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14 Common Application Performance Issues & How to Fix Them

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How Middleware Helps Catch Deployment Issues Before They Hit Users

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How Log Patterns Help You Debug Faster and Monitor Smarter


<p>Error tracking and performance monitoring of the platform or applications have been stuck in the past with static dashboards and manual detective work to sift through logs to find bugs. Currently, developers spend almost 60% of their time on finding the root cause of errors and fixing them. </p>



<p>At <a href="https://middleware.io/">Middleware</a>, our mission has been to help companies keep their apps available to customers all the time by providing unified <a href="https://middleware.io/blog/observability/">observability</a> by collating logs, metrics, and <a href="https://middleware.io/product/distributed-tracing/">traces</a> in one place. We have always been thinking about how to increase <a href="https://middleware.io/blog/developer-productivity/">developer productivity</a>.</p>



<p>As we are in the early years of the AI era, we started thinking about building a tool suitable for the new age of computing. New-age programming tools are helping developers generate billions of lines of code every day. </p>



<p>While that means teams are shipping more code faster, it also results in more errors creeping into the codebase and breaking production systems. As the codebase grows, it becomes harder to monitor bugs.</p>



<p>For that, we need an ever-aware tool to observe your codebase. But that’s not enough. Apart from detecting errors, it should also fix these exceptions so your engineers don’t need to hamper their sleep cycle at 3:00 AM.&nbsp;</p>



<p>That’s why we built <strong>Ops AI</strong>, our new <strong>AI co-pilot</strong> that spots and explains errors while finding ready fixes for you to apply within seconds.&nbsp;</p>



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<h2 class="wp-block-heading" id="h-why-use-ops-ai">Why use Ops AI?</h2>



<p>Ops AI is an AI observability co-pilot that works across any platform, language, or framework. Ops AI can detect and fix issues across both your frontend and backend. With full awareness of your codebase, it delivers accurate, pinpoint insights into exceptions.</p>



<p>Built on top of Middleware’s trusted <a href="https://middleware.io/product/apm/">APM</a> solution, it leverages deep context from logs, traces, and metrics to provide fast, reliable fixes.</p>



<p>At Middleware, we want to create an environment of trust and safety. That’s why we started using Ops AI for our own system and got robust results.&nbsp;</p>



<figure class="wp-block-pullquote"><blockquote><p>“We started using Ops AI at Middleware, and it now resolves over half of our production issues automatically. In tests with multiple customers, we&#8217;ve seen a detection-to-resolution rate of over 70%. We believe this is a game-changer for observability,”</p><cite>Laduram Vishnoi, Founder and CEO of Middleware</cite></blockquote></figure>



<p>The new Ops AI tool boosts on-call developer productivity by more than 80% and reduces mean time to respond (MTTR) by 5 times, allowing developers to concentrate on important tasks for creating applications.</p>



<p>Ops AI also learns from historical patterns, and as it catches and fixes more bugs, it becomes smarter as you use it more.</p>



<p>We have created this new tool with privacy-first principles. We don’t use your data to train our AI models.&nbsp;</p>



<h2 class="wp-block-heading" id="h-how-does-ops-ai-work">How does Ops AI work?</h2>



<p>On a high level, Ops AI pulls together all issues across your frontend (<a href="https://middleware.io/product/real-user-monitoring/">real-user monitoring</a>) and back-end applications (application performance monitoring) —&nbsp; including syntax errors, code exceptions, and runtime errors in a single report. These issues include details such as error type, frequency, and timeline.&nbsp;</p>



<p>After installing our latest <a href="https://github.com/middleware-labs/mw-agent">APM agent</a>, customers can connect their GitHub and let our observability AI do its work. You can connect your GitHub account, and Ops AI will automatically collect errors from all your repositories. It helps you identify and fix issues with just one click.&nbsp;</p>



<h3 class="wp-block-heading" id="h-error-issue-detection">Error/Issue Detection</h3>



<p>On a more granular level, once you install the agent, it starts monitoring your application stack. When it catches a code exception, syntax or runtime error, it will use information from traces, logs, request headers and body, error messages, and environment to create a detailed report.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1140" height="654" src="https://middleware.io/backend/wp-content/uploads/2025/06/Error-or-Issue-detection-by-OpsAI.webp" alt="Error or Issue detection by OpsAI" class="wp-image-11126"/></figure>



<p>Through GitHub integration, it helps fetch the file and code context to show the exact error location, including the file and line number in your codebase.&nbsp;</p>



<h3 class="wp-block-heading" id="h-solution-finding">Solution Finding</h3>



<p>Before solving the error, Ops AI acts as an investigator to find the root cause of the bug using context from the Middleware’s full-stack observability suite. </p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1140" height="655" src="https://middleware.io/backend/wp-content/uploads/2025/06/Code-updates-from-the-suggested-fixes-by-OpsAI-Observability-co-pilot.webp" alt="Code updates from the suggested fixes by OpsAI Observability co-pilot" class="wp-image-11127"/></figure>



<p>It observes commits made before the error, code dependencies, past data about similar errors, and other developer activities to build the complete picture to understand what caused the breakdown of the system.&nbsp;</p>



<h3 class="wp-block-heading" id="h-generating-pr-and-solutions">Generating PR and Solutions</h3>



<figure class="wp-block-image size-large is-resized"><img loading="lazy" decoding="async" width="1140" height="655" src="https://middleware.io/backend/wp-content/uploads/2025/06/Generating-PR-and-solutions-by-OpsAI-Observability-Co-pilot.webp" alt="Generating PR and solutions by OpsAI Observability Co-pilot" class="wp-image-11128" style="width:620px;height:auto"/></figure>



<p>All these details are passed to our refactoring agent to get suggestions for the bug fix. Finally, apply them to the code. Ops AI only creates a PR if it&#8217;s more than 95% confident of the fix. </p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1140" height="629" src="https://middleware.io/backend/wp-content/uploads/2025/06/Fix-development-application-issue-by-OpsAI-observability-co-pilot.webp" alt="Fix development application issue by OpsAI observability co-pilot" class="wp-image-11130"/></figure>



<p>For a lower confidence score, it provides all the context for the error to engineers to start debugging without having to spend time going through logs.</p>



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<h2 class="wp-block-heading" id="h-what-can-aiops-co-pilot-do">What Can AIOps Co-Pilot Do?</h2>



<ul class="wp-block-list">
<li><strong>Error Monitoring and Summarization: </strong>The tool collects all errors from the front-end, back-end, error logs, and code exceptions to present them in an easily readable format that shows error type, error message, exception, and error code line with complete stack trace. You can also track and manage errors in a more efficient way by assigning statuses like &#8216;reviewed&#8217;, &#8216;resolved&#8217;, and &#8216;ignored&#8217;.</li>



<li><strong>Detailed Root Cause Analysis: </strong>Find the exact place that caused the error<strong> </strong>through a link to the codebase. It provides detailed error information, including the file name, code line, stack trace, and even related variables and version details. This makes it easy to understand what went wrong, and you can start fixing issues right away without wasting time searching through logs or code.</li>



<li><strong>One-click error resolution: </strong>With Ops AI, you can view the root cause and a recommended one-click fix on a single screen. You can also generate a PR with fixed bugs through this interface to save time and get your application up and running again.</li>



<li><strong>Preserves your data privacy: </strong>Ops AI is designed to maintain the security and integrity of your data. All your inputs and responses are private, and only your team can access them. Plus, the tool doesn’t share AI-generated responses publicly outside your organization.&nbsp;</li>
</ul>



<p>This AI observability co-pilot summarizes errors, provides root cause analysis, and even auto-generates fixes.</p>



<h2 class="wp-block-heading" id="h-limitations-and-what-is-next">Limitations and What is next?</h2>



<p>This launch is the first step towards the future of <strong><em>AI-powered observability</em></strong>. We are working on building upgrades to Ops AI to cover Logs and Kubernetes monitoring.</p>



<p>Our goal is for Ops AI to generate a ready-to-use root cause analysis report before system engineers start investigating. This gives them a head start and saves valuable debugging time.</p>



<p>Middleware and Ops AI currently support Node.js, Golang, Python, Java, and JavaScript. However, we are scaling our development to be compatible with other languages, including Ruby, .NET, PHP, and more. On the version control front, we already have GitHub integration with GitLab and Bitbucket support in the works.&nbsp;</p>



<h2 class="wp-block-heading" id="h-how-to-get-started-with-ops-ai-nbsp">How to get started with Ops AI&nbsp;</h2>



<p>At Middleware, we have been helping our clients be AI-ready for a while now. First, our AI-powered search allowed our customers to use natural language to make sense of their data. Then, we used AI to boost <a href="https://middleware.io/blog/anomaly-detection/">anomaly detection</a> and make the systems of our clients more reliable. With Ops AI, we are stepping into the agentic era to create a reliable partner for engineering teams to seamlessly handle production issues.&nbsp;</p>



<p>Ops AI is available for all Middleware’s existing and new customers starting from 25th June. You can <a href="https://app.middleware.io/auth/register/">try</a> it for 14 days, no extra setup required.&nbsp;</p>



<p>If you have any questions or feedback, we are happy to listen to them. Please <a href="https://middleware.io/contact-us/">reach out to us</a>.&nbsp;</p>



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We’re excited to launch Ops AI — your new AI observability co-pilot. Built on Middleware’s APM, it helps developers detect, debug, and fix issues faster across the stack. Boost productivity and cut MTTR by 5x with every bug it resolves.

Introducing Ops AI: Observability Co-Pilot to Resolve Your Production Issues Instantly


<p>Before <a href="https://middleware.io/blog/understanding-the-docker-ecosystem/">Docker</a> existed, developers had a hard time trying to package, ship and deploy their applications consistently across various environments. By enabling the much-needed <a href="https://middleware.io/blog/containerization/">containerization</a>, Docker gives you the superpower that ensures your application behaves the same way regardless of the environment. However, with this great power comes the great responsibility of monitoring and troubleshooting your applications effectively.&nbsp;</p>



<p>In this roundup, we’ll introduce you to some robust logging mechanisms that you can utilize for your&nbsp; Docker container logs to provide your application the reliability and performance it needs.&nbsp;</p>



<p>We’ll dive deep into Docker container logs, explore some common Docker container logs commands, and understand how you can access and manage them.&nbsp;</p>



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<h2 class="wp-block-heading" id="h-what-are-docker-container-logs">What Are Docker Container Logs?</h2>



<p>When you run applications within containers, Docker container logs capture the standard output and error messages your application generates. Think of these as application logs, but specifically for your container.&nbsp;</p>



<p>Typically, docker container logs give you information about any exceptions or errors your application throws, status messages, events emitted by your application, relevant performance metrics, etc. These logs are extremely valuable to provide insights for troubleshooting and understanding the behavior of your application.&nbsp;</p>



<h2 class="wp-block-heading" id="h-accessing-docker-container-logs">Accessing Docker Container Logs</h2>



<p>To access your Docker container logs,&nbsp; you can run the following command using Docker CLI:</p>



<pre class="wp-block-code"><code>docker logs my-container</code></pre>



<p>You can also enable these logs to run in real-time by using the -f flag and view Docker container logs:</p>



<pre class="wp-block-code"><code>docker logs -f my-container</code></pre>



<p>Alternately, you can also view logs for a specific service using Docker Compose:</p>



<pre class="wp-block-code"><code>docker-compose logs web-backend</code></pre>



<p>The above command uses Docker Compose to show you logs for your service called “web-backend”.&nbsp;</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>Understand the fundamentals of <a href="https://middleware.io/blog/what-is-log-monitoring/">log monitoring</a> and how it helps proactively detect issues before they impact users.</p>
</blockquote>



<h3 class="wp-block-heading" id="h-log-drivers-and-why-they-re-important">Log Drivers and why they’re important</h3>



<p>Log drivers in Docker determine where and how your container logs should be stored and managed. This could be a JSON file, which is the default log driver that stores all your Docker logs as JSON.&nbsp;</p>



<p>Other options for log drivers include syslog, which logs your Docker container logs to the syslog daemon, journald, which integrates with systemd journal, or <strong>gelf</strong>, <strong>fluentd</strong>, <strong>awslogs</strong> for centralized logging.</p>



<p>Each log driver has its driver-specific features, including storage persistence, scalability, and ease of use.</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>Understanding <a href="https://middleware.io/blog/docker-logging-tutorial/">Docker Logging</a>. Explore how Docker handles logs, best practices, and advanced options for managing them efficiently.</p>
</blockquote>



<h3 class="wp-block-heading" id="h-configuring-log-drivers">Configuring Log Drivers</h3>



<p>Docker CLI lets you configure the log driver for your Docker container logs at the time of creating your container:</p>



<pre class="wp-block-code"><code>docker run --log-driver syslog --log-opt syslog-address=udp://192.168.0.42:514 my-container</code></pre>



<p>In the above command, the main <code>--log-driver</code> flag specifies the log driver we’re using, which in this case is the syslog. Then, the -log-opt flag provides options specific to the selected log driver. After that, we tell Docker the address and port of the syslog server using syslog-address. Finally, the container called “<code>my-container</code>” is run, whose logs are routed to the syslog server at the specified address.&nbsp;</p>



<p>To directly configure the log driver options in the <code>/etc/docker/daemon.json</code> file, modify it’s contents as shown below:</p>



<pre class="wp-block-code"><code>{
  "log-driver": "json-file",
  "log-opts": {
    "max-size": "10m",
    "max-file": "3"
  }
}</code></pre>



<h2 class="wp-block-heading" id="h-managing-and-viewing-docker-logs">Managing and Viewing Docker Logs</h2>



<p>Developers often burn entire afternoons chasing down what turned out to be straightforward configuration errors, all because they were drowning in log noise. When you can effectively manage and view your logs, you can narrow down the ones that can cause critical problems. </p>



<p>Moreover, staying on top of your container logs can help you with deeper log analysis and avoid problems such as your disk space suddenly running out and causing your entire system to halt.&nbsp;</p>



<h3 class="wp-block-heading" id="h-viewing-and-filtering-logs">Viewing and Filtering Logs</h3>



<p>Docker provides several different ways to view Docker container logs and filter them. This eventually helps you focus on specific logs that you need to focus on during debugging and troubleshooting.&nbsp;</p>



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<p>For instance, the below command will show you all the logs from the last 30 minutes:</p>



<pre class="wp-block-code"><code>docker logs --since 30m my-container&nbsp;</code></pre>



<p>But you’re not just limited by a time duration, you can view logs for a precise moment in time, like a specific timestamp:</p>



<pre class="wp-block-code"><code>docker logs --since "2024-01-15T10:30:00" my-container&nbsp;</code></pre>



<p>The above command will show you all the logs taken on the exact timestamp specified. Moreover, you can also view logs until a specific time:</p>



<pre class="wp-block-code"><code>docker logs --until "2024-01-15T11:00:00" my-container&nbsp;</code></pre>



<p>The <code>--until</code> flag returns all the logs till that moment in time. You can combine the <code>--since</code> flag to get a window of time and view all logs in that window, as shown below:</p>



<pre class="wp-block-code"><code>docker logs --since "1h30m" --until "30m" my-container</code></pre>



<p>You can also follow logs in real-time with timestamps:</p>



<pre class="wp-block-code"><code>docker logs -f -t my-container&nbsp;</code></pre>



<p>The above will show you logs alongside their timestamp. To reduce noise from unwanted logs, you can also stream logs from the last 50 lines or so:</p>



<pre class="wp-block-code"><code>docker logs -f --tail 50 my-container</code></pre>



<p>The -f flag is like your messiah when it comes to debugging during development. Watching your application’s behavior in real-time makes it easier to spot any issues as they happen.&nbsp;</p>



<p>But how do you see logs for a specific pattern? Using advanced log filtering, you can specify a pattern using grep:</p>



<pre class="wp-block-code"><code>docker logs my-container 2&gt;&amp;1 | grep "ERROR"</code></pre>



<p>The above log redirects logs containing the word <code>“ERROR”</code> to your window. Let’s say you have a web application whose container logs look like this:</p>



<pre class="wp-block-code"><code>2024-01-15 10:30:15 INFO Starting application
2024-01-15 10:30:16 INFO Database connected
2024-01-15 10:30:20 ERROR Failed to connect to Redis
2024-01-15 10:30:25 INFO Request processed successfully
2024-01-15 10:30:30 ERROR Timeout connecting to external API</code></pre>



<p>As you can see, the container logs show that your application failed to connect to Redis and timed out connecting to an external API. To dig deeper, you can retrieve these relevant logs using the above command.&nbsp;</p>



<h3 class="wp-block-heading" id="h-log-size-management-and-rotation">Log Size Management and Rotation</h3>



<p>Uncontrolled logs can get out of hand easily and start taking up more disk space than needed. This can lead to crashing your entire system. To prevent this, you should set limits to your logs when creating a container:</p>



<pre class="wp-block-code"><code>docker run -d \ --name my-app \ --log-driver json-file \ --log-opt max-size=10m \ --log-opt max-file=5 \ nginx:latest</code></pre>



<p>Using the <code>max-size=10m</code>, you specify the maximum size of a file that your logs can take. Similarly, using the&nbsp; <code>max-file=5</code>, you can declare the maximum number of files that will be used for your <a href="https://middleware.io/product/container-monitoring/">container logs</a>. All in all, this will set a size limit of 50MB.&nbsp; When the current log file reaches 10MB, Docker creates a new one and eventually removes the oldest when the limit is exceeded.</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>If you’re facing storage bloat, learn how to free up unused containers, volumes, and images in our <a href="https://middleware.io/blog/docker-cleanup/">Docker Cleanup</a> Guide.</p>
</blockquote>



<h3 class="wp-block-heading" id="h-log-file-manipulation">Log File Manipulation</h3>



<p>There are cases where you might directly need to access the log files and manipulate the logs. For example, when you need more power and flexibility, such as advanced text processing, performance upside with large log files, etc. </p>



<p>For instance, consider the below docker command that logs the entire log into memory, slowing down the process for large logs in GBs:</p>



<pre class="wp-block-code"><code>docker logs huge-container | grep "ERROR"</code></pre>



<p>You can directly access the file instead, which would be much faster:</p>



<pre class="wp-block-code"><code>grep "ERROR" $(docker inspect --format='{{.LogPath}}' huge-container)</code></pre>



<p>Here’s another example where you might want to get lines <code>1000-1500</code> from a log file, which is harder to do directly with Docker CLI. Instead, you can directly access the file and retrieve those lines using file manipulation:</p>



<pre class="wp-block-code"><code>tail -n +1000 $(docker inspect --format='{{.LogPath}}' my-container) | head -500&nbsp;</code></pre>



<p>You can still use Docker CLI for the exact Docker container logs location:</p>



<pre class="wp-block-code"><code>docker inspect --format='{{.LogPath}}' my-container&nbsp;</code></pre>



<h2 class="wp-block-heading" id="h-best-practices-for-efficient-log-management">Best Practices for Efficient Log Management</h2>



<p>Here are some quick best practices you should keep in mind to get an efficient log management system for your Docker container logs:</p>



<ul class="wp-block-list">
<li><strong>Rotate logs regularly:</strong> Disk overflow is a common consequence of not rotating Docker container logs regularly. Configuring the daemon with rotation settings can ensure that you always have enough disk space available.</li>
</ul>



<ul class="wp-block-list">
<li><strong>Set log size limits: </strong>Use flags such as max-size and max-file, as we explored in the previous section, to ensure proper file size restrictions are in place. An unpredictable surge in traffic on your application could easily eat up a lot of unwanted memory if size limits are not in place.&nbsp;</li>
</ul>



<ul class="wp-block-list">
<li><strong>Follow structured logging:</strong> When streaming Docker container logs, parsing or running custom analytics, use structured formats only, like <code>JSON</code>. This can help you not get tangled in processing your logs and give you more flexibility in terms of working on top of the logs.&nbsp;</li>
</ul>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>Learn how to structure logs using formats like JSON for better readability and machine processing. See <a href="https://middleware.io/blog/log-formatting/">Log Formatting</a> Best Practices.</p>
</blockquote>



<h2 class="wp-block-heading" id="h-centralized-logging-and-routing-logs-nbsp">Centralized Logging and Routing Logs&nbsp;</h2>



<p>Modern applications have complex architectures, interfaces and communicate extensively with external APIs. Practically, it’s best to have all your Docker container logs centralized in one place for viewing, accessing, managing and processing. Not only does centralized logging simplify management across multiple containers and environments, but using dedicated logging tools can give you benefits such as advanced filtering and real-time analytics.&nbsp;</p>



<p>The given command shows how you can route your Docker container logs to a popular monitoring platform <a href="https://middleware.io/">Middleware</a>:</p>



<pre class="wp-block-code"><code>docker run --log-driver fluentd --log-opt fluentd-address=middleware.io:24224 my-app</code></pre>



<h2 class="wp-block-heading" id="h-best-practices-for-docker-logging">Best Practices for Docker Logging</h2>



<p>As we’re close to a wrap, let’s also understand some best practices that can help us utilize Docker container logs effectively. We’ve already explored how structured logging formats like JSON can be helpful for readability and parsing logs for further analysis.&nbsp;</p>



<p>Security is paramount for every organization and their customers. A common security consideration often missed is logging sensitive data. You should never log any kind of sensitive data like passwords, auth tokens, payment information, etc.&nbsp;</p>



<p>You can save countless hours of log analysis and management by using a reliable <a href="https://middleware.io/blog/log-management/">log management</a> tool like Middleware. Which do all the heavy lifting and offer extremely valuable features like <a href="https://middleware.io/blog/log-aggregation/">log aggregations</a>, automated notifications and alerting for critical log events.</p>



<p>Finally, ensure you regularly audit and archive your container logs to meet any compliance standards necessary. Log management solutions automate audit trails and simplify regulatory compliance out of the box, so you can use them directly to meet compliance standards conveniently.</p>



<h3 class="wp-block-heading" id="h-conclusion">Conclusion</h3>



<p>By combining the power of your Docker container logs with effective log management and dedicated log management tools like Middleware, you can ensure reliability for your application. Following the best practices is just as crucial, so make sure you’re not missing them.&nbsp;</p>



<h3 class="wp-block-heading" id="h-faqs">FAQs</h3>


		<section		class="sc_fs_faq sc_card    "
				>
				<h3>How do I check Docker container logs?</h3>				<div>
						<div class="sc_fs_faq__content">
				

<p>To check the Docker container logs, you can use the Docker CLI command docker logs my-container</p>

			</div>
		</div>
		</section>
				<section		class="sc_fs_faq sc_card    "
				>
				<h3>How can I follow Docker container logs in real-time?</h3>				<div>
						<div class="sc_fs_faq__content">
				

<p>When you use the command docker logs -f my-container, your Docker container logs are streamed in real-time.</p>

			</div>
		</div>
		</section>
				<section		class="sc_fs_faq sc_card    "
				>
				<h3>What are the best practices for managing Docker container logs?</h3>				<div>
						<div class="sc_fs_faq__content">
				

<p>Some best practices for managing Docker container logs include regularly rotating logs, setting size limits using flags like max-size and max-file, using structured log formats like JSON, avoiding sensitive information in logs, and implementing centralized log management tools such as Middleware.</p>

			</div>
		</div>
		</section>
				<section		class="sc_fs_faq sc_card    "
				>
				<h3>How do I filter Docker container logs for specific errors?</h3>				<div>
						<div class="sc_fs_faq__content">
				

<p>The command docker logs my-container 2&gt;&amp;1 | grep &#8220;ERROR&#8221; can be used to filter Docker container logs for specific errors</p>

			</div>
		</div>
		</section>
				<section		class="sc_fs_faq sc_card    "
				>
				<h3>What is structured logging, and why is it important?</h3>				<div>
						<div class="sc_fs_faq__content">
				

<p>When you format your Docker container logs into a structured format, such as JSON, it becomes easier to parse, analyse, and automate monitoring.</p>

			</div>
		</div>
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<p>At Middleware, our motto is to help and transform the process of how developers and organizations handle <a href="https://middleware.io/blog/observability/">observability</a> at scale. We are on a mission to provide a full-stack observability platform that empowers developers to build, monitor, and troubleshoot infrastructure and applications with unparalleled ease and efficiency. </p>



<p>We are currently helping out many companies with their observability problems, and we believe that observability is not just a tool but the cornerstone of modern applications.</p>



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<p>The belief in helping organizations has driven us to relentlessly innovate and create a platform that seamlessly integrates logs, <a href="https://middleware.io/blog/what-is-distributed-tracing/">traces</a>, and metrics into a single pane of glass. With detailed insight, we are helping organizations to rapidly identify issues and effortlessly solve them, enhancing operational efficiency, minimizing downtime, and reducing observability costs.</p>



<p>At Middleware we strongly believe in innovation and excellence and with that motto comes a lot of responsibility, we constantly enhance our platform and add new <a href="https://middleware.io/platform/integrations/">integrations</a> to make the platform better and have more access to data. </p>



<p>Our goal is to provide developers with the most comprehensive and seamless observability experience possible. That’s why we have integrated Kapstan into our ecosystem to further elevate the developer experience. This integration ensures that developers have the tools they need to succeed in today’s fast-paced, cloud-native world.</p>



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<p>Today, we are thrilled to take a significant step forward in our journey by announcing our strategic partnership with Kapstan, a leader in Internal Developer Platforms and Kubernetes management.</p>



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<h2 class="wp-block-heading" id="h-making-the-deployments-more-insightful-a-shared-vision-with-kapstan">Making the Deployments More Insightful: A Shared Vision with Kapstan</h2>



<p>Kapstan’s vision deeply resonates with our own as they also believe that “A well-observed system is the only well-behaved system”. We are gearing up together to enhance the developer experience through observability and automating the deployment process.</p>



<p><a href="https://www.kapstan.io/">Kapstan</a> is a comprehensive platform designed to simplify Kubernetes management while unlocking its full potential. It empowers developers to deploy applications using intent-based workflows without worrying about the configuration files. </p>



<p>Middleware steps in here to make the developer experience more seamless by providing complete end-to-end observability, streamlining insights, and ultimately optimizing their technology stack for success.</p>



<h2 class="wp-block-heading" id="h-announcing-our-partnership-with-kapstan-io">Announcing Our Partnership with Kapstan.io</h2>



<p>In that spirit, we are thrilled to announce our strategic partnership with Kapstan.io!</p>



<p>Kapstan helps developers instantly provision, deploy and manage complex cloud infrastructure in their own cloud account. They give engineers world-class developer experience in managing infrastructure and deploying applications, databases, jobs, and more without the infrastructure sprawl or the platform lock-in of Vercel/Heroku.</p>



<p>For larger engineering organizations with DevOps teams, Kapstan offers a fully customizable abstraction over Kubernetes clusters with getting bogged down in the intricacies of infrastructure management such as Kubernetes upgrades, repetitive tasks, and one-off tickets from developers to help them provision an S3 bucket.&nbsp;</p>



<h2 class="wp-block-heading" id="h-introducing-the-middleware-x-kapstan-integration">Introducing the Middleware x Kapstan Integration</h2>



<p>We are excited to announce a full-fledged integration of Middleware for Kapstan users to ease their Observability headache. With just a single click, Kapstan customers can now observe their environments within Middleware with the integration, gaining access to a unified platform that brings together logs, traces, and metrics. </p>



<p>This integration gives developers a holistic view of their applications, enabling faster issue resolution and smoother operations.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1890" height="1013" src="https://middleware.io/backend/wp-content/uploads/2025/04/Middleware-Kapstan-Integration-1.webp" alt="Middleware Kapstan Integration" class="wp-image-10846"/></figure>



<h2 class="wp-block-heading" id="h-a-shared-commitment-to-developer-experience">A Shared Commitment to Developer Experience</h2>



<p>This partnership is more than just a collaboration—it’s our shared commitment to improving the developer experience together. By combining Middleware’s observability expertise with Kapstan’s intuitive IDP and ZeroOps Kubernetes solution, we would create a tech ecosystem where developers can focus on what truly matters: building innovative, reliable, and scalable applications.</p>



<p>We are thrilled to embark on this journey with Kapstan and look forward to the incredible value this partnership will bring to developers worldwide. Stay tuned for more updates as we continue to innovate and redefine the future of the developer experience together!</p>



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<p>I’m sure, as an engineer, there’s no bigger nightmare than a production outage at 3 AM. We live in a world where software is becoming more complex by the minute. From distributed architectures to <a href="https://middleware.io/blog/what-is-container-orchestration/">container orchestration</a> and microservices, an <a href="https://middleware.io/blog/observability/tools/">observability tool</a> is a must-have digital vigilante for your system and infrastructure.&nbsp;</p>



<p>Prometheus is a powerful open-source monitoring tool that enables system administrators and engineers to collect useful data points about their system&#8217;s health and performance. </p>



<p>These data points or metrics are called Prometheus metrics, help in tracking resource utilization, identifying system bottlenecks, and ensuring smooth operations. If you&#8217;re wondering <strong>what is Prometheus metric</strong>, it is essentially a time-series data point that represents system behavior over time.</p>



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<p>In this guide, we&#8217;ll unravel how Prometheus metrics provide visibility into your systems. By the end of this post, you&#8217;ll have everything under your belt to make the most of Prometheus metrics.</p>



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<h2 class="wp-block-heading" id="h-what-are-prometheus-metrics">What Are Prometheus Metrics?</h2>



<p>Prometheus collects data points that represent the behavior and <a href="https://middleware.io/blog/what-is-application-performance-monitoring/">performance of your application</a> or infrastructure. These data points, also known as <strong>Prometheus metrics</strong>, are stored in a <strong>Prometheus metrics format</strong>, which follows a structured approach to time-series data. </p>



<p>For instance, a data point that measures your <a href="https://middleware.io/blog/efficient-server-utilization/">server&#8217;s CPU consumption</a>, the number of requests your service receives, or the average response time of your server are all examples of Prometheus metrics.</p>



<p>Let’s look at a <strong>Prometheus metrics example</strong> to understand how these metrics work in <a href="https://middleware.io/blog/what-is-real-user-monitoring/">real-time monitoring</a>. Consider <a href="https://middleware.io/blog/prevent-cpu-overload-on-linux-servers/">CPU usage</a> and average response time for a web server:</p>



<p><strong>CPU usage:</strong></p>



<p>&#8211; &nbsp; 10:00:00 AM: 45% utilization</p>



<p>&#8211; &nbsp; 10:00:15 AM: 48% utilization</p>



<p>&#8211; &nbsp; 10:00:30 AM: 73% utilization (sudden spike)</p>



<p><strong>Average response time:</strong></p>



<p>&#8211; 10:00:00 AM: 85 ms</p>



<p>&#8211; 10:00:15 AM: 90 ms</p>



<p>&#8211; 10:00:30 AM: 250 ms</p>



<p>Prometheus stores metrics as time-series data in a structured <strong>Prometheus metrics format</strong>, allowing engineers to query, visualize, and analyze them to identify performance trends. </p>



<p>You can effectively monitor system health and make data-driven decisions by understanding different&nbsp;Prometheus metrics types, such as counters, gauges, histograms, and summaries. </p>



<p>For instance, in the above example, the time-series data can be used to visualize the correlation between increased request rate and the CPU/memory spikes. We see that there was a sudden spike that happened at 10:00:30 AM, so Prometheus can also send alerts for visibility.&nbsp;</p>



<h2 class="wp-block-heading" id="h-understanding-prometheus-metrics-types-and-their-role">Understanding Prometheus Metrics Types and Their Role</h2>



<p>Before we dive deeper into each metric type and their role, let&#8217;s first understand some key concepts related to Prometheus metrics.&nbsp;</p>



<h3 class="wp-block-heading" id="h-1-nbsp-labels-nbsp-nbsp">1.&nbsp; Labels&nbsp;&nbsp;</h3>



<p>Labels are key-value pairs that give dimension to the metrics like instance, method,&nbsp;service, etc.</p>



<p>For instance, a metric like <strong><code>http_requests_total</code></strong> might include labels such as <code><strong>method="POST</strong>"</code>, <strong><code>service="checkout"</code></strong>, and<strong> <code>instance="server-1"</code></strong>.&nbsp;</p>



<p>If you wish to query metrics specifically for your checkout service, you can do so using a label selector in PromQL:</p>



<pre class="wp-block-code"><code>http_requests_total{service="checkout"}</code></pre>



<p>These queries fetch data from the <strong>Prometheus metrics endpoint</strong>, which serves the latest metric values collected from various services. The <strong>Prometheus metrics endpoint</strong> allows tools like Grafana and Middleware to retrieve metrics in real time for visualization and alerting.</p>



<h3 class="wp-block-heading" id="h-2-nbsp-metric-name-and-value-nbsp-nbsp">2.&nbsp; Metric Name and Value&nbsp;&nbsp;</h3>



<p>The name of a metric describes what the metric represents, whereas the value is the actual data point associated with that metric. For instance, a metric used to represent CPU usage is called</p>



<p>&nbsp;<strong><code>cpu_usage_seconds_total</code></strong>, which tells you how many seconds the CPU has been used in total.&nbsp;&nbsp;&nbsp;</p>



<p>For instance, if the <strong><code>cpu_usage_seconds_total</code></strong> metric has a value of <strong>450</strong> for <strong><code>instance="server-2"</code></strong>. This means that your server-2&#8217;s CPU has been used for 450 seconds since last it was started or reset.&nbsp;</p>



<p>Also, with PromQL, you can query the rate of change for this metric.</p>



<pre class="wp-block-code"><code>rate(cpu_usage_seconds_total{instance="server-2"}&#91;5m])</code></pre>



<p>The result of the above query tells you how fast your CPU usage has increased in the last 5 minutes. You can check the same for any specified period. You can use this information to decide if you need to scale up resources. </p>



<p>For example, a value of 0.75 or higher may indicate a significant spike in CPU usage that you may need to address.&nbsp;</p>



<h3 class="wp-block-heading" id="h-3-nbsp-prometheus-metric-types-nbsp-nbsp">3.&nbsp; Prometheus Metric Types&nbsp;&nbsp;</h3>



<p>We&#8217;ve seen earlier the four core metrics that Prometheus provides, but let&#8217;s examine them more thoroughly.</p>



<h4 class="wp-block-heading" id="h-counters">Counters</h4>



<p>A counter metric, as the name suggests, is a metric that counts or cumulates the value. It only gets incremented or reset to 0. You can use counter metrics to track the number of requests a service has handled, tasks a job has completed, the number of errors or crashes a system has encountered, etc.&nbsp;</p>



<p>For instance, you could have a counter metric <strong><code>requests_total</code></strong>. When its value changes from 100 to 150, we know there have been 50 new requests since the last measurement.&nbsp;</p>



<p>Using PromQL, we can also get a _rate_ of requests over time as shown below:</p>



<pre class="wp-block-code"><code>rate(requests_total&#91;5m])</code></pre>



<h4 class="wp-block-heading" id="h-gauges">Gauges</h4>



<p>To track values that can fluctuate over time, a gauge metric is used. It can increase in value, or go down/decrease in value. You can use a gauge to measure how much memory is used or the number of active sessions for your users.&nbsp;</p>



<p>For instance, consider a gauge metric called <strong><code>memory_usage_bytes</code></strong> . If it has a value of 2,147,483,648 it means that 2GB of memory has been consumed up to that point.&nbsp;</p>



<h4 class="wp-block-heading" id="h-histograms">Histograms</h4>



<p>Let&#8217;s say you want to understand how quick or slow your application appears to your users. Measuring response time would be helpful here. However, not every user may get the same response time. So it may be more helpful to get a range of response times, say from 20ms to 1000ms. </p>



<p>To get a clearer picture, you might want to see these response times sorted with a count telling you how many requests fall into each category.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td>Response Time20ms</td><td>Count150</td></tr><tr><td>50ms</td><td>200</td></tr><tr><td>100ms</td><td>400</td></tr><tr><td>500ms</td><td>150</td></tr></tbody></table></figure>



<p>Histograms in Prometheus are like organized bucket systems that help you understand how long things take or how big things are.&nbsp;</p>



<p>A histogram metric might look like this (in a simplified text format):</p>



<pre class="wp-block-code"><code># HELP request_duration_seconds Request duration in seconds
&nbsp; &nbsp; # TYPE request_duration_seconds histogram
&nbsp; &nbsp; request_duration_seconds_bucket{le="0.05"} 240
&nbsp; &nbsp; request_duration_seconds_bucket{le="0.1"} 638
&nbsp; &nbsp; request_duration_seconds_bucket{le="0.2"} 912
&nbsp; &nbsp; request_duration_seconds_bucket{le="0.5"} 980
&nbsp; &nbsp; request_duration_seconds_bucket{le="1"} 995
&nbsp; &nbsp; request_duration_seconds_bucket{le="+Inf"} 1000
&nbsp; &nbsp; request_duration_seconds_sum 123
&nbsp; &nbsp; request_duration_seconds_count 1000</code></pre>



<p>From this data, you can calculate the distribution of request durations. Some useful features of Histograms are statistics like median response time (50th percentile) and 90th percentile (the time below which 90% of your requests are completed) to get a broader sense of how your application is performing.</p>



<h4 class="wp-block-heading" id="h-summaries">Summaries</h4>



<p>Summaries are similar to histograms but they also give you information like the sum of all the observations and configurable quantiles like the 95th percentile directly, without the need for any buckets.&nbsp;&nbsp;</p>



<p>Let&#8217;s say you&#8217;re now interested in understanding how long it takes for your web application to process a login request for a user. Using Prometheus, you can set up a summary metric called <strong><code>login_duration_seconds</code></strong>.</p>



