Curious about observability? This guide covers everything from the basics to more advanced concepts, and shows you how it works in detail. observability can help you more effectively manage your systems, diagnose problems, and prevent outages. Learn more here.
Observability has been gaining a lot of attention over the last few years. It has been a buzzword after an increasing number of companies making a move to microservice-based architectures, yet with the added complexity of coordinating those services, organizations find it more difficult to monitor and troubleshoot in and after production.
Even more challenging is the emergence of multi-cloud environments with multiple providers and the difficulty of identifying behavioral anomalies at a macro level across disparate services. It has gone from an obscure and rarely used technique to a critical aspect of modern distributed systems.
Applying observability to your existing system requires a bit of rethinking and robust implementation. This guide will explore the brief introduction, benefits, how it works, and challenges in microservice observability and provide actionable insights for microservice practitioners and observability practitioners.
What is observability?
Observability is the ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces. It helps you see inside modern distributed systems so you can quickly identify and fix problems.
Observability is an emerging field of software engineering and DevOps. The goal is to measure, collect and identify software behavior to understand and improve the software system and the development process.
In control theory, observability is how engineers infer the internal states of a system from its external outputs.
Why do we need observability?
Observability gives you more control over complex systems. Simple systems are easier to handle because they have fewer moving parts. Monitoring CPU, memory, databases, and network conditions are enough to understand simple systems and apply the right solution to a problem.
But because distributed systems contain so many interconnected parts, the number and type of errors are much more significant. In addition, distributed systems update regularly, and each change can introduce a new type of bug.
It is difficult to understand an actual problem in a distributed context because it creates more “unknowns” than simpler systems. As a result, monitoring typically cannot adequately address issues in these complex systems because it requires “known unknowns.”
Benefits of observability
Observability simplifies complex workflows and offers the following advantages.
1. Make sense of the complexity
Observability gives engineering teams a complete view of their architecture. This makes it easier for teams to understand data in a complex system, from third-party apps and APIs to distributed services.
2. Speed up troubleshooting
Observability empowers IT teams to spot hard-to-detect issues, which improves troubleshooting time and reduces Mean Time to Identify (MTTI), Mean Time to Acknowledge (MTTA) and Mean Time to Restore (MTTR). Achieving low MTTI, MTTA, and MTTR is the site reliability engineer’s goal in today’s fast-paced world.
3. Increased team productivity
A well-configured observability system helps in easy and accurate error identification. This allows developers to deal directly with solving the root cause of the problems instead of focusing primarily on error identification. It also reduces alert fatigue, one of the biggest productivity killers.
4. Improved user experience
With improved error detection and speeding up the troubleshooting process, monitoring systems achieve high system availability, providing an improved user experience.
5. Reduced time to market
An observability solution doesn’t just help you identify or fix errors. It also allows you to lay your cloud infrastructure’s foundation by simply integrating complex systems and measuring the external states of two systems, shortening a product’s time to market.
6. Business Analytics
Businesses can combine business context with full-stack application analytics and performance to comprehend real-time business impact, enhance conversion optimization, ensure that software releases achieve anticipated business objectives, and verify that the business is abiding by internal and external SLAs.
How observability is different than monitoring
Knowing what each phrase means on its own can help you comprehend the differences between them.
Monitoring collects and displays data and tracks an application’s overall health.
Monitoring is the process through which IT teams gather logs, data, and traces and transfer them to a destination where they can be evaluated.
Most monitoring solutions use dashboards to display performance indicators, which IT professionals use to find or fix IT problems. However, because your team built such dashboards, they only show performance irregularities or concerns your team may foresee. This makes it challenging for security and performance monitoring teams to monitor complex cloud-native apps and containerized environments, where security threats are frequently multi-faceted and unpredictable.
In contrast, observability software uses logs, traces, and metrics gathered throughout your application infrastructure to alert DevOps engineers before potential problems and assist them in system debugging. In addition, they can leverage observability infrastructure to measure all the inputs and outputs across microservices, servers, and databases. Overall observability analyzes a system’s inputs and outputs to determine its health.
Achieve complete observability in your existing infrastructure with Middleware.
Observability provides actionable insights into the health of your system. It identifies flaws or weak attack vectors at the first sign of aberrant performance by comprehending the relationships between IT systems.
It compiles the system’s performance regarding access speeds, connectivity, downtime, and bottlenecks. On the other hand, observability delves into the “what” and “why” of application operations by offering detailed and contextual information on failure modes.
Three pillars of observability
The three key pillars of observability are logs, metrics, and traces. While access to these pillars doesn’t guarantee enhanced system visibility, they’re powerful tools that can help construct better systems.
Logs give you the necessary insights into raw system information to figure out what happens to your database. An event log is a time-stamped, immutable record of discrete events over a period. Event logs can come in three different formats, but they all contain the same information: date and payload with some context.
