As system architecture becomes more complex, IT operations, QA teams, and SRE teams are challenged to effectively track and respond to issues in their multi-cloud environments. They need better observability in increasingly diverse and complex computing setups.
Applying observability to your existing system requires a bit of rethinking and robust implementation. Here’s a deeper dive into observability, its importance and benefits, and how to integrate observability tools into your architecture.
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 thoroughly understand a system by examining the inputs. In control theory, observability is how engineers infer the internal states of a system from its external outputs.
Difference between observability and monitoring
Monitoring collects and displays data, while observability analyzes a system's inputs and outputs to determine its health. Monitoring tracks an application’s overall health.
It compiles information on the system's performance in terms of 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. Metrics comprise properties such as name, value, label, and timestamp that convey data about SLAs, SLOs, and SLIs.
Metrics are quantifiable values derived from system performance, as opposed to 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, lack of tags or labels was 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.
Why is observability important?
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. Distributed systems update regularly, and each change can introduce a new type of bug.
Understanding an actual problem in a distributed context is difficult, in part 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.
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 an overwhelming amount of 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 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.
Top 3 observability tools
Observability tools allow organizations to track their applications’ overall health. Here are the top 3 observability tools on the market.
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 for the following reasons:
- Enable digital transformation and cloud migration
- Foster collaboration between development, operations, security, and business teams
- Accelerate the time to market
- Secure applications and infrastructure
- Understand user behavior
- Track key business metrics
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.
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?
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 your 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. Make sure 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. Make sure you benchmark your observability tool against key business key performance indicators (KPIs) like deployment time, system stability, and customer satisfaction.
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 unknowns” 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. This access helps you increase conversions, 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 in DevOps?
Observability in DevOps focuses on helping IT organizations understand an application's processes by examining its output.
- What is observability in software?
In IT and cloud computing, observability is the ability to monitor a system’s current state based on the data it produces, such as logs, metrics, and traces. Observability uses telemetry generated by instrumenting endpoints and services in your multi-cloud computing setups.
- What is observability in a control system?
In a control system, observability refers to its ability to discern internal states by looking at the output over a finite period.