<pre class="wp-block-code"><code> login_duration_seconds{instance="app-server-1",quantile="0.5"} 0.042
&nbsp; &nbsp; login_duration_seconds{instance="app-server-1",quantile="0.9"} 0.123
&nbsp; &nbsp; login_duration_seconds{instance="app-server-1",quantile="0.95"} 0.187
&nbsp; &nbsp; login_duration_seconds_sum{instance="app-server-1"} 75.3
&nbsp; &nbsp; login_duration_seconds_count{instance="app-server-1"} 1250</code></pre>



<p>You can see from the above summary that the average login time is 0.042 seconds (42ms). This is the median or the 50th percentile.&nbsp;</p>



<p>The trade-off here is that summaries are somewhat more resource-intensive and less flexible compared to histograms when it comes to combining data across multiple instances. </p>



<p>However, they can be incredibly useful if you want immediate, per-instance percentile calculations without having to define buckets as with histograms.</p>



<h2 class="wp-block-heading" id="h-best-practices-for-working-with-various-prometheus-metrics">Best Practices for Working with Various Prometheus Metrics</h2>



<p>We’ve seen how useful Prometheus metrics can be, but now let’s understand some best practices we can adopt when working with these metrics.&nbsp;</p>



<h3 class="wp-block-heading" id="h-1-nbsp-organize-metric-names-and-labels-clearly-nbsp">1.&nbsp; Organize Metric Names and Labels Clearly&nbsp;</h3>



<p>Ensure that your metrics follow a consistent naming convention. For instance, the metric name <code>node_cpu_usage_seconds_total</code>&nbsp; for CPU usage is self-descriptive in nature. </p>



<p>Organizing metric names and labels clearly will make it easier for anyone to understand what the metric is for. Also, avoid creating labels that can have too many values to keep your metrics database simple and reliable.&nbsp;</p>



<h3 class="wp-block-heading" id="h-2-nbsp-choosing-the-right-metric-type">2.&nbsp; Choosing the Right Metric Type</h3>



<p>Each Prometheus metric type has its own well-defined purpose. Be very strict about using the right metric type.&nbsp;</p>



<p>For a metric with continuously incrementing values, use &nbsp;Counters . For metric values that can go up or down, like current state measurements, use&nbsp; Gauges. If you need on-the-fly quantiles, for instance, use&nbsp;Summaries.&nbsp; To measure the frequency or distribution of metrics across a range of values, use Histograms.&nbsp;&nbsp;&nbsp;</p>



<h3 class="wp-block-heading" id="h-3-nbsp-avoid-overusing-histograms-nbsp-nbsp-nbsp">3.&nbsp; Avoid Overusing Histograms&nbsp;&nbsp;&nbsp;</h3>



<p>While histograms are powerful, each histogram can generate many time series based on bucket definitions. This can lead to high cardinality and storage overhead. If you don’t need bucket-based distributions, a gauge or counter might suffice.</p>



<p>Whenever you do need to use histograms, make sure you carefully select the buckets and consider using fewer buckets for less critical metrics.&nbsp;</p>



<h3 class="wp-block-heading" id="h-4-nbsp-use-clear-and-meaningful-labels">4.&nbsp; Use Clear and Meaningful Labels</h3>



<p>If a label can have thousands or millions of values, it&#8217;s probably not suitable for Prometheus. So avoid using labels for data that has large cardinality like UserID, email addresses, etc.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>



<h3 class="wp-block-heading" id="h-5-nbsp-keep-alerting-thresholds-realistic-nbsp-nbsp-nbsp-nbsp">5.&nbsp; Keep Alerting Thresholds Realistic&nbsp;&nbsp;&nbsp;&nbsp;</h3>



<p>Poorly defined alerts will defeat their purpose. Define sensible thresholds that account for known fluctuations. For instance, setting an alert for CPU usage above 50% might be too sensitive. A reasonable alert threshold might be close to 90% or even 95%.&nbsp;</p>



<p>Also, you should combine multiple metrics for more accurate alerting. For example, CPU usage and memory usage can be combined into a single alert.</p>



<h2 class="wp-block-heading" id="h-collecting-and-visualizing-prometheus-metrics-with-middleware">Collecting and Visualizing Prometheus Metrics with Middleware</h2>



<p>You can integrate Prometheus into your own system. A lot of teams prefer to use external tools for Prometheus custom metrics dashboards and visualizations of their Prometheus metrics. You can use an observability tool like Middleware to create custom visualizations and dashboards for common metrics like CPU, memory, disk I/O, etc.&nbsp;</p>



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<h3 class="wp-block-heading" id="h-prometheus-integration-with-middleware">Prometheus Integration with Middleware</h3>



<p>Integrating Prometheus with Middleware is a breeze. You can follow along with this setup guide or refer to the <a href="https://docs.middleware.io/integrations/prometheus-integration?_gl=1*1vlajmz*_ga*MTY1NTAxODc3MS4xNzIxNjMxODU2*_ga_2KBHR7YB2G*MTc0MTYyNjU3MC40NDQuMS4xNzQxNjI2NjQzLjAuMC4w">official docs</a> as well.&nbsp;</p>



<p>Login to Middleware and go to the installation page from the left-hand bottom side of the home screen.</p>



<p>Then, go to the <a href="https://app.middleware.io/installation#integration/allintegration">Integration Section</a></p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="2339" height="1168" src="https://middleware.io/backend/wp-content/uploads/2025/03/Prometheus-Integration-with-Middleware.jpg" alt="Prometheus Integration with Middleware" class="wp-image-9659"/></figure>



<p>Search for &#8220;Prometheus Integration&#8221; and click on the Prometheus card from inside the &#8220;Available&#8221; tab.&nbsp; Here, you can select the host and configure your cluster. You’ll need to add the required Prometheus annotations in your Kubernetes deployment’s metadata as mentioned in the instructions.</p>



<p>Let&#8217;s go with &#8220;Annotation Based Scraping&#8221; for now. Click on the &#8220;Save&#8221; button.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="2319" height="1138" src="https://middleware.io/backend/wp-content/uploads/2025/03/Annotation-Based-Scraping-in-prometheus.jpg" alt="Annotation Based Scraping in Prometheus" class="wp-image-9656"/></figure>



<p>Next, we&#8217;ll create a dashboard. You can create a dashboard from scratch or simply add a new widget to any of your custom dashboards.</p>



<p>Add a custom widget using the &#8220;Add Widget button&#8221;. The Integration Widget can be used to collect data from Prometheus.&nbsp;</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="2299" height="1103" src="https://middleware.io/backend/wp-content/uploads/2025/03/collect-data-metrics-from-Prometheus.jpg" alt="collect data metrics from Prometheus" class="wp-image-9657"/></figure>



<p>&nbsp;Next, we&#8217;ll need to mention the details of our widget.&nbsp;</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="2320" height="1113" src="https://middleware.io/backend/wp-content/uploads/2025/03/update-widget-for-prometheus-integration.jpg" alt="update widget for prometheus integration" class="wp-image-9661"/></figure>



<p>&nbsp;Once you click &#8220;Save&#8221;, you should see the data in the widget.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="2322" height="1048" src="https://middleware.io/backend/wp-content/uploads/2025/03/prometheus-metrics-visibility.jpg" alt="prometheus metrics visibility" class="wp-image-9660"/></figure>



<p>Your metrics from Prometheus will be automatically discovered and rendered here on your Middleware dashboard.&nbsp;</p>



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<p><a href="https://middleware.io/comparison/newrelic-alternative/">New Relic</a> has long been the gold standard in the <a href="https://middleware.io/blog/observability/">observability</a> space, renowned for its comprehensive <a href="https://middleware.io/blog/observability-vs-monitoring/">monitoring</a> and analytics capabilities. Since its founding in 2008, the company has empowered developers to optimize application performance, identify bottlenecks, and enhance overall user experience. In fact, their customer roster includes over <a href="https://newrelic.com/sites/default/files/2023-11/Company_Fact_Sheet_11_21_23.pdf">16,000</a> global organizations, such as Domino&#8217;s, Flight Centre, Tokopedia, and Serko.&nbsp;&nbsp;</p>



<p>However, beneath the surface, many customers have begun to voice concerns about New Relic&#8217;s complexity, steep learning curve, and – notably – pricing model. Most of them have experienced sticker shock as their observability costs skyrocket, leading to unforeseen expenses and budgetary strain.</p>



<blockquote class="reddit-embed-bq" style="height:316px" data-embed-height="316"><a href="https://www.reddit.com/r/devops/comments/19avcu8/newrelic_unethical_billing/">NewRelic unethical billing</a><br> by<a href="https://www.reddit.com/user/jaxn/">u/jaxn</a> in<a href="https://www.reddit.com/r/devops/">devops</a></blockquote><script async="" src="https://embed.reddit.com/widgets.js" charset="UTF-8"></script>



<p>To be more specific, customers have criticized New Relic&#8217;s pricing for its lack of transparency, with complex tiers, hidden costs, and per-user pricing. Additional costs for data storage, retention, and advanced features like AI-powered analytics and mobile monitoring can quickly add up. Long-term contracts, minimum commitment requirements, and limited flexibility in pricing also have been major.&nbsp;</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>“Our monthly spend with NewRelic surpassed the value we were getting out of it. The specific incident that triggered us to look at other vendors was when we turned on JVM-level telemetry and were not aware that it incurred a &#8220;custom event&#8221; cost, which skyrocketed our bill 10X for a month.”</p>
<cite>John D&#8217;Emic, Co-Founder and CTO at <a href="https://middleware.io/customers/revenium/">Revenium</a></cite></blockquote>



<p>Furthermore, extra fees for premium support and implementation can surprise customers, making New Relic&#8217;s total cost of ownership higher than expected. Not to mention, the steep learning curve doesn’t sweeten the pot whatsoever.</p>



<blockquote class="reddit-embed-bq" style="height:316px" data-embed-height="316"><a href="https://www.reddit.com/r/newrelic/comments/10lz884/am_istupid/">Am i&#8230;stupid?</a><br> by<a href="https://www.reddit.com/user/Andrewofredstone/">u/Andrewofredstone</a> in<a href="https://www.reddit.com/r/newrelic/">newrelic</a></blockquote><script async="" src="https://embed.reddit.com/widgets.js" charset="UTF-8"></script>



<p>This disparity between New Relic&#8217;s stellar observability ratings and its problematic pricing model raises questions about the true value proposition for customers. </p>



<p>If you are also wondering if it&#8217;s worth the investment, join <a href="https://middleware.io/">Middleware</a> as we delve into the details of New Relic&#8217;s pricing, explore its strengths and weaknesses, and compare it to our own observability solutions. Hopefully, it will help you make an informed decision and optimize your observability costs.</p>



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<h2 class="wp-block-heading" id="h-complexity-and-lack-of-transparency">Complexity and lack of transparency</h2>



<p>New Relic&#8217;s pricing tiers are divided into four categories: Free, Standard, Pro, and Enterprise.&nbsp;</p>



<ol class="wp-block-list">
<li>The Free tier offers 100 GB of data ingest, one full platform user, and unlimited basic users, but with limited features.&nbsp;</li>



<li>The Standard tier costs $49 per core user, with 100 GB of data ingest, limited features, and support.&nbsp;</li>



<li>The Pro tier is priced at $349 per full platform user, with commitment options, priority support, and advanced features.&nbsp;</li>



<li>The Enterprise tier offers custom pricing, advanced security, priority support, and tailored solutions.</li>
</ol>



<p>However, the complexity of New Relic&#8217;s pricing model makes it challenging for enterprises to accurately predict their costs. The various tiers and add-ons, such as data ingest costs, user-based pricing, and additional features, can lead to unexpected expenses. Furthermore, the platform&#8217;s pricing structure is not transparent, making it difficult for enterprises to understand the true cost of the service.&nbsp;</p>



<p>In contrast, Middleware offers a transparent and flexible pricing approach. For a limited time, all services are completely free of charge, allowing users to experience the platform&#8217;s benefits and effortlessly scale their infrastructure. After this period, Middleware follows a pay-as-you-go pricing plan, where costs are calculated based on actual usage rather than peak usage. This approach allows for significant cost savings, up to five times compared to New Relic&#8217;s pricing.&nbsp;</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>“Middleware’s pricing model incentivizes us to send more data, which means we get better results. This was very important for us because other tools would penalize us for higher data usage, leading to worse outcomes.”</p>
<cite><strong><em>— Rangaraj Tirumala, Founding Engineer at <a href="https://middleware.io/customers/hotplate/">Hotplate</a>&nbsp;</em></strong></cite></blockquote>



<p>Overall, Middleware&#8217;s pricing is transparent, with no traditional pricing tiers and full access to all features without restrictions. Each use case has its own associated cost, ensuring that users only pay for what they use. </p>



<p>Regardless of how users choose to utilize or prioritize their data, they gain full access to all Middleware features without any restrictions. This transparent and scalable pricing model eliminates the complexity and uncertainty associated with traditional pricing tiers.</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>“Middleware turned out to be 10X more cost-effective than competitors, and it provided the comprehensive monitoring capabilities we needed. I really feel like I’m getting real value out of it for the dollars I spent. I’m able to actually see the use, which is not always true of a lot of these tools.”</p>
<cite><strong><em>— Rangaraj Tirumala, Founding Engineer at Hotplate&nbsp;</em></strong></cite></blockquote>



<h2 class="wp-block-heading" id="h-data-ingestion-costs">Data ingestion costs</h2>



<p>New Relic charges $0.35 per GB for data ingest beyond the free 100 GB limit. For instance, an enterprise with 1 TB of data ingest would incur an additional $350 per month. This cost can quickly add up, especially for organizations with complex systems and high traffic.</p>



<p>Middleware also offers a free tier of 100 GB, but its Pay As You Go pricing plan is more cost-effective. Users pay only $0.3 per GB of metrics, logs, and traces beyond the free tier. For the same 1 TB of data ingest, users would pay approximately $270, representing significant savings of up to 22.86%.</p>



<p>Moreover, Middleware&#8217;s Ingestion Control Model provides unparalleled flexibility in managing data intake. This model enables users to optimize data ingestion by selecting only the most critical data, such as metrics, traces, logs, and profiling data. Users can further refine their data intake using granular filtering options, ingesting logs from specific services or filesystems.</p>



<p>While New Relic offers some data ingestion control features, including data ingest limits, data filtering, and data sampling, Middleware&#8217;s approach is more comprehensive and flexible. New Relic&#8217;s data filtering capabilities, for example, are based on attributes, whereas Middleware&#8217;s feature flags offer more precise control.</p>



<p>By controlling data ingestion, Middleware&#8217;s model helps enterprises:</p>



<ul class="wp-block-list">
<li>Reduce unnecessary data intake</li>



<li>Lower data ingest costs</li>



<li>Improve data quality and relevance</li>



<li>Enhance overall observability</li>
</ul>



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<h2 class="wp-block-heading" id="h-user-based-pricing">User-based pricing</h2>



<p>New Relic&#8217;s user-based pricing model can be restrictive for enterprises with multiple teams and departments. The platform charges $49 per core user and $349 per full platform user, which can become costly for large organizations. Additionally, the platform&#8217;s user-based pricing model does not account for varying user roles and permissions, leading to unnecessary expenses.</p>



<p>In contrast, Middleware&#8217;s pricing is based on data ingestion and usage, eliminating user-based costs and providing a more scalable and flexible solution for enterprises. In essence, there are no restrictions on user access or roles, ensuring that all team members can utilize the platform without additional costs.</p>



<h2 class="wp-block-heading" id="h-implementation-and-onboarding">Implementation and onboarding</h2>



<p>New Relic&#8217;s implementation and onboarding process has garnered criticism from users on social platforms, citing complexities and frustrations that hinder seamless integration. </p>



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="950" height="709" src="https://middleware.io/backend/wp-content/uploads/2024/11/New-Relic-DashBoard.webp" alt="New Relic Dashboard" class="wp-image-8261"/></figure>



<p>The process is often described as overly technical, requiring extensive expertise, with a steep learning curve that slows adoption. Users struggle with agent installation, configuration, and integration with other tools, while insufficient documentation and support exacerbate the challenges. The lengthy onboarding process, sometimes spanning weeks or months, can be overwhelming due to the platform&#8217;s vast feature set.</p>



<p>In contrast, Middleware&#8217;s implementation and onboarding process has received praise for its simplicity and efficiency. Users report a significantly streamlined experience, with a <a href="https://content.techgig.com/leadership/achieve-observability-in-60-seconds-middleware/articleshow/104532516.cms">lightweight agent</a> installation that takes mere minutes to complete. Middleware&#8217;s extensive documentation and 24/7 expert support ensure a smooth transition, eliminating the need for extensive technical expertise. </p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1920" height="1090" src="https://middleware.io/backend/wp-content/uploads/2024/11/Final.jpg" alt="Middleware Dashboard" class="wp-image-8262"/></figure>



<p>With over 200 seamless <a href="https://middleware.io/platform/integrations/">integrations</a>, users can easily adapt the platform to their existing ecosystem. Additionally, Middleware&#8217;s intuitive design enables users to quickly instrument services without encountering a steep learning curve. </p>



<p>According to Elijah Smith, Software Engineer III at Generation Esports, </p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>&#8220;The integration with Middleware was significantly easier and less time-consuming. We received real-time support from the Middleware team via Slack, which made the entire experience seamless.&#8221;</p>
<cite><strong><em>— Elijah Smith, Software Engineer III at <a href="https://middleware.io/customers/generationesports/">Generation Esports</a></em></strong></cite></blockquote>



<h2 class="wp-block-heading" id="h-add-ons-and-bundles">Add-ons and bundles</h2>



<p>New Relic offers various add-ons, including additional synthetic monitor checks, extended data retention, and Vulnerability Management. These add-ons can increase the overall cost of the platform, making it less competitive with alternative solutions.</p>



<p>On the other hand, Middleware includes all features and capabilities in its paid plan, eliminating the need for add-ons or bundles. This simple and transparent pricing structure ensures that enterprises only pay for what they use, without surprise costs or complexity.</p>



<h2 class="wp-block-heading" id="h-customer-service">Customer service</h2>



<p>New Relic&#8217;s post-sales support has been a disaster for many customers. The technical support is mediocre at best, with customers struggling to get in touch with competent representatives over the phone. Email communication is slow, with templated responses that often point to help articles, rather than providing personalized solutions. This has led to painful and cumbersome integration processes between customers&#8217; systems and New Relic.</p>



<figure class="wp-block-image size-large"><a href="https://www.g2.com/products/new-relic/reviews/new-relic-review-6489163"><img loading="lazy" decoding="async" width="2448" height="1572" src="https://middleware.io/backend/wp-content/uploads/2024/11/Screenshot-2024-11-06-at-12.07.11 AM.png" alt="New Relic G2 Review" class="wp-image-8257"/></a><figcaption class="wp-element-caption">Read the full review on G2. </figcaption></figure>



<p>In stark contrast, Middleware prioritizes customer satisfaction through responsive and knowledgeable support teams, flexible account management, proactive issue resolution, and transparent communication. By putting customers first, Middleware ensures that enterprises receive the support they need to optimize their observability and monitoring capabilities.</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>“Middleware’s team is incredibly responsive to feedback and often implements requested features or fixes within days. I have a monthly sync with one of their team members, and it’s something I actually look forward to. They listen to our concerns and actively work with us to make the platform better.”&nbsp;</p>
<cite><em><strong>— Aaron Hawkey, Co-Founder &amp; CTO, </strong></em><a previewlistener="true" href="https://middleware.io/customers/generationesports/"><strong>Generation Esports</strong></a></cite></blockquote>



<h2 class="wp-block-heading">New Relic v/s Middleware</h2>



<p>Here’s a side-by-side comparison of New Relic v/s Middleware based on key factors:</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="1144" src="https://middleware.io/backend/wp-content/uploads/2024/11/New-Relic-Vs-Middleware.png" alt="New Relic v/s Middleware" class="wp-image-8258"/></figure>



<h2 class="wp-block-heading">What are the people saying?</h2>



<p>We analyzed user reviews on G2, a leading software buying marketplace, to see what users are saying about both of the tools:</p>



<h3 class="wp-block-heading" id="h-new-relic-s-review-on-g2-nbsp"><strong>New Relic’s review on G2&nbsp;</strong></h3>



<p>Despite New Relic&#8217;s 4.3-star rating out of 5 on G2, numerous customers have publicly expressed disappointment with the platform, citing various reasons.</p>



<figure class="wp-block-image size-large"><a href="https://www.g2.com/products/new-relic/reviews/new-relic-review-4459142"><img loading="lazy" decoding="async" width="1600" height="589" src="https://middleware.io/backend/wp-content/uploads/2024/11/New-Relic-g2-review.png" alt="New Relic G2 Review 2" class="wp-image-8259"/></a><figcaption class="wp-element-caption">Read the full review on G2. </figcaption></figure>



<h3 class="wp-block-heading" id="h-middleware-s-review-on-g2"><strong>Middleware’s review on G2</strong></h3>



<p>Middleware holds a rating of 4.5 stars out of 5 on G2. We analyzed our reviews, and some users referred us to a leading cost-effective New Relic alternative:</p>



<figure class="wp-block-image size-large"><a href="https://www.g2.com/products/middleware/reviews/middleware-review-9715190"><img loading="lazy" decoding="async" width="1600" height="299" src="https://middleware.io/backend/wp-content/uploads/2024/11/Middleware-g2-review.png" alt="Middleware G2 Review" class="wp-image-8260"/></a><figcaption class="wp-element-caption">Read the full review on G2. </figcaption></figure>



<h2 class="wp-block-heading" id="h-final-verdict">Final verdict</h2>



<p>New Relic is undoubtedly a great platform, but its complex pricing, lack of transparency, restrictive scalability, and disappointing customer support have led to widespread dissatisfaction. In contrast, Middleware excels in these areas, offering a compelling alternative through its flexible pricing, scalable architecture, and unwavering commitment to customer success.</p>



<p>With Middleware, enterprises have achieved:</p>



<ol class="wp-block-list">
<li>10X reduction in observability costs</li>



<li>75% improvement in operational efficiency</li>



<li>20% faster MTTR</li>



<li>25% improvement in developer productivity</li>
</ol>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>“For people, by people. Middleware isn’t just another company’s profit machine; it’s a platform that actually works with us. The experience has been great, and it’s made a huge difference in how we handle observability.”</p>
<cite><strong><em>— Elijah Smith, Software Engineer III at Generation Esports&nbsp;</em></strong></cite></blockquote>



<p>So why wait? Join the ranks of <a href="https://middleware.io/customers/">satisfied enterprises</a> who&#8217;ve made the switch to Middleware and discover a better way to optimize performance, reduce costs, and drive success. Book a <a href="https://middleware.io/demo-request/">demo</a> today! &nbsp;</p>



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Explore New Relic's pricing model, its limitations, and unexpected costs. Discover if the observability benefits outweigh the expenses and consider alternative solutions.

New Relic Pricing: Still Worth it in 2025?

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<p>Think of a busy online shop with holiday sales and thousands of people waiting to make a purchase. Then, out of nowhere, the site is as slow as molasses; it takes forever for pages to load, and visitors abandon their shopping carts in disgust. Every business&#8217;s worst fear is this scenario — but <a href="https://middleware.io/blog/what-is-application-performance-monitoring/">Application Performance Monitoring</a> (APM) offers a vital safety net, ensuring optimal performance and minimizing downtime.</p>



<p>APM tracks key software performance metrics using <a href="https://middleware.io/blog/apm-tools/">monitoring software</a> and telemetry data. These tools help enterprises identify and resolve performance bottlenecks,&nbsp; ensuring smoother and faster application interactions. APM is used in many scenarios, but most enterprises use it to make the customer experience a breeze.&nbsp;</p>



<p>As shown in <a href="https://www.gartner.com/imagesrv/media-products/pdf/Catchpoint/Catchpoint1-31I1NU5.pdf">Gartner’s</a> survey, <strong>83%</strong> of enterprises prioritize improving troubleshooting, while <strong>81%</strong> focus on enhancing customer experience through APM.&nbsp; The report found that end-user experience monitoring was the most critical APM dimension, with 49% of respondents citing &#8220;<strong>enhancing CX quality</strong>&#8221; as their top reason for investing in APM.</p>



<p>This article explores the role of APM in driving customer satisfaction, measuring its business value, and implementing it on Middleware, featuring real-world applications and case studies.</p>



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<h2 class="wp-block-heading" id="h-how-apm-drives-customer-satisfaction">How APM drives customer satisfaction</h2>



<p>Application Performance Monitoring is the practice of using real-time data to <a href="https://middleware.io/blog/why-is-monitoring-your-application-important/">monitor application performance</a> and the digital experiences of end users. It does this by tracking different metrics (which we will discuss later) in real-time and showing performance bottlenecks.&nbsp;</p>



<p>Here’s what APM does to the application to drive customer satisfaction:</p>



<ol class="wp-block-list">
<li>​<strong>Improved application performance</strong>: APM plays a major part in improving application performance through real-time monitoring, including response time and errors. When these <a href="https://middleware.io/blog/devops-metrics-you-should-be-monitoring/">metrics</a> decrease to a certain level, the team is aware of possible issues.&nbsp;</li>
</ol>



<ol start="2" class="wp-block-list">
<li><strong>Reduced downtime</strong>: Issues in a software system are inevitable, but the time required to fix them can be reduced. APM helps lower the mean time to detect (MTTD) and the mean time to resolve (<a href="https://middleware.io/blog/how-to-reduce-mttr/">MTTR</a>). MTTD measures the duration a problem persists in an IT deployment before it&#8217;s noticed, while MTTR is the average time taken to restore a system to operational condition after a failure or cyberattack notification. Once the issue is detected early, the negative impact on the customer can be greatly reduced. </li>
</ol>



<p>Interested to know more about MTTR and MTTD? Click <a href="https://middleware.io/blog/mttr-vs-mttd/">here</a>. </p>



<ol start="3" class="wp-block-list">
<li><strong>Enhanced user experience</strong>: Smoother application performance greatly improves <a href="https://middleware.io/blog/mobile-app-performance-best-practices/">user experience</a> by ensuring applications run efficiently and responsively. Research by <a href="https://newsroom.cisco.com/c/r/newsroom/en/us/a/y2021/m07/tbd-ac.html">Cisco AppDynamics shows that 60% of customers</a> blame performance issues on the application or brand. An application is made up of different elements which are interrelated in some way. However, in a problem-solving process, trying to handle these aspects on your own can be quite challenging. APM are a great helping hand in figuring out such aspects.</li>
</ol>



<p>APM excels at managing these aspects, but how can you be sure it&#8217;s performing effectively? The answer is in defining the right metrics, which we will see in the next section.</p>



<h2 class="wp-block-heading" id="h-quantifying-the-impact-of-apm">Quantifying the impact of APM</h2>



<p>When hard-earned money and time are invested in Application Performance Monitoring (APM), it’s crucial to ensure that it’s hitting the mark for stakeholders. </p>



<p>The impact of APM on customer experience can be quantified using several key metrics. Keeping an eye on these will help us make informed decisions and steer us in the right direction:</p>



<ul class="wp-block-list">
<li><strong>CPU &amp; Memory Utilization: </strong>CPU and memory utilization show system resource usage. APM tracks these metrics in real-time to <a href="https://middleware.io/blog/efficient-server-utilization/">prevent server overloads</a> and potential slowdowns.</li>



<li><strong>Customer Support Tickets</strong>: This feature is responsible for managing the quantity and the kind of support tickets that pertain to application performance. If the number is high, it means that there are some problems with the <a href="https://middleware.io/blog/how-to-optimize-website-performance-for-the-holidays/">poor performance</a> of different operations.</li>



<li><strong>Page Load Times</strong>: It quantifies the amount of time a web page or an application takes to load. Generally, higher speeds of loading the webpage increase usability and decrease the level of frustration.</li>



<li><strong>Garbage Collection:</strong> It improves performance by automatically reclaiming memory from unused objects. It deletes redundant data and moves active objects to a new memory pool. <a href="https://middleware.io/blog/java-garbage-collection/">Optimal GC</a> frequency is crucial—too often can cause overhead, too infrequent can lead to memory shortages.</li>



<li><strong>Error Rates</strong>: Records the number of errors or failures the users go through. Lowering <a href="https://middleware.io/product/real-user-monitoring/error-tracking/">error rates</a> imply that APM is able to detect and solve problems with performance effectively.</li>



<li><strong>User Satisfaction Scores</strong>: This measure is a way of getting immediate feedback from the users with the help of questionnaires or ratings. Higher satisfaction scores tend to embody such positive experiences connected to the issue of enhanced application performance.</li>
</ul>



<h3 class="wp-block-heading" id="h-apm-on-middleware">APM on Middleware</h3>



<p><a href="https://middleware.io/product/apm/">Middleware&#8217;s APM</a> solution can help correlate application performance improvements with positive customer behavior changes by:</p>



<ul class="wp-block-list">
<li><strong>Real-time monitoring:</strong> <a href="https://middleware.io/">Middleware</a> continuously monitors application performance. This allows you to find and resolve issues before they impact users. Improved performance metrics, like reduced page load times, are directly linked to increased user engagement and satisfaction.</li>



<li><strong>User experience tracking:</strong> By analyzing metrics like response times and error rates, Middleware helps correlate better application performance with improved user experiences, such as fewer drop-offs and increased session durations.</li>



<li><strong>Data-driven insights:</strong> The APM solution offers detailed reports that show how performance improvements, like faster transaction processing, result in positive customer behaviors, such as higher conversion rates or lower abandonment rates.</li>
</ul>



<p>Furthermore, the development process is simplified without requiring extensive learning, as will be explained in the next section.</p>



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<h2 class="wp-block-heading" id="h-implementing-middleware-s-apm-for-a-python-application">Implementing Middleware&#8217;s APM for a Python application</h2>



<p>This section will create a Python application in FastAPI and use APM to monitor metrics, logs, and traces. Let’s see it step-by-step.</p>



<h3 class="wp-block-heading">Prerequisites</h3>



<p>To get started with setting up, follow these general steps:</p>



<ul class="wp-block-list">
<li><strong>Access the Installation Documentation</strong>: Visit the <a href="https://docs.middleware.io/getting-started">installation documentation</a> for detailed instructions specific to your operating system (OS).</li>



<li><strong>Run the Installation Command</strong>: Copy and execute the command provided on the documentation page.</li>



<li><strong>Verify Installation</strong>: After running the command, ensure the installation is completed.</li>
</ul>



<p>For more detailed and up-to-date instructions, refer to the official <a href="https://docs.middleware.io/quickstart">installation guide</a>.</p>



<h3 class="wp-block-heading" id="h-create-fastapi-application">Create FastAPI application</h3>



<p>To create a FastAPI application, we will need to create a virtual environment to isolate the dependencies and add our code. Follow these steps: </p>



<ul class="wp-block-list">
<li>Create a Virtual Environment</li>



<li>Activate the Virtual Environment</li>



<li>Install FastAPI and Uvicorn</li>



<li>Create Endpoints</li>



<li>Enable Middleware APM</li>
</ul>



<h3 class="wp-block-heading" id="h-running-the-application">Running the application</h3>



<p>Upon successfully running the application using the Middleware installation manual&#8217;s commands, you can monitor metrics and performance through the APM. </p>



<p>The dashboard presented below shows all the APIs&#8217; metrics and performance.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="858" src="https://middleware.io/backend/wp-content/uploads/2024/10/unnamed-2.png" alt="Running the Application on Middleware" class="wp-image-8197"/></figure>



<p>From the above, we can observe our Application Performance Monitoring (APM) in action. It is tracking both success and error rates, indicating that the API currently has a success rate of 95.5%. If we further want to dig up the problematic request that the API didn’t handle, go to <strong>Spans</strong>.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="856" src="https://middleware.io/backend/wp-content/uploads/2024/10/APM-2.jpg" alt="Middleware’s APM Spans of Requests" class="wp-image-8198"/></figure>



<p>From the above, we can see all the requests that were made, including both successful ones and those that resulted in errors.</p>



<h2 class="wp-block-heading" id="h-case-studies">Case studies</h2>



<p>Application monitoring is an important part of customer satisfaction. The following case studies from different companies testify to the improvement of customer experience through APM.&nbsp;</p>



<h3 class="wp-block-heading" id="h-kurt-geiger">Kurt-Geiger</h3>



<p>A fashion retailer operating five websites and retail stores across the UK and Ireland faced challenges with low Core Web Vitals (CWV) scores. It affected their e-commerce performance and customer satisfaction. The lack of detailed insights made it difficult to address issues. Kurt Geiger monitored CWV&nbsp; in real-time, identified performance bottlenecks, and improved their score from <a href="https://newrelic.com/blog/best-practices/kurt-geiger-core-web-vitals">45-50 to 85-90</a> within six months by implementing an APM solution. This APM-driven approach led to 99% of customers enjoying a positive online shopping experience.</p>



<h3 class="wp-block-heading" id="h-hotplate">Hotplate</h3>



<p>Middleware&#8217;s APM helped <a previewlistener="true" href="https://middleware.io/customers/hotplate/">Hotplate achieve cost savings</a>, approximately 10 times more than other observability solutions. The detailed traces and session replays allowed Hotplate to pinpoint and address performance bottlenecks efficiently, enhancing overall customer interactions. As a result, Middleware&#8217;s Real-User Monitoring (RUM) now manages over 1.5 million sessions per month for Hotplate. This ensures a smooth user experience during high-traffic events.</p>



<h3 class="wp-block-heading" id="h-trademarkia">Trademarkia</h3>



<p>One of the <a href="https://middleware.io/customers/trademarkia/">largest visual search engines</a> for trademarked logos, names, and slogans faced challenges with its outdated tech stack. This affected efficiency and scalability. Trademarkia adopted Middleware’s full-stack cloud <a href="https://middleware.io/blog/observability/">observability</a> platform to modernize its infrastructure. Middleware&#8217;s user-friendly interface and Slack integration boosted collaboration, productivity, and response times. These, in turn, positively impacted the CX.</p>



<h3 class="wp-block-heading" id="h-mercado-libre">Mercado Libre</h3>



<p>The largest e-commerce platform in Latin-America, faced challenges in scaling, performance, and downtime as its business grew. With APM solution, they gained real-time insights into application performance, which enabled their teams to identify and resolve issues quickly. This led to a significant reduction in errors, improved <a previewlistener="true" href="https://newrelic.com/customers/mercadolibre">response times to under 50 milliseconds</a>, and enhanced the overall user experience.&nbsp;</p>



<p>Attaching an APM provides a clear view of existing bottlenecks, making it easier to address them. These case studies prove the importance of APM in improving customer experience.&nbsp;</p>



<h2 class="wp-block-heading">Conclusion</h2>



<p>The 21st century is striving for automation. So, why manually monitor the application&#8217;s performance? It is important that the APM handles the heavy lifting of continuous monitoring and informs the team why customers are not returning. When customers feel their needs are prioritized, businesses see an increase in return rates and customer loyalty.</p>



<p>With Middleware&#8217;s APM, businesses can enhance application performance, boosting customer satisfaction and loyalty by providing real-time insights directly correlating with positive customer behavior. Middleware can extend its capabilities to RUM (Real User Monitoring) and Synthetic Monitoring to 10x the customer experience.</p>



<p>The full suite of Middleware provides monitoring tools to deliver superior customer experiences and drive business success. So, why wait? Sign up for <a previewlistener="true" href="http://middleware.io">Middleware</a> now.</p>



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Explore the role of APM in driving customer satisfaction, measuring its business value, and implementing it on Middleware.

How Can APM Transform Customer Satisfaction?