- Plain text: A log record can be free text. This is also the most popular log format.
- Structured: This type sends logs in JSON format.
- Binary: Protobuf logs, MySQL BinLogs for replication and point-in-time recovery, Systemd journal logs, and the PFLOG format used by the BSD firewall pf are frequently used as a backup system.
Metrics are numerical representations of data that can identify the overall behavior of a service or component over time. It comprises name, value, label, and timestamp properties that convey data about SLAs, SLOs, and SLIs data.
Metrics are quantifiable values derived from system performance instead of an event log that captures individual events. They save time because they can easily correlate across infrastructure components to provide a comprehensive picture of system health and performance. They also enable quicker data search and advanced data preservation.
Metric discovery didn’t lend itself well to exploratory analysis or filtering. In early versions of Graphite, the hierarchical metric approach and lack of tags or labels were disadvantageous. Each time series was represented by a metric name and additional key-value pairs, now called labels in modern monitoring systems such as Prometheus and later versions of Graphite with high dimensionality.
While logs and metrics evaluate individual system behavior and performance, they’re rarely useful in determining a request’s lifecycle in a distributed system. Instead, a different observability approach called tracing is used to observe and understand the full lifetime of a request or action across multiple systems.
A trace shows the complete path of a request or action through a distributed system’s nodes. Traces help you profile and monitor systems, especially containerized applications, serverless, and microservices architectures. You can assess overall system health, identify bottlenecks, spot and fix problems faster, and select valuable areas for tweaks and improvements by evaluating trace data.
How does observability work?
Observability platforms continuously identify and gather performance telemetry by integrating existing instrumentation embedded into application and infrastructure components and offering tools to add instrumentation to these components.
The platform collects logs, traces, and metrics. Then, it correlates them in real-time to give DevOps teams, site reliability engineering (SRE) teams, and IT staff comprehensive, contextual information — the what, where, and why of any event that might point to, cause, or be used to address an application performance issue.
Numerous observability platforms continuously search for new telemetry sources that may be present in the system (such as a recent API call to another software application). Many platforms also feature AIOps (artificial intelligence for operations) capabilities that separate the signals – indicators of real problems – from noise because they deal with much more data than a traditional APM solution (data unrelated to issues).
Challenges of observability
Traditional monitoring solutions are often designed for monolithic systems and oversee the health and activity of just one application or server. Below are some common challenges of observability.
1. Dynamic complex environments
The rapid release and implementation of new technologies create overwhelming data and increasingly complicated, dynamic monitoring settings. Manual tools and traditional monitoring make it difficult for IT teams to understand how everything in their environment works together. Teams need tools to understand dependencies and minimize blind spots in ever-changing situations.
2. Containers and microservices monitoring
Containers and microservices give modern application development the necessary speed and agility. However, due to microservices architecture’s dynamic nature, real-time visibility into container workloads is an issue.
IT teams cannot perform end-to-end tracing of user requests across microservices without the right tools to determine the root cause of anomalies. As a result, they either consult the engineers and architects who designed the system or (worst case) just assume what went wrong.
3. Data and alert volume, velocity, and variety
Teams sift through a growing volume of data using various tools and dashboards to establish specific behavioral standards in an ever-changing environment. But how can you track issues you don’t know about? IT often pieces data together from multiple static dashboards using timestamps or guesswork to determine how unique events led to the system’s failure.
4. The business impact of observability is difficult to quantify
While most engineers understand the need for up-to-date observability tools and best practices, building the business case for these tools can be difficult.
Real-time cloud-native observability platform at scale.
Top 5 observability tools
Observability tools allow organizations to track their applications’ overall health. Here are the top 3 observability tools on the market.
Middleware is a cloud-native observability platform that will help you un-silo your data and insights from all your containers, empower you to identify root causes and solve issues in real-time and give you the best value for money with a platform that fits your specific needs.
Bring all your metrics, logs, and traces into a single timeline, and empower your developers and DevOps to debug and fix the issue faster—reducing downtime and improving the user experience.
The tool also comes with a unified dashboard that displays all core and essential services in one place.
Splunk is a sophisticated analytics system that correlates and applies machine learning to data to enable predictive, real-time performance monitoring and a fully integrated IT management solution. It allows teams to detect, respond to, and resolve events in one place.
Datadog is a cloud monitoring tool for IT, development, and operations teams that want to transform the massive amounts of data created by their applications, tools, and services into actionable intelligence.
Companies of all sizes use Datagod across a variety of industries.
Dynatrace is a cloud-based, on-premises, hybrid application and SaaS monitoring platform. It provides continuous APM self-learning and predictive alerts for proactive issue resolution using AI-assisted algorithms. Dynatrace offers an easy-to-use interface with a wide range of products to generate detailed monthly reports on app performance and SLAs.