<p>Many businesses struggle with understanding real-world user experiences, leading to unresolved issues and user frustration. Slow load times, navigation difficulties, and unexpected errors often go unnoticed until they significantly impact user satisfaction.</p>



<p><a href="https://middleware.io/blog/what-is-real-user-monitoring/"><strong>Real User Monitoring</strong></a><strong> (RUM)</strong> is a performance monitoring practice that captures and analyzes every user interaction with an application or website in real-time. By providing detailed insights into how users engage with digital platforms, it plays a crucial role in understanding and enhancing real-world user experiences.</p>



<p>It helps identify pain points such as slow load times and navigation issues, enabling businesses to make data-driven decisions to improve user satisfaction and optimize the overall experience. In this article, we will see how you can use RUM to increase user satisfaction.</p>



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<h2 class="wp-block-heading" id="h-benefits-of-rum-for-user-satisfaction">Benefits of RUM for user satisfaction</h2>



<p>Let’s dive into how RUM can enhance user satisfaction:</p>



<h3 class="wp-block-heading" id="h-user-behavior-insights">User behavior insights</h3>



<p>Understanding how users navigate through an application is crucial for enhancing user satisfaction. Real User Monitoring provides businesses with the ability to track and analyze user journeys in real-time, revealing the paths users take, the actions they perform, and the obstacles they encounter. By examining these user journeys, businesses can identify common friction points that disrupt the user experience.</p>



<p>For example, if users frequently abandon a process at a particular step, RUM data can highlight this issue. This could be due to confusing navigation, lengthy forms, or unclear instructions. By pinpointing these issues, businesses can take targeted actions to streamline the process, making it more intuitive and user-friendly. Additionally, RUM can uncover patterns in user behavior, such as frequently visited pages or popular features, which can inform decisions on where to focus development efforts.</p>



<h3 class="wp-block-heading" id="h-identify-user-frustrations">Identify user frustrations</h3>



<p>RUM not only provides a macro view of user journeys but also dives deep into specific instances of user frustration. Detailed user session data allows businesses to detect issues such as repeated clicks on unresponsive buttons, error messages, or slow load times. These frustrations can significantly impact user satisfaction, leading to decreased engagement and higher churn rates.</p>



<p>For instance, if users encounter a slow-loading page, RUM data can reveal the extent of the delay and its impact on user behavior. Businesses can then prioritize performance improvements for that page, such as optimizing images, refining server-side processes, or enhancing content delivery networks (CDNs). By addressing these frustrations promptly, businesses can enhance the overall user experience and reduce the likelihood of users abandoning the application.</p>



<h3 class="wp-block-heading" id="h-understand-user-interactions">Understand user interactions</h3>



<p>RUM provides a comprehensive view of how users interact with an application. This includes tracking clicks, scrolls, form submissions, and other interactions. By analyzing these interactions, businesses can gain a deeper understanding of how users engage with different features and content.</p>



<p>For example, if users frequently interact with a particular feature, such as a search bar or a filter option, businesses can optimize these elements to enhance usability and efficiency. Conversely, if certain features are rarely used, it may indicate that they are not meeting user needs or are difficult to find. In such cases, businesses can make design adjustments to improve visibility and usability.</p>



<h2 class="wp-block-heading" id="h-middleware-s-real-user-monitoring-nbsp">Middleware&#8217;s Real User Monitoring&nbsp;</h2>



<p><a href="https://middleware.io/">Middleware</a>’s full-stack cloud <a href="https://middleware.io/blog/observability/">observability</a> platform offers a robust RUM solution designed to capture detailed user session data and provide comprehensive user behavior analytics. With Middleware&#8217;s RUM, businesses can visualize user interactions through <a href="https://middleware.io/product/real-user-monitoring/session-replays/">session replays</a>, offering a clear picture of user behavior and identifying patterns that indicate potential issues.</p>



<p>Session replays allow businesses to watch recordings of actual user sessions, providing invaluable insights into how users experience the application. This visual representation helps identify usability issues that might not be apparent through traditional analytics. For example, session replays can show where users get stuck, how they navigate complex workflows, and where they <a href="https://middleware.io/product/real-user-monitoring/error-tracking/">encounter errors</a>.</p>



<p><a href="https://middleware.io/product/real-user-monitoring/">Middleware&#8217;s RUM solution</a> also includes advanced analytics capabilities, enabling businesses to segment user data based on various criteria such as demographics, device type, and location. This allows for a more granular analysis of user behavior, helping businesses understand the specific needs and preferences of different user segments.&nbsp;</p>



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<h2 class="wp-block-heading" id="h-implementing-middleware-s-rum-nbsp">Implementing Middleware’s RUM&nbsp;</h2>



<p>Let’s see how to implement Middleware’s RUM to enhance your user experience. By following these steps, you can effectively track user journeys, correlate data, and prioritize key issues to <a href="https://middleware.io/blog/what-is-application-performance-monitoring/">optimize application performance</a>.</p>



<h3 class="wp-block-heading" id="h-1-focus-on-user-journeys">1. Focus on user journeys</h3>



<p>User journeys refer to the paths users take through your application, from their initial interaction to their final action, such as completing a purchase or signing up for a service. Tracking user journeys involves <a href="https://middleware.io/blog/monitoring-best-practices-for-developer/">monitoring</a> and analyzing these paths to understand how users interact with various features and to identify any points of friction or frustration.</p>



<p>Understanding user journeys is crucial for optimizing the user experience. By examining these journeys, businesses can identify where users encounter difficulties, drop off, or experience delays. This insight helps in improving application design, enhancing usability, and ultimately increasing user satisfaction and retention.</p>



<p><em><strong>Example</strong></em>: Consider an <a href="https://middleware.io/blog/how-to-optimize-website-performance-for-the-holidays/">e-commerce website</a> where users typically search for products, add them to their cart, and proceed to checkout. By tracking this user journey, you might discover that users frequently abandon their carts during the checkout process. This could indicate issues such as a lengthy form, unexpected shipping costs, or a confusing payment interface.</p>



<h3 class="wp-block-heading" id="h-what-data-to-collect">What data to collect?</h3>



<p>To effectively track user journeys, collect the following data:</p>



<ul class="wp-block-list">
<li><strong>User actions:</strong> Record clicks, page views, form submissions, and other interactions.</li>



<li><strong>Session duration:</strong> Measure the time spent on each page or section.</li>



<li><strong>Conversion points</strong>: Track critical actions such as adding items to the cart, initiating checkout, or completing a purchase.</li>



<li><strong>Drop-off points:</strong> Identify where users abandon their journey, such as leaving a form incomplete or exiting the checkout process.</li>
</ul>



<h3 class="wp-block-heading" id="h-how-to-track-user-journeys-with-middleware">How to Track User Journeys with Middleware?</h3>



<ol class="wp-block-list">
<li><strong>Define key user journeys: </strong>Start by identifying the most important paths within your application, such as onboarding processes, product searches, or transaction flows.</li>



<li><strong>Configure Middleware’s RUM</strong>: Set up Middleware’s RUM to track these key journeys. For instance, you can configure the RUM to capture interactions on the product page, track the steps in the checkout process, and record user actions throughout the journey.</li>



<li><strong>Utilize session replays:</strong> Middleware’s session replays allow you to watch recordings of user interactions. Use this feature to visually analyze how users navigate through your application and identify any issues or friction points.</li>



<li><strong>Analyze data:</strong> Review the collected data to understand user behavior, pinpoint areas of friction, and gain insights into user interactions. Look for patterns such as frequent clicks on non-interactive elements or repeated navigation between pages.</li>
</ol>



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<h3 class="wp-block-heading">2. Correlate RUM data</h3>



<p>Correlating RUM data involves integrating and comparing user interaction data with other performance metrics to gain a comprehensive understanding of user experience. This approach helps in identifying the <a href="https://middleware.io/blog/mttr-vs-mttd/">root causes of issues</a> that span both frontend and backend systems.</p>



<p>Correlating RUM data with other metrics, such as server performance or error rates, enables businesses to diagnose complex issues more effectively. It helps in distinguishing between problems caused by frontend design flaws and those resulting from backend performance issues.</p>



<p><em>Example: </em>Suppose users report slow load times on a specific page, and RUM data shows high abandonment rates on that page. By correlating this data with server performance metrics, you might discover that the slow load times are due to high server response times or inefficient database queries.</p>



<h3 class="wp-block-heading" id="h-what-data-to-correlate">What data to correlate?</h3>



<ul class="wp-block-list">
<li><strong>User interaction data: </strong>Includes click rates, page load times, session durations, and error occurrences.</li>



<li><strong>Server performance metrics:</strong> Covers server response times, error rates, and resource utilization.</li>



<li><strong>Backend logs:</strong> Provides information on database queries, API response times, and application errors.</li>
</ul>



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<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1200" height="952" data-id="8177" src="https://middleware.io/backend/wp-content/uploads/2024/09/RUM-3.png" alt="How can you enhance user satisfaction with RUM - 3" class="wp-image-8177"/></figure>
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<h3 class="wp-block-heading" id="h-how-to-correlate-data-with-middleware-nbsp">How to correlate data with Middleware?&nbsp;</h3>



<ol class="wp-block-list">
<li><strong>Integrate Middleware’s RUM with other tools: </strong>Connect Middleware’s RUM with your backend performance monitoring tools to gather comprehensive data on both frontend and backend performance. As things stand, Middleware offers over 200+ built-in <a href="https://middleware.io/platform/integrations/">integrations</a>.&nbsp;</li>



<li><strong>Analyze correlated data:</strong> Use Middleware’s platform to visualize and analyze the correlated data. Identify patterns and correlations to diagnose the root causes of performance issues and user dissatisfaction.</li>
</ol>



<h3 class="wp-block-heading">3. Prioritize user impact</h3>



<p>Prioritizing user impact involves focusing on the issues that most significantly affect user satisfaction and experience. By addressing high-impact problems first, businesses can make meaningful improvements that enhance the overall <a href="https://middleware.io/blog/mobile-app-performance-best-practices/">user experience</a>.</p>



<p>Focusing on high-impact issues ensures that resources are allocated effectively to resolve problems that have the most significant effect on user satisfaction. It helps in improving key metrics such as conversion rates, user retention, and overall engagement.</p>



<p><em>Example: </em>If RUM data reveals that users frequently abandon the checkout process due to slow loading times, addressing this issue should be a priority. Slow load times can directly impact conversion rates and customer satisfaction, making it essential to implement performance optimizations.</p>



<h3 class="wp-block-heading" id="h-what-data-to-prioritize">What data to prioritize?</h3>



<ul class="wp-block-list">
<li><strong>Frequency of issues:</strong> Focus on issues that occur frequently across many user sessions.</li>



<li><strong>Severity of issues</strong>: Prioritize issues that have a significant impact on user experience, such as major performance bottlenecks or critical errors.</li>



<li><strong>User feedback:</strong> Consider user feedback and complaints related to specific issues.</li>
</ul>



<h3 class="wp-block-heading" id="h-how-to-prioritize-and-address-issues-with-middleware">How to prioritize and address issues with Middleware?</h3>



<ol class="wp-block-list">
<li><strong>Identify high-impact issues</strong>: Review RUM data to identify the most frequent and severe issues affecting user satisfaction. For example, slow loading times on key pages or frequent form submission errors.</li>



<li><strong>Assess impact:</strong> Evaluate the potential impact of each issue on user satisfaction and business goals. For instance, determine how slow loading times on a checkout page affect conversion rates.</li>



<li><strong>Implement solutions:</strong> Develop and implement targeted solutions to address high-impact issues. This might involve optimizing page load times, fixing broken features, or improving form usability.</li>



<li><strong>Monitor and iterate: </strong>After implementing solutions, continue to monitor RUM data to ensure that the changes have resolved the issues and improved user satisfaction. Iterate on the solutions based on ongoing feedback and data.</li>
</ol>



<p>By effectively tracking user journeys, correlating RUM data with performance metrics, and prioritizing high-impact issues, businesses can leverage Middleware’s RUM solution to enhance user satisfaction and optimize application performance.</p>



<p>Next, set up Middleware’s RUM in your application, start tracking user interactions, and use the data to spot and fix issues. Our advice? Focus on the most critical problems first to improve user satisfaction &#8211; and give your users something to smile about!&nbsp;</p>



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Boost user satisfaction with Middleware's RUM by identifying pain points and optimizing real-time user experiences. 

How can you enhance user satisfaction with RUM?


<p>Website reliability is essential for business success, and there is no second thought about it! A reliable website ensures customers can access services without disruptions, building trust and loyalty. On the other hand, downtime or performance issues can lead to lost revenue and damage a brand&#8217;s reputation.</p>



<p>Picture this: you&#8217;re hosting a massive online sale, and just as customers are filling their carts, the checkout page crashes. It&#8217;s a nightmare scenario, risking lost sales and unhappy shoppers. Much like you&#8217;d never wait for customers to point out broken equipment in a physical store, businesses can&#8217;t afford to wait for users to report website issues.</p>



<p>That&#8217;s where <a href="https://middleware.io/blog/what-is-synthetic-monitoring/">synthetic monitoring</a> comes in. This proactive approach acts like a virtual user, regularly testing critical parts of a website to catch problems before real customers do. By simulating typical user actions, synthetic monitoring helps <a href="https://middleware.io/blog/best-practices-to-avoid-website-outages-on-black-friday/">prevent outages</a> and keeps websites running smoothly.</p>



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</div>
</div>



<h2 class="wp-block-heading" id="h-how-synthetic-monitoring-works">How synthetic monitoring works</h2>



<p>Synthetic monitoring proactively ensures website and application reliability by simulating user interactions. It helps identify issues before they affect real users. Here’s how it is generally implemented.</p>



<ol class="wp-block-list">
<li><strong>Script creation</strong>: Develop scripts that simulate user actions, such as navigating a website or making API calls (Or you can use a full-stack cloud <a href="https://middleware.io/blog/observability/">observability</a> platform like <a href="https://middleware.io">Middleware</a> for the same).</li>



<li><strong>Simulation execution</strong>: Run these scripts regularly from various global locations to detect regional performance differences.</li>



<li><strong>Data collection and analysis</strong>: Gather data on <a href="https://middleware.io/blog/devops-metrics-you-should-be-monitoring/">metrics</a> like load times and error rates and analyze them for anomalies.</li>



<li><strong>Alerting and reporting</strong>: Generate alerts for detected issues and provide detailed reports on performance trends.</li>
</ol>



<h2 class="wp-block-heading">Benefits of synthetic monitoring for reliability</h2>



<h3 class="wp-block-heading" id="h-proactive-identification-of-issues">Proactive identification of issues</h3>



<p>Imagine launching a new feature on your website, only to have customers report bugs after it goes live. It&#8217;s a frustrating experience that could&#8217;ve been avoided with synthetic monitoring. By simulating user journeys and transactions, it allows businesses to detect website errors and performance issues before they affect real users.<br><br>For instance, Middleware offers robust synthetic monitoring capabilities that test API functionality from various locations. This proactive approach ensures that potential problems are caught early, preventing disruptions and maintaining a smooth user experience.<br><br>For example, if an online platform was updated to use a newly rolled out API, synthetic monitoring through Middleware can test the same, catching any glitches before the real users experience such errors.</p>



<h3 class="wp-block-heading" id="h-global-monitoring">Global monitoring</h3>



<p>A website&#8217;s performance can&#8217;t just be reliable in one location; it must be consistent globally. Picture an e-commerce site that&#8217;s fast and responsive in New York but sluggish in Sydney. Such disparities can drive away international customers.</p>



<ol class="wp-block-list">
<li></li>
</ol>



<p>Middleware addresses this challenge with its geographically distributed location-based testing. We can simulate API interactions from different regions, helping businesses monitor and ensure a consistent experience worldwide. This global perspective is crucial for businesses aiming to provide seamless service, regardless of where their customers are.</p>



<p>Here’s another example. Imagine a global travel booking site experiencing slow response times in Asia, affecting bookings. Global monitoring from Middleware can reveal that server configuration in that region needs optimization.</p>



<h3 class="wp-block-heading" id="h-performance-monitoring">Performance monitoring</h3>



<p>Understanding a website&#8217;s normal performance is key to spotting issues before they escalate. By establishing performance baselines, businesses can track deviations that indicate underlying problems. For example, if an API response time suddenly spikes, it could signal a technical issue or an inefficient new feature. In the real world, it looks like this: A SaaS company launches a software update, and performance monitoring shows an increase in response times. This data helps the team quickly isolate a problematic code change.<br><br>Middleware&#8217;s synthetic monitoring tools help set these benchmarks and monitor performance trends over time. By identifying regressions early, businesses can address issues promptly, maintaining optimal <a href="https://middleware.io/blog/how-to-optimize-website-performance-for-the-holidays/">website performance</a> and user satisfaction.</p>



<h2 class="wp-block-heading">Using synthetic monitoring effectively with Middleware</h2>



<p>Here are some insights on optimizing your synthetic monitoring and using all the best features of Middleware effectively.</p>



<h3 class="wp-block-heading">Monitor API performance</h3>



<p>APIs are the backbone of many web services, facilitating communication between different systems. Monitoring API performance is vital for ensuring these integrations work smoothly. Middleware&#8217;s synthetic monitoring tools can simulate API requests and track response times and error rates. This proactive approach helps identify bottlenecks or failures in API endpoints, ensuring that services dependent on these APIs remain reliable and responsive. Using an example, let’s explore how to set up an <a href="https://middleware.io/blog/api-monitoring/">API monitor</a> with Middleware.</p>



<ol class="wp-block-list">
<li>Let’s use a public API that returns the current Bitcoin price. We can use the steps provided with any such API as required.</li>



<li>First, access the synthetic monitoring tab on the left-hand panel of the Middleware platform.</li>



<li>Click the “Add a new test” option.</li>



<li>This will open a configuration menu to create your synthetic API monitor. Fill in the details of your API endpoint as required.</li>



<li>Next, proceed to configure the required assertions. You can configure multiple assertions to reflect the preferred behavior of your API, like the response time for different status codes and the response type.</li>



<li>You can also configure other settings like location, frequency of monitoring, and where you would like to push your alerts and then finally create the monitor.</li>



<li>Once the monitor is created, you can check the dashboard for your results. Here’s what a typical dashboard looks like once you have configured everything.</li>
</ol>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="1056" src="https://middleware.io/backend/wp-content/uploads/2024/09/How-can-synthetic-monitoring-enhance-website-reliability.png" alt="How can synthetic monitoring enhance website reliability" class="wp-image-8136"/></figure>



<h3 class="wp-block-heading">Monitor multi-step API</h3>



<p>Complex applications often rely on multi-step API calls to complete user transactions, such as logging in, processing payments, or retrieving data. Middleware enables businesses to monitor the uptime and reliability of these multi-step API processes. For instance, an e-commerce platform can use synthetic monitoring to test the entire checkout process, from adding items to the cart to confirming payment. By simulating these multi-step interactions, Middleware helps ensure each step functions correctly, preventing disruptions that could lead to customer frustration or abandoned transactions.</p>



<p>To configure a multi-step API monitor, select the “Multi-step API tests” tab under “Define request” and configure the assertions for multiple APIs in the desired sequence.</p>



<p>One of the key features of a multi-step API test is the ability to use the response values from a previous step in the next API call. The example below shows that the response preview of step 1 contains three fields– amount, base, and currency under the data object.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="930" src="https://middleware.io/backend/wp-content/uploads/2024/09/How-can-synthetic-monitoring-enhance-website-reliability-2.png" alt="Monitor multi-step API" class="wp-image-8137"/></figure>



<p>You can access these values in subsequent steps using variable names like {{$0.data.amount}}, {{$0.data.base}}, and {{$0.data.currency}}. For example, If you want to use these values in making a new API call in step 2, you may access them as follows &#8211;</p>



<pre class="wp-block-code"><code>"https://api.coinbase.com/v2/prices/{{$0.data.base}}-{{$0.data.currency}}/spot”</code></pre>



<h3 class="wp-block-heading">Monitor API/Endpoint performance from any location</h3>



<p>Global businesses must ensure that their APIs perform consistently across different regions. Middleware provides the ability to monitor API and endpoint performance from various geographical locations. This feature is essential for identifying region-specific issues, such as network latency or server misconfigurations, that could impact user experience. For example, a streaming service can use Middleware to test video delivery APIs from multiple countries, ensuring that users worldwide enjoy the same high-quality experience.</p>



<p>While creating a synthetic monitor, you can configure the locations to test under the “Select locations” section. The screenshot below showcases the available locations via Middleware.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1285" height="625" src="https://middleware.io/backend/wp-content/uploads/2024/09/How-can-synthetic-monitoring-enhance-website-reliability-3.png" alt="Monitor API/Endpoint performance from any location" class="wp-image-8138"/></figure>



<h3 class="wp-block-heading">Set alert thresholds</h3>



<p><a href="https://middleware.io/blog/proactive-monitoring/">Proactive monitoring</a> is most effective when paired with timely alerts. Middleware allows users to set the frequency to check the required thresholds for various metrics, such as response times, error rates, or downtime duration. By defining these thresholds, businesses can receive immediate notifications when performance deviates from the norm. For example, an online banking app might set an alert for high transaction times, prompting the IT team to investigate and resolve the issue before it affects customers. This feature ensures that potential problems are addressed quickly, minimizing their impact on end users.</p>



<p>Middleware currently supports <a href="https://middleware.io/platform/alerts/">alert</a> integrations with Slack, Email, Opengenie, and PagerDuty.</p>



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<h2 class="wp-block-heading">Common challenges with synthetic monitoring</h2>



<h3 class="wp-block-heading">Avoiding false positives</h3>



<p>One common challenge with synthetic monitoring is the risk of false positives—instances where monitoring incorrectly reports issues that don&#8217;t exist. False positives can lead to unnecessary alarms and wasted time investigating non-issues.</p>



<p>To mitigate this, one must regularly update and validate your monitoring scripts to reflect the most current version of your website or application. Use conditional checks to ensure that only genuine issues trigger alerts.</p>



<h3 class="wp-block-heading">Balancing synthetic and real user monitoring</h3>



<p>While synthetic monitoring proactively tests website functionality, it doesn&#8217;t capture the full spectrum of real user experiences. Balancing synthetic monitoring with real user monitoring (RUM) provides a more comprehensive view of website performance.</p>



<p>A good approach would be implementing a hybrid monitoring strategy that uses synthetic monitoring and RUM. Synthetic monitoring helps catch potential issues before they impact users, while RUM provides insights into actual user behavior and experiences. This combination allows for a more complete understanding of your website&#8217;s performance. Middleware also provides features for <a href="https://middleware.io/blog/what-is-real-user-monitoring/">real user monitoring</a>.</p>



<h2 class="wp-block-heading">Conclusion</h2>



<p>Synthetic monitoring is an essential tool for proactive website reliability management. By simulating user interactions it allows businesses to identify and resolve potential issues before they impact real users, ensuring a seamless online experience. Middleware&#8217;s <a href="https://docs.middleware.io/workflow/synthetic-monitoring/synthetic-monitoring">synthetic monitoring solution</a> stands out by offering features like API performance monitoring, multi-step API uptime checks, and global performance analysis. These capabilities enable businesses to maintain consistent website performance and reliability across different regions and devices.</p>



<p>With Middleware, companies can also set customizable alert thresholds, ensuring they are immediately informed of deviations from expected performance metrics. This proactive approach prevents outages and performance degradation and protects the business&#8217;s reputation and revenue.</p>



<p>Want to ensure your website&#8217;s reliability and provide a flawless user experience across the globe? Discover how <a href="https://middleware.io/product/synthetic-monitoring/">Middleware&#8217;s synthetic monitoring</a> can help your business avoid potential issues and deliver consistent performance. Start your journey to a more reliable website today!</p>


Understand how you can proactively enhance your website’s performance and reliability with Middleware’s synthetic monitoring.

How can synthetic monitoring enhance website reliability?


<p>Gone are the days when a hardware failure led to an organization&#8217;s entire infrastructure coming to a complete standstill. With digital databases and <a href="https://middleware.io/blog/cloud-native-infrastructure-challenges/">cloud-based architecture</a>, we now have sophisticated mechanisms to ensure that your database is protected and can stay running despite operational issues.</p>



<p>However, just because it is safer doesn&#8217;t mean it can never experience downtime or failures. Database failures remain a persistent threat for most businesses and can cause significant financial losses or reputational damages if they happen at a crucial time.&nbsp;</p>



<p>But why do database failures occur? The reasons can range from hardware malfunctions to human errors, and even the minutest of mistakes can cause an entire database to fail. To mitigate these risks, organizations must adopt a <a href="https://middleware.io/blog/proactive-monitoring/">proactive approach</a> to database management. This involves implementing a robust set of strategies to prevent failures, protect data integrity, and ensure business continuity.&nbsp;</p>



<p>In this comprehensive guide, we will explore nine essential safety tips for safeguarding your database and minimizing the impact of potential disruptions.</p>



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<h2 class="wp-block-heading" id="h-9-ways-to-prevent-database-failures"><strong>9 ways to prevent database failures</strong></h2>



<p>So, despite being safer than traditional hardware-based infrastructure, why do database failures still occur? The answer can be several, like hardware failures, software failures, network failures, bugs in the database, or security breaches.</p>



<p>Surprisingly, 70% of organizations in a <a href="https://www.proofpoint.com/us/newsroom/press-releases/proofpoint-recognized-2024-gartner-peer-insights-customers-choice-data#:~:text=In%20fact%2C%20Proofpoint's%20inaugural%202024,while%20fewer%20than%2050%25%20cited">global report</a> mentioned “careless users” as the cause for data losses, and fewer than 50% cited technical issues as the reason for it.</p>



<p>Thus, the ideal approach to database failures is to undertake preventive steps to minimize the risks of failure and ensure that even if a failure occurs, its impact can be contained. Here are nine proactive steps you can implement to keep your data safe and ensure business continuity even if a failure occurs.</p>



<h2 class="wp-block-heading"><strong>1. Regular backups</strong></h2>



<p>The best way to avoid data loss during a database failure is always to have a backup. It’s that simple!&nbsp;</p>



<p>By regularly backing up your data, you can instantly use the last saved instance and get back in action, ensuring that your data loss due to hardware failures, cyberattacks, or human errors is minimized. Depending on your organization’s dependency on the database and business usage, you can backup the data by determining:</p>



<ul class="wp-block-list">
<li><strong>Frequency:</strong> Determine the optimal backup frequency based on data criticality and business requirements. As per Recovery Point Objective (RPO) protocols, a business cannot afford data loss of more than 10 hours.</li>



<li><strong>Backup types:</strong> Consider full, incremental, and differential backups to balance data protection and storage efficiency.</li>



<li><strong>Backup media:</strong> Choose reliable storage options like tapes, disks, or cloud-based storage.</li>



<li><strong>Backup testing:</strong> Regularly test your backup and restore processes to verify data integrity and recovery time objectives.</li>



<li><strong>Backup retention:</strong> Define backup retention policies to manage storage space and compliance requirements. The Recovery Time Objective (RTO) mentions that a database must run within 3 hours after a failure occurs.&nbsp;</li>
</ul>



<h2 class="wp-block-heading"><strong>2. Database monitoring</strong></h2>



<p><a href="https://middleware.io/product/database-monitoring/">Database monitoring</a> is a systematic approach that helps you monitor your database systems and closely monitor their network and database performance.&nbsp;</p>



<p>This allows you to gain deep insights into the behavior and performance of your entire database, enabling you to identify slow queries, address bottlenecks, and take action regarding resource constraints that could impact your overall performance. An effective database monitoring process usually involves the following:</p>



<ul class="wp-block-list">
<li>Tracking key metrics, such as CPU utilization, memory usage, disk I/O, query performance, and error rates.</li>



<li>Real-time monitoring using advanced <a previewlistener="true" href="https://middleware.io/blog/database-monitoring-software/">database monitoring and observability tools</a> to gain insights into the health and performance of your database.</li>



<li>Setting up alerts to notify you of abnormal behavior or performance deviations in your database.</li>



<li>Monitoring performance data 24/7 to identify and address performance bottlenecks before they can lead to a slowdown or database failures.</li>



<li>Forecasting resource requirements based on database growth and workload trends.</li>
</ul>



<p><a href="https://middleware.io/blog/observability/">Comprehensive observability solutions</a> like Middleware can help you proactively monitor and manage your database activities. Middleware is an all-encompassing tool that provides detailed insights into your database&#8217;s health and performance, enabling you to identify potential issues before they escalate into major problems.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1845" height="929" src="https://middleware.io/backend/wp-content/uploads/2024/08/image.png" alt="Database monitoring to prevent database failures" class="wp-image-8093"/></figure>



<p>This proactive database monitoring allows you to perform activities like:</p>



<ul class="wp-block-list">
<li><strong>Real-time monitoring:</strong> Continuously monitor your database metrics such as CPU utilization, memory usage, disk I/O, query performance, and error rates.</li>



<li><strong>Anomaly detection</strong>: Leverage advanced analytics to identify unusual patterns and deviations from normal behavior, <a href="https://middleware.io/platform/alerts/">alerting</a> you to potential issues before they impact your database.</li>



<li><strong>Root-cause analysis</strong>: Middleware helps pinpoint the root cause of database performance issues by correlating database metrics with other system data, such as <a href="https://middleware.io/blog/what-is-log-monitoring/">application logs</a> and <a href="https://middleware.io/blog/what-is-infrastructure-monitoring/">infrastructure metrics</a>.</li>



<li><strong>Capacity planning</strong>: By analyzing database growth trends, Middleware helps you anticipate future resource requirements and avoid performance bottlenecks.</li>



<li><strong>Index management:</strong> Middleware offers recommendations for creating, dropping, or rebuilding indexes to improve query performance.</li>



<li><strong>Improved MTTR:</strong> By providing a comprehensive view of your system, Middleware helps reduce the time it takes to resolve database problems, improving your <a href="https://middleware.io/blog/how-to-reduce-mttr/">mean time to repair</a> (MTTR).</li>



<li><strong>Optimized MTTD:</strong> Middleware&#8217;s early warning capabilities help detect issues sooner, reducing your <a href="https://middleware.io/blog/mttr-vs-mttd/">mean time to detect</a> (MTTD).</li>
</ul>



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<h2 class="wp-block-heading"><strong>3. Schema optimization</strong></h2>



<p>Regularly reviewing and optimizing your database schema is crucial for maintaining performance and preventing bottlenecks. Some of the key aspects of schema optimization include:</p>



<ul class="wp-block-list">
<li><strong>Normalization:</strong> Ensure data is organized into tables with minimal redundancy and dependencies.</li>



<li><strong>Indexing:</strong> Create appropriate indexes to speed up query execution on frequently accessed columns.</li>



<li><strong>Data types:</strong> Choose the correct data types for columns to optimize storage and performance.</li>



<li><strong>Table partitioning:</strong> Divide large tables into smaller, more manageable partitions for improved query performance and manageability.</li>



<li><strong>Regular review:</strong> Periodically assess your schema for potential optimization opportunities.</li>
</ul>



<h2 class="wp-block-heading"><strong>4. Query optimization</strong></h2>



<p>Most databases can be accessed using queries, making query execution essential to maintaining database performance and effectiveness. Some key strategies for <a href="https://middleware.io/platform/query-language/">query optimization</a> are:</p>



<ul class="wp-block-list">
<li><strong>Query analysis:</strong> Identify slow-performing queries and analyze their execution plans.</li>



<li><strong>Indexing:</strong> Ensure appropriate indexes are in place to support query execution.</li>



<li><strong>Query rewriting:</strong> Rewrite inefficient queries to improve performance.</li>



<li><strong>Parameterization:</strong> Use parameterized queries to reduce query compilation overhead.</li>



<li><strong>Caching:</strong> Implement query caching to store frequently executed queries and their results.</li>



<li><strong>Query tuning:</strong> Fine-tune query parameters and execution plans for optimal performance.</li>
</ul>



<h2 class="wp-block-heading"><strong>5. Resource management</strong></h2>



<p>Allocating sufficient resources to your database is essential for optimal performance and preventing bottlenecks. Proper resource management involves:</p>



<ul class="wp-block-list">
<li><strong>Hardware resources:</strong> Ensure adequate CPU, memory, and storage capacity to handle the database workload.</li>



<li><strong>Software resources:</strong> Allocate sufficient licenses for database software and related tools.</li>



<li><strong>Monitoring resource utilization:</strong> Track resource consumption to identify potential bottlenecks and adjust allocations as needed.</li>



<li><strong>Capacity planning:</strong> Forecast future resource requirements based on database growth and workload trends.</li>
</ul>



<h2 class="wp-block-heading"><strong>6. Access control</strong></h2>



<p>Protecting your database from unauthorized access is crucial for maintaining data integrity and security. Access controls allow you to provide every user with limited access to your network and overall infrastructure, protecting it from any unauthorized access. You can define access controls using parameters like:</p>



<ul class="wp-block-list">
<li><strong>Authentication:</strong> Verify the identity of users attempting to access the database through strong password policies, multi-factor authentication, and biometric verification.</li>



<li><strong>Authorization:</strong> Define user permissions based on roles and responsibilities, granting access only to necessary data and functions.</li>



<li><strong>Least privilege principle:</strong> Grant users the minimum privileges required to perform their tasks.</li>



<li><strong>Regular reviews:</strong> Periodically review and update user permissions to ensure they align with current roles and responsibilities.</li>



<li><strong>Security awareness training:</strong> Educate users about the importance of data security and best practices for protecting sensitive information.</li>
</ul>



<h2 class="wp-block-heading"><strong>7. Patch management</strong></h2>



<p>Keeping your database software up-to-date with the latest patches is essential for maintaining security and <a href="https://middleware.io/blog/cloud-load-balancing/">performance</a>. Regular patch management helps <a rel="follow" href="https://www.getastra.com/blog/security-audit/vulnerability-assessment-scanning-tools/">address vulnerabilities</a>, improve stability, and enhance overall database reliability.&nbsp;</p>



<p>Key aspects of patch management include:</p>



<ul class="wp-block-list">
<li><strong>Patch testing:</strong> Thoroughly test patches in a controlled environment before deploying them to production.</li>



<li><strong>Patch scheduling:</strong> Develop a patch deployment schedule to minimize disruptions to database operations.</li>



<li><strong>Change management:</strong> Implement change management processes to document patch installations and track their impact.</li>



<li><strong>Vulnerability assessment:</strong> Regularly assess your database environment for vulnerabilities and prioritize patch applications accordingly.</li>



<li><strong>Emergency patching:</strong> Plan to apply critical patches outside of regular maintenance windows in case of security threats.</li>
</ul>



<h2 class="wp-block-heading"><strong>8. Disaster recovery plan</strong></h2>



<p>Since database failures are sometimes unavoidable, you will need a comprehensive disaster recovery plan to help you in this stage. The plan should include the steps and resources you can use to get back up to speed and minimize downtime, data loss, or any sort of disruption to your business.&nbsp;</p>



<p>A complete disaster recovery plan should include:</p>



<ul class="wp-block-list">
<li><strong>Risk assessment:</strong> Identify potential threats and vulnerabilities to your database environment.</li>



<li><strong>Recovery objectives:</strong> Based on business requirements, define recovery time objectives (RTO) and recovery point objectives (RPO).</li>



<li><strong>Backup and recovery procedures:</strong> Develop detailed procedures for backing up and restoring database data.</li>



<li><strong>Testing and validation:</strong> Regularly test your disaster recovery plan to ensure its effectiveness.</li>



<li><strong>Communication plan:</strong> Establish communication channels for coordinating disaster recovery efforts.</li>



<li><strong>Documentation:</strong> Maintain up-to-date documentation of the disaster recovery plan and procedures.</li>
</ul>



<h2 class="wp-block-heading"><strong>9. Database high availability</strong></h2>



<p>A major reason for database failures is the continuous and high usage of its resources. To ensure uninterrupted database access and minimize downtime, consider implementing high-availability solutions. These solutions provide redundancy and failover mechanisms to protect against database failures. Some of the approaches for managing database availability include:</p>



<ul class="wp-block-list">
<li><strong>Database clustering:</strong> Create a cluster of database servers where one acts as the primary and others as standbys.</li>



<li><strong>Database replication:</strong> Maintain multiple copies of the database across different servers.</li>



<li><strong>Load balancing:</strong> Distribute database traffic across multiple servers to improve performance and availability.</li>



<li><strong>Failover mechanisms:</strong> Implement automatic failover to a standby database in case of primary database failure.</li>



<li><strong>Regular testing:</strong> Test high-availability configurations to ensure their effectiveness.</li>
</ul>



<h2 class="wp-block-heading"><strong>Conclusion</strong></h2>



<p>Database failures can be a nightmare, but with proper precautions, their frequency and impact can be controlled. The strategies outlined in this guide will help you reduce the risk of these failures and ensure smooth operations. However, to note any impending disaster, you need constant monitoring to ensure that any issue or anomaly gets flagged early on, giving you ample time to mitigate it.</p>



<p>Database monitoring tools like Middleware offer several features that help you proactively monitor your database, preventing failures and optimizing overall performance. This also lets you gain valuable insights into your database performance and take the right steps to safeguard it.Ready to explore how<a previewlistener="true" href="https://middleware.io/"> Middleware</a> can help you achieve database resilience and peace of mind? <a previewlistener="true" href="https://app.middleware.io/auth/register/">Try us</a> for free today!</p>



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Safeguard your database from unexpected failures with our expert advice. Discover proven methods to enhance database reliability, performance, and resilience.

Top 9 Ways To Prevent Database Failures

Take control of your AWS cloud environment! Discover how ECS monitoring helps you track performance, pinpoint issues, and maximize resource efficiency.

What is ECS Monitoring?

Learn how to set up, monitor, and analyze AWS CloudTrail logs for enhanced security, compliance, and operational troubleshooting.

Best Monitoring Practices for AWS CloudTrail Logs


<p>Analyzing CloudWatch Logs is an essential practice for any organization aiming to optimize their AWS expenditure and enhance the overall visibility of their cloud environment.&nbsp;</p>



<p>These logs contain data that, when properly queried and analyzed, enables teams to respond more effectively and efficiently to operational issues. For instance, if an unexpected issue arises, CloudWatch Logs Insights can be utilized to perform in-depth queries, helping to quickly identify potential causes and verify the effectiveness of deployed fixes.</p>



<p>In this article, we&#8217;ll dive into several practical methods for analyzing CloudWatch Logs using <a href="https://middleware.io/">Middleware</a>. You&#8217;ll learn how to use built-in tools like metric streams and custom dashboards&nbsp; to perform deep-dive queries, rapidly identify issues, and validate the effectiveness of the solutions you deploy.</p>



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<h2 class="wp-block-heading" id="h-understanding-aws-cloudwatch-nbsp"><strong>Understanding AWS CloudWatch&nbsp;</strong></h2>



<p><a href="https://docs.middleware.io/integrations/aws-integration">Amazon CloudWatch</a> is a comprehensive monitoring service offered by AWS that tracks the performance and health of your AWS resources and applications. It collects and stores operational metrics and log files from various AWS resources such as EC2 instances, RDS databases, and Lambda functions. </p>



<p>CloudWatch enables you to monitor your AWS account and resources effectively, reacting to system-wide performance changes, optimizing resource utilization, and gaining a unified view of operational health. Think of it like the go-to tool for cloud visibility and monitoring.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1431" height="894" src="https://middleware.io/backend/wp-content/uploads/2024/07/AWS.png" alt="6 Ways to Analyze AWS CloudWatch Resources, Data, &amp; Metrics with Middleware" class="wp-image-7979"/></figure>



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<h2 class="wp-block-heading"><strong>What are AWS CloudWatch logs?</strong></h2>



<p>CloudWatch Logs is a feature within CloudWatch that allows you to monitor, store, and access logging information from Amazon EC2 instances, AWS CloudTrail, and other sources. This service is crucial for centralizing <a href="https://middleware.io/blog/what-is-log-monitoring/">logs</a> from all your applications, systems, and AWS services into a single, highly scalable service.&nbsp;</p>



<p>With CloudWatch Logs, you can streamline log management by viewing, searching, and filtering logs irrespective of their origin, thus maintaining a consistent and chronological flow of events.</p>



<h2 class="wp-block-heading" id="h-how-to-analyze-aws-cloudwatch-resources-data-amp-metrics-using-middleware"><strong>How to analyze AWS CloudWatch resources, data &amp; metrics using Middleware</strong>?</h2>



<p>AWS CloudWatch provides robust <a href="https://middleware.io/blog/website-monitoring-tools/">monitoring</a> capabilities, and integrating it with Middleware can enhance your ability to analyze resources, data, and metrics.&nbsp;</p>



<p>This comprehensive guide will walk you through the 3 steps to start <a href="https://middleware.io/blog/aws-monitoring/">monitoring your AWS environment</a> with Middleware effectively.</p>



<h2 class="wp-block-heading"><strong>Step 1: Integrate AWS CloudWatch or ECS with Middleware</strong></h2>



<p>To begin streaming AWS CloudWatch metrics in real-time, we can use Metric Streams, a service provided by Amazon Web Services. This integration involves setting up a Delivery stream and a Metric stream.<br><br><strong><em><a previewlistener="true" href="https://docs.middleware.io/integrations/aws-ecs-monitoring">Here’s a link to the documentation on how to set it up</a>.</em></strong></p>



<p>With the Middleware Agent, you can also monitor your ECS containers and tasks running on EC2 instances and ECS Fargate. This ensures comprehensive monitoring of your <a href="https://middleware.io/blog/containerization/">containerized environments</a>.</p>



<p><strong><em>For more detailed instructions, refer to the<a href="https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/CloudWatch-Metric-Streams.html"> AWS Metric Streams documentation</a> and the <a href="https://docs.middleware.io/integrations/kinesis-streams">Middleware integration documentation</a>.</em></strong></p>



<h2 class="wp-block-heading"><strong>Step 2: Display metrics and logs in Middleware log section</strong></h2>



<p>Once you have integrated AWS services with Middleware, the next step is to display and analyze the metrics and log data. Here’s how you can make the most of the Middleware dashboard:</p>



<h3 class="wp-block-heading"><strong>Viewing metrics</strong></h3>



<p>After successful integration, Middleware’s dashboard will automatically start pulling in metrics and log data from AWS CloudWatch and ECS. Some of the key metrics you can monitor include:</p>



<ul class="wp-block-list">
<li><strong>Task health</strong>: Check the health status of your tasks to ensure they are running smoothly.</li>



<li><strong>Container health</strong>: Monitor the health of your containers to detect any potential issues early.</li>



<li><strong>Container memory usage</strong>: Keep track of the memory usage of your containers to optimize performance.</li>



<li><strong>Task memory percent</strong>: View the percentage of memory utilized by tasks to manage resources effectively.</li>
</ul>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="773" src="https://middleware.io/backend/wp-content/uploads/2024/07/AWS_Middleware-Screen-1.png" alt="AWS_Middleware Screen 1" class="wp-image-7980"/></figure>



<h3 class="wp-block-heading"><strong>Custom dashboard builder</strong></h3>



<p>To tailor the monitoring experience to your specific needs, you can create custom dashboards. Here’s a step-by-step guide:</p>



<ol class="wp-block-list">
<li><strong>Enter custom dashboard builder</strong>: Navigate to the custom dashboard builder from the Middleware dashboard.</li>



<li><strong>Select widget type</strong>: Choose from various widget types such as Time Series, Bar, Heatmaps, and more.</li>



<li><strong>Choose data source and metrics</strong>: Select the appropriate data source (e.g., &#8220;cloud&#8221;) and metrics stream. Choose the specific metrics you want to monitor.</li>



<li><strong>Configure parameters</strong>: Set up additional parameters and filters to refine the data visualization according to your requirements.</li>
</ol>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="812" src="https://middleware.io/backend/wp-content/uploads/2024/07/AWS_Middleware-Screen-2.png" alt="AWS Middleware Screen 2" class="wp-image-7981"/></figure>



<p>You can always navigate back to the default dashboard under the AWS CloudWatch section in the left-hand menu of the Middleware platform.</p>



<h2 class="wp-block-heading" id="h-step-3-visualize-your-metrics"><strong>Step 3: Visualize your metrics</strong></h2>



<p>Visualizing your metrics effectively is crucial for gaining insights into your AWS environment. Middleware offers two primary ways to visualize your data:</p>



<h3 class="wp-block-heading"><strong>Middleware&#8217;s default AWS dashboards</strong></h3>



<p>Middleware provides pre-configured dashboards for popular AWS services. These dashboards come with a variety of graphs and visualizations representing key metrics for each service. For instance, an EC2 namespace dashboard might include metrics such as CPU utilization, network traffic, and disk I/O.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="810" src="https://middleware.io/backend/wp-content/uploads/2024/07/AWS-Middleware-Screen-3.png" alt="Middleware’s default AWS dashboards" class="wp-image-7982"/></figure>



<p>These default dashboards are designed to give you a comprehensive view of your AWS services&#8217; performance and health. You can explore similar dashboards for other AWS services within Middleware’s default dashboard collection.</p>



<h3 class="wp-block-heading"><strong>Build custom widgets using Middleware&#8217;s dashboard builder</strong></h3>



<p>If you need a more customized view, you can build your own dashboards from scratch. Here’s how:</p>



<ol class="wp-block-list">
<li><strong>Add new widget</strong>: To any existing dashboard, add a new widget.</li>



<li><strong>Select AWS metrics</strong>: Choose from the list of available AWS metrics under the &#8220;cloud&#8221; data source.</li>



<li><strong>Widget types</strong>: Visualise your data using various widget types such as Time Series, Bar Chart, Pie Chart, etc.</li>



<li><strong>Customize data visualization</strong>: Tailor the widgets to display the metrics that are most important to you.</li>
</ol>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="812" src="https://middleware.io/backend/wp-content/uploads/2024/07/AWS-Middleware-Screen-4.png" alt="AWS Middleware Screen 4" class="wp-image-7983"/></figure>



<p>By customizing your dashboard, you can focus on the specific metrics and data points that matter most to your operations, providing a tailored monitoring experience.</p>



<p><strong><em>How much does AWS CloudWatch Cost?&nbsp;You can read about AWS CloudWatch Pricing, or try out the <a href="https://www.economize.cloud/resources/aws/pricing/cloudwatch/">AWS Cloudwatch Pricing Catalog</a> to view direct, indirect, and hidden costs associated with using CloudWatch.</em></strong></p>



<h2 class="wp-block-heading"><strong>Conclusion</strong></h2>



<p>Integrating AWS CloudWatch with Middleware significantly enhances your ability to monitor and analyze your AWS environment.&nbsp;</p>



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<p>By following the steps outlined in this guide, you can set up real-time streaming of metrics, customize your dashboards, and gain deep insights into your AWS resources.Whether you utilize default dashboards or build custom views, Middleware provides powerful tools to manage and optimize your AWS infrastructure effectively.</p>


Explore how you can analyze AWS CloudWatch logs using Middleware. Learn to integrate, display, and visualize metrics with custom dashboards to optimize your AWS environment.

How to Analyze AWS CloudWatch Resources, Data, & Metrics with Middleware


<p>Imagine your Java application is a thriving online store, buzzing with activity. On a <a href="https://middleware.io/blog/best-practices-to-avoid-website-outages-on-black-friday/">big sale day</a>, the excitement peaks—but suddenly, your application starts slowing down, orders get delayed, and then it crashes.&nbsp;</p>



<p>While there are many reasons an application might slow down or crash, such as high CPU usage, slow database queries, or inefficient code, one of the <strong>main reasons is often memory management issues</strong>. This is where Java Garbage Collection comes in. By automatically reclaiming memory from objects that are no longer in use, GC helps keep your <a href="https://middleware.io/blog/why-is-monitoring-your-application-important/">application running smoothly</a>.&nbsp;</p>



<p>Java GC acts like a diligent housekeeper for your application&#8217;s memory, automatically clearing out those unused objects to prevent such disastrous crashes. Let&#8217;s explore what Java Garbage Collection is, why it’s vital, and how you can ensure it works optimally for your applications.</p>



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<h2 class="wp-block-heading" id="h-what-is-java-garbage-collection-nbsp">What is Java garbage collection?&nbsp;</h2>



<p>Java Garbage Collection (GC) is an automatic memory management system that is a fundamental part of the <a href="https://middleware.io/blog/java-performance-monitoring/">Java runtime environment</a>. Its primary role is to identify and reclaim memory occupied by objects that are no longer in use by the application. By doing so, it prevents <a href="https://middleware.io/blog/memory-leaks/">memory leaks</a> and optimizes memory usage without requiring developers to manually manage memory allocation and deallocation</p>



<ul class="wp-block-list">
<li><strong>Automatic memory management:</strong> GC automatically frees up memory by removing unused objects, eliminating the need for manual memory management.</li>



<li><strong>Prevents memory leaks:</strong> By identifying and cleaning up objects that are no longer referenced, GC prevents memory leaks that can lead to application crashes.</li>



<li><strong>Optimizes performance:</strong> Efficient memory management through GC helps maintain application performance by ensuring that the heap space is used effectively.</li>



<li><strong>Simplifies development:</strong> With GC handling memory cleanup, developers can focus on writing business logic without worrying about memory allocation details.</li>
</ul>



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<h2 class="wp-block-heading">Why is Java garbage collection important?</h2>



<p>Java Garbage Collection (GC) plays a vital role in maintaining the health and performance of Java applications. Here’s why:</p>



<h3 class="wp-block-heading" id="h-preventing-memory-leak-nbsp">Preventing memory leak&nbsp;</h3>



<p>GC identifies and removes unused objects, preventing memory leaks that could lead to memory exhaustion and application crashes. By reclaiming memory from unused objects, GC ensures that there is always available memory for new objects, preventing sudden application failures.</p>



<h3 class="wp-block-heading">Improved performance</h3>



<p>GC optimizes memory usage by continuously freeing up space occupied by unused objects, leading to smoother application execution. By managing memory allocation and deallocation, GC reduces fragmentation, ensuring faster and more efficient memory operations.</p>



<h3 class="wp-block-heading">Simplified development</h3>



<p>Developers are freed from the complexities of manual memory allocation and deallocation, allowing them to focus on writing functional code. Automated memory management by GC reduces the likelihood of memory-related bugs, such as dangling pointers and memory leaks.</p>



<h3 class="wp-block-heading">Enhanced scalability</h3>



<p>GC helps in managing memory resources efficiently, allowing <a href="https://middleware.io/wp-content/uploads/2021/12/the-ultimate-guide-to-auto-scaling-your-application-infrastructure.pdf">applications to scale up</a> to handle more data and users. By ensuring optimal memory management, GC makes it possible to build and maintain large-scale, enterprise-level applications.</p>



<h2 class="wp-block-heading">Understanding Java memory management</h2>



<p>Java Heap Memory is a dedicated memory space used for dynamic memory allocation where all Java objects are stored. This heap is managed by the Java Garbage Collector (GC), which ensures that memory is used efficiently by reclaiming space from objects that are no longer in use.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1200" height="700" src="https://middleware.io/backend/wp-content/uploads/2024/07/Java-garbage-collection-What-is-it-and-how-does-it-work.png" alt="Java memory management" class="wp-image-7969"/></figure>



<h3 class="wp-block-heading">Generational garbage collection</h3>



<p>Java uses a generational approach to garbage collection, which categorizes objects based on their lifecycle:</p>



<h4 class="wp-block-heading" id="h-young-generation">Young generation</h4>



<ul class="wp-block-list">
<li><strong>Frequent GC cycles:</strong> The Young Generation is designed for short-lived objects. It consists of the Eden space and two Survivor spaces (S0 and S1).</li>



<li><strong>Eden space:</strong> New objects are initially allocated in the Eden space.</li>



<li><strong>Minor GC:</strong> When the Eden space fills up, a minor GC cycle occurs, moving surviving objects to the Survivor spaces. This process is quick and happens frequently.</li>
</ul>



<h4 class="wp-block-heading" id="h-tenured-old-generation">Tenured/old generation</h4>



<ul class="wp-block-list">
<li><strong>Longer-lived objects:</strong> Objects that survive multiple minor GC cycles in the Young Generation are moved to the Tenured Generation (also known as the Old Generation).</li>
</ul>



<ul class="wp-block-list">
<li><strong>Less frequent GC cycles:</strong> Collections in the Tenured Generation are less frequent but more time-consuming compared to the Young Generation.</li>
</ul>



<h4 class="wp-block-heading" id="h-permanent-generation">Permanent generation</h4>



<ul class="wp-block-list">
<li><strong>Static data storage:</strong> In versions of Java before 8, the Permanent Generation stored metadata such as class information and method <a href="https://middleware.io/blog/data-driven-approach-engineering/">data</a>. This has been replaced by Metaspace in Java 8 and later versions.</li>
</ul>



<h3 class="wp-block-heading">Garbage collection algorithms</h3>



<p>Java uses different algorithms to manage memory efficiently and reclaim unused objects. Let&#8217;s dive into two key algorithms:</p>



<h4 class="wp-block-heading">Marking and sweeping</h4>



<p><strong>Marking phase:</strong></p>



<ul class="wp-block-list">
<li><strong>Objective:</strong> Identify all reachable objects starting from GC roots.</li>



<li><strong>Process:</strong> During the marking phase, the Garbage Collector (GC) traverses through the heap, marking all objects that are accessible directly or indirectly from GC roots<em> (e.g., local variables, static variables).</em> These marked objects are considered alive and are retained in memory.</li>
</ul>



<p><strong>Sweeping phase:</strong></p>



<ul class="wp-block-list">
<li><strong>Objective:</strong> Reclaim memory from objects that were not marked as reachable.</li>



<li><strong>Process:</strong> After marking, the GC performs the sweeping phase. It scans through the heap again and identifies objects that were not marked during the previous phase. These unmarked objects are no longer in use by the application and are reclaimed, freeing up memory for future allocations.</li>
</ul>



<h4 class="wp-block-heading">Copy collection</h4>



<p><strong>Copying phase:</strong></p>



<ul class="wp-block-list">
<li><strong>Objective:</strong> Optimize memory usage by relocating surviving objects.</li>



<li><strong>Process:</strong> In the copy collection algorithm, the GC copies surviving objects from one area of memory to another. Typically, this involves moving objects from the Eden space and Survivor spaces to a new area of memory. By doing so, it compacts memory usage, reduces fragmentation, and prepares the original memory space for new allocations.</li>
</ul>



<p>Understanding these algorithms helps developers optimize memory management in Java applications, ensuring smoother performance and avoiding issues like memory leaks or excessive pauses during garbage collection cycles.</p>



<h3 class="wp-block-heading">GC terminology</h3>



<p>To understand Java Garbage Collection (GC) better, let&#8217;s explore key terminology that forms the foundation of how GC manages memory in Java applications:</p>



<h4 class="wp-block-heading">GC roots</h4>



<p>GC roots are critical reference points in the Java runtime environment that serve as starting points for the Garbage Collector to traverse and identify reachable objects.</p>



<p><strong>Types of GC roots:</strong></p>



<ul class="wp-block-list">
<li><strong>Local variables:</strong> Variables defined within methods and currently in scope.</li>



<li><strong>Active threads:</strong> Threads currently running in the application.</li>



<li><strong>Static variables:</strong> Class-level variables that are referenced from a class.</li>



<li><strong>JNI (Java native interface) references:</strong> References from native code to Java objects.</li>
</ul>



<p>GC roots ensure that any object reachable directly or indirectly from these roots is considered alive and not eligible for garbage collection.</p>



<h4 class="wp-block-heading">Reachability</h4>



<p>Reachability in the context of Java GC refers to the accessibility of an object starting from the GC roots. An object is said to be reachable if there exists a path of references from one or more GC roots that lead to the object.</p>



<p><strong>Types of reachability:</strong></p>



<ul class="wp-block-list">
<li><strong>Strong reachability:</strong> An object is strongly reachable if it can be accessed directly from a GC root or through a chain of strong references.</li>



<li><strong>Soft reachability:</strong> Objects that are reachable through soft references, which are cleared by the GC only when memory is low.</li>



<li><strong>Weak reachability:</strong> Objects that are reachable only through weak references, which are cleared by the GC during the next garbage collection cycle.</li>



<li><strong>Phantom reachability:</strong> Objects that are reachable only through phantom references, which are enqueued after the object is finalized but before it is reclaimed.</li>
</ul>



<p>GC uses reachability to determine which objects are eligible for garbage collection. Objects that are not reachable are considered dead and can be safely reclaimed to free up memory.</p>



<h4 class="wp-block-heading">Minor vs. Major GC collections</h4>



<p><strong>Definition:</strong></p>



<ul class="wp-block-list">
<li><strong>Minor GC (young generation GC):</strong> This type of GC collects garbage in the Young Generation, where short-lived objects are allocated. It is characterized by fast and frequent collections, usually involving the Eden space and Survivor spaces.</li>



<li><strong>Major GC (full GC):</strong> A major GC involves the entire heap, including both the Young Generation and the Tenured Generation (Old Generation). It is less frequent but more time-consuming compared to a minor GC.</li>
</ul>



<p><strong>Purpose:</strong></p>



<ul class="wp-block-list">
<li><strong>Minor GC:</strong> Cleans up short-lived objects and promotes surviving objects to the Old Generation.</li>



<li><strong>Major GC:</strong> Reclaims memory across the entire heap, including long-lived objects in the Old Generation, and often involves compaction of memory to reduce fragmentation.</li>
</ul>



<p>Understanding the distinction between minor and major GC collections helps developers optimize memory usage and anticipate the impact of garbage collection cycles on <a href="https://middleware.io/blog/what-is-application-performance-monitoring/">application performance</a>.</p>



<p>These GC-related terms form the basis of understanding how Java manages memory dynamically, ensuring efficient memory utilization and stable application performance.</p>



<h2 class="wp-block-heading">Monitoring and tuning Java garbage collection</h2>



<p>Java Garbage Collection (GC) is pivotal for maintaining Java application performance by efficiently managing memory. <a href="https://middleware.io/blog/monitoring-best-practices-for-developer/">Effective monitoring</a> and tuning of GC parameters are essential practices to ensure optimal resource utilization and application stability.</p>



<h3 class="wp-block-heading">Java monitoring tools</h3>



<p><a href="https://middleware.io/blog/website-monitoring-tools/">Monitoring tools</a> like Middleware, VisualVM, jmap, and jstat provide invaluable insights into GC activity:</p>



<ul class="wp-block-list">
<li><strong>Middleware</strong>: Offering comprehensive monitoring for Java applications, Middleware&#8217;s <a href="https://middleware.io/product/apm/">APM</a> provides end-to-end visibility, real-time metrics, and <a href="https://middleware.io/product/distributed-tracing/">distributed tracing</a> to quickly identify and resolve performance issues.</li>



<li><strong>VisualVM:</strong> Integrated with the JDK, VisualVM offers real-time monitoring of heap usage, GC cycles, thread behavior, and more through a graphical interface.</li>



<li><strong>jmap:</strong> This command-line utility generates heap dumps and provides details on heap memory usage, aiding in diagnosing memory-related issues.</li>



<li><strong>jstat:</strong> Another command-line tool, jstat reports JVM performance statistics including GC behavior, memory usage, and more, useful for analyzing application performance over time.</li>
</ul>



<h3 class="wp-block-heading">Interpreting GC output</h3>



<p>Understanding GC output involves monitoring key metrics:</p>



<ul class="wp-block-list">
<li><strong>Collection times:</strong> Track the duration of GC cycles to identify if they are causing application pauses or delays.</li>



<li><strong>Memory usage:</strong> Monitor heap usage trends to ensure it remains within optimal limits and detect memory leaks early.</li>



<li><strong>Object counts:</strong> Analyze the number of live and dead objects during GC cycles to understand memory allocation patterns and optimize accordingly.</li>
</ul>



<h3 class="wp-block-heading">Fine-tuning GC for optimal performance</h3>



<p>Optimizing GC behavior involves configuring JVM parameters:</p>



<ul class="wp-block-list">
<li><strong>Xmx and Xms flags:</strong> Set maximum (Xmx) and initial (Xms) heap sizes based on application requirements to balance memory usage and performance.</li>



<li><strong>GC logging and verbose flags:</strong> Enable GC logging (-verbose:gc, -Xlog:gc) to capture detailed information about GC activities, helping diagnose performance issues related to memory management.</li>



<li><strong>Permanent generation tuning:</strong> For older Java versions, tuning the Permanent Generation (PermGen) for class <a href="https://middleware.io/blog/structured-vs-unstructured-data/">metadata</a> and interned strings was crucial. In Java 8 and later, this has been replaced by Metaspace, which requires adjusting parameters based on application demands.</li>
</ul>



<p>By leveraging these tools and techniques, developers can proactively manage memory resources, optimize application performance, and ensure a stable and responsive Java application environment.</p>



<h2 class="wp-block-heading">Common Java GC issues and troubleshooting</h2>



<p>Java Garbage Collection (GC) is essential for managing memory efficiently in Java applications, but it can encounter several critical issues:</p>



<h3 class="wp-block-heading">Memory leaks</h3>



<p><strong>Causes:</strong> Objects not properly dereferenced, static references holding unnecessary objects, and mismanagement of thread-local variables.</p>



<p><strong>Solution:</strong> Use tools like VisualVM for heap analysis to identify memory-consuming objects. Ensure proper object dereferencing, manage static references carefully, and use weak references for non-critical caches.</p>



<h3 class="wp-block-heading">Full GC pauses and long GC times</h3>



<p><strong>Issue: </strong>Full GC cycles cause significant pauses in application execution, leading to degraded user experience and increased latency.</p>



<p><strong>Solution: </strong>&nbsp;Select GC algorithms like CMS (Concurrent Mark-Sweep) or G1 (Garbage-First) that minimize pause times.&nbsp; Adjust settings to reduce the frequency and duration of Full GC cycles based on application requirements.</p>



<h3 class="wp-block-heading">Fragmentation issues</h3>



<p><strong>Issue: </strong>Fragmentation occurs when memory becomes divided into small, non-contiguous blocks over time due to object allocation and deallocation.</p>



<p><strong>Solution:</strong> Consider GC algorithms like G1 that perform heap compaction to reduce fragmentation and improve memory utilization.Design applications to minimize short-lived objects and reduce fragmentation over time.</p>



<h2 class="wp-block-heading" id="h-final-thoughts">Final thoughts</h2>



<p>Java Garbage Collection (GC) automates memory management by reclaiming unused memory, preventing issues like memory leaks and OutOfMemoryError exceptions. Its importance lies in optimizing memory usage for smoother application performance and simplifying development by reducing manual memory management tasks.&nbsp;</p>



<p>Next steps would be to continuously optimize GC settings and monitor performance metrics using tools like <a previewlistener="true" href="https://middleware.io/">Middleware</a>. Stay updated on best practices and experiment with different configurations to enhance Java application efficiency.</p>



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Discover the importance of Java Garbage Collection in maintaining your application's performance, even on the busiest sale days.

Java Garbage Collection: What is it, and How does it Work?


<p><strong><em>Middleware&#8217;s new and improved platform provides complete control over telemetry data and costs, new monitoring capabilities, faster root cause analysis, and broader integration support.</em></strong></p>



<p></p>



<p>Over the last few years, we&#8217;ve witnessed a fundamental shift, driven by the relentless growth of complex, distributed systems. Monolithic applications are giving way to microservices architectures, and with them, a growing IT industry that <a href="https://middleware.io/blog/observability-vs-monitoring/">traditional monitoring tools</a> simply can&#8217;t handle.</p>



<p>Two years ago, we recognized this challenge as an opportunity. The goal was to move beyond basic metrics to <a href="https://middleware.io/blog/observability/">true observability</a>, where we could ingest and analyze massive datasets – logs, traces, metrics – to paint a holistic picture of application health in a cost-effective way.&nbsp;</p>



<p><a href="https://middleware.io/">Middleware</a> was born to empower development and operation teams to not just react to fires, but to proactively identify and prevent issues before they disrupt users. The ability to monitor applications and infrastructure in real-time allowed developers to improve end-user experience, boost operational efficiency, minimize downtime, and ensure a positive impact on the bottom line.&nbsp;</p>



<figure class="wp-block-pullquote"><blockquote><p>“Developers no longer want outdated debugging systems. They seek faster, cost-effective, automated solutions for monitoring distributed architectures with scalable and easy-to-learn real-time observability capabilities. Back in 2022, our goal was to drive adoption by providing cost-effective, end-to-end observability.&#8221;</p><cite><a href="https://www.linkedin.com/in/laduramvishnoi"><strong>Laduram Vishnoi</strong></a><strong>, </strong>Founder and CEO, Middleware.</cite></blockquote></figure>



<p>However, a lot has changed since. Observability is no longer a buzzword or an afterthought, but a core principle woven into the fabric of modern software development. It&#8217;s a collaborative effort, where <a href="https://middleware.io/blog/monitoring-best-practices-for-developer/">developers</a>, SREs, and operations teams work together to ensure a seamless <a href="https://middleware.io/blog/mobile-app-performance-best-practices/">user experience</a> (UX). This requires not just powerful tools, but a commitment to knowledge sharing and a deep understanding of the systems we&#8217;re building.&nbsp;</p>



<p>To achieve this, Middleware needed to be more intuitive and user-friendly while providing comprehensive insights into every layer of the tech stack, from the infrastructure to the application, without the complexity that typically comes with such solutions.</p>



<p><em>&#8220;By analyzing global customer insights across various industries, we&#8217;ve pinpointed where users struggled and the features they desired. This feedback has been crucial in shaping the new Middleware platform,&#8221;</em> <strong>Vishnoi adds. </strong></p>



<figure class="wp-block-pullquote"><blockquote><p>&#8220;Our mission is to simplify the observability process to the extent that the dev and ops teams can use the platform intuitively within a minute. This update is a testament to that mission. We&#8217;ve made it much easier for users to set up services and get started quickly, without having to deal with a steep learning curve. We&#8217;ve also incorporated significant changes, both minor and major, to enhance the overall user experience and functionality of the platform.&#8221;</p><cite><a href="https://www.linkedin.com/in/laduramvishnoi"><strong>Laduram Vishnoi</strong></a><strong>, </strong>Founder and CEO, Middleware.</cite></blockquote></figure>



<h2 class="wp-block-heading" id="h-what-s-improved-within-middleware">What’s improved within Middleware?</h2>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1920" height="1090" src="https://middleware.io/backend/wp-content/uploads/2024/07/1.1.jpg" alt="Middleware’s Observability Platform Gets a Big Makeover" class="wp-image-7949"/></figure>



<h2 class="wp-block-heading">UI/UX enhancements</h2>



<p>Users expressed appreciation for the depth of information provided in the first version of Middleware but desired a more intuitive workflow for correlating signals. </p>



<p>To address this, we&#8217;ve included the following updates in the latest version:&nbsp;</p>



<h3 class="wp-block-heading" id="h-1-new-ui-with-reusable-components-nbsp">1. New UI with reusable components&nbsp;</h3>



<p>Over 300 screens have undergone a comprehensive redesign with reusable components that ensure a consistent and streamlined UX throughout the Middleware. Not only does this improve visual aesthetics, but it enhances functionality and simplifies navigation.&nbsp;</p>



<h3 class="wp-block-heading" id="h-2-enhanced-dashboard-builder">2. Enhanced dashboard builder</h3>



<p>Middleware’s dashboard builder has undergone a complete overhaul. We have added alerts on custom metrics and introduced 100 new dashboards. The improved UI/UX ensures that users can easily build and customize dashboards to suit their specific needs.</p>



<h3 class="wp-block-heading" id="h-3-simpler-installation-and-documentation">3. Simpler installation and documentation</h3>



<p>The installation process and documentation website have been thoroughly revamped to help users set up and utilize Middleware&#8217;s features with ease, reducing onboarding friction and accelerating time to value.&nbsp;</p>



<h3 class="wp-block-heading" id="h-4-interactive-features-for-data-correlation">4. Interactive features for data correlation</h3>



<p>Our platform can facilitate data correlation across multiple graphs. Users can now move the mouse along the X-axis of one graph to view corresponding data points on all graphs simultaneously. This streamlines data analysis and improves overall usability.&nbsp;</p>



<h3 class="wp-block-heading" id="h-5-date-picker">5. Date picker</h3>



<p>We have standardized the date picker across all features, ensuring a consistent user experience and simplifying date selection and navigation throughout the platform.</p>



<h3 class="wp-block-heading" id="h-6-high-performance-charting-library">6. High-performance charting library</h3>



<p>Middleware now features Highcharts to enhance data visualization capabilities. Developers can now gain a clearer and more interactive representation of data for quick and effective insights.</p>



<h2 class="wp-block-heading">Technology enhancements (features and functionalities)&nbsp;</h2>



<h3 class="wp-block-heading" id="h-1-datadog-agent-support-nbsp">1. Datadog agent support&nbsp;</h3>



<p>Developers can now send logs, metrics, and traces from <a href="https://middleware.io/blog/datadog-pricing/">Datadog</a> agents directly to Middleware. This eliminates the need for new agents or configurations, making the transition to Middleware effortless.&nbsp;</p>



<h3 class="wp-block-heading" id="h-2-auto-instrumentation">2. Auto-instrumentation</h3>



<p>Middleware now supports auto instrumentation for applications written in <a href="https://middleware.io/blog/python-performance-monitoring/">Python</a>, <a href="http://node.js">Node.js</a>, <a href="https://middleware.io/blog/java-performance-monitoring/">Java</a>, .NET, and <a href="https://middleware.io/blog/golang-monitoring/">Golang</a> using the <a href="https://middleware.io/blog/opentelemetry-observability/">OpenTelemetry operator</a>. This allows for distributed tracing with zero code changes and simplifies the instrumentation process.</p>



<h3 class="wp-block-heading" id="h-3-new-integrations">3. New integrations</h3>



<p>Middleware now supports <a href="https://middleware.io/blog/aws-monitoring/">AWS integration</a> for collecting logs, metrics, and events from ECS, EC2, S3, RDS, Firehose, Lambda, and EBS, simplifying AWS monitoring with less complex configurations. Middleware can also ingest logs from <a href="https://middleware.io/blog/logstash-alternatives/">Elastic Logstash</a>, thereby replacing Elastic Search &amp; Open Search.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1920" height="1090" src="https://middleware.io/backend/wp-content/uploads/2024/07/2.1.jpg" alt="Middleware’s Observability Platform Gets a Big Makeover" class="wp-image-7951"/></figure>



<h3 class="wp-block-heading" id="h-4-status-page-nbsp">4. Status page&nbsp;</h3>



<p>Users can now create status pages and publish them to their websites. This feature allows for synthetic checks and incident management, providing a public-facing status page that displays the availability of services/products. Users can also send notifications via Slack and email, keeping stakeholders informed during outages.</p>



<h3 class="wp-block-heading" id="h-5-product-performance-monitoring-within-real-user-monitoring-rum-nbsp">5. Product performance monitoring within real user monitoring (RUM)&nbsp;</h3>



<p>Middleware&#8217;s <a href="https://middleware.io/blog/what-is-real-user-monitoring/">RUM</a> now tracks core web vitals such as First Contentful Paint (FCP), Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS). It will also support native iOS and Android apps, delivering insights on performance metrics and user interactions across <a href="https://middleware.io/blog/mobile-app-monitoring/">mobile platforms</a>.&nbsp;</p>



<h3 class="wp-block-heading" id="h-6-log-patterns">6. Log patterns</h3>



<p>Middleware&#8217;s new <a href="https://middleware.io/blog/debug-faster-with-log-patterns/">log pattern</a> feature enables users to quickly identify logs by creating a pattern for the log message. It shows users which machines are emitting similar logs, helping them recognize that the issue may lie in the code rather than a specific machine or code replica.</p>



<h2 class="wp-block-heading">Infrastructure enhancements</h2>



<h3 class="wp-block-heading" id="h-1-query-optimization-nbsp">1. Query optimization&nbsp;</h3>



<p>Several backend queries have been revisited and optimized to enhance query response times and overall system performance. These optimizations ensure that users receive timely and accurate insights into their infrastructure and applications.</p>



<h3 class="wp-block-heading" id="h-2-database-schema-optimization">2. Database schema optimization</h3>



<p>The database schema has been refined to optimize storage efficiency and reduce costs associated with data retention and retrieval. These improvements contribute to a more efficient use of resources and enable Middleware to offer competitive pricing while maintaining high performance standards.</p>



<h3 class="wp-block-heading" id="h-3-ingestion-control-and-pipelines">3. Ingestion control and pipelines</h3>



<p>Middleware&#8217;s ingestion control model allows you to optimize data intake through customizable &#8220;feature flags.&#8221; This ensures that we only ingest the telemetry data that is crucial to you, such as metrics, traces, logs, and profiling data. Within these categories, we offer granular filtering options. For example, you can choose to ingest logs from specific services or filesystems, keeping your data intake streamlined and cost-efficient.<br></p>



<p class="has-accent-3-background-color has-background"><strong><em>Read more about how Middleware makes a difference in this exclusive story by </em></strong><a href="https://analyticsindiamag.com/ai-trends/this-2-year-old-startup-drawing-customers-away-datadog-new-relic/"><strong><em>AIM</em></strong></a><strong><em>.</em></strong></p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1920" height="1090" src="https://middleware.io/backend/wp-content/uploads/2024/07/3.1.jpg" alt="Middleware’s Observability Platform Gets a Big Makeover" class="wp-image-7952"/></figure>



<figure class="wp-block-pullquote"><blockquote><p>&#8220;<strong><em>At Middleware, we are committed to redefining cloud native observability by making our tools more intuitive, faster, and cost-effective by using cutting-edge technologies like OpenTelemetry, cloud object storage, and fast analytical query processing. With revamped visuals, seamless integrations, and advanced capabilities like log patterns and mobile RUM, we aim to empower SREs, DevOps, and developers to achieve unparalleled insights and efficiency</em></strong>.<strong><em> To further our goal, we will also leverage AI to detect anomalies in the telemetry data, provide alert recommendations, and enable querying using natural language.</em></strong>&#8220;</p><cite><a previewlistener="true" href="https://www.linkedin.com/in/tejaskokje">Tejas Kokje</a>, Head of Engineering at Middleware.</cite></blockquote></figure>



<p>This is just the beginning for us. In the months to come, we’d be introducing many more exciting features and functionalities. Stay tuned!<br><br><strong><em>Want to try the latest version of Middleware’s full-stack cloud observability platform for free? </em></strong><a href="https://app.middleware.io/auth/register/"><strong><em>Click here</em></strong></a><strong><em>.&nbsp;</em></strong></p>


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<p>Organizations often struggle with the massive influx of data flooding their systems daily. This deluge presents a significant hurdle in efficiently processing, managing, and analyzing information in real-time.&nbsp;</p>



<p>Kafka emerges as a solution to this predicament, offering a robust platform designed to handle the continuous flow of data. Its <a href="https://middleware.io/blog/serverless-architecture/">architecture</a> facilitates the smooth transmission of data across systems, enabling organizations to ingest, process, and distribute data at scale with fault tolerance and low latency.&nbsp;</p>



<p>In this article, we will dive into the world of Kafka, exploring its architecture and essential metrics for monitoring performance using <a href="https://middleware.io/">Middleware</a>.</p>



<div id="accordian" class="table-of-content">
<div class="toc-heading accordion" data-toggle="collapse" data-target="#toc">Table of Contents</div>
<div id="toc" class="collapse" data-parent="#accordian">
<div class="toc-list">&nbsp;</div>
</div>
</div>



<h2 class="wp-block-heading" id="h-what-is-kafka">What is Kafka?</h2>



<p>Kafka is an <a href="https://middleware.io/blog/open-source-vs-managed-observability-platforms/">open-source</a> distributed streaming platform designed for building real-time data pipelines and streaming applications. It provides a unified, high-throughput, fault-tolerant architecture for handling streams of data.&nbsp;</p>



<p>Users use Kafka for its ability to efficiently process, store, and transmit large volumes of data in real-time, making it ideal for use cases such as event-driven architectures, real-time analytics, <a href="https://middleware.io/blog/search-logs-effectively-with-log-management/">log aggregation</a>, and messaging systems.</p>



<h2 class="wp-block-heading" id="h-understanding-the-kafka-architecture">Understanding the Kafka architecture</h2>



<p>Kafka&#8217;s architecture comprises several key components that work together to provide a scalable, fault-tolerant, and high-throughput distributed streaming platform. At its core, Kafka architecture includes brokers, topics, partitions, producers, consumers, and ZooKeeper.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1137" height="675" src="https://middleware.io/backend/wp-content/uploads/2024/05/Frame-1000002645-1.jpg" alt="Monitoring Kafka Performance Metrics with Middleware- Understanding the Kafka architecture" class="wp-image-7671"/></figure>



<ul class="wp-block-list">
<li><strong>Brokers</strong>: Manage data storage and exchange.</li>



<li><strong>Topics</strong>: Categorize data streams logically.</li>



<li><strong>Partitions</strong>: Distribute data for scalability, replicated for fault tolerance.</li>



<li><strong>Producers</strong>: Publish records to topics.</li>



<li><strong>Consumers</strong>: Subscribe to consume records.</li>



<li><strong>ZooKeeper</strong>: Coordinates cluster management tasks.</li>
</ul>



<p>This setup enables Kafka to handle high-throughput, fault-tolerant data streams efficiently, supporting real-time analytics, event-driven architectures, and messaging systems.</p>