5. Observe Inc
Observe is a SaaS Observability tool. It provides a dashboard that shows you applications’ top issues and the system’s overall health. Since it’s a cloud-based platform, it’s fully elastic.
Observe uses open-source agents to collect and process data, so the setup process is relatively quick and easy.
What to look for in an observability tool
Engineers and developers have many monitoring options, so how do they know which one is right for them?
Consider the number of services, data volume, degree of transparency, and corporate goals when selecting an observability platform. It would be prudent to choose a solution that addresses both the constraints and considers how the volume of data directly affects cost and performance.
When starting your observability journey, neither the most expensive nor the cheapest solution is the best option. Here are some key considerations when choosing an observability solution for your project.
1. User-friendly interface
User-friendly dashboards help management narrow down their efforts and provide a clear picture of what’s going wrong at different levels in a system. The more commercial value you can derive from a solution, the better.
Since your solution will affect many people in the company, it should be user-friendly and easy to implement. Otherwise, it won’t fit into your established procedures, and key stakeholders will quickly lose interest.
2. Supplies real-time data
Gathering real-time data is critical, as stale data complicates determining the best course of action. Therefore, you should use current event-handling techniques and APIs to collect real-time data and put everything in perspective. You won’t know about the data if you don’t have it.
3. Works on open-source agents
When choosing an observability tool, it’s important to consider how it retrieves and processes data about your environment.
It’s advisable to use an observability tool that uses open-source agents to fetch and process data in two ways: by reducing your system’s CPU and memory consumption and offering appropriate security and easier configuration than agents explicitly developed by an organization.
Observe is an excellent example of an observability tool running on open-source agents. Datadog is another great example of a tool running on its own agents.
4. Easy to implement
Check how easy or difficult it is to implement a specific observability tool. A support team and knowledge would be great for easy implementation.
5. Integrates with current tools
Your observability attempts will fail if your tools don’t work with your current stack. Ensure they support your environment’s frameworks and languages, container platform, messaging platform, and other important software.
6. Delivers business value
Choosing what to monitor and how is a big part of selecting the right tool. Some devices are better than others at certain tasks. Ensure you benchmark your observability tool against key business performance indicators (KPIs) like deployment time, system stability, and customer satisfaction.
Choose the right observability platform.
A log and metrics repository, querying engine, and visualization dashboard are the elements at the center of an observability configuration. These skills are mapped across multiple platforms. Certain of them operate exceptionally well together to build a complete observability system. Each must be carefully chosen to fulfill the requirements of the business and system.
Work cross-functionally with observability
Observability helps cross-functional teams understand and answer specific questions about “what is happening in massively distributed systems.” For example, observability allows you to spot sluggish or bad data and what needs to be addressed to improve performance. With an observability solution, teams can receive proactive notifications of issues and fix them before they impact users.
Because current cloud infrastructures are dynamic and constantly growing in size and complexity, most issues are not understood or monitored. Observability addresses the “unknown” problem by enabling you to automatically and continuously understand new types of experiments as they arise.
The benefits of observability are not limited to IT applications. Once you collect and analyze observability data, you have a good insight into the commercial impact of your digital services. It can help you to increase conversions and ensure software releases align with business goals. Track the results of your user experience SLOs, and prioritize business decisions based on the criteria that matter most.
With an observability system that analyzes user experience data using synthetic and real-user monitoring, you can find problems before your users do and improve their experience based on real-time input.
What is observability?
Observability is the ability to monitor a system’s current state based on the data it produces, such as logs, metrics, and traces. Or in other words, observability refers to its ability to discern internal states by looking at the output over a finite period. It uses telemetry data from instrumenting endpoints and services in your distributed systems.
Why is observability important?
Observability is important because it gives you greater control and complete visibility over complex distributed systems. Simple systems have fewer moving parts, making them easier to manage. But in complex distributed systems, you need to monitor CPU, logs, traces, memory, databases and networking conditions to understand these systems and apply the appropriate fix to a problem.
What are the three pillars of observability?
The 3 pillars of observability: Logs, metrics and traces.
Logs: These give you the necessary insights into raw system information to determine what happens to your database. An event log is a time-stamped, immutable record of discrete events over a period.
Metrics: Metrics are numerical representations of data that can identify the overall behavior of a service or component over time. Metrics comprise properties such as name, value, label, and timestamp that convey data about SLAs, SLOs, and SLIs.
Traces: A trace shows the complete path of a request or action through a distributed system’s nodes. Traces help you profile and monitor systems, especially containerized applications, serverless, and microservices architectures.
How do I implement observability?
Your systems and apps need proper tooling to collect the appropriate telemetry data to achieve observability. You use open-source software or a commercial observability solution to make an observable system by building your own tools. Typically, four components are involved in implementing observability: logs, traces, metrics and events.