<figure class="wp-block-image size-large is-resized"><img loading="lazy" decoding="async" width="1036" height="711" src="https://middleware.io/backend/wp-content/uploads/2024/05/Monitoring-Kafka-performance-metrics-with-Middleware-Kafka-Architecture-2.png" alt="Monitoring Kafka performance metrics with Middleware - Kafka Architecture 2" class="wp-image-7661" style="width:620px;height:auto"/></figure>



<h2 class="wp-block-heading" id="h-why-should-we-monitor-kafka-nbsp">Why should we monitor Kafka?&nbsp;</h2>



<p>Organizations struggle to find reliable, scalable solutions capable of efficiently managing large amounts of data. Traditional systems often falter under such demands, leading to bottlenecks, latency issues, and scalability constraints.&nbsp;</p>



<p>Kafka addresses these challenges by providing a distributed streaming platform capable of processing and managing large-scale data streams in real-time.</p>



<ul class="wp-block-list">
<li><strong>Performance optimization:</strong> Monitoring Kafka allows for the timely identification of bottlenecks, latency issues, and scalability constraints, enabling organizations to optimize performance and ensure smooth data processing.</li>



<li><strong>Fault detection:</strong> <a href="https://middleware.io/blog/proactive-monitoring/">Proactive monitoring</a> helps in the early detection of issues such as broker failures or replication lag, minimizing the risk of data loss and service disruptions.</li>



<li><strong>Scalability planning:</strong> By analyzing Kafka <a href="https://middleware.io/blog/devops-metrics-you-should-be-monitoring/">metrics</a> related to resource utilization and throughput, organizations can assess scalability requirements and plan for capacity upgrades as needed.</li>



<li><strong>Resource efficiency: </strong>Monitoring helps in optimizing resource allocation within Kafka clusters, reducing operational costs and improving ROI.</li>



<li><strong>Security and compliance:</strong> Monitoring security-related metrics ensures the integrity and confidentiality of data processed by Kafka, helping organizations meet compliance requirements and prevent security breaches.</li>



<li><strong>Operational insights:</strong> Analyzing historical metrics provides valuable insights into Kafka cluster behavior, enabling informed decision-making and long-term optimization strategies.</li>
</ul>



<p class="has-base-2-color has-accent-3-background-color has-text-color has-background has-link-color wp-elements-1e095a5787193eda07e296329f90134a"><strong><em>Learn more about Kafka Monitoring by clicking <a href="https://middleware.io/blog/kafka-monitoring/">here</a>.</em></strong></p>



<h2 class="wp-block-heading" id="h-top-4-kafka-metrics-to-monitor">Top 4 Kafka metrics to monitor</h2>



<p>With Kafka, numerous metrics can impact performance, but focusing on key indicators is essential. Here, we highlight the top Kafka metrics critical for monitoring and optimizing your deployment&#8217;s health and efficiency.</p>



<h3 class="wp-block-heading" id="h-1-kafka-broker-metrics">1. Kafka broker metrics</h3>



<p>Kafka broker metrics refer to performance indicators of individual Kafka brokers within a cluster. These metrics provide insights into the health, resource utilization, and operational efficiency of each broker.</p>



<h3 class="wp-block-heading">Reasons to monitor</h3>



<ul class="wp-block-list">
<li>Monitoring broker metrics helps ensure that Kafka brokers are available and responsive, <a href="https://middleware.io/blog/best-practices-to-avoid-website-outages-on-black-friday/">minimizing downtime</a> and service disruptions.</li>



<li>By monitoring resource utilization metrics, organizations can optimize resource allocation and prevent overloading or underutilization of Kafka brokers.</li>



<li>Monitoring Kafka broker metrics allows organizations to identify and address performance bottlenecks, such as high CPU usage or disk latency, ensuring smooth data processing.</li>
</ul>



<h3 class="wp-block-heading" id="h-key-metrics-to-monitor">Key metrics to monitor</h3>



<p><strong>CPU usage</strong></p>



<pre class="wp-block-code"><code>Name: kafka.server:type=KafkaServer,name=BrokerTopicMetrics.BrokerTopicMetrics:AllTopicsBytesInPerSec</code></pre>



<p>CPU usage reflects the percentage of CPU resources utilized by a Kafka broker for processing requests and executing tasks. Monitoring CPU usage helps identify processing bottlenecks, enabling optimization of Kafka configurations and resource scaling.&nbsp;</p>



<p><strong>Disk utilization</strong></p>



<pre class="wp-block-code"><code>Name:  kafka.server:type=Log,name=Size</code></pre>



<p>What it is: Disk utilization indicates the amount of disk space used by Kafka for storing message logs and index files. Monitoring disk utilization prevents disk space exhaustion, ensuring uninterrupted data ingestion and retention.</p>



<p><strong>Network metrics</strong></p>



<pre class="wp-block-code"><code>Name: kafka.network:type=SocketServer,name=NetworkProcessorAvgIdlePercent</code></pre>



<p>Network metrics include indicators like throughput and traffic, reflecting network performance. Monitoring network metrics detects anomalies, congestion, and ensures smooth communication between Kafka brokers and clients.</p>



<p><strong>Partition metrics</strong></p>



<pre class="wp-block-code"><code>Name: kafka.server:type=ReplicaManager,name=PartitionCount</code></pre>



<p>Partition metrics provide insights into the health of Kafka partitions, including ISR and lag. Monitoring partition metrics ensures data replication, distribution, and high availability, minimizing data loss for consumers.</p>



<h3 class="wp-block-heading" id="h-2-producer-metrics">2. Producer metrics</h3>



<p>Kafka producer metrics encompass performance indicators that track the behavior and efficiency of Kafka producers. These metrics provide insights into the rate of message production, latency in message delivery, and occurrences of errors during message transmission.</p>



<h3 class="wp-block-heading">Reasons to monitor</h3>



<ul class="wp-block-list">
<li>Monitoring producer metrics allows organizations to assess the efficiency and throughput of message production, ensuring optimal performance of Kafka producers.</li>



<li>By tracking metrics related to message delivery latency, organizations can identify and address issues that may cause delays in message transmission, ensuring timely data processing.</li>



<li>Monitoring producer metrics helps in detecting errors encountered during message transmission, enabling prompt resolution and minimizing data loss or disruption in data pipelines.</li>
</ul>



<h3 class="wp-block-heading" id="h-key-metrics-to-monitor-0">Key metrics to monitor</h3>



<p><strong>Message send rate</strong></p>



<pre class="wp-block-code"><code>Name: kafka.producer:type=producer-metrics,client-id=*ProducerClientId*,topic=*TopicName*,metric-name=record-send-rate</code></pre>



<p>Message send rate reflects the rate at which Kafka producers publish messages to Kafka topics. Monitoring message send rate provides insights into the efficiency of message production, enabling organizations to assess workload distribution and scale resources as needed to meet demand.</p>



<p><strong>Latency</strong></p>



<pre class="wp-block-code"><code>Name: kafka.producer:type=producer-metrics,client-id=*ProducerClientId*,topic=*TopicName*,metric-name=record-send-rate</code></pre>



<p>Producer latency measures the time taken for data generated by producers to be successfully transmitted and acknowledged by Kafka topics. </p>



<p>Monitoring producer latency helps in identifying delays in data transmission, ensuring timely processing and reducing latency in downstream data consumption, which is crucial for <a href="https://middleware.io/blog/what-is-application-performance-monitoring/">real-time applications.</a></p>



<h3 class="wp-block-heading" id="h-3-consumer-metrics">3. Consumer metrics</h3>



<p>Kafka Consumer metrics encompass <a href="https://middleware.io/blog/incident-management-kpis/">performance indicators</a> that track the behavior and efficiency of Kafka consumers. These metrics provide insights into how <a href="https://middleware.io/blog/what-is-real-user-monitoring/">consumers</a> are ingesting messages from Kafka topics, processing them, and managing partition assignments.</p>



<h3 class="wp-block-heading">Reasons to monitor</h3>



<ul class="wp-block-list">
<li>By tracking metrics related to message consumption latency and consumer lag, organizations can identify and address delays in message processing, ensuring timely data consumption and downstream processing.</li>



<li>Tracking errors and retries encountered by consumers provides insights into the stability and reliability of consumer applications, enabling prompt detection and resolution of issues to prevent data loss or processing disruptions.</li>
</ul>



<h3 class="wp-block-heading">Key metrics to monitor</h3>



<p><strong>Consumer lag</strong></p>



<pre class="wp-block-code"><code>Name: kafka.consumer:type=consumer-fetch-manager-metrics,client-id=*ConsumerClientId*,topic=*TopicName*,partition=*PartitionId*,metric-name=records-lag-max</code></pre>



<p>It measures the difference between the latest message offset in a partition and the consumer&#8217;s current offset. Monitoring consumer lag helps identify processing delays, bottlenecks, or issues in consumer performance.&nbsp;</p>



<p><strong>Number of consumed messages&nbsp;</strong></p>



<pre class="wp-block-code"><code>Name: kafka.consumer:type=consumer-fetch-manager-metrics,client-id=*ConsumerClientId*,topic=*TopicName*,partition=*PartitionId*,metric-name=records-consumed-rate</code></pre>



<p>Tracks the volume of messages consumed by Kafka consumers over a specified period. Monitoring the number of consumed messages provides insights into consumption rates, helping assess consumer workload and ensuring efficient data processing.</p>



<h3 class="wp-block-heading" id="h-4-zookeeper-metrics">4. ZooKeeper metrics</h3>



<p>ZooKeeper metrics comprise performance indicators that track the health, activity, and resource utilization of ZooKeeper servers within a Kafka ecosystem. These metrics provide insights into the stability and efficiency of ZooKeeper, a critical component for coordinating and managing Kafka clusters.</p>



<h3 class="wp-block-heading">Reasons to monitor</h3>



<ul class="wp-block-list">
<li>ZooKeeper is responsible for maintaining metadata, coordinating leader election, and managing cluster membership in Kafka. Monitoring ZooKeeper metrics ensures the proper functioning of these critical tasks, facilitating the stability and reliability of Kafka clusters.</li>



<li>Monitoring ZooKeeper metrics aids in early <a href="https://middleware.io/blog/how-to-reduce-mttr/">detection of issues</a>, such as node failures, connectivity problems, or performance degradation, enabling proactive measures to prevent service disruptions and data loss.</li>



<li>By analyzing ZooKeeper metrics, organizations can identify potential bottlenecks or inefficiencies in ZooKeeper operations and optimize configurations to enhance performance and scalability.</li>
</ul>



<h3 class="wp-block-heading" id="h-key-metrics-to-monitor-1">Key metrics to monitor</h3>



<p><strong>ZooKeeper quorum size</strong></p>



<pre class="wp-block-code"><code>Name: kafka.server:type=controller-channel-metrics,name=LeaderCount</code></pre>



<p>ZooKeeper quorum size refers to the number of nodes in a ZooKeeper ensemble required for achieving consensus. Monitoring ZooKeeper quorum size ensures the integrity and reliability of distributed coordination tasks.</p>



<p><strong>ZooKeeper watcher counts</strong></p>



<pre class="wp-block-code"><code>Name: kafka.server:type=socket-server-metrics,name=NetworkProcessorAvgIdlePercent</code></pre>



<p>Tracks the number of watchers registered with ZooKeeper nodes for monitoring changes to <strong>znodes</strong>. Monitoring watcher counts helps in assessing the workload and usage patterns of ZooKeeper, facilitating capacity planning and resource allocation for optimal performance.</p>



<p><strong>ZooKeeper pending requests</strong></p>



<pre class="wp-block-code"><code>Name: kafka.server:type=request-channel-metrics,name=RequestQueueSize</code></pre>



<p>Measures the number of pending client requests awaiting processing by ZooKeeper servers. Monitoring pending requests helps in identifying overload or saturation of ZooKeeper services, enabling organizations to scale resources and optimize configurations for improved performance and responsiveness.</p>



<h2 class="wp-block-heading" id="h-monitor-your-kafka-deployment-using-middleware">Monitor your Kafka deployment using Middleware</h2>



<p>With Middleware&#8217;s <a href="https://docs.middleware.io/integrations/kafka-integration#default-kafka-dashboard">Kafka Integration</a>, you can actively monitor various aspects of your Kafka environment, including brokers, partitions, consumer groups, and JMX metrics.</p>



<figure class="wp-block-image size-large is-resized"><img loading="lazy" decoding="async" width="1850" height="931" src="https://middleware.io/backend/wp-content/uploads/2024/05/image-47.png" alt="Monitoring Kafka performance metrics with Middleware" class="wp-image-7662" style="width:620px;height:auto"/></figure>



<ol class="wp-block-list">
<li><strong>Setup Middleware host agent:</strong> Install Middleware Host Agent (MW Agent) v1.5.0+ on your local machine as a prerequisite.</li>



<li><strong>Access Kafka integration:</strong> Log in to your Middleware account and navigate to the Kafka Integration section.</li>



<li><strong>Connect Kafka:</strong> Configure the JMX Server and Broker Host settings to connect Middleware with your Kafka deployment. This allows Middleware to collect relevant metrics from Kafka brokers.</li>



<li><strong>Visualize data:</strong> Explore Middleware&#8217;s default Kafka dashboard to gain insights into key metrics such as broker health, partition status, and consumer lag. Customize the dashboard with widgets to focus on specific aspects of your Kafka environment.</li>



<li><strong>Create alerts:</strong> Set up alerts to receive notifications when certain Kafka metrics exceed predefined thresholds. This enables proactive monitoring and timely response to potential issues.</li>



<li><strong>Dashboard customization:</strong> Customize the Kafka dashboard to suit your specific monitoring needs. Add or remove widgets, adjust visualizations, and arrange metrics to optimize your monitoring experience.</li>



<li><strong>Explore additional metrics:</strong> Explore Middleware&#8217;s extensive list of Kafka metrics to monitor various aspects of your Kafka deployment comprehensively. Monitor metrics related to producers, consumers, topics, partitions, and more to ensure the smooth operation of your Kafka ecosystem.</li>



<li><strong>Analyze performance trends:</strong> Use Middleware&#8217;s analytical tools to analyze performance trends over time. Identify patterns, anomalies, and areas for optimization to continuously improve the performance and reliability of your Kafka deployment.</li>
</ol>



<p>By following these steps and leveraging Middleware&#8217;s Kafka Integration, you can gain valuable insights into your Kafka deployment&#8217;s performance, health, and efficiency.</p>



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<p>Monitoring our Kafka deployment is key to ensuring its smooth operation and performance. Middleware&#8217;s Kafka Integration provides real-time insights into various aspects of our Kafka environment, from brokers to partitions and consumer groups.</p>



<p>The next steps involve implementing Middleware&#8217;s Kafka integration, setting up monitoring dashboards, and configuring alerts to keep us informed of any issues. With Middleware&#8217;s help, we can proactively manage our Kafka deployment, optimize its performance, and ensure reliable data processing.</p>


Discover how Kafka handles data influx, enabling real-time processing and distribution with low latency. Learn about Kafka's architecture and key performance metrics to monitor with Middleware.

Monitoring Kafka Performance Metrics with Middleware


<p>The <a href="https://middleware.io/blog/what-is-devops/">DevOps</a> landscape continues to evolve rapidly, and 2025 promises to showcase its full potential in software development and IT operations. As <a href="https://middleware.io/blog/devops-tools/">DevOps tools</a> and practices advance, professionals must stay updated with the latest trends and innovations to remain competitive.</p>



<p>One of the best ways to keep up with these changes is by engaging with the DevOps community through online groups, forums, and industry conferences. These events provide valuable networking opportunities, insights from experts, and exposure to emerging technologies shaping the future of DevOps.</p>



<p>With numerous DevOps conferences scheduled this year, attending all may not be feasible. Instead, prioritizing the most impactful events will ensure maximum learning and networking. These top conferences should be on the radar of every <a href="https://middleware.io/blog/devops-roadmap/">DevOps Engineer</a> or enthusiast looking to stay ahead in this dynamic field.</p>



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<h2 class="wp-block-heading" id="h-top-15-devops-conferences-amp-events">Top 15 DevOps conferences &amp; events</h2>



<h3 class="wp-block-heading" id="h-1-cloud-expo-europe-nbsp-nbsp"><strong>1. </strong>Cloud Expo Europe&nbsp;&nbsp;</h3>



<p><a href="https://www.cloudaiinfrastructure.com/">Cloud Expo Europe</a> is a premier event that brings together cloud computing professionals and industry leaders to discuss the latest trends, technologies, and innovations in the cloud space. Attendees can network, attend workshops, and explore solutions from leading cloud providers.</p>



<ul class="wp-block-list">
<li><strong>Date: </strong>March 12-13&nbsp;&nbsp;</li>



<li><strong>Location:</strong> London, UK&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-2-srecon-25-america">2. SRECon 25 America</h3>



<p><a href="https://www.usenix.org/conference/srecon">SRECon 25 America</a> focuses on Site Reliability Engineering (SRE) practices and principles. This conference gathers SRE professionals to share insights, best practices, and case studies, fostering collaboration and learning within the SRE community.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> March 25-27&nbsp;&nbsp;</li>



<li><strong>Location:</strong> Santa Clara, USA&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-3-kubecon-cloudnativecon-europe-nbsp">3. KubeCon + CloudNativeCon Europe&nbsp;</h3>



<p><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-europe/">KubeCon + CloudNativeCon Europe</a> is the flagship conference for Kubernetes and cloud-native technologies. It features keynotes, breakout sessions, and hands-on workshops, providing a platform for developers, operators, and decision-makers to connect and learn about the latest advancements in the ecosystem.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> April 1-4&nbsp;&nbsp;</li>



<li><strong>Location: </strong>Europe&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-4-sreday-nbsp-nbsp">4. SREDay&nbsp;&nbsp;</h3>



<p><a href="https://sreday.com/">SREDay</a> is a dedicated event for Site Reliability Engineers to explore the latest tools, techniques, and practices in SRE. The event includes talks, panels, and networking opportunities aimed at enhancing the skills and knowledge of SRE professionals.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> April 11&nbsp;&nbsp;</li>



<li><strong>Location:</strong> San Francisco, USA&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-5-devopscon-london-nbsp">5. DevopsCon London&nbsp;</h3>



<p><a href="https://devopscon.io/london/">DevopsCon London</a> is a conference that focuses on DevOps practices, tools, and culture. It features expert speakers, workshops, and networking opportunities, providing insights into how organizations can improve collaboration and efficiency in software delivery.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> May 12-16&nbsp;&nbsp;</li>



<li><strong>Location:</strong> London, UK</li>
</ul>



<h3 class="wp-block-heading" id="h-6-devopsdays-chicago-nbsp">6. DevopsDays Chicago&nbsp;</h3>



<p><a href="https://devopsdays.org/events/2025-chicago/welcome/">DevopsDays Chicago</a> is a community-driven event that brings together DevOps practitioners to share experiences, ideas, and best practices. The event includes talks, open spaces, and networking opportunities, fostering collaboration within the DevOps community.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> May 18&nbsp;&nbsp;</li>



<li><strong>Location:</strong> Chicago, USA&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-7-devopspro-europe-2025-nbsp">7. DevOpsPro Europe 2025&nbsp;</h3>



<p><a href="https://devopspro.lt/">DevOpsPro Europe 2025</a> is a conference dedicated to DevOps professionals, focusing on advanced practices, tools, and methodologies. Attendees can participate in workshops, keynotes, and discussions aimed at enhancing their DevOps skills and knowledge.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> May 21-23&nbsp;&nbsp;</li>



<li><strong>Location:</strong> Vilnius, Lithuania&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-8-kubecon-cloudnativecon-china-nbsp-nbsp">8. KubeCon + CloudNativeCon China&nbsp;&nbsp;</h3>



<p><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-china/">KubeCon + CloudNativeCon China</a> is a key event for the cloud-native community in Asia. It features sessions on Kubernetes, microservices, and cloud-native technologies, providing a platform for networking and knowledge sharing among industry leaders and practitioners.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> June 10-11&nbsp;&nbsp;</li>



<li><strong>Location:</strong> China&nbsp;&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-9-kubecon-cloudnativecon-japan-nbsp">9. KubeCon + CloudNativeCon Japan&nbsp;</h3>



<p><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-japan/">KubeCon + CloudNativeCon Japan</a> brings together cloud-native enthusiasts and professionals to discuss the latest developments in Kubernetes and related technologies. The event includes talks, workshops, and networking opportunities to foster collaboration in the region.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> June 16-17&nbsp;&nbsp;</li>



<li><strong>Location:</strong> Japan&nbsp;&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-10-devopsdays-amsterdam-nbsp">10. DevopsDays Amsterdam &nbsp;</h3>



<p><a href="https://devopsdays.org/events/2025-amsterdam/welcome/">DevopsDays Amsterdam</a> is a community-focused event that explores the intersection of development and operations. It features talks, open spaces, and networking opportunities, encouraging participants to share their experiences and learn from one another.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> June 18-20&nbsp;&nbsp;</li>



<li><strong>Location:</strong> Amsterdam, Netherlands&nbsp;&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-11-kubecon-cloudnativecon-india">11. KubeCon + CloudNativeCon India</h3>



<p><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-india/">KubeCon + CloudNativeCon India</a> is a significant gathering for cloud-native professionals in the region. The conference showcases the latest innovations in Kubernetes and cloud-native technologies, offering sessions, workshops, and networking opportunities.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> August 6-7&nbsp;&nbsp;</li>



<li><strong>Location:</strong> India&nbsp;&nbsp;</li>
</ul>



<div class="container-cta">
  
<div><h3>Meet the Middleware team at KubeCon India!</h3>
  <p> We’re excited to join the conversation on Kubernetes and cloud-native advancements. Drop by our booth or connect with us to explore collaboration opportunities!
 </p>
   <a href="https://middleware.io/events/kubecon-india-2025/" target="_blank" rel="noopener"><button class="secondary white">Visit Our Event Page</button></a>
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</div>



<h3 class="wp-block-heading" id="h-12-devopsdays-washington-nbsp">12. DevopsDays Washington&nbsp;</h3>



<p><a href="https://devopsdays.org/events/2025-washington-dc/welcome/">DevopsDays Washington</a> is a community-driven event that brings together DevOps practitioners to share insights and experiences. The event includes talks, open spaces, and networking opportunities, fostering collaboration and learning within the DevOps community.</p>



<ul class="wp-block-list">
<li><strong>Date:</strong> September 17-18&nbsp;&nbsp;</li>



<li><strong>Location:</strong> Washington, DC, USA&nbsp;&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-13-kubernetes-community-day">13. <a href="https://community.cncf.io/cloud-native-edinburgh/">Kubernetes Community Day</a></h3>



<p><a href="https://community.cncf.io/cloud-native-edinburgh/">Kubernetes Community Day</a> is an event focused on bringing together Kubernetes enthusiasts and professionals. It features talks, discussions, and networking opportunities aimed at fostering community engagement and knowledge sharing.</p>



<ul class="wp-block-list">
<li><strong>Date: </strong>October 21&nbsp;&nbsp;</li>



<li><strong>Location: </strong>Edinburgh, UK&nbsp;&nbsp;</li>
</ul>



<h3 class="wp-block-heading" id="h-14-kubecon-cloudnativecon-north-america-nbsp">14. KubeCon + CloudNativeCon North America&nbsp; </h3>



<p><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america-2025/">KubeCon + CloudNativeCon North America</a> is the largest gathering of Kubernetes and cloud-native professionals in the region. The conference includes keynotes, breakout sessions, and hands-on workshops, providing a comprehensive platform for learning about the latest advancements in cloud-native technologies, networking with industry leaders, and sharing best practices.</p>



<ul class="wp-block-list">
<li>Date: November 10-13&nbsp;&nbsp;</li>



<li>Location: NA, USA&nbsp;&nbsp;</li>
</ul>



<div class="container-cta">
  
<div><h3>Meet the Middleware team at KubeCon NA!</h3>
  <p> Join us for insightful discussions, networking, and the latest cloud-native innovations.
 </p>
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</div>



<h3 class="wp-block-heading" id="h-15-kubernetes-community-day-nbsp-nbsp">15. Kubernetes Community Day&nbsp;&nbsp;</h3>



<p>Kubernetes Community Day in Geneva is an event dedicated to fostering community engagement around Kubernetes. It features talks, discussions, and networking opportunities for Kubernetes users and enthusiasts, promoting collaboration and knowledge sharing within the global Kubernetes community.</p>



<ul class="wp-block-list">
<li><strong>Date: </strong>December 5&nbsp;&nbsp;</li>



<li><strong>Location: </strong>Geneva, Switzerland&nbsp;&nbsp;</li>
</ul>



<p>Many of the events mentioned are major gatherings that draw thousands of attendees each year. Take advantage of this chance to attend and expand your knowledge significantly. Don’t miss the chance to learn a lot.</p>



<p><strong>KubeCon</strong> is one of the premier events in the Cloud Native Ecosystem. Starting this year, it will be held in five different countries.<br>Here are the details:</p>



<ul class="wp-block-list">
<li><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-europe/">KubeCon + CloudNativeCon Europe</a> &#8211; April 1-4, 2025</li>



<li><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-china/">KubeCon + CloudNativeCon China</a> &#8211; June 10-11, 2025</li>



<li><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-japan/">KubeCon + CloudNativeCon Japan</a> &#8211; June 16-17, 2025</li>



<li><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-india/">KubeCon + CloudNativeCon India</a> &#8211; August 6-7, 2025</li>



<li><a href="https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america-2025/">KubeCon + CloudNativeCon North America</a> – Nov 10–13, 2025</li>
</ul>



<p>Events like DevopsDays and Kubernetes Community Day are renowned and organized in many countries worldwide. Here you can get more details about them.</p>



<ul class="wp-block-list">
<li><a href="https://devopsdays.org/">DevopsDays Conferences</a></li>



<li><a href="https://www.cncf.io/kcds/">Kubernetes Community Days</a></li>
</ul>



<p>The Middleware team is pretty active at various events every month, so be sure to follow our <a href="https://middleware.io/events/">event page</a>. This is your chance to meet the Middleware team in person and snag some amazing swag! Don’t miss out!</p>



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="720" height="420" src="https://middleware.io/backend/wp-content/uploads/2025/03/developer-conferences-to-attend-1.png" alt="amazing swag by attending Middleware developer events to attend" class="wp-image-9399"/></figure>


Mark your calendars! We have curated a list of the top DevOps conferences and events happening in 2025, including dates and locations, that you can’t miss.

Top 15 DevOps Conferences to Attend in 2025


<p>The world of <a href="https://middleware.io/blog/observability/tools/">observability tools</a> is split between two extremes: open-source and managed observability platforms. Both options come with their own set of advantages and disadvantages, making it even more difficult for organizations to pick the right option.</p>



<p>Open-source platforms are distributed with their source code, enabling organizations to use and distribute it as part of their existing applications. They are also seen as highly collaborative, often coming with their own library and shared resources maintained by developers across the globe. Managed observability platforms, on the other hand, are out-of-the-box solutions that are licensed or sold to the organization. They are delivered as a SaaS product and include additional features that make them ideal for business use.</p>



<p>So, how can you pick the best option for your needs? This blog explores the world of open-source vs. managed observability options, dissecting their strengths and weaknesses to help you make an informed decision.</p>



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<h2 class="wp-block-heading" id="h-what-are-open-source-observability-platforms">What are open-source observability platforms?</h2>



<p>As per the 2024 Open-source Security Risk Analysis Report (OSSRA), 96% of codebases contained open-source, given the many benefits of using this platform.</p>



<p>Open-source observability platforms offer a compelling value proposition: flexibility, customization, and cost-effectiveness. These platforms are freely available, allowing you to tinker, modify, and tailor them to your unique infrastructure.</p>



<p>Some of the most powerful open-source observability platforms include:</p>



<h3 class="wp-block-heading" id="h-grafana"><strong>Grafana</strong></h3>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="3360" height="1878" src="https://middleware.io/backend/wp-content/uploads/2024/05/Grafana.png" alt="Grafana Open-source Observability Platforms" class="wp-image-7637"/></figure>



<p>A powerful open-source observability platform that offers flexible visualization and analytical capabilities. It empowers organizations to create tailored monitoring solutions based on their specific preferences and can be used to track metrics, logs, and traces.</p>



<h3 class="wp-block-heading" id="h-zipkin"><strong>Zipkin</strong></h3>



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="974" height="514" src="https://middleware.io/backend/wp-content/uploads/2024/05/Zipkin-.png" alt="Zipkin Open-source Observability Platform" class="wp-image-7638"/></figure>



<p>An open-source <a href="https://middleware.io/blog/what-is-distributed-tracing/">distributed tracing</a> platform often used for troubleshooting issues with latency in software services. It allows users to gather data needed for troubleshooting latency problems and helps you track requests across your entire application ecosystem, pinpointing performance bottlenecks.</p>



<h3 class="wp-block-heading" id="h-prometheus"><strong>Prometheus</strong> </h3>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1305" height="709" src="https://middleware.io/backend/wp-content/uploads/2024/05/Prometheus-.png" alt="Prometheus Open-source Observability Platform" class="wp-image-7639"/></figure>



<p>A widely adopted open-source tool for collecting and storing metrics, allowing for <a href="https://middleware.io/blog/monitoring-best-practices-for-developer/">real-time monitoring</a> and alerting. It offers excellent visualizations, PromQL queries for data slicing and monitoring, and several third-party integrations, making it ideal for most system architecture.&nbsp;</p>



<p>While open-source platforms are free (with some paid add-ons, as seen with Grafana), there are some things to remember when adopting it. These include:</p>



<ul class="wp-block-list">
<li><strong>Installation and maintenance:</strong> Setting up and maintaining an open-source observability platform requires significant in-house expertise. You&#8217;ll need to manage infrastructure, configure tools, and handle ongoing updates.</li>



<li><strong>Limited support:</strong> While open-source communities can be helpful, they may not provide the level of dedicated support you might find with a managed platform. Troubleshooting complex issues can be time-consuming since the community comprises fellow developers and users who can help out only if the issue is something they are familiar with.</li>



<li><strong>Security concerns:</strong> Securing your open-source stack requires constant vigilance. Patching vulnerabilities and staying on top of security best practices falls entirely on your shoulders. As per the OSSRA report, 84% of codebases contained at least one open-source vulnerability, with 49% of codebases with open-source having no development in the last two years.</li>
</ul>



<p>There are additional open-source observability and <a href="https://middleware.io/blog/why-is-monitoring-your-application-important/">monitoring tools</a> like Logz.io, Signoz, OpsVerse, and more which can help organizations to gain similar observability features.</p>



<p>However, overly depending on the open-source platform comes with the inherent risks we discussed. This is one of the major reasons why organizations should consider managed observability platforms.</p>



<h2 class="wp-block-heading" id="h-what-are-managed-observability-platforms">What are managed observability platforms?</h2>



<p>Managed observability platforms take the opposite approach, offering a complete, out-of-the-box solution. These platforms are delivered as a service (SaaS), often cloud-based, removing the burden of infrastructure management and configuration. The key features of a managed observability platform include:</p>



<h3 class="wp-block-heading" id="h-ease-of-use"><strong>Ease of use</strong></h3>



<p>Managed platforms are designed for quick deployment and minimal configuration. They often offer intuitive interfaces and pre-built dashboards, allowing users to get started monitoring their systems quickly.</p>



<h3 class="wp-block-heading" id="h-scalability"><strong>Scalability</strong></h3>



<p>Managed platforms can automatically scale to meet your growing needs, eliminating the need to provision additional resources constantly.</p>



<h3 class="wp-block-heading" id="h-enterprise-grade-support"><strong>Enterprise-grade support</strong></h3>



<p>Managed providers offer dedicated support teams with deep expertise in their platform. They handle troubleshooting, updates, and security concerns, freeing your team to focus on core business activities.</p>



<h3 class="wp-block-heading" id="h-pre-built-integrations"><strong>Pre-built integrations</strong></h3>



<p>Many managed platforms come with pre-built integrations for popular applications and infrastructure tools, simplifying data collection and analysis.</p>



<p>Some of the top-managed <a href="https://middleware.io/blog/observability/">observability</a> platforms include:</p>



<h3 class="wp-block-heading" id="h-middleware"><strong>Middleware</strong></h3>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1345" height="712" src="https://middleware.io/backend/wp-content/uploads/2024/04/10-Monitoring-BP.jpeg" alt="Middleware Managed Observability Platform" class="wp-image-7517"/></figure>



<p>It is a <a href="https://middleware.io/">full-stack observability platform</a> that provides a comprehensive suite of tools for <a href="https://middleware.io/blog/what-is-application-performance-monitoring/">application performance monitoring</a>, <a href="https://middleware.io/blog/log-management/">log management</a>, and tracing. It offers an intuitive interface, pre-built dashboards, and dedicated support to ensure a smooth and efficient monitoring experience.</p>



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<h3 class="wp-block-heading" id="h-datadog"><strong>Datadog</strong></h3>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="2472" height="1416" src="https://middleware.io/backend/wp-content/uploads/2024/03/Datadog-MySQL-1.jpeg" alt="Datadog Managed Observability Platform" class="wp-image-7345"/></figure>



<p>A leading provider of cloud-based monitoring solutions, Datadog offers a wide range of features, including <a href="https://middleware.io/blog/what-is-infrastructure-monitoring/">infrastructure monitoring</a>, application performance monitoring, and log management. It is known for its user-friendly platform and extensive integrations.</p>



<h3 class="wp-block-heading" id="h-new-relic"><strong>New Relic</strong></h3>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1362" height="1174" src="https://middleware.io/backend/wp-content/uploads/2024/02/Newrelic-cassandra.png.webp" alt="New Relic Managed Observability Platform" class="wp-image-7223"/></figure>



<p>A popular managed observability platform with a focus on application performance monitoring, New Relic provides deep application insights, real-time alerting, and code-level troubleshooting capabilities. It excels in offering solutions tailored to specific application types (e.g., web, mobile).</p>



<p>However, managed solutions come with their own set of considerations:</p>



<ul class="wp-block-list">
<li><strong>Vendor lock-In:</strong> Reliance on a single vendor can lead to lock-in, making it difficult and expensive to switch platforms in the future.</li>



<li><strong>Cost:</strong> Managed platforms typically have subscription fees, which can add up over time compared to the free nature of open-source tools.</li>



<li><strong>Limited customization:</strong> While some managed platforms offer customization options, they may not provide the same level of flexibility as open-source solutions.</li>
</ul>



<h2 class="wp-block-heading" id="h-open-source-or-managed-observability-platform">Open-source or managed observability platform?</h2>



<p>So, which option should you pick for your enterprise needs? Here is a side-by-side comparison of open-source vs. managed observation platforms to help you out.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1493" height="1285" src="https://middleware.io/backend/wp-content/uploads/2024/05/Group-1000003869.png" alt="Open-source Vs. managed observability" class="wp-image-7624"/></figure>



<p>Thus, when it comes to open-source observability platforms, they are:</p>



<ul class="wp-block-list">
<li>Ideal for teams with in-house expertise on how to manage and customize the observability stack.</li>



<li>Cost effective.</li>



<li>Flexible and easy to tweak as per your needs.</li>
</ul>



<p>However, they require significant investment in time and resources.</p>



<p>Managed observability platforms, on the other hand, are:</p>



<ul class="wp-block-list">
<li>User-friendly and easy to use.</li>



<li>Low maintenance.</li>



<li>Easy to set up and deploy.</li>



<li>Automatic scaling.</li>
</ul>



<p>They are ideal for organizations seeking a solution that provides access to dedicated support and offers out-of-the-box features. However, the trade-off lies in potential vendor lock-in and ongoing subscription costs.</p>



<h2 class="wp-block-heading" id="h-when-should-you-use-a-managed-solution-over-open-source">When should you use a managed solution over open-source?</h2>



<p>At first glance, most organizations may find open-source observability platforms to be more appealing. They are free to use, and the popular options come with community support, which is great initially. However, first impressions can be deceiving. As you build upon the open-source solution and encounter more complex issues, they can be tough to navigate, and the support you get in such situations is often limited.</p>



<p>Thus, here is a checklist to know if you should pick open-source or managed observability platforms:</p>



<h3 class="wp-block-heading" id="h-compare-features"><strong>Compare features</strong></h3>



<p>Focus on the features and agility that both solutions offer. Managed observability platforms often provide add-on features and functionalities that make them ideal for business setup.</p>



<h3 class="wp-block-heading" id="h-access-in-house-expertise"><strong>Access in-house expertise</strong></h3>



<p>If your organization lacks the in-house expertise to manage and maintain a complex open-source observability stack, a managed platform can alleviate this burden. Their user-friendly interfaces and dedicated support teams can ensure smooth operation without requiring extensive technical knowledge from your staff.</p>



<h3 class="wp-block-heading" id="h-scalability-0"><strong>Scalability</strong></h3>



<p>Understand how large and fast your solution needs to scale according to your needs. Open-source is flexible but requires in-house resources to help scale the solution. </p>



<p>For organizations with rapidly scaling infrastructure, a managed platform can automatically adjust its resources to meet your growing demands. This eliminates the need for your team to constantly provision and manage additional hardware or cloud resources but can add an associated cost as you scale.</p>



<h3 class="wp-block-heading" id="h-understand-in-direct-costs"><strong>Understand in-direct costs</strong></h3>



<p>While open-source solutions are often completely free to use, be sure to factor in the in-direct expenses you will incur when using them. These include the costs associated with hiring or the time your resources spend managing open-source platforms. </p>



<p>Managed observability platforms may have a small fee and other associated costs, but they will be easier to use and free up your resources, making them <a href="https://middleware.io/blog/reduce-observability-costs/">cost-effective</a> in the long run.</p>



<h3 class="wp-block-heading" id="h-support"><strong>Support</strong></h3>



<p>While open-source observability platforms will help you get community support and help, managed solutions offer customer support. This is effective, especially if you are stuck with a specific issue that requires dedicated help.</p>



<h3 class="wp-block-heading" id="h-security-focus"><strong>Security focus</strong></h3>



<p>A managed platform can offer significant advantages if maintaining robust security is a top priority. Leading managed observability providers invest heavily in securing their platforms and keeping them up-to-date with the latest security patches. This frees your team from the constant vigilance required with open-source solutions.</p>



<h3 class="wp-block-heading" id="h-compliance-requirements"><strong>Compliance requirements</strong> </h3>



<p>For organizations subject to strict compliance regulations, a managed platform can offer peace of mind. Many providers ensure their platforms adhere to industry security and data privacy standards, reducing your compliance burden.</p>



<h2 class="wp-block-heading"><strong>Conclusion</strong></h2>



<p>While both open source and managed observability tools have benefits, this checklist can help you make an informed choice. It will empower your organization to gain valuable insights into your application performance and  ensure system health. </p>



<p>With managed observability platforms, you gain a strategic advantage. Observability tools should go beyond simply <a href="https://middleware.io/blog/observability-vs-monitoring/">monitoring</a> and capturing metrics, logs, and traces of your IT infrastructure and applications.</p>



<p>When it comes to such detailed monitoring capabilities, managed observability platforms like Middleware stand out. It offers a comprehensive suite of managed observability solutions designed to streamline your monitoring process and help you to make data-driven decisions.</p>



<p>With simplified installation, no-code drag-and-drop customizable dashboards, comprehensive support, and more, you gain real-time visibility and insights into your infrastructure, no matter how complex the systems or architecture is.</p>



<figure class="wp-block-pullquote"><blockquote><p><a href="https://app.middleware.io/auth/register/">Get started with Middleware</a> for free, or <a href="https://calendly.com/middleware_io/30min?back=1&amp;month=2022-11">contact us</a> for a personalized demo.</p></blockquote></figure>


Struggling to choose between Open-source and Managed Observability platforms? We'll break down the pros and cons of each platform, to help you pick the perfect fit for your needs.

Open-source vs. Managed Observability Platforms


<p><a href="https://middleware.io/blog/observability/">Observability</a> is crucial for any modern organization since we are now more heavily reliant on software and hardware systems than ever before. An estimated <a href="https://www.g2.com/articles/big-data-statistics">100 zettabytes of data</a> will be stored in the cloud by 2025, with data volumes growing at an average of <a href="https://www.g2.com/articles/big-data-statistics">63% each month</a> in a company.</p>



<p>With the rising amount of data comes the risk of managing, storing, and analyzing this monumental data. <a href="https://middleware.io/blog/observability/tools/">Observability tools</a> help organizations proactively identify and resolve performance issues, optimize resource allocation, and ensure exceptional user experiences. However, building a robust observability stack can sometimes lead to concerns about cost management.</p>



<p>This is a big challenge for most data-driven companies, and it is important to strike a balance between achieving comprehensive visibility and keeping your observability costs in check. This blog dives deep into five practical tips that will help you optimize your observability setup without sacrificing the valuable insights it delivers.</p>



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<div class="toc-list">&nbsp;</div>
</div>
</div>



<h2 class="wp-block-heading" id="h-5-tips-to-reduce-your-observability-costs-without-sacrificing-visibility">5 tips to reduce your observability costs without sacrificing visibility</h2>



<p>As with any IT infrastructure, performance issues will depend on the size and complexity of an activity. Highly intensive processes or large-scale activities can lead to slow load times, lags, or, at times, even outages. This is a major reason why the DevOps teams need to <a href="https://middleware.io/blog/why-is-monitoring-your-application-important/">continuously monitor system behavior</a>, utilizing observability pillars like metrics, logs, and traces to ensure that the system is running efficiently and that any spike in activity can be mitigated before it can make a huge impact on the overall system or specific applications.&nbsp;</p>



<p>However, with rising data, organizations today are looking to mitigate observability costs without sacrificing visibility. To do so, here are five key tips to optimize your observability setup and streamline your spending:</p>



<h3 class="wp-block-heading" id="h-1-optimize-your-data-ingestion">1. Optimize your data ingestion</h3>



<p>In most organizations, data quality is a process that begins only after the data makes its way into a common storage or warehouse. However, many observability tools charge based on the volume of data ingested, and this can often lead to higher costs for observability. Hence, the first step to cost reduction is identifying and capturing only the data that truly matters for your specific needs.</p>



<p>Here&#8217;s how to refine your data ingestion strategy for cost-effectiveness:</p>



<ul class="wp-block-list">
<li><strong>Focus on Critical Metrics:</strong> Prioritize capturing metrics that directly impact application performance and user experience. These might include:
<ul class="wp-block-list">
<li><strong>Application Response Times:</strong> Identify and track key metrics like server response times, API latencies, and page load times.</li>



<li><strong>Resource Utilization:</strong> Monitor CPU, memory, and network usage across your infrastructure to identify potential bottlenecks.</li>



<li><strong>Error Rates:</strong> Track the occurrence of errors and exceptions within your applications.</li>
</ul>
</li>



<li><strong>Filter and Whitelist Data Sources:</strong> Many observability platforms like<a href="https://middleware.io/"> Middleware</a> allow you to filter data at the source. Leverage this functionality to exclude irrelevant data like debug logs or information from non-critical services. Whitelist only the specific data sources that provide valuable insights for your monitoring needs.</li>



<li><strong>Sample Strategically:</strong> For high-volume data streams, consider implementing data sampling techniques. This allows you to capture a statistically significant subset of the data, reducing storage and processing costs while still providing valuable insights into overall system behavior. However, be mindful of the sampling rate to ensure you capture enough data to identify anomalies or trends.</li>



<li><strong>Leverage Data Transformation Rules:</strong> Transform raw data into a more compact and efficient format before ingestion. Tools like Logstash can be used to parse logs, extract relevant fields, and remove unnecessary information. This reduces the overall volume of data stored and minimizes processing overhead.</li>



<li><strong>Utilize Compression Techniques:</strong> Some observability platforms offer data compression options. Compressing data before storage can significantly reduce storage requirements and associated costs. With Middleware, you can leverage <a href="https://middleware.io/product/container-monitoring/">container monitoring</a> to gain end-to-end visibility into the performance of a containerized environment. This ensures that you gain deeper insights into a specific containerized environment, which can include defined applications, multi-cloud and hybrid environments, and other aspects of a defined container.</li>
</ul>



<h3 class="wp-block-heading" id="h-2-lower-data-retention">2. Lower data retention</h3>



<p>After data ingestion comes data retention policies; these play a crucial role in observability costs, as observability often requires logs and traces for analysis. When stored, they provide a historical record of what has been happening in your IT systems, helping you to resolve any troubleshooting errors and gain insights into the system’s healthy behavior. However, storing logs and traces for a long period of time can be expensive and increases both the volume and cost of maintaining this infrastructure.</p>



<p>To reduce these costs and lessen resolution time, organizations can do the following:</p>



<ul class="wp-block-list">
<li><strong>Identify Retention Requirements:</strong> Define clear retention periods for different data types. Critical metrics and logs essential for day-to-day operations may require longer retention (e.g., a week or a month). Less critical data, like debug logs or traces from infrequent events, can have shorter retention periods (e.g., a few days).</li>



<li><strong>Utilize Tiered Storage:</strong> Many observability platforms offer tiered storage options. Frequently accessed data can be stored in high-performance but potentially more expensive storage tiers. Meanwhile, less frequently accessed historical data can be archived to cost-effective cold storage solutions.</li>



<li><strong>Leverage Data Lifecycle Management (DLM):</strong> Implement automated data lifecycle management tools to automate data archiving and deletion based on predefined retention policies. This ensures you&#8217;re not paying to store data that has outlived its usefulness.</li>
</ul>



<h3 class="wp-block-heading" id="h-3-embrace-dynamic-observability">3. Embrace dynamic observability</h3>



<p>Traditional, static observability approaches often collect data at a constant rate, regardless of system activity. This &#8220;one-size-fits-all&#8221; method can be inefficient and costly, especially for applications with fluctuating workloads. <a href="https://middleware.io/platform/unified-experience/">Dynamic observability</a> allows you to be more cost-effective, using specialized methods like:</p>



<ul class="wp-block-list">
<li><strong>Scaling Data Collection Based on Need:</strong> Dynamic observability tools can automatically adjust the amount of data collected based on real-time system behavior. During periods of high traffic or potential issues, data collection can be increased to provide deeper insights. Conversely, during low-activity periods, data collection can be reduced, minimizing storage and processing overhead.</li>



<li><strong>Focusing on Anomaly Detection:</strong> Leverage AI-powered <a href="https://middleware.io/blog/anomaly-detection/">anomaly detection</a> capabilities within your observability platform. These tools can pinpoint deviations from normal system behavior, allowing you to focus data collection efforts on identifying and resolving potential issues. This targeted approach reduces unnecessary data capture and associated costs.</li>



<li><strong>Utilizing Ephemeral Tracing:</strong> For short-lived tasks or microservices with transient execution, consider using ephemeral tracing techniques. Ephemeral traces are designed for short-term analysis and automatically expire after a predefined period, eliminating the need for long-term storage and associated costs.</li>
</ul>



<h3 class="wp-block-heading" id="h-4-consolidate-tooling">4. Consolidate tooling</h3>



<p>One of the biggest challenges most organizations face when it comes to observability is maintaining data and real-time monitoring across multiple tools. A typical tech stack in an organization includes multiple apps, systems, and environments. </p>



<p>Unfortunately, this large-scale dependence on specialized tools can lead to what is known as observability tool sprawl, increasing complexity and driving up costs.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="606" src="https://middleware.io/backend/wp-content/uploads/2024/05/Tool-Consolidation-.jpeg" alt="Consolidate tooling - Reduce Observability Costs " class="wp-image-7593"/></figure>



<p>To resolve this, you can leverage the <a href="https://middleware.io/blog/what-is-tool-consolidation/">consolidate tooling</a> method, which includes:</p>



<h4 class="wp-block-heading">Step 1: Consolidate your observability stack</h4>



<p>Multiple tools often collect overlapping data sets, leading to unnecessary redundancy and inflated storage costs. Consolidating your observability stack onto a unified platform eliminates duplication and streamlines data management.</p>



<h4 class="wp-block-heading">Step 2: Simplify tooling into a single platform</h4>



<p>Managing a multitude of tools can be time-consuming and resource-intensive. A single platform simplifies configuration, maintenance, and user access, reducing administrative overhead and associated costs.</p>



<h4 class="wp-block-heading">Step 3: Execute</h4>



<p>Once you have evaluated the options, start consolidating your tools to the singular platform by purchasing new software or simplifying internal processes. Consolidation fosters a holistic view of your system&#8217;s health. By correlating data from various sources within a single platform, you gain deeper insights into application performance and user experience, allowing for more proactive problem-solving.</p>



<h3 class="wp-block-heading" id="h-5-choose-the-right-tool-for-your-setup">5. Choose the right tool for your setup</h3>



<p>Not all observability platforms are created equal. Selecting the right tool for your specific needs is crucial for optimizing costs. Some factors you should consider when selecting an observability platform include:</p>



<ul class="wp-block-list">
<li><strong>Deployment model:</strong> Choose a tool that aligns with your deployment model (on-premises, cloud, or hybrid). On-premise solutions may require upfront infrastructure costs, while cloud-based options typically offer flexible pricing models based on usage.</li>



<li><strong>Feature set:</strong> Carefully evaluate the features offered by different platforms. Prioritize tools that provide the functionalities you truly need, avoiding feature bloat that can increase costs.&nbsp;</li>



<li><strong>Scalability:</strong> Select a platform that can scale alongside your evolving infrastructure needs. Opt for solutions with flexible pricing tiers that adapt to your data volume and user base, ensuring you only pay for the resources you utilize.</li>
</ul>



<p>Middleware is a full-stack cloud observability platform that allows businesses to monitor their <a href="https://middleware.io/blog/cloud-infrastructure-guide/">cloud infrastructure</a> in real-time. It consolidates <a href="https://middleware.io/blog/devops-metrics-you-should-be-monitoring/">metrics</a>, <a href="https://middleware.io/blog/what-is-log-monitoring/">logs</a>, <a href="https://middleware.io/blog/what-is-distributed-tracing/">traces</a>, and events into a unified timeline. Plus, its core features can be used across multiple environments, including on-prem, cloud, and hybrid deployment options. This ensures comprehensive viability at a cost-effective price point.</p>



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<h2 class="wp-block-heading">Conclusion</h2>



<p>Optimizing your observability setup for cost-effectiveness requires a proactive approach. By implementing the strategies outlined above, you can achieve a balance between gaining valuable insights and keeping your observability costs under control.</p>



<p>To get a quick recap of our blog, remember that to keep your costs in control, you should:</p>



<ul class="wp-block-list">
<li>Focus on collecting only the data that truly matters for your specific needs</li>



<li>Implement data retention policies and leverage tiered storage for cost-efficiency</li>



<li>Embrace dynamic observability to scale data collection based on system activity</li>



<li>Consolidate your observability tools to reduce redundancy and streamline management</li>



<li>Choose the right tool that aligns with your deployment model, feature needs, and scalability requirements</li>
</ul>



<p>Middleware offers a <a href="https://middleware.io/pricing/">modern pricing model</a> ideal for your agile approach to help you simplify your observability pricing. It offers three pricing models, which you can choose from depending on your usage and requirements.</p>



<p>For specific features, such as infrastructure monitoring, log monitoring, application monitoring, synthetic testing, database monitoring, serverless monitoring, or real user monitoring, you can add them to your overall pricing model and pay-as-you-go. This ensures you&#8217;re not paying for unnecessary features or data storage.</p>



<p><a href="https://middleware.io/contact-us/">Contact Middleware</a> today to learn more about how our cost-effective observability solutions can empower your teams and optimize your infrastructure performance.</p>


Worried about skyrocketing observability costs? We've got you covered! Discover 5 practical tips to optimize your observability spending. 

5 Tips to Reduce Your Observability Costs


<p>Ask any system administrator or network expert about downtime, and they will probably tell you that it is “everything that can go wrong in a system at the exact moment when it should function optimally.”</p>



<p>Since organizations today are highly reliant on technology and digital processes, there is a lot that can happen if the system fails, especially just minutes before an important event.</p>



<p>As per the <a href="https://datacenter.uptimeinstitute.com/rs/711-RIA-145/images/AnnualOutageAnalysis2023.03092023.pdf">Annual Outages Analysis Report 2023</a>, 60% of organizations have faced some kind of outage or downtime in the past three years. For context, the <a href="https://www.pingdom.com/outages/average-cost-of-downtime-per-industry/">cost of downtime</a> is estimated to be around $9,000 per minute, with 98% of companies claiming that just an hour of downtime costs them roughly $100,000.&nbsp;</p>



<p>To combat downtime effectively, organizations rely on two <a href="https://middleware.io/blog/incident-management-kpis/">key incident management KPIs</a>: Mean Time to Repair (MTTR) and Mean Time to Detect (MTTD). While their acronyms may sound similar, their roles within the incident management life cycle are distinct.</p>



<p>MTTD is essentially the time it takes to detect an issue, while MTTR tells us how long it takes to repair it. These two metrics form the core of any <a href="https://middleware.io/blog/why-is-monitoring-your-application-important/">incident management process</a>.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="776" src="https://middleware.io/backend/wp-content/uploads/2024/04/MTTR-vs.-MTTD.png" alt="MTTR vs. MTTD" class="wp-image-7547"/></figure>



<p>By utilizing these metrics strategically, organizations can identify and diagnose the root cause of any outage and decrease the time it takes to repair it. To understand this in-depth, let&#8217;s explore these metrics and the critical differences between MTTR and MTTD.</p>



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<h2 class="wp-block-heading" id="h-what-is-mttd">What is MTTD?</h2>



<p>Let us start by understanding the first crucial KPI, Mean Time To Detect (MTTD). MTTD signifies the average time it takes to identify an incident after it has actually occurred in your IT infrastructure. It is calculated from the time the system experiences failure or outage and the time it takes to detect or fully diagnose the issue.</p>



<iframe src="https://gifer.com/embed/1p4I" width=480 height=364.000 frameBorder="0" allowFullScreen></iframe><p><a href="https://gifer.com">via GIFER</a></p>



<p>This metric essentially reflects the effectiveness of your monitoring and alerting systems in spotting issues before they snowball into major problems. A lower MTTD translates to faster issue detection, enabling your team to take swift action and minimize downtime.</p>



<p>To calculate MTTD, you can use the following formula:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>MTTD = (the sum of all incident detection times) / (the total number of incidents reported in that time period)</p>
</blockquote>



<p>For example, if the total number of incidents in a month is 5, and the total time to detect the incident is 251 minutes, the MTTD will be 251/5 = 50.2. </p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="693" src="https://middleware.io/backend/wp-content/uploads/2024/04/What-is-MTTD.png" alt="What is MTTD?" class="wp-image-7548"/></figure>



<p>MTTD is crucial for incident detection and management and is influenced by the following factors:</p>



<ul class="wp-block-list">
<li><strong>Monitoring capabilities:</strong> <a href="https://middleware.io/blog/cloud-monitoring-tools/">Comprehensive monitoring tools</a> that capture a wide range of system metrics and logs are crucial for the early detection of anomalies.</li>



<li><strong>Alert configuration:</strong> Effectively configured alerts, with clear notification thresholds and appropriate channels, ensure timely awareness of potential issues.</li>



<li><strong>Data correlation:</strong> The ability to correlate data from various sources allows for a holistic view of system health and helps identify the root cause of problems faster.</li>
</ul>



<p>By using a real-time and unified observability platform like <a href="https://middleware.io">Middleware</a>, organizations can significantly reduce MTTD. This platform should centralize data collection from all your systems and applications, providing a single dashboard for monitoring systems. This helps you uncover potential threats and patterns before they can make a huge impact, even raising triggers to notify your team about suspicious activities or fluctuations.</p>



<p>Detecting incidents in time allows your incident management teams to resolve issues before they impact the business. Similarly, downtime can be detected in real-time and diagnosed faster.</p>



<h2 class="wp-block-heading" id="h-what-is-mttr">What is MTTR?</h2>



<p>Once an issue or downtime is effectively diagnosed, we come to the next crucial metric &#8211; the time it takes to repair or fix this issue. This is calculated using Mean Time to Repair (MTTR).</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="710" src="https://middleware.io/backend/wp-content/uploads/2024/04/What-is-MTTR.png" alt="What is MTTR?" class="wp-image-7549"/></figure>



<p><a href="https://middleware.io/blog/how-to-reduce-mttr/">MTTR,</a> also referred to as Mean Time to Resolve or Recover,&nbsp; measures the average time it takes to diagnose and fix an incident after it&#8217;s been discovered. </p>



<p>This metric essentially reflects the efficiency of your troubleshooting and resolution processes. A lower MTTR signifies a faster recovery time, minimizing the impact of downtime on your business operations.</p>



<p>It can be calculated using the formula:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>MTTR = Total Time to Resolve All Incidents / Number of Incidents</p>
</blockquote>



<p>Wherein,</p>



<ul class="wp-block-list">
<li><strong><em>Total Time to Resolve All Incidents</em></strong><strong>:</strong> This represents the cumulative time spent resolving all incidents within a specific timeframe (e.g., a week, month, or quarter).</li>



<li><strong><em>Number of Incidents</em></strong><strong>:</strong> Refers to the total number of incidents encountered during the chosen timeframe.</li>
</ul>



<p>The following factors influence MTTR:</p>



<ul class="wp-block-list">
<li><strong>Technical complexity:</strong> Intricate system issues naturally take longer to diagnose and resolve compared to simpler ones.</li>



<li><strong>Team expertise:</strong> A skilled and experienced team can pinpoint and address problems faster than a team with limited knowledge.</li>



<li><strong>Availability of resources:</strong> The readily available tools and resources streamline the troubleshooting process.</li>



<li><strong>Standardized procedures:</strong> Clearly defined incident response protocols ensure efficient collaboration and minimize wasted time.</li>
</ul>



<p>To reduce MTTR, organizations can invest in a <a href="https://middleware.io/blog/proactive-monitoring/">proactive monitoring</a> tool, which allows them to <a href="https://middleware.io/blog/what-is-infrastructure-monitoring/">monitor all their IT infrastructure</a> and identify potential threats before they escalate into significant issues.</p>



<p>If the issue cannot be diagnosed early on, the incident management teams can swing into action quickly and take appropriate steps to resolve or repair it before it makes a bigger impact.</p>



<p>By focusing on these aspects, organizations can significantly reduce MTTR and minimize the disruption caused by system outages.</p>



<h2 class="wp-block-heading" id="h-mttr-vs-mttd-which-metric-matters-most">MTTR vs MTTD: Which metric matters most?</h2>



<p>In simple words, both matter! This is because both KPIs are essentially correlated. The faster your team can detect and diagnose an issue, the lower the MTTD. And by identifying issues faster with an accurate diagnosis, your teams can resolve them effectively, translating into improved MTTR.&nbsp;</p>



<p>Traditionally, reducing MTTR (Mean Time to Repair) and MTTD (Mean Time to Detect) has been a balancing act. Efforts to improve issue detection (MTTD) often relied on generating a high volume of alerts, which could overwhelm teams and slow down the resolution (MTTR) process.</p>



<p>However, Middleware&#8217;s <a href="https://middleware.io/platform/unified-experience/">innovative observability platform</a> bridges this gap by offering a robust suite of features designed to tackle both metrics simultaneously.</p>



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<h2 class="wp-block-heading">How to reduce MTTR and MTTD using Middleware?&nbsp;</h2>



<p>Here&#8217;s how Middleware empowers organizations to achieve a significant reduction in both MTTR and MTTD:</p>



<h3 class="wp-block-heading">Unified dashboard</h3>



<p>Middleware provides a central dashboard that offers a real-time, consolidated view of all your system metrics, <a href="https://middleware.io/blog/what-is-log-monitoring/">logs</a>, traces, and events. This eliminates the need to toggle between disparate tools, allowing your team to quickly identify anomalies and potential issues.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="805" src="https://middleware.io/backend/wp-content/uploads/2024/04/Middleware_Unified-dashboard-.png" alt="Middleware Unified dashboard (mttr vs. mttd)" class="wp-image-7550"/></figure>



<h3 class="wp-block-heading">Logs and traces</h3>



<p>Middleware empowers users to drill down into specific metrics and <a href="https://middleware.io/blog/what-is-distributed-tracing/">traces</a> associated with an identified issue. This granular visibility allows for a deeper understanding of the problem and facilitates faster troubleshooting.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="802" src="https://middleware.io/backend/wp-content/uploads/2024/04/Middleware_Logs-and-traces.png" alt="Middleware Logs and Traces (mttr vs. mttd)" class="wp-image-7551"/></figure>



<h3 class="wp-block-heading">Real-time alert notifications</h3>



<p>Middleware allows for the configuration of <a href="https://middleware.io/blog/what-is-alert-fatigue/">intelligent alerts</a> that prioritize critical issues and provide context-rich notifications. This ensures your team receives actionable information, allowing them to focus on resolving the most impactful problems first.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="802" src="https://middleware.io/backend/wp-content/uploads/2024/04/Middleware_Real-time-alert-notifications.png" alt="Middleware Real-time alert notifications (mttr vs. mttd)" class="wp-image-7552"/></figure>



<h3 class="wp-block-heading">Centralized knowledge base</h3>



<p>Middleware facilitates the creation and maintenance of a centralized knowledge base where past incidents and their resolutions are documented. This empowers teams to learn from previous experiences and resolve similar issues faster in the future.</p>



<p>In essence, Middleware acts as a force multiplier for your IT team. The unified dashboard and advanced data correlation features empower faster issue detection (MTTD reduction).</p>



<p>Once an issue is identified, metric and trace analysis coupled with intelligent alerts streamline resolution (MTTR reduction). This holistic approach ensures your team is prepared to tackle any challenge that arises, minimizing downtime and maximizing system uptime.</p>



<h2 class="wp-block-heading">Conclusion</h2>



<p>Optimizing MTTR and MTTD is a continuous pursuit for any organization striving for <a href="https://middleware.io/blog/what-is-application-performance-monitoring/">optimal system performance</a>. By understanding the nuances of these metrics and implementing the right strategies, you can significantly reduce downtime and ensure a more efficient incident response process.</p>



<p><a href="https://middleware.io/blog/observability/tools/">End-to-end observability tools</a> like Middleware don&#8217;t just offer a platform; they empower a proactive approach to system health. With its comprehensive observability features, Middleware equips your team to identify potential problems before they escalate into major incidents.</p>



<p>This proactive approach minimizes the impact of downtime and fosters a culture of continuous improvement within your IT operations.</p>



<p class="has-base-background-color has-background"><strong><em>Ready to unlock the power of faster issue detection and resolution? </em></strong></p>



<p class="has-base-background-color has-background">Explore how <a href="https://middleware.io/contact-us/">Middleware&#8217;s observability platform</a> can transform your incident response capabilities and ensure a seamless user experience.</p>


Confused by MTTR and MTTD? Read on to understand it better and how it can help improve your team’s incident response and minimize downtime.

MTTR vs. MTTD: What’s the difference?

Explore top 10 monitoring best practices for developers to optimize performance, ensure reliability, and comply with regulations.

10 Monitoring Best Practices Every Developer Should Know


<p>With over 40 years on the data processing market, Oracle is a leading database for online transaction processing and data warehousing. Designed for enterprise grid computing, Oracle manages information, applications and provides logical data storage using tablespaces. It is used by Wells Fargo, Samsung Electronics, Honeywell, among others.&nbsp;</p>



<p>If you are having a bad day with your Oracle database performance, this article will put an end to it. In this article, we highlight key oracle database metrics to keep a tab on for root-cause analysis and optimum performance. We also explore top monitoring tools that will help you simplify the monitoring exercise.</p>



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<h2 class="wp-block-heading" id="h-what-is-oracle">What is Oracle?</h2>



<p>Oracle is a feature-rich relational <a href="https://middleware.io/blog/database-monitoring-software/">database management system</a> (RDBMS) known for its scalability, reliability, security features, and comprehensive toolset. It supports a rich data model that includes relational tables, views, sequences, indexes, and constraints for structuring and organizing data. </p>



<p>Oracle also supports advanced data types such as LOBs, XML, spatial data, multimedia data, and JSON data. It uses SQL as the standard language for interacting with the database, while also supporting PL/SQL, a procedural language extension to SQL that allows developers to create stored procedures, functions, and triggers within the database.</p>



<p>An Oracle instance consists of a set of memory structures and background processes and tasks such as data caching, query processing, and transaction management. In the Oracle database, there are physical data files that store the actual data, control files containing metadata about the database, and redo logs that record changes made to the database. </p>



<p>Oracle provides features like Real Application Clusters (RAC), Data Guard, and Automatic Storage Management (ASM) for achieving high availability and disaster recovery capabilities.</p>



<p>Oracle supports both vertical and horizontal scalability through features like partitioning, parallel execution, and distributed databases, allowing developers to handle growing data volumes and user loads. </p>



<p>Alongside security features such as user authentication, role-based access control, encryption, and auditing, it also provides utilities for backup and recovery, data replication, data compression, and data archival to ensure data availability, reliability, and compliance with regulatory requirements. </p>



<p>Oracle&#8217;s comprehensive toolsets for database administration and performance tuning include SQL*Plus, SQL Developer, Enterprise Manager, Data Pump, and many others to streamline database management tasks.</p>



<h2 class="wp-block-heading" id="h-why-is-monitoring-oracledb-important">Why is monitoring OracleDB important?</h2>



<p>Monitoring OracleDB is important for the following reasons:</p>



<h3 class="wp-block-heading" id="h-capacity-planning">Capacity planning</h3>



<p>Monitoring resource utilization trends over time enables you to anticipate future capacity requirements and help you plan accordingly. This ensures that the database has sufficient resources to handle growing data volumes and user demands.</p>



<h3 class="wp-block-heading" id="h-troubleshooting">Troubleshooting</h3>



<p>Monitoring allows you to quickly detect and fix problems with your database, such as errors, failures, and performance issues. This helps you minimize downtime and maintain the reliability of the database. It also helps you identify slow-performing queries, resource bottlenecks, and other issues that can impact the database&#8217;s performance.</p>



<h3 class="wp-block-heading" id="h-cost-efficiency">Cost efficiency</h3>



<p>Effective monitoring helps you optimize resource usage, identify unused or underutilized resources, and avoid unnecessary expenditures on hardware or software. This leads to cost savings and a more efficient use of resources.</p>



<h3 class="wp-block-heading" id="h-security-and-compliance">Security and compliance</h3>



<p>Continuous monitoring of your Oracle database helps you detect unauthorized access, unusual activity, and security breaches in real-time. It also ensures that the database complies with data security regulations and internal policies.</p>



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<h2 class="wp-block-heading" id="h-key-oracledb-metrics-you-should-be-monitoring">Key OracleDB metrics you should be monitoring</h2>



<p>The following are key OracleDB <a href="https://middleware.io/blog/devops-metrics-you-should-be-monitoring/">metrics you should be monitoring</a>. Monitoring them enables you to proactively manage your database performance, detect issues early, and optimize system efficiency.</p>



<h3 class="wp-block-heading" id="h-oracledb-cpu-time">Oracledb.cpu_time</h3>



<p>This metric represents the total amount of CPU time consumed by the Oracle database. Monitoring it helps you understand the workload on the database server. High CPU time usage could indicate inefficient queries, resource contention, or a need for additional hardware resources. </p>



<p>When you keep a tab on this metric, you&#8217;ll understand how to optimize query performance, identify the root cause of bottlenecks, and ensure that the database functions efficiently.</p>



<h3 class="wp-block-heading" id="h-oracledb-enqueue-deadlocks">Oracledb.enqueue_deadlocks</h3>



<p>This metric indicates the number of deadlocks that occur during enqueue operations in the Oracle database. Monitoring enqueue deadlocks helps you identify concurrency issues within the database. </p>



<p>Deadlocks lead to performance degradation and application errors. By tracking this metric, you&#8217;ll proactively address locking conflicts, optimize transaction management, and prevent application downtime.</p>



<h3 class="wp-block-heading" id="h-oracledb-exchange-deadlocks">Oracledb.exchange_deadlocks</h3>



<p>This metric reflects the number of exchange deadlocks encountered in the Oracle database, especially as it relates to parallel query operations. Monitoring exchange deadlocks helps you detect and resolve parallel processing issues. </p>



<p>Exchange deadlocks impact query performance and scalability, thus, monitoring it enables you to fine-tune parallel query settings, optimize resource allocation, and ensure smooth data processing in the database.</p>



<h3 class="wp-block-heading" id="h-oracledb-logical-reads">Oracledb.logical_reads</h3>



<p>This metric measures the number of data blocks read from the buffer cache during query execution. Monitoring the metric provides insight into the efficiency of data retrieval operations. </p>



<p>High numbers of logical reads indicates inefficient SQL queries, missing indexes, or database design issues. Tracking this metric enables you to optimize query performance, implement appropriate indexing strategies, and reduce I/O overhead.</p>



<h3 class="wp-block-heading" id="h-oracledb-hard-parses">Oracledb.hard_parses</h3>



<p>This metric represents the number of times SQL statements are hard-parsed in the Oracle database. Hard parses are resource-intensive operations that impact database responsiveness. </p>



<p>When you monitor hard parses, you&#8217;ll easily identify poorly performing SQL statements, promote SQL plan reuse, and enhance overall system efficiency.</p>



<h3 class="wp-block-heading" id="h-oracledb-parse-calls">Oracledb.parse_calls</h3>



<p>This metric indicates the total number of parse calls made to the Oracle database. Monitoring parse calls provides insights into the workload generated by SQL statement parsing. </p>



<p>Excessive parse calls often result in CPU overhead and reduced performance. Tracking this metric enables you to optimize query caching, encourages statement reusability, and minimizes parsing-related bottlenecks.</p>



<h2 class="wp-block-heading" id="h-top-5-oracledb-monitoring-tools">Top 5 OracleDB monitoring tools</h2>



<p>Here are the top 5 Oracle database monitoring tools to look out for, along with their key features which will help you monitor, troubleshoot, and optimize the performance of your database:</p>



<h3 class="wp-block-heading" id="h-middleware">Middleware</h3>



<p><a href="https://middleware.io/">Middleware </a>is a full-stack <a href="https://middleware.io/blog/observability/">observability</a> platform that offers centralized <a href="https://middleware.io/blog/what-is-infrastructure-monitoring/">monitoring for infrastructure</a> and applications. It provides monitoring capabilities for databases, including Oracle Database and its various components.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1910" height="983" src="https://middleware.io/backend/wp-content/uploads/2024/04/image-1.png" alt="What is Oracle Monitoring - Middleware" class="wp-image-7458"/></figure>



<p>Middleware database monitoring captures performance metrics such as CPU utilization, memory usage, query execution times, resource utilization, and much more.</p>



<p>It dives deep into key metrics such as oracledb.cpu_time, oracledb.exchange_deadlocks, oracledb.enqueue_deadlocks, oracledb.hard_parses, oracledb.enqueue_locks.usage, oracledb.enqueue_resources.limit, oracledb.blocks_gets, oracledb.consistent_gets, among others. </p>



<p>With its user-friendly dashboard, Middleware provides end-to-end visibility into the performance of your Oracle database system.</p>



<p>While offering correlation of metrics for in-depth analysis, Middleware also provides a causation-to-correlation feature that spots when an issue begins and where it ends. </p>



<p>Read the Middleware <a href="https://docs.middleware.io/integrations/oracledb-integration">Oracle Integration</a> doc for more information.</p>



<h4 class="wp-block-heading" id="h-features">Features</h4>



<ul class="wp-block-list">
<li>Customizable dashboard.</li>
</ul>



<ul class="wp-block-list">
<li><a href="https://middleware.io/blog/real-user-monitoring-with-middleware/">Real-time monitoring</a> of performance metrics. </li>
</ul>



<ul class="wp-block-list">
<li>Historical performance data analysis.</li>
</ul>



<ul class="wp-block-list">
<li>Alerting and notification capabilities.</li>
</ul>



<h4 class="wp-block-heading" id="h-pricing">Pricing</h4>



<p>Middleware pricing varies based on the number of infrastructure and APM hosts. It offers a <a href="https://middleware.io/pricing/">free 14-day trial</a> that allows you to monitor as many servers as you like. </p>



<h3 class="wp-block-heading" id="h-datadog">Datadog</h3>



<p>Datadog is a comprehensive monitoring tool for infrastructure and applications. It provides health and performance metrics for databases, including Oracle, in near real-time. </p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1200" height="630" src="https://middleware.io/backend/wp-content/uploads/2024/04/unnamed.jpg" alt="Oracle Monitoring - Datadog" class="wp-image-7459"/></figure>



<p>Datadog enables you to visualize these metrics through its dashboard and alerts you on your Oracle database states. </p>



<p>In addition to the standard integration features, Datadog provides query-level metrics, live and historical query snapshots, wait event analysis, database load, query explain plans, and blocking query insights.</p>



<h4 class="wp-block-heading" id="h-features-0">Features</h4>



<ul class="wp-block-list">
<li>Real-time performance monitoring.</li>
</ul>



<ul class="wp-block-list">
<li>Customizable dashboards.</li>
</ul>



<ul class="wp-block-list">
<li>Automatic <a href="https://middleware.io/blog/anomaly-detection/">anomaly detection</a>.</li>
</ul>



<ul class="wp-block-list">
<li>Integration with 200+ database tools.</li>
</ul>



<h4 class="wp-block-heading" id="h-pricing-0">Pricing</h4>



<p>Datadog offers multiple pricing plans based on the number of hosts and features required. Custom pricing is however available for large enterprises.</p>



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<h3 class="wp-block-heading" id="h-new-relic">New Relic</h3>



<p>New Relic provides monitoring and observability for cloud-native applications and infrastructure. It offers deep insights into the health and behavior of databases, including Oracle.</p>



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="784" height="391" src="https://middleware.io/backend/wp-content/uploads/2024/04/unnamed-1.jpg" alt="Oracle Database Monitoring - New Relic " class="wp-image-7460"/></figure>



<p>New Relic&#8217;s Oracle monitoring collects key performance metrics on the database instances, and you can customize your configuration to collect even more metrics, giving you a detailed characterization of database performance. With its customizable dashboard, you can perform the “slice and dice” operations on your database metrics.</p>



<h4 class="wp-block-heading" id="h-features-1">Features</h4>



<ul class="wp-block-list">
<li>End-to-end performance monitoring.</li>
</ul>



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<li>Transaction monitoring and resource utilization tracking.</li>
</ul>



<ul class="wp-block-list">
<li>Anomaly detection and alerting.</li>
</ul>



<ul class="wp-block-list">
<li>Integration with Oracle monitoring plugins and extensions.</li>
</ul>



<h4 class="wp-block-heading" id="h-pricing-1">Pricing</h4>



<p>New Relic pricing varies based on the selected product and features.</p>



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<h3 class="wp-block-heading" id="h-signoz">SigNoz</h3>



<p>SigNoz is an open-source data observability and performance monitoring tool. It helps you monitor your system and databases, including Oracle, and troubleshoot problems.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1080" height="519" src="https://middleware.io/backend/wp-content/uploads/2024/04/Signoz.jpeg" alt="Oracle Monitoring Tools - Signoz" class="wp-image-7461"/></figure>



<p>SigNoz provides deep visibility into performance metrics and displays the data on its customizable dashboard, using graphs or flowcharts. Its metrics include availability, table sizes, cache ratios, and other key metrics.</p>



<h4 class="wp-block-heading" id="h-features-2">Features</h4>



<ul class="wp-block-list">
<li>Performance metrics visualization.</li>
</ul>



<ul class="wp-block-list">
<li>Anomaly detection.</li>
</ul>



<ul class="wp-block-list">
<li>Root cause analysis.</li>
</ul>



<ul class="wp-block-list">
<li>Community-driven development and support.</li>
</ul>



<h4 class="wp-block-heading" id="h-pricing-2">Pricing</h4>



<p>SigNoz follows an open-source model. It offers a free self-hosted version and a paid managed service option based on the volume of data and features required.</p>



<h3 class="wp-block-heading" id="h-site24x7">Site24x7</h3>



<p>Site24x7 is a <a href="https://middleware.io/blog/cloud-strategy/">cloud-based infrastructure</a> and application monitoring tool. It monitors all kinds of databases and has a specialized screen for Oracle monitoring.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1360" height="687" src="https://middleware.io/backend/wp-content/uploads/2024/04/site.png" alt="Oracle Monitoring Tools - Site24x7" class="wp-image-7462"/></figure>



<p>Site24x7 automatically detects all database instances within an environment and continuously monitors their performance. It also monitors the underlying system for the database and makes optimization recommendations.</p>



<h4 class="wp-block-heading" id="h-features-3">Features</h4>



<ul class="wp-block-list">
<li>Threshold-based alerting and notification.</li>
</ul>



<ul class="wp-block-list">
<li>Customizable dashboard.</li>
</ul>



<ul class="wp-block-list">
<li>Automatic database discovery.</li>
</ul>



<ul class="wp-block-list">
<li>Reporting and analytics capabilities.</li>
</ul>



<h4 class="wp-block-heading" id="h-pricing-3">Pricing</h4>



<p>Site24x7 offers free monitoring service for five servers, and a paid subscription that has a 30-day free trial.</p>



<h2 class="wp-block-heading" id="h-conclusion">Conclusion</h2>



<p>Database monitoring involves monitoring which ensures optimum performance and management. The right Oracle monitoring tool must combine both functions for unified and centralized observability. </p>



<p>Middleware executes these qualities effortlessly by offering root-cause analysis of database performance, and showing you who did what and when it occurred.</p>



<p><a href="https://docs.middleware.io/integrations/oracledb-integration">Try Middleware</a> today for a firsthand experience of its monitoring deftness.</p>


Explore key Oracle database metrics and top monitoring tools. Learn how to analyze data for root causes and ensure seamless operations.

What is Oracle Monitoring?


<p>IT downtime can be costly. And despite the best of our efforts, circumstances make it unavoidable. A minute of downtime can cost almost <a href="https://www.encomputers.com/2024/03/small-business-cost-of-downtime/#:~:text=Industry%20statistics%20for%20the%20cost%20of%20IT%20downtime,-For%20many%20years&amp;text=About%2044%25%20of%20those%20polled,or%20about%20%24100%2C000%20an%20hour.">$1670 per minute</a> or $100,000 per hour, and this will only increase if you are a large IT organization. It also translates into lost productivity, user frustration, and potential repercussions for the company.</p>



<p>While IT downtime can have a lasting impact, what makes a whole difference is how organizations react when they experience these unfavorable circumstances, particularly in terms of their Mean Time to Repair (MTTR).&nbsp;</p>



<p>MTTR allows organizations to track the average time it takes to resolve an incident. The lower this number, the more efficient your organization is at tackling such risks.</p>



<p>In this article, we will examine what MTTR is, why it is essential, and some ways to reduce MTTR within your organization.</p>



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<h2 class="wp-block-heading" id="h-what-is-mttr">What is MTTR?</h2>



<p>By definition, MTTR is the average time it takes to identify, diagnose, and resolve an incident that disrupts normal system function.</p>



<p>It essentially measures the efficiency of your incident response process and is defined by four ‘R’:</p>



<ul class="wp-block-list">
<li>Report: Time taken to report a particular incident.</li>



<li>Response: Time taken to respond to the incident.</li>



<li>Resolution: Time taken to completely resolve the incident.</li>



<li>Recovery: Time taken to recover from the incident. </li>
</ul>



<p>Reducing the MTTR means you have implemented disaster management processes, allowing your customers and DevOps teams to get back to order faster in case of any incident.</p>



<h2 class="wp-block-heading" id="h-how-is-mttr-calculated">How is MTTR calculated?</h2>



<p>MTTR is a <a href="https://middleware.io/blog/incident-management-kpis/">crucial metric for any incident management</a> process. It is measured using a straightforward formula:</p>



<p><strong>MTTR = Total Time to Resolve All Incidents / Number of Incidents</strong></p>



<p>In this formula,</p>



<ul class="wp-block-list">
<li><strong>Total Time to Resolve All Incidents:</strong> This represents the cumulative amount of time spent resolving all incidents within a specific timeframe (e.g., a week, month, or quarter).</li>



<li><strong>Number of Incidents:</strong> Refers to the total number of incidents encountered during the chosen timeframe.</li>
</ul>



<p>By calculating MTTR, you gain valuable insight into the effectiveness of your existing incident response strategies. As per most industry analyses, the ideal MTTR for most industries should be <a href="https://llumin.com/what-is-a-good-mean-time-to-repair-llu/#:~:text=In%20this%20regard%2C%20the%20MTTR,be%20less%20than%20five%20hours.">less than 5 hours</a>.</p>



<p>This is keeping all factors in mind, including the time it takes not just to resolve a particular incident but also to recover from it (which includes damages in terms of finances, lost business, customer ratings, or other indicators).</p>



<h2 class="wp-block-heading" id="h-how-to-reduce-mttr">How to Reduce MTTR?</h2>



<p>Now, let us focus on understanding how we can reduce MTTR for your incident management. Even if the resolution time is less than 5 hours, it can still significantly impact the business. </p>



<p>But fret not, for this section equips you with a battle plan—a comprehensive strategy designed to help you reduce MTTR and achieve lightning-fast incident resolution.</p>



<h3 class="wp-block-heading" id="h-1-set-up-an-incident-response-plan">1. Set up an incident response plan</h3>



<p>The first step to reducing MTTR is to start with a plan. Establish a clear policy to document the basics:</p>



<ul class="wp-block-list">
<li><strong>Incident Classification and Escalation:</strong> Establish a transparent system for classifying incidents based on severity, allowing for swift prioritization and resource allocation.</li>



<li><strong>Communication Channels:</strong> Ensure seamless communication across all teams – from IT to DevOps – with designated communication channels and clear escalation protocols.</li>



<li><strong>Documentation &amp; Knowledge Repository</strong>: Compile a comprehensive knowledge base of past incidents, resolutions, and troubleshooting procedures. This empowers your team to leverage past experiences and expedite future resolutions.</li>
</ul>



<p>By meticulously crafting an incident response plan, you establish a unified command center and ensure a coordinated and rapid response to any disruption.</p>



<h3 class="wp-block-heading" id="h-2-define-roles-in-your-incident-management-command-structure">2. Define roles in your incident management command structure</h3>



<p>Even if you have a well-planned incident management system, the key is defining the roles and responsibilities of each individual. This is crucial because when an incident is reported, the right teams need to be informed to take instant action.&nbsp;</p>



<p>So once you have a plan, do not forget to assign the following:</p>



<ul class="wp-block-list">
<li><strong>Roles and Responsibilities:</strong> Define ownership. Every team member must understand their role and responsibilities during an incident, eliminating confusion and delays.</li>



<li><strong>Communication Channels:</strong> How should the incident be reported? Ensure seamless communication across all teams – from IT to DevOps – with designated communication channels and clear escalation protocols.</li>
</ul>



<p>This will speed up operations once an incident is detected and ensure the right teams get the required information.</p>



<h3 class="wp-block-heading" id="h-3-detect-diagnose-and-resolve-incidents-faster-with-aiops">3. Detect, diagnose, and resolve incidents faster with AIOps</h3>



<p>If you are still relying on the traditional methods, <a href="https://middleware.io/blog/anomaly-detection/">anomaly detection</a> can often leave critical blind spots. To create a more <a href="https://middleware.io/blog/why-is-monitoring-your-application-important/">proactive monitoring</a> approach, AIOPs are crucial for your incident detection and tracking processes. Using the power of AI, you can improve your overall process, including:</p>



<ul class="wp-block-list">
<li><strong>Proactively Detect Anomalies:</strong> AI algorithms can analyze vast amounts of data to identify subtle deviations from normal system behavior, enabling you to intercept potential incidents before they erupt into full-blown outages.</li>



<li><strong>Accelerate Root Cause Analysis:</strong> AIOps can sift through mountains of data, pinpointing the root cause of incidents with laser focus. This eliminates the time-consuming process of manual troubleshooting, allowing your team to focus on swift resolution.</li>



<li><strong>Predict and Prevent:</strong> Advanced AI can learn from past incidents and identify patterns that could lead to future disruptions. This proactive approach empowers you to prevent outages before they even occur.</li>
</ul>



<p>By integrating AIOps into your MTTR reduction strategy, you gain an invaluable advantage: the power of artificial intelligence to analyze, identify, and predict, ensuring a faster and more effective response to any digital threat.</p>



<h3 class="wp-block-heading" id="h-4-proactively-monitor-nbsp">4. Proactively monitor&nbsp;</h3>



<p>To measure efficiency, you need to have clear insights into your incitement management and response processes. This is why comprehensive <a href="https://middleware.io/blog/what-is-infrastructure-monitoring/">infrastructure monitoring</a> is crucial for reducing MTTR.</p>



<p>Infrastructure monitoring helps you get a clear picture of your entire IT landscape. It ensures that your systems are in perfect order and allows you to identify issues early on to resolve them quickly.</p>



<p>Establishing a culture of constant vigilance with <a href="https://middleware.io/blog/proactive-monitoring/">proactive monitoring</a> ensures early detection of issues, enabling faster intervention and a significant reduction in MTTR.</p>



<h3 class="wp-block-heading" id="h-5-set-reliable-alerting">5. Set reliable alerting</h3>



<p>But simply monitoring isn’t enough. You need intelligent alerting or the power of observability tools. <a href="https://middleware.io/blog/observability/tools/">Observability tools</a> like Middleware help you configure alerts to trigger only when anomalies or deviations from normal behavior occur. This lets your team focus on critical issues and react swiftly to threats.</p>



<p>To set up a reliable alerting system, you need to:</p>



<ol class="wp-block-list">
<li>Set up a monitoring process where your entire infrastructure is constantly monitored</li>



<li>Define the metrics for normal or ideal behavior. For example, if the ideal usage of your processor is below 50%, the system will consider this normal behavior. However, if it goes beyond 85%, you can set up the system to raise alerts. This will help you take proactive actions, such as reducing unwanted resources or tasks and minimizing usage to avoid any downtime or lag in the system.</li>



<li>Configure alerts to trigger based on correlations between different data points. This helps identify the root cause of an incident faster, eliminating time spent chasing false positives.</li>



<li>Implement a tiered alerting system that prioritizes critical issues demanding immediate attention, ensuring your team focuses on the most impactful problems first.</li>
</ol>



<p>Establishing a robust alerting system that focuses on actionable insights and prioritization empowers your team to react swiftly and effectively to real threats, significantly reducing MTTR.</p>



<h3 class="wp-block-heading" id="h-6-automate-repeated-actions">6. Automate repeated actions</h3>



<p>Most IT and operations teams spend considerable time on mundane tasks, such as monitoring metrics or system activities. Automation is your secret weapon for liberating your team from these mundane activities.</p>



<p>This can also help to reduce MTTR using methods such as:</p>



<ul class="wp-block-list">
<li><strong>Automated Incident Response Actions:</strong> Configure automated responses for repetitive tasks like system restarts or service escalations. This frees up your team to focus on complex problem-solving and expedite resolution times.</li>



<li><strong>Automated Reporting and Analysis:</strong> Automate reports and incident analysis generation, providing valuable insights into trends and recurring issues. This empowers proactive problem-solving and continuous improvement of your MTTR.</li>
</ul>



<p>By strategically implementing automation, you streamline your incident response process, allowing your team to focus on high-value activities that truly improve MTTR.</p>



<h3 class="wp-block-heading" id="h-7-build-a-robust-change-management-strategy-nbsp">7. Build a robust change management strategy&nbsp;</h3>



<p>While often necessary, change can be a breeding ground for IT disruptions. This is where tight change management processes become your defensive wall:</p>



<ul class="wp-block-list">
<li><strong>Impact assessments:</strong> Before implementing any change, conduct thorough impact assessments to identify potential risks and ensure minimal disruption. This proactive approach helps prevent disruptions before they even occur.</li>



<li><strong>Version control and rollback plans:</strong> Maintain meticulous version control and develop clear rollback plans. This allows for a swift recovery in case of unforeseen complications arising from a change.</li>



<li><strong>Communication is paramount:</strong> Ensure clear and comprehensive communication regarding upcoming changes to all stakeholders, minimizing confusion and potential disruptions. You can anticipate and address potential roadblocks before they impact system stability by keeping everyone informed.</li>
</ul>



<p>By fortifying your change management processes, you proactively address potential roadblocks and minimize the likelihood of incidents, ultimately contributing to a lower MTTR.</p>



<h2 class="wp-block-heading" id="h-leveraging-middleware-to-reduce-mttr">Leveraging Middleware to reduce MTTR</h2>



<p>Implementing these seven powerful MTTR reduction strategies can significantly improve your incident response efficiency, minimize downtime, and ensure a more resilient IT infrastructure. </p>



<p>However, the ultimate goal is not just a lower MTTR but the relentless pursuit of zero downtime – a goal well within your grasp with the right tools and strategies.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1902" height="816" src="https://middleware.io/backend/wp-content/uploads/2024/04/Unified-Observability-Platform-Middleware-1.png" alt="Leveraging Middleware to reduce MTTR" class="wp-image-7434"/></figure>



<p>This is exactly where observability tools like <a href="https://middleware.io">Middleware</a>, an end-to-end full-stack <a href="https://middleware.io/blog/observability/">observability</a> platform, become your secret weapon. The platform is designed to provide real-time monitoring and alerting capabilities alongside advanced analytics and reporting tools.</p>



<p>Thus, you get a unified view of metrics, logs, traces, and events, helping you accelerate troubleshooting and also leverage AIOps for better infrastructure and <a href="https://middleware.io/blog/what-is-application-performance-monitoring/">application performance</a>.</p>



<p>Middleware ushers in a new era of proactive IT management with its advanced capabilities and features, such as:</p>



<ul class="wp-block-list">
<li><strong>Performance benchmarking:</strong> Middleware allows you to establish performance baselines for your IT infrastructure. This enables you to identify areas for improvement and proactively optimize your systems to prevent future slowdowns or outages.</li>



<li><strong>Predictive analytics:</strong> The platform leverages historical data to predict potential bottlenecks and system vulnerabilities before they erupt into incidents. This allows you to address potential issues preemptively, preventing downtime altogether.</li>



<li><strong>Root cause analysis:</strong> Middleware goes beyond simply identifying the root cause of an incident. It analyzes trends and patterns across historical data, helping you identify systemic weaknesses and implement preventive measures to prevent similar incidents from recurring in the future.&nbsp;</li>



<li><strong>Data correlation</strong>: By linking metrics, logs, traces, and network data, you can easily move from correlation to causation. Middleware facilitates this correlation, enabling users to quickly investigate alerts without switching screens. This streamlined process is instrumental in minimizing MTTR.</li>
</ul>



<p>Thus, by leveraging Middleware&#8217;s feature set and tailoring it to your specific needs, you can significantly enhance the effectiveness of each MTTR reduction strategy.</p>



<h2 class="wp-block-heading" id="h-conclusion">Conclusion</h2>



<p>As we conclude this article, you have been equipped with all the necessary strategies to tackle incidents and reduce MTTR, outlining the strategies that can help you achieve this goal. By implementing these strategies and leveraging Middleware&#8217;s advanced features, you can achieve significant results:</p>



<ul class="wp-block-list">
<li><strong>Reduced Downtime:</strong> Faster incident resolution translates to less downtime, minimizing user disruption and safeguarding business continuity.</li>



<li><strong>Improved Efficiency:</strong> Streamlined workflows and automated tasks free up your IT team to focus on high-value activities, maximizing their productivity.</li>



<li><strong>Proactive Prevention:</strong> AI-powered insights empower you to predict and prevent incidents before they occur, eliminating downtime altogether.</li>



<li><strong>Enhanced Collaboration:</strong> Unified communication and knowledge sharing across teams fosters a culture of collaboration, strengthening your overall IT resilience.</li>
</ul>



<p>The journey towards zero downtime doesn&#8217;t end here. It&#8217;s a continuous pursuit of optimization and improvement. By embracing a proactive approach and leveraging the power of Middleware, you can transform your IT infrastructure from vulnerable to resilient, ensuring a foundation for success in today&#8217;s ever-demanding digital landscape.</p>



<p>Ready to <a href="https://middleware.io/blog/best-practices-to-avoid-website-outages-on-black-friday/">achieve zero downtime</a> and unlock the full potential of your IT infrastructure? <a href="https://app.middleware.io/auth/register/">Sign up</a> today to learn more about Middleware Observability Solution for your business!</p>


Cut downtime! Discover proven methods to reduce MTTR & leverage Middleware's AI-powered monitoring for faster recovery.

How to reduce MTTR?


<p>Streaming live events and managing event data only became a walk in the park when tools such as Redpanda entered the market. In fact, it was built with the sole purpose of eliminating the complexities common to Apache <a href="https://middleware.io/blog/kafka-monitoring/">Kafka</a>, improving performance, and making the architecture more safe and resilient.</p>



<p>However, the infamous Kafka alternative is not immune to anomalies. Hence, <a href="https://middleware.io/blog/observability-vs-monitoring/">monitoring</a> Redpanda is essential for developers to detect issues early, resolve them quickly, prevent <a href="https://middleware.io/blog/best-practices-to-avoid-website-outages-on-black-friday/">downtime</a>, and improve the overall data streaming experience.&nbsp;</p>



<p>In this article, we will dive into the importance of Redpanda monitoring, highlight the key metrics to observe, and top 3 Redpanda monitoring tools for 2024.</p>



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<h2 class="wp-block-heading" id="h-what-is-redpanda">What is Redpanda?</h2>



<p>Redpanda is a modern event streaming platform developed using C++ and built on top of the Raft consensus algorithm. It is protocol and API-compatible with Apache Kafka, and famous for low-latency data streaming—up to 10x lower—due to its patent-pending thread-per-core architecture.</p>



<p>Redpanda has an easy-to-use interface and tools for setting up and monitoring event streaming pipelines. It is used by developers as a cost-effective drop-in replacement for Kafka, for use cases like stream processing, real-time analytics, and event-driven architectures.</p>



<h2 class="wp-block-heading" id="h-why-is-monitoring-redpanda-important">Why is monitoring Redpanda important?</h2>



<p>Monitoring Redpanda helps you get actionable insights that empower you to effectively manage and optimize your Redpanda cluster. </p>



<p>Here are some top reasons why monitoring Redpanda is important:</p>



<h3 class="wp-block-heading" id="h-capacity-planning">Capacity planning</h3>



<p>Monitoring helps you track resource usage, data growth, and cluster health trends. This information is valuable for capacity planning to ensure that your Redpanda cluster can scale to meet future demands.&nbsp;</p>



<p>It involves monitoring key resource metrics such as CPU utilization, memory usage, disk I/O, network traffic, and storage capacity across Redpanda nodes. Assessing the efficiency of the cluster helps you identify potential bottlenecks under different load conditions.&nbsp;</p>



<p>Capacity planning also means tracking data ingestion rates, retention policies, and storage usage patterns. This enables you to estimate how quickly your data volumes are increasing and plan storage expansion accordingly. </p>



<p>By understanding the characteristics of the workloads running on your Redpanda cluster, you can create workload models to simulate different scenarios and predict how changes in workload intensity or data volume impact resource requirements.</p>



<h3 class="wp-block-heading" id="h-performance-optimization">Performance optimization</h3>



<p>By monitoring key metrics like throughput, latency, and resource utilization, you will easily identify performance bottlenecks and optimize configurations to ensure optimal performance of your Redpanda cluster.&nbsp;</p>



<p>It begins with monitoring key metrics such as message throughput, message latency, request rates, peak traffic periods, data access patterns, disk utilization, memory usage, and cluster health indicators.&nbsp;</p>



<p>Analyzing these metrics helps you pinpoint areas of the system that are limiting <a href="https://middleware.io/blog/how-to-optimize-website-performance-for-the-holidays/">performance</a> and take corrective actions. Monitoring also helps in fine-tuning settings related to buffer sizes, replication factors, batch sizes, and other parameters to achieve better throughput, lower latency, and improved resource utilization.&nbsp;</p>



<p>It also makes it easy to assess whether scaling out (adding more nodes) or scaling up (increasing resources on existing nodes) is the best approach to maintain or improve performance levels.</p>



<h3 class="wp-block-heading" id="h-efficient-troubleshooting">Efficient troubleshooting</h3>



<p>Monitoring enables you to detect issues such as node failures, disk errors, or network problems promptly. With this, you&#8217;ll quickly troubleshoot and resolve issues before they impact your application. It also helps you minimize downtime and ensure the reliability of your event streaming platform.&nbsp;</p>



<p>As part of monitoring techniques, setting up alerting rules based on predefined thresholds or <a href="https://middleware.io/blog/anomaly-detection/">anomaly detection</a> algorithms gets you notified about potential issues that require immediate attention. </p>



<p>Detailed analysis and correlation of these metrics helps you easily diagnose the root cause of issues and pinpoint where failures are occurring within the Redpanda cluster. Monitoring also facilitates collaborative troubleshooting which involves providing a shared understanding of the system&#8217;s state among team members.&nbsp;</p>



<p>This encourages the establishment of standardized troubleshooting playbooks based on common issues and best practices to streamline the troubleshooting process. By documenting known solutions, escalation procedures, and diagnostic steps, you&#8217;ll expedite the resolution of recurring problems and improve your system reliability.</p>



<h3 class="wp-block-heading" id="h-security-and-compliance">Security and compliance</h3>



<p>Monitoring helps in identifying security threats, unauthorized access attempts, and compliance violations within your Redpanda cluster. Redpanda supports end-to-end encryption, Kerberos authentication, Kafka-compatible ACLs, cloud IAM roles, Console SSO, and more.&nbsp;</p>



<p>Monitoring access control mechanisms within Redpanda, such as authentication, authorization, and encryption, helps you ensure that only authorized users and applications can interact with the cluster.&nbsp;</p>



<p>Data encryption mechanisms, such as SSL/TLS for data in transit and encryption-at-rest for stored data, helps you safeguard sensitive information within the Redpanda cluster. In furtherance, monitoring audit logs means tracing security incidents, compliance violations, and unauthorized activities within the cluster, helping in forensics, incident response, and compliance audits.&nbsp;</p>



<p>When you monitor security-related metrics, you&#8217;ll prevent data breaches, maintain data integrity and meet regulatory compliance.</p>



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<h2 class="wp-block-heading" id="h-what-metrics-should-you-monitor-in-redpanda">What metrics should you monitor in Redpanda?</h2>



<h3 class="wp-block-heading" id="h-public-metrics">Public metrics</h3>



<h4 class="wp-block-heading" id="h-1-cluster-metrics">1. Cluster metrics</h4>



<p>Cluster metrics are operational data collected from various nodes within the Redpanda cluster. They include information on resource utilization, node health, and other key parameters for optimizing the cluster&#8217;s performance.</p>



<h5 class="wp-block-heading" id="h-i-redpanda-cluster-brokers">i. Redpanda_cluster_brokers</h5>



<p>This metric indicates the total number of Redpanda brokers currently active in the cluster. Monitoring it helps you ensure the cluster&#8217;s health and detect any discrepancies in the expected broker count. You can monitor it by setting a threshold with an alert system to notify you when the threshold is surpassed.</p>



<h5 class="wp-block-heading" id="h-ii-redpanda-cluster-controller-log-limit-requests-dropped">ii. Redpanda_cluster_controller_log_limit_requests_dropped</h5>



<p>This metric indicates the number of requests dropped due to hitting the log limit in the Redpanda cluster&#8217;s controller. Monitoring this metric helps you understand if there are any issues with log limits affecting request processing.</p>



<h5 class="wp-block-heading" id="h-iii-redpanda-cluster-partition-moving-from-node">iii. redpanda_cluster_partition_moving_from_node</h5>



<p>This metric tracks the partitions currently moving from one node to another within the Redpanda cluster. It helps you optimize partition movement for better cluster performance. When this gauge shows a value of non-zero, check if partition replicas are being reassigned as expected or unexpectedly. This helps identify if there are any unusual movements of partition replicas.</p>



<h4 class="wp-block-heading" id="h-2-infrastructure-metrics">2. Infrastructure metrics</h4>



<p>With the following metrics, you can manage your system infrastructure and optimize its margins to handle peaks in processing, storage, and I/O loads:</p>



<h5 class="wp-block-heading" id="h-i-redpanda-cpu-busy-seconds-total">i. Redpanda_cpu_busy_seconds_total</h5>



<p>This metric measures the total CPU busy time in seconds within the Redpanda infrastructure. You can find out if the CPU is not being used by checking how much of the CPU core is active compared to its total capacity at a specific moment. This helps identify times when the CPU might be idle or not working as expected.</p>



<h5 class="wp-block-heading" id="h-ii-redpanda-io-queue-total-read-ops">ii. redpanda_io_queue_total_read_ops</h5>



<p>This metric denotes the total number of read operations in the I/O queue within Redpanda infrastructure. Monitoring this metric helps you track I/O performance and detect potential read operation bottlenecks.</p>



<h5 class="wp-block-heading" id="h-iii-redpanda-memory-allocated-memory">iii. Redpanda_memory_allocated_memory</h5>



<p>This metric indicates the memory allocated in bytes within the Redpanda infrastructure. Monitoring it helps you understand memory usage patterns and optimize memory allocation for efficient cluster operations.</p>



<h5 class="wp-block-heading" id="h-iv-redpanda-memory-available-memory">iv. redpanda_memory_available_memory</h5>



<p>This metric reflects the amount of available memory within the Redpanda infrastructure. It helps you ensure sufficient memory availability for smooth cluster operation and prevent memory-related issues.</p>



<h3 class="wp-block-heading" id="h-internal-metrics">Internal metrics</h3>



<p>Internal metrics are more specific to the internal workings and components of the Redpanda cluster. They provide detailed insights into the functioning of the cluster, including low-level details such as partition offsets, schema validation records, application uptime, and other internal parameters.</p>



<h5 class="wp-block-heading" id="h-1-vectorized-application-uptime">1. Vectorized_application_uptime</h5>



<p>This metric tracks the uptime of the vectorized application within the Redpanda cluster in milliseconds. It provides insights into the application&#8217;s availability and reliability.</p>



<h5 class="wp-block-heading" id="h-2-vectorized-cluster-partition-last-stable-offset">2. Vectorized_cluster_partition_last_stable_offset</h5>



<p>This metric indicates the last stable offset for partitions within the vectorized cluster. It helps in assessing data consistency and partition stability. If this is the last record the cluster has received, it means the cluster is fully updated and ready for maintenance.</p>



<h5 class="wp-block-heading" id="h-3-vectorized-cluster-partition-schema-id-validation-records-failed">3. Vectorized_cluster_partition_schema_id_validation_records_failed</h5>



<p>This metric monitors the number of failed schema ID validation records for partitions in the vectorized cluster. This metric helps you identify and resolve schema validation failures for data integrity and consistency.</p>



<h2 class="wp-block-heading" id="h-top-3-redpanda-monitoring-tools">Top 3 Redpanda Monitoring Tools</h2>



<h3 class="wp-block-heading" id="h-middleware">Middleware</h3>



<p><a href="https://middleware.io/">Middleware</a> is a full-stack cloud observability platform, offering intuitive dashboards for holistic insights and streamlined data analysis for various applications, databases, and platforms, including Redpanda. It automatically collects performance metrics from Redpanda and presents them within a centralized customizable dashboard.&nbsp;</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1600" height="810" src="https://middleware.io/backend/wp-content/uploads/2024/03/Redpanda-Monitoring-1.png" alt="Redpanda monitoring on Middleware" class="wp-image-7384"/></figure>



<p>Middleware also offers a bespoke alert system equipped with algorithms for root cause analysis and actionable recommendations. You can configure the alerts to trigger anomaly detection and receive notifications when specific thresholds are exceeded. This proactive approach enables quick identification and resolution of issues across Redpanda clusters.</p>



<p>To monitor Redpanda with Middleware, <a href="https://docs.middleware.io/agent-installation/overview">click here.</a></p>



<p>Middleware supports all the key Redpanda metrics including but not limited to redpanda_cluster_controller_log_limit_requests_dropped, redpanda_cluster_partition_moving_from_node, redpanda_cluster_partition_node_cancelling_movements, redpanda_cluster_topics, redpanda_cpu_busy_seconds_total, redpanda_memory_allocated_memory, redpanda_rpc_request_errors_total, redpanda_schema_registry_request_errors_total, redpanda_storage_disk_free_bytes.</p>



<p>While offering correlation of metrics for in-depth analysis, Middleware also provides a causation-to-correlation feature that spots when an issue begins and where it ends. Read the Middleware <a href="https://docs.middleware.io/integrations/redpanda-integration">Redpanda integration documentation</a> for more information.</p>



<h3 class="wp-block-heading" id="h-datadog">Datadog</h3>



<p><a href="https://middleware.io/comparison/datadog-alternative/">Datadog</a> is a monitoring tool for infrastructures and applications. It integrates with several systems, servers, databases and tools, including Redpanda.&nbsp;</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1206" height="624" src="https://middleware.io/backend/wp-content/uploads/2024/03/Redpanda-monitoring-2.png" alt="Redpanda monitoring on Datadog" class="wp-image-7385"/></figure>



<p>Datadog has Redpanda integration and provides the needed Redpanda metrics for a detailed understanding of the streaming platform&#8217;s operations. Additionally, Datadog employs anomaly detection and alerting supported by machine learning algorithms to prevent alert fatigue, allowing you to concentrate on critical insights concerning your Redpanda clusters.</p>



<h3 class="wp-block-heading" id="h-new-relic">New Relic</h3>



<p><a href="https://middleware.io/comparison/newrelic-alternative/">New Relic</a> provides a comprehensive monitoring of systems and applications, offering full visibility into their clusters and execution environment.&nbsp;</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1080" height="568" src="https://middleware.io/backend/wp-content/uploads/2024/03/Redpanda-Monitoring-3.jpeg" alt="Redpanda monitoring on New Relic" class="wp-image-7386"/></figure>



<p>With its alerting system and query language, New Relic equips you with all the necessary tools to manage and monitor Redpanda clusters. It provides in-depth analysis for Redpanda metrics and simple visualization through its dashboard. New Relic seamlessly monitors Redpanda on major cloud providers and environments, including AWS, Azure, and Kubernetes.</p>



<h2 class="wp-block-heading" id="h-final-thoughts">Final thoughts</h2>



<p>Although Redpanda simplifies—and cheapens—data streaming, monitoring the tool is as important as the data you are streaming. But you can only monitor effectively by using the appropriate monitoring tool.&nbsp;</p>



<p>Middleware is your surest bet at comprehensive Redpanda monitoring. How? See for yourself and <a href="https://app.middleware.io/auth/register/?_gl=1*1gut4pw*_ga*MTIzMDgwMDYxOC4xNjkyMzM4MDIw*_ga_2KBHR7YB2G*MTcxMTkwNjk4MS4zNjcuMS4xNzExOTA3Mjc1LjAuMC4w">try Middleware now</a>.&nbsp;</p>


Discover why monitoring Redpanda is crucial for seamless event streaming. Learn key metrics and top tools for 2024.

What is Redpanda Monitoring?


<p>As a software developer, you are working on a high-traffic application that heavily relies on Redis for caching and real-time data processing.&nbsp;</p>



<p>Unfortunately, you are faced with a Redis server outage that leads to unexpected performance issues. The sudden outage not only disrupts service but also harms your users&#8217; experience.&nbsp;</p>



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<p>In this article, we highlight key Redis metrics to monitor that help you prevent performance issues and take proactive steps to optimize your Redis instances. We also mention monitoring solutions that help you monitor all of your key metrics from a single view.</p>



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<h2 class="wp-block-heading" id="h-what-is-redis">What is Redis?</h2>



<p>Redis (Remote Dictionary Server) is an open-source, NoSQL advanced key-value data store that serves as an in-memory data structure. It is well-suited for various use cases, including database, caching, real-time analytics, message brokering, and general-purpose data storage. </p>



<p>Widely used for caching frequently accessed data, Redis reduces the load on primary <a href="https://middleware.io/blog/database-monitoring-software/">databases</a> and improves application performance.</p>



<p>Because Redis primarily stores its data in memory, it makes it incredibly fast for read and write operations. Its support for various data structures such as strings, lists, sets, and hashes, makes it flexible and efficient for data storage and manipulation. Redis is highly scalable and allows <a href="https://middleware.io/blog/vertical-vs-horizontal-scaling/">horizontal scaling</a> for more instances.</p>



<p>Redis persists data to disk, a feature that eases data recovery in the event of a server restart. It also supports publish/subscribe messaging, enabling message brokering and real-time analytics, such as <a href="https://middleware.io/blog/what-is-real-user-monitoring/">tracking user behavior </a>and managing real-time dashboards. </p>



<p>Many web applications therefore use Redis to store user sessions to quickly access and retrieve session data.</p>



<h2 class="wp-block-heading" id="h-top-5-redis-metrics-to-monitor">Top 5 Redis metrics to monitor</h2>



<p>Despite Redis functionalities and the ability for low latency when working on large numbers of queries, it is still prone to performance bottlenecks in a complex architecture. </p>



<p>Monitoring its key metrics is the only way to get the best out of the Redis deployment and overcome performance issues. The following are key metrics to observe:</p>



<h3 class="wp-block-heading" id="h-1-performance-metrics">1. Performance metrics</h3>



<p><a href="https://middleware.io/blog/what-is-application-performance-monitoring/">Monitoring performance</a> metrics ensures that Redis operates optimally, and provides fast and reliable data access for your applications. The following are Redis performance metrics:</p>



<h4 class="wp-block-heading" id="h-a-latency">a. Latency:</h4>



<p>This metric measures the time it takes for Redis to process a request. Monitoring latency ensures that Redis commands are executing within an acceptable timeframe, thereby preventing slowdowns in application performance. </p>



<p>This is because high latency often leads to delays in data retrieval and system responsiveness. To optimize latency, ensure you understand network latency, disk I/O latency, and Redis server processing time. </p>



<p>This understanding will help you fine-tune Redis configuration, data layout, and the system architecture to minimize delays in data access.</p>



<p>You can access the Redis server using the Redis command-line interface, “redis-cli”. For Redis latency, you can run the following command:</p>



<div class="code-block content-copy">
<div class="pre" id="code-blog"><pre><code>
<span> bash </span>
redis-cli --latency -h <host> -p <port> -n <db_number> -a <password>
</code>
</pre>
</div>
<a class="copy-btn" id="first-button"></a>
</div>



<p>Replace `<code>&lt;host&gt;</code>`, `<code>&lt;port&gt;</code>`, `<code>&lt;db_number&gt;</code>`, and `<code>&lt;password&gt;</code>` with your Redis server&#8217;s actual values.</p>



<p>A result like the below will be returned:</p>



<div class="code-block content-copy">
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<span> plaintext </span>
min: 0, max: 24, avg: 0.13 (1089 samples)
</code>
</pre>
</div>
<a class="copy-btn" id="first-button"></a>
</div>



<p>In this example, the result shows the minimum latency, maximum latency, average latency, and the number of samples collected. This information shows the performance of the Redis server in terms of response time. </p>



<p>But when external monitoring tools are used, you’ll capture and analyze latency metrics over time without running commands.</p>



<h4 class="wp-block-heading" id="h-b-cpu-usage">b. CPU usage:</h4>



<p>CPU usage represents the amount of processing power that Redis consumes. High CPU usage indicates heavy computational load or inefficient resource utilization. Monitoring CPU usage provides insight into the server&#8217;s workload and helps you identify resource constraints. </p>



<p>When you understand the CPU usage patterns, it enables effective capacity planning. Overtime monitoring of CPU usage helps you assess load trends, predict scaling needs, and implement optimizations to maximize CPU utilization.</p>



<h4 class="wp-block-heading" id="h-c-hit-rate">c. Hit rate:</h4>



<p>The hit rate measures the percentage of successful key lookups in Redis&#8217; cache, compared to overall lookups, indicating the effectiveness of caching strategies. While a high hit rate signifies efficient cache utilization, a low hit rate points to suboptimal caching, prompting a reevaluation of caching strategies and data access patterns. </p>



<p>The hit rate can be calculated using formulas based on the number of cache hits and misses, or by leveraging other Redis metrics for cache utilization and hit ratio. Monitoring and analyzing hit rates help you refine caching strategies, adjust cache sizes, and fine-tune data access patterns.</p>



<h3 class="wp-block-heading" id="h-2-memory-metrics">2. Memory metrics</h3>



<p>Monitoring memory metrics is crucial for maintaining optimal memory utilization, and proactive management of memory resources. The metrics are as follows:</p>



<h4 class="wp-block-heading" id="h-a-memory-usage">a. Memory usage:</h4>



<p>Memory usage refers to the amount of physical memory consumed by Redis to store data and metadata. High memory usage results in out-of-memory errors, performance degradation, and even server crashes. You can monitor memory usage through Redis commands such as `MEMORY USAGE` and by examining the &#8220;used_memory&#8221; metric from the INFO command.</p>



<h4 class="wp-block-heading" id="h-b-memory-fragmentation-ratio">b. Memory fragmentation ratio:</h4>



<p>Memory fragmentation occurs when memory is allocated and deallocated, which results in fragmented memory blocks and inefficiencies. The memory fragmentation ratio measures the degree of fragmentation within Redis and reflects how fragmented the memory allocation has become.&nbsp;</p>



<p>High fragmentation often leads to inefficiency in memory usage, and slower responses. You can monitor the memory fragmentation ratio by calculating the ratio of memory used to memory allocated. </p>



<p>However, Redis provides limited visibility into this metric through native commands. Specialized tools such as <a href="http://middleware.io">Middleware</a> are therefore required to gain a deeper understanding of the metric.</p>



<h4 class="wp-block-heading" id="h-c-key-evictions">c. Key evictions:</h4>



<p>This metric tracks the rate at which keys are evicted from the Redis cache due to memory constraints. Key evictions occur when Redis needs to free memory and removes keys according to its configured eviction policy. Monitoring key evictions helps you understand the impact of memory limits and management on the dataset and system.&nbsp;</p>



<p>This helps you determine if the eviction policy needs adjustment or if additional memory resources are required. You can monitor key evictions by periodically querying the &#8220;evicted_keys&#8221; metric from the INFO command and assessing the eviction rate over time. </p>



<p>However, with monitoring tools, you will gain historical data on key eviction patterns and their impact on Redis performance.</p>



<h3 class="wp-block-heading" id="h-3-basic-activity-metrics">3. Basic activity metrics</h3>



<p>Monitoring basic activity metrics helps ensure the stability, scalability, and reliability of the Redis deployment. The following are the metrics within the basic activity metrics:</p>



<h4 class="wp-block-heading" id="h-a-connected-clients">a. Connected clients:</h4>



<p>This metric monitors the number of clients connected to the Redis server. High numbers of connected clients increase the server load and may impact responsiveness. </p>



<p>You can retrieve the count of connected clients using the INFO command. By monitoring this metric, you&#8217;ll identify usage patterns, peak connection times, and client connection limit issues.</p>



<h4 class="wp-block-heading" id="h-b-connected-slaves">b. Connected slaves:</h4>



<p>This tracks the number of slave instances connected to the Redis master server, providing insights into replication status and issues related to the slave nodes. It also helps you understand the load on the master server. </p>



<p>Sudden drops in the number of connected slaves or inconsistencies in replication can be quickly identified through this metric. You can retrieve the count of connected slaves using the INFO command. </p>



<p>And you need to monitor changes over time to ensure that replication is functioning as expected.</p>



<h4 class="wp-block-heading" id="h-c-master-last-io-seconds-ago">c. Master_last_io_seconds_ago:</h4>



<p>This metric indicates the time elapsed since the last interaction with the master node for each connected replica. Monitoring this metric helps you assess the liveness of the master node. </p>



<p>Observing an increasing value for &#8220;<code>master_last_io_seconds_ago</code>&#8221; could indicate potential replication issues or network connectivity problems. You can access the &#8220;<code>master_last_io_seconds_ago</code>&#8221; metric through the INFO command for each connected replica and set up alerting mechanisms to identify anomalies.</p>



<h4 class="wp-block-heading" id="h-d-keyspace">d. Keyspace:</h4>



<p>The &#8220;keyspace&#8221; metric provides information about the number of keys in each defined keyspace, along with additional metadata such as expiration details. Monitoring the keyspace metric offers insights into the distribution of data across different key spaces. </p>



<p>You can retrieve information about the keyspace using the INFO command. This allows you to analyze the distribution of keys, assess expiration patterns, and plan for data sharding or partitioning.</p>



<h3 class="wp-block-heading" id="h-4-error-metrics">4. Error metrics</h3>



<p>Error metrics in Redis encompass a wide range of issues including command failures, connection errors, and other unexpected behavior within the Redis environment. Monitoring and understanding these metrics help you maintain system reliability and performance.&nbsp;</p>



<p>From a technical standpoint, to detect and address error metrics, you&#8217;ll need to regularly review Redis logs to identify various errors, warnings, and exceptions that may impact the system&#8217;s stability. </p>



<p>You’ll also need to leverage Redis&#8217; built-in error reporting which provides detailed error messages for various operations. This enables you to diagnose and rectify issues proactively.</p>



<h3 class="wp-block-heading" id="h-5-expired-evicted-keys">5. Expired/Evicted keys</h3>



<p>Keeping track of the number of expired and evicted keys helps you gauge the efficiency of the key eviction policy and assess the impact of key expiration on system performance. </p>



<p>You can monitor the number of expired keys and the frequency of expiration using Redis commands, such as `<em>TTL</em>` and `<em>PTTL</em>`, and by analyzing the expiration metadata for keys. Understanding expiration patterns allows you to properly optimize data retention and caching strategies. </p>



<p>This ensures that expired data does not linger unnecessarily in the system.&nbsp;</p>



<p>Evicted keys represent data removed from the Redis cache due to memory constraints and its configured eviction policy. To monitor key eviction rates and patterns, use the &#8220;evicted_keys&#8221; metric from the INFO command. External monitoring tools will however provide deeper and detailed analysis of the metric. </p>



<p>Analyzing this metric enables you to assess the impact of memory management on the dataset and adjust eviction policies as needed to maximize cache efficiency.</p>



<h2 class="wp-block-heading" id="h-redis-performance-monitoring-3-options">Redis performance monitoring: 3 options</h2>



<p>Each of the following options offers a unique approach to Redis performance monitoring, providing you with the functionalities and insights needed to optimize the performance of your Redis deployments.</p>



<h3 class="wp-block-heading" id="h-1-using-tools-like-middleware">1. Using tools like Middleware</h3>



<p><a href="https://middleware.io/">Middleware</a> is a full-stack cloud observability platform, offering intuitive dashboards for holistic insights and streamlined data analysis for various applications and databases, including&nbsp; Redis. The platform also offers a dedicated database monitoring section where you can find detailed view and data for database performance</p>



<p><a href="https://docs.middleware.io/integrations/redis-integration">Seamlessly integrated with Redis</a>, it automatically collects performance metrics and presents them within a centralized dashboard. The customizable dashboard empowers you to tailor the visualizations to your specific needs.</p>



<p>Middleware also offers a <a href="https://middleware.io/platform/alerts/">smart alert system</a> equipped with algorithms for root cause analysis. You can configure alerts to trigger anomaly detection and receive notifications when specific thresholds are exceeded. </p>



<p>This proactive approach enables swift identification and resolution of issues across Redis instances, ensuring optimal system performance.</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1913" height="970" src="https://middleware.io/backend/wp-content/uploads/2024/03/Middleware-Redis-1.png" alt="Redis Monitoring Tool, Middleware" class="wp-image-7363"/></figure>



<p>Middleware supports all the key Redis metrics including but not limited to <em>redis.cpu.time, redis.clients.connected, redis.keys.expired, redis.keys.evicted, redis.memory.peak, redis.keyspace.hits, redis.replication.offset,</em> and <em>redis.memory.fragmentation_ratio</em>. </p>



<p>While offering correlation of metrics for in-depth analysis, Middleware also provides a causation-to-correlation feature that spots when an issue begins and where it ends.</p>



<h3 class="wp-block-heading" id="h-2-redis-enterprise-built-in-monitoring-capabilities">2. Redis enterprise built-in monitoring capabilities</h3>



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="924" height="850" src="https://middleware.io/backend/wp-content/uploads/2024/03/Enterprise-Redis.jpeg" alt="Redis enterprise built-in monitoring tool" class="wp-image-7364"/></figure>



<p>The built-in monitoring capability for Redis is the Redis Enterprise cluster management UI monitoring console. It does not require any configuration or installation efforts since it is built-in. </p>



<p>With the “minute” interval, Redis monitoring console works almost real time. Its interface represents a comprehensive dashboard that provides metrics for the cluster, its nodes, and databases, on separate screens.</p>



<p>The Redis Enterprise monitoring console also has an alert system that allows you to set alerts for each metric, and to configure these notifications to be sent by email. Note however that the Redis Enterprise monitoring console is only available for Enterprise users of Redis, and not the open-source users.</p>



<h3 class="wp-block-heading" id="h-3-rest-api">3. REST API</h3>



<p>Redis provides a REST API that allows users to programmatically retrieve performance metrics and operational data from the Redis server. The REST API offers a flexible and convenient way to integrate Redis monitoring with custom solutions or third-party tools.&nbsp;</p>



<p>This enables seamless incorporation of Redis telemetry and operational functionality within the broader IT ecosystem. By leveraging the REST API, you can query for specific metrics and integrate them into third-party tools. REST API is also available only for Redis Enterprise users.</p>



<h2 class="wp-block-heading" id="h-conclusion">Conclusion</h2>



<p>While it is a great asset to know and understand key Redis metrics to observe, it would not be worth the effort when you do not have the right monitoring tool at your disposal.&nbsp;</p>



<p>One such tool is Middleware, which offers a comprehensive solution for Redis, capturing vital metrics and providing features that aid analysis.&nbsp;</p>



<p>So, why are you waiting? <a href="https://app.middleware.io/auth/register/">Sign up </a>to Middleware for free and embrace the true potential of Redis monitoring.&nbsp;</p>


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<p>Did you know that, according to a FRSecure report, only 45% of businesses are leveraging the use of incident response plans and incident management KPIs optimally?</p>



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<p>In today&#8217;s high-stakes, cloud-centric landscape, a comprehensive understanding of these KPIs for incident management holds the key to driving efficiency and maintaining infrastructure stability.</p>



<p>If you&#8217;re among the technology leaders, enterprise decision-makers, DevOps engineers, software engineers, or investors striving to fortify your incident response and management, then this blog post is for you. We will explore the top 9 incident management KPIs to keep an eye on.&nbsp;</p>



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<h2 class="wp-block-heading" id="h-what-is-a-kpi-in-incident-management"><strong>What is a KPI in incident management?</strong></h2>



<p>A Key Performance Indicator (KPI) in incident management is a quantifiable measure that IT teams use to track, analyze, and measure the effectiveness of their incident response processes.</p>



<p>These KPIs are of great importance because they provide tangible data, offering insights into how efficiently an organization is handling its IT concerns. They serve as a critical feedback mechanism for continuous operational improvement, shining a spotlight on areas that need attention.</p>



<p>When we talk about incident management KPIs, we&#8217;re referring to measurements that can provide a comprehensive view of your system&#8217;s health and your team&#8217;s efficiency in addressing and resolving issues. </p>



<p>This enables teams to identify trends, make informed decisions, <a href="https://middleware.io/blog/best-practices-to-avoid-website-outages-on-black-friday/">reduce system downtime</a>, and improve overall service quality.</p>



<h2 class="wp-block-heading" id="h-top-9-incident-management-kpis"><strong>Top 9 Incident Management KPIs</strong></h2>



<p>If you&#8217;re in the business of running a top-notch IT department, you know that mastering incident management KPIs is just as much art as it is science. From quicker resolution times to enhanced decision-making, these ten important KPIs for incident management are pivotal for optimized operations.&nbsp;</p>



<h3 class="wp-block-heading" id="h-incidents-over-time-nbsp"><strong>Incidents Over Time&nbsp;</strong></h3>



<p>When it comes to monitoring the effectiveness of your incident management process, one significant incident management KPI to keep on your radar is &#8216;Incidents Over Time.&#8217;&nbsp;</p>



<p>This KPI helps track the number of incidents recorded in a specific timeframe. To decode this further, it provides vital insights into the periodic trends and patterns, enabling you to proactively pinpoint and remediate potential problem areas.&nbsp;</p>



<p>For example: If a certain software component consistently triggers an uptick in incidents during peak use hours, your team can strategically use these insights to streamline processes, allocate resources, or even alert system users in advance.&nbsp;</p>



<p>To highlight further:&nbsp;</p>



<ul class="wp-block-list">
<li>It monitors the volume of incidents over a defined timeframe, which helps quickly identify surges or drops.</li>



<li>Shows cyclical trends and helps in forecasting future incident volumes and their patterns.</li>



<li>Assists in allocating resources optimally based on incident volumes and expected trends.</li>
</ul>



<p>The journey toward<a href="https://middleware.io/blog/how-to-optimize-website-performance-for-the-holidays/"> improved system health</a> is never a one-size-fits-all journey &#8211; but with the right KPIs for incident management, you&#8217;re certainly on the right path!</p>



<h3 class="wp-block-heading" id="h-mean-time-to-acknowledge-mtta"><strong>Mean Time to Acknowledge (MTTA)</strong></h3>



<p>When it comes to the incident management world, the Mean Time to Acknowledge (MTTA) scores some real points. It&#8217;s a critical key performance indicator (KPI) that signifies the average time it takes for your team to acknowledge an incident once it&#8217;s reported.</p>



<p>The sooner you acknowledge a problem, the quicker you can work on resolving it. Thus, keeping a low MTTA is necessary for any organization that offers top-tier service.&nbsp;</p>



<p>A well-monitored MTTA metric implies your team is prompt at recognizing incidents, leading to hastier troubleshooting.</p>



<ul class="wp-block-list">
<li><strong>Why is it important?</strong><strong><br></strong>The MTTA measures an organization&#8217;s responsiveness, which is vital to effectively handle any incident.</li>



<li><strong>What does it show?</strong><br>A shorter MTTA indicates a team’s swift acknowledgment of incidents, leading to quicker resolution times.</li>



<li><strong>Example:</strong> If your team&#8217;s weekly average MTTA is 30 minutes, it means that, on average, it takes your team half an hour to acknowledge an incident after it&#8217;s been reported.</li>
</ul>



<h3 class="wp-block-heading" id="h-mean-time-to-detect-mttd"><strong>Mean Time to Detect (MTTD)</strong></h3>



<p>Mean Time to Detect (MTTD) is one of the critical incident management KPIs technology leaders should focus on.</p>



<p>MTTD is the average time it takes to discover an incident occurring within an <a href="https://middleware.io/product/infrastructure-monitoring/">IT infrastructure</a>. This metric is crucial as it reflects the efficiency of your monitoring system and can play a significant role in minimizing business disruption. Keep in mind that a lower MTTD typically reflects a more effective incident management process.&nbsp;</p>



<ul class="wp-block-list">
<li><strong>Importance: </strong>MTTD helps to evaluate the performance of your IT detection systems and crisis response. The lower the MTTD, the faster your response.</li>



<li><strong>Example: </strong>If in a month your system detected incidents at 2 hours, 1.5 hours, and 1 hour, respectively, your MTTD would be the average of these times.</li>
</ul>



<h3 class="wp-block-heading" id="h-mean-time-to-resolution-mttr"><strong>Mean Time to Resolution (MTTR)</strong></h3>



<p>The Mean Time to Resolution (MTTR) is a critical incident management KPI you cannot afford to ignore. It provides an average timespan within which your <a href="https://middleware.io/blog/what-is-devops/">DevOps</a> squad remedies an issue that interrupts standard workflows. It quantifies a system&#8217;s downtime and is an essential gauge of the efficiency of your response team.&nbsp;</p>



<p>In a nutshell, a lower MTTR is always better. <strong><em>Why?</em></strong>&nbsp;</p>



<p>Let&#8217;s consider this: imagine you run an enterprise-grade, mission-critical cloud service. An outage can wreak havoc, not only affecting your company&#8217;s productivity but even dribbling down to your customers.&nbsp;</p>



<ul class="wp-block-list">
<li>For instance, if your average MTTR stands at two hours, it suggests that users can expect a resolution within about two hours when an incident strikes.</li>



<li>However, if your team can put a leash on this incident management metric and lower the MTTR to, say, one hour, your service reliability tends to feel far less of an impact when an incident does occur.</li>
</ul>



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<h3 class="wp-block-heading" id="h-first-touch-resolution-rate-ftrr"><strong>First Touch Resolution Rate (FTRR)</strong></h3>



<p>First Touch Resolution Rate (FTRR) is indeed a crucial KPI in incident management. It measures the percentage of incidents resolved on the first contact without escalating to upper support levels.</p>



<p>By monitoring this metric, you gain insights into your team&#8217;s efficiency and the effectiveness of your troubleshooting guides. Understanding the underpinnings of your FTRR can help you better optimize your processes and enhance your incident management strategy.&nbsp;</p>



<ul class="wp-block-list">
<li>FTRR directly speaks to the capabilities of your ground-level support team.</li>



<li>High FTRR signifies a well-equipped, knowledgeable team that can solve issues swiftly and in one go.</li>



<li>It also indicates clear and effective troubleshooting guides that aid the first-contact resolution.</li>
</ul>



<p>For example, if your support team resolves 70 out of 100 incidents on the first touch, your FTRR would stand at an impressive 70%.</p>



<h3 class="wp-block-heading" id="h-on-call-time"><strong>On-call time</strong></h3>



<p>On-call time is one of the critical incident management KPIs that&#8217;s worth keeping an eye on. Essentially, this measures the period an IT professional or a team is actively on duty to address any potential issues. The point here is about availability and the ability to respond effectively and quickly.&nbsp;</p>



<ul class="wp-block-list">
<li>It reflects on resource allocation: A high on-call time may indicate an overburdened team.</li>



<li>It impacts responsiveness: Quick on-call responses can prevent major incidents or reduce their impact.</li>



<li>It helps in decision-making: Mapping on-call times against incidents can guide shift scheduling for better coverage.</li>
</ul>



<p>For instance, a company might track its DevOps team&#8217;s on-call time to see if there&#8217;s a correlation between high on-call times and the incidence of system problems. Such insights could lead to better staffing decisions and enhanced incident management protocols.</p>



<h3 class="wp-block-heading" id="h-escalation-rate"><strong>Escalation rate</strong></h3>



<p>Keeping a balanced eye on your escalation rate can truly be a game-changer in your incident management strategy. Essentially, the escalation rate refers to the frequency at which incidents require assistance from higher-level support teams or experts to achieve resolution.</p>



<p>It serves as a revealing mirror, reflecting the complexity of the issues being encountered, and indirectly showcases your front-line support teams&#8217; efficacy and technical prowess.&nbsp;</p>



<ul class="wp-block-list">
<li>Indicates if incidents often surpass front-line support capabilities</li>



<li>This may point to a possible need for better training or tools for lower-tier teams</li>



<li>High escalation rates can mean increased resolution times and costs</li>
</ul>



<p>For instance, if your escalation rate is steadily rising, it may be the ideal time to re-evaluate your current incident management practices and training methodologies thoroughly.</p>



<h3 class="wp-block-heading" id="h-cost-per-ticket"><strong>Cost per ticket</strong></h3>



<p>In the business world, we must keep tabs on every penny spent to enhance our incident management. Enter the Cost per ticket, a critical component of incident management KPIs.</p>



<p>This metric measures the overall cost involved in resolving an individual incident or ticket. It&#8217;s calculated by dividing the total costs attached to resolving incidents by the number of tickets resolved in the given timeframe.&nbsp;</p>



<p>Let&#8217;s break it down:&nbsp;</p>



<ul class="wp-block-list">
<li><strong>What it means:</strong> Cost per ticket indicates the efficiency of your support team and the value of the resources invested in incident management.</li>



<li><strong>What it shows:</strong> Tracking this KPI can shed light on your operational efficiency and incident response budgeting. For instance, a high cost per ticket could hint at needing more staff training or, potentially, automation opportunities.</li>
</ul>



<p>For example, If your organization spent $10,000 in a month to resolve 1000 incidents, your cost per ticket would be $10. Keeping an eye on this figure! Over time, it helps in optimizing resources and budgets for your incident management process.</p>



<h3 class="wp-block-heading" id="h-uptime"><strong>Uptime</strong></h3>



<p>A crucial element in your stable of incident management KPIs is uptime. By definition, uptime refers to the total time a system or application is fully operational, available, and unbroken. </p>



<p>It directly impacts companies&#8217; bottom line since more uptime means increased productivity, hence enhanced profitability.&nbsp;</p>



<ul class="wp-block-list">
<li>It paints an extremely accurate picture of a company&#8217;s reliability.</li>



<li>Why is it important? For technology leaders, maintaining high uptime percentages is critical to keeping the digital aspect of their businesses running seamlessly.</li>



<li>Let&#8217;s consider an example: If a SaaS (Software as a Service) platform brags of 99.999% uptime, it means the service promises to be down less than 5.26 minutes per year, excluding scheduled maintenance.&nbsp;</li>
</ul>



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<h2 class="wp-block-heading" id="h-importance-of-incident-management-metrics"><strong>Importance of incident management metrics</strong></h2>



<p>Incident management KPIs or Key Performance Indicators allow you to proactively monitor your systems, measure your team&#8217;s effectiveness, and continuously improve your processes.&nbsp;</p>



<p>Here are the key reasons why focusing on the right incident management metrics is vital:&nbsp;</p>



<ul class="wp-block-list">
<li><strong>Improved system availability:</strong> By using KPIs for incident management, teams can determine system vulnerabilities, enabling proactive fixes that reduce downtime. It promotes system availability, which is paramount for delivering user satisfaction and business continuity.</li>



<li><strong>Enhanced operational efficiency:</strong> Measuring Incident management KPIs like MTTD (Mean Time to Detect), and MTTR (Mean Time to Resolution), allows for informed decision-making. This not only enhances operational efficiency but also boosts the <a href="https://middleware.io/blog/developer-productivity/">productivity of your IT team</a>.</li>



<li><strong>Cost optimization:</strong> Incident management metrics aid in identifying resource wastage and the cost per ticket. Measures can then be taken to streamline processes and reduce the total cost of operations in the long run.</li>



<li><strong>Improved quality of service:</strong> Harnessing these metrics effectively contributes to improved resolution times and service quality, elevating the overall customer experience.</li>



<li><strong>Strategic decision-making:</strong> With<a href="https://middleware.io/blog/data-driven-approach-engineering/"> real-time data and insights</a> provided by incident management metrics, leadership can make strategic decisions that align with business goals, creating a roadmap for digital transformation.</li>
</ul>



<p>In a nutshell, incident management KPIs are integral to not just maintaining high-performing IT services but continuously improving upon them.&nbsp;</p>



<h2 class="wp-block-heading" id="h-final-thoughts"><strong>Final thoughts</strong></h2>



<p>Reflection upon these top 9 incident management KPIs underscores their crucial role in fine-tuning support efforts, mitigating disruption, and enhancing overall service quality. </p>



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<p>Properly implementing and tracking these incident management metrics act as a beacon, guiding you towards an improved incident response landscape. It&#8217;s your lens to granular visibility and informed decision-making.</p>



<p>Ultimately, we achieve agility in innovation without looming threats of potential downtime, all thanks to these KPIs for incident management. Remember, consistency in observation and data-driven improvement are keys to effective incident management. And with this, you&#8217;re all set to excel in that arena!</p>


There are 30+ incident management KPIs. But not all are important. Here are the top 9 KPIs you should be keeping an eye on!

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<h2 class="wp-block-heading" id="h-what-is-a-flame-graph">What is a Flame Graph?</h2>



<p>A Flame Graph is a visual representation of software stack traces, portraying the execution flow of a program. It offers a holistic view of system performance, condensing intricate stack trace data into a comprehensible format.</p>



<ul class="wp-block-list">
<li><strong>Rectangular Frames: </strong>Each frame represents a function in the software stack.</li>



<li><strong>Vertical Stacking:</strong> Frames are stacked vertically, showcasing the call hierarchy.</li>



<li><strong>Width Indicates CPU Usage: </strong>The width of each frame corresponds to the function&#8217;s CPU usage, aiding in the quick identification of resource-intensive components.</li>
</ul>



<h2 class="wp-block-heading" id="h-flame-chart-vs-flame-graph-what-s-the-difference-nbsp">Flame chart vs flame graph: What’s the difference?&nbsp;</h2>



<p>Flame charts and flame graphs share similarities but differ in their visual representations and purposes:</p>



<ul class="wp-block-list">
<li><strong>Orientation</strong>: Flame charts typically display data horizontally, offering a chronological view of stack traces over time. On the other hand, flame graphs represent information vertically, providing a compact and intuitive visualization of the entire stack at a glance.</li>



<li><strong>Chronological vs. Hierarchical:</strong> Flame charts excel in presenting the chronological order of events, making them suitable for tracking changes over time. Flame graphs, with their vertical stacking, emphasize the hierarchical structure of function calls, facilitating a quick assessment of resource utilization.</li>



<li><strong>Compactness</strong>: Flame graphs are known for their space efficiency, presenting a condensed yet comprehensive overview of the entire call stack in a single view. This compactness aids in quickly identifying performance bottlenecks.</li>
</ul>



<p>Understanding these differences allows users to choose between flame charts and flame graphs based on their specific needs for performance analysis.</p>



<h2 class="wp-block-heading" id="h-why-are-flame-graphs-important">Why are Flame Graphs important?</h2>



<p>Flame graphs, visual representations of software stack <a href="https://middleware.io/product/apm/">traces</a>, serve as pivotal tools in performance analysis, offering a unique perspective on program execution.</p>



<p><strong>Holistic View of Execution Flow: </strong>Flame graphs provide developers with a bird&#8217;s-eye view of a program&#8217;s execution, showcasing the entire stack trace in a single, comprehensible visual. This holistic perspective is invaluable for understanding how different functions contribute to overall performance.</p>



<p><strong>Efficient Bottleneck Identification:</strong> The width of each frame in a flame graph directly correlates with CPU usage, allowing developers to quickly identify resource-intensive components. This efficiency in pinpointing bottlenecks accelerates the troubleshooting process and streamlines optimization efforts.</p>



<p><strong>Quick Troubleshooting: </strong>The graphical nature of flame graphs enables rapid troubleshooting and root cause analysis. Developers can visually trace the execution path, making it easier to identify problematic areas and optimize code for better performance.</p>



<p><strong>Space-Efficient Overview:</strong> Flame graphs present a compact yet comprehensive representation of the entire call stack. This space efficiency enhances usability, especially when dealing with large and intricate codebases, making it easier for developers to navigate and understand the program&#8217;s structure.</p>



<p><strong>Transcending Profiling Limitations: </strong>Flame graphs go beyond the capabilities of traditional profiling tools by offering a dynamic and condensed visualization of stack traces. This transcendence empowers developers with a more <a href="https://middleware.io/blog/apm-tools/">intuitive tool for strategic optimization</a> efforts, ultimately resulting in high-performing software applications.</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>Get a holistic view of your application&#8217;s performance with Flame Graphs. <a href="https://app.middleware.io/auth/register/">Get Started Free</a>.</p>
</blockquote>



<h2 class="wp-block-heading" id="h-how-to-interpret-a-flame-graph">How to Interpret a Flame Graph?</h2>



<p>Interpreting a flame graph involves understanding its key elements to gain insights into a program&#8217;s performance.&nbsp;</p>



<p>The fundamental concept behind a flame graph is to visually represent a program&#8217;s function call stack as a &#8220;flame&#8221;-shaped image. Each rectangle in the graph corresponds to a function, with its width representing the proportion of resources occupied by that function and its height indicating the call depth (number of layers of recursive calls).&nbsp;</p>



<p>Analyzing flame graphs at different time points helps swiftly identify performance bottlenecks, enabling targeted optimization. A wide rectangle at the top of the stack typically signals a bottleneck that requires analysis and optimization.</p>



<ul class="wp-block-list">
<li><strong>Width of Rectangles: </strong>Indicates the proportion of resources (CPU time) consumed by each function.</li>



<li><strong>Height of Rectangles:</strong> Represents the call depth, showing the number of recursive call layers.</li>



<li><strong>Position on the Graph</strong>: Top-wide rectangles may signal potential bottlenecks for analysis.</li>



<li><strong>Color Coding</strong>: Enhances readability by distinguishing different functions or categories.</li>
</ul>



<figure class="wp-block-image aligncenter size-large"><img loading="lazy" decoding="async" width="1583" height="861" src="https://middleware.io/backend/wp-content/uploads/2024/02/color-coded-flame-graph.jpg" alt="Middleware's color-coded Flame Graph" class="wp-image-10214"/><figcaption class="wp-element-caption">Middleware&#8217;s color-coded Flame Graph</figcaption></figure>



<h3 class="wp-block-heading" id="h-interpreting-in-middleware-flame-graph-view"><strong>Interpreting in Middleware: Flame Graph View</strong></h3>



<ul class="wp-block-list">
<li>Hierarchical visualization where each level represents a function or method.</li>



<li>The width of graph segments corresponds to resource consumption.</li>



<li>Enables quick identification of performance hotspots and understanding of program execution flow.</li>
</ul>



<figure class="wp-block-image aligncenter size-large"><img loading="lazy" decoding="async" width="1959" height="963" src="https://middleware.io/backend/wp-content/uploads/2024/02/How-to-Interpret-a-Flame-Graph.jpg" alt="How to Interpret a Flame Graph" class="wp-image-10217"/><figcaption class="wp-element-caption">Flame Graph view in Middleware’s APM module
</figcaption></figure>



<h3 class="wp-block-heading" id="h-flame-graph-types">Flame Graph Types:</h3>



<ul class="wp-block-list">
<li><strong>Flame Graph (Narrow Sense):</strong>
<ul class="wp-block-list">
<li>Root is at the bottom, and child nodes are displayed above their parent nodes.</li>



<li>Useful for visualizing the call hierarchy and understanding function relationships.</li>
</ul>
</li>



<li><strong>Icicle Graph:</strong>
<ul class="wp-block-list">
<li>Root is at the top, and child nodes are displayed below their parent nodes.</li>



<li>Provides an alternative visualization perspective while retaining the same underlying data.</li>
</ul>
</li>
</ul>



<h2 class="wp-block-heading">Flame Graph Examples</h2>



<p>Let&#8217;s explore different types of flame graphs and their unique characteristics:</p>



<h3 class="wp-block-heading" id="h-cpu">CPU</h3>



<p>A CPU flame graph is a visual representation of CPU usage, illustrating how time is distributed across various functions or code paths during program execution.</p>



<figure class="wp-block-image aligncenter size-large"><img loading="lazy" decoding="async" width="1956" height="966" src="https://middleware.io/backend/wp-content/uploads/2024/02/CPU-usage-Flame-Graph-view-in-APM-module-1.jpg" alt="CPU usage Flame Graph view in APM module" class="wp-image-10219"/><figcaption class="wp-element-caption">CPU usage Flame Graph view in APM module</figcaption></figure>



<p>From the left sidebar, you can select <strong>CPU usage </strong>options, which allow you to filter data based on a table or graph view. This interactive feature allows for a more detailed examination of CPU performance metrics.</p>



<ul class="wp-block-list">
<li>Developers and system administrators use CPU flame graphs to identify bottlenecks, hotspots, or inefficient code in their <a href="https://middleware.io/blog/what-is-application-performance-monitoring/">applications</a>.</li>



<li>You can compare different instances of CPU flame graphs to establish baselines, track changes, and assess the impact of optimizations over time.</li>
</ul>



<h3 class="wp-block-heading" id="h-memory">Memory</h3>



<p>A Memory Flame Graph visually represents memory consumption, showcasing the allocation distribution across different functions or code paths during program execution.</p>



<figure class="wp-block-image aligncenter size-large is-resized"><img loading="lazy" decoding="async" width="1959" height="962" src="https://middleware.io/backend/wp-content/uploads/2024/02/Memory-Flame-Graph-in-Middleware.jpg" alt="Memory Flame Graph in Middleware" class="wp-image-10220" style="width:620px;height:auto"/><figcaption class="wp-element-caption">Memory usage Flame Graph view in Middleware</figcaption></figure>



<ul class="wp-block-list">
<li>Optimizing memory usage can reduce resource overhead. Unnecessary memory consumption can lead to increased costs, especially in cloud environments where resources are billed based on usage.</li>



<li>Developers and system administrators utilize Memory Flame Graphs to identify memory-related issues, such as leaks or inefficient usage, facilitating the optimization of memory allocation within applications.</li>
</ul>



<h3 class="wp-block-heading" id="h-off-cpu">Off-CPU</h3>



<p>An Off-CPU Flame Graph provides a visual representation of CPU idle time or waiting periods, highlighting moments when the CPU is inactive during program execution.</p>



<p>It is a valuable tool for developers and system administrators to identify and understand the reasons behind CPU stalls or waits, contributing to a comprehensive analysis of system performance.</p>



<ul class="wp-block-list">
<li>You can compare different instances of Off-CPU Flame Graphs to establish baselines, track changes, and assess the impact of optimizations on CPU idle time over time. This aids in understanding the effectiveness of performance improvements.</li>



<li>Understanding and addressing off-CPU issues contribute to effective capacity planning, preventing resource contention and optimizing resource utilization.</li>
</ul>



<h3 class="wp-block-heading" id="h-hot-cold">Hot/Cold</h3>



<p>A Hot/Cold Flame Graph represents the frequency or intensity of execution for different functions or code paths during program runtime. &#8220;Hot&#8221; paths are frequently executed, while &#8220;Cold&#8221; paths are less commonly traversed.</p>



<p>Developers use Hot/Cold Flame Graphs to identify critical code paths (hotspots) that contribute significantly to overall execution time, aiding in optimization efforts.</p>



<h3 class="wp-block-heading" id="h-differential">Differential</h3>



<p>A Differential Flame Graph compares two sets of profiling data, providing a visual representation of the differences between them. It is a powerful tool for understanding changes in code performance over time or after specific modifications.</p>



<p>Differential Flame Graphs support a culture of continuous improvement by providing insights into the effectiveness of changes and guiding further optimizations.</p>



<p>Developers use Differential Flame Graphs to assess how code changes, optimizations, or updates impact CPU usage, memory consumption, or other performance metrics.</p>



<h2 class="wp-block-heading" id="h-profiling-flame-graphs-vs-tracing-flame-graphs">Profiling Flame Graphs vs. Tracing Flame Graphs</h2>



<p>Profiling Flame Graphs and Tracing Flame Graphs are two distinct techniques used in performance analysis, each offering unique insights into the behavior and efficiency of software applications.&nbsp;</p>



<h3 class="wp-block-heading" id="h-profiling-flame-graphs">Profiling Flame Graphs</h3>



<p>Profiling Flame Graphs are typically generated using a sampling approach. The profiler periodically samples the program&#8217;s state to gather information on which functions are active at that moment.</p>



<ul class="wp-block-list">
<li>Profiling provides a more high-level and coarse-grained view of program execution. It identifies the functions that consume the most CPU time over the sampling period.</li>



<li>Profiling Flame Graphs are especially useful for identifying hotspots in the code—areas where the program spends a significant amount of time.</li>



<li>Profiling typically incurs lower overhead compared to <a href="https://middleware.io/blog/what-is-distributed-tracing/">tracing</a>, making it suitable for capturing performance data with minimal impact on the running application.</li>
</ul>



<h3 class="wp-block-heading" id="h-tracing-flame-graphs">Tracing Flame Graphs</h3>



<p>Tracing Flame Graphs are generated by capturing detailed <strong>events </strong>during the program&#8217;s execution. Tracers record specific events, such as function calls, system calls, or memory allocations.</p>



<ul class="wp-block-list">
<li>Tracing provides a more fine-grained view of program execution, capturing detailed information about function calls, system calls, and other events.</li>



<li>Tracing Flame Graphs is powerful for gaining detailed insights into the sequence of events and interactions within the program. They are valuable for diagnosing complex issues and understanding the complete program flow.</li>



<li>Tracing typically involves more overhead compared to profiling, as it captures a more detailed set of events. This increased overhead might be a concern in performance-sensitive applications.</li>
</ul>



<h3 class="wp-block-heading" id="h-considerations">Considerations</h3>



<p><strong>Data Volume</strong></p>



<ul class="wp-block-list">
<li>Profiling: Generates concise profiles based on sampled data, providing a quick overview.</li>



<li>Tracing: Captures a larger volume of detailed data, offering a comprehensive understanding of the program&#8217;s behavior.</li>
</ul>



<p><strong>Ease of Use</strong></p>



<ul class="wp-block-list">
<li>Profiling: Generally easier to set up and use, with lower impact on the application.</li>



<li>Tracing: Requires more configuration and may impact performance more significantly but offers a more detailed and comprehensive view.</li>
</ul>



<p><strong>Use Case</strong></p>



<ul class="wp-block-list">
<li>Profiling: Well-suited for identifying performance bottlenecks and hotspots.</li>



<li>Tracing: Ideal for in-depth analysis, debugging, and understanding complex interactions within the application.</li>
</ul>



<p>The choice between Profiling Flame Graphs and Tracing Flame Graphs depends on the specific goals of performance analysis, the level of detail required, and the acceptable level of impact on the running application. Often, a combination of both techniques may provide a well-rounded understanding of application performance.</p>



<h2 class="wp-block-heading" id="h-final-thoughts">Final Thoughts</h2>



<p>Flame graphs illuminate the path to optimized software performance, revealing critical insights into CPU usage, memory consumption, and code behavior.&nbsp;</p>



<p>As developers integrate these visualizations into their toolset, the strategic use of flame graphs becomes a cornerstone in the continuous quest for efficient and responsive applications.</p>



<p>The next steps involve a practical application, ongoing learning, and the integration of flame graphs within a holistic performance analysis strategy.</p>



<p><a href="https://app.middleware.io/auth/register/">Get started with Middleware for free</a> and start visualizing your tech stack&#8217;s performance with flame graph view. </p>


A Flame Graph is a visual representation of software stack traces, portraying the execution flow of a program. Know more about it here: 

Flame Graphs: What They Are? And How To Use It

Alert fatigue carries a significant implication in the IT sphere. In fact, 28 % of teams forget to review a critical alert because of it. Learn more about it here: 

What is Alert Fatigue? A DevOps Guide On How to Avoid It

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