Observability Patterns for Distributed Systems

Digital Analytics
8 min
Digital Analytics
Observability Patterns for Distributed Systems

When designing complex distributed systems, it can be difficult to know how to implement observability. Many legacy monitoring solutions are not designed for the modern distributed architecture of today's applications. These services often do not provide enough visibility into custom application dependencies and microservices. In this article, we’ll discuss observability patterns for distributed systems that allow you to collect relevant data, visualize it in a meaningful way and store it for future analysis.

What are Observability Patterns for Distributed Systems and Why You Should Care

Troubleshooting a production system can be an overwhelming and frustrating experience. Especially if you don’t have the information needed to get to the bottom of things. However, troubleshooting becomes much more effective when you have insightful information on which to begin your search. That information is even more helpful when you can analyze the data for system metrics. That’s where observability patterns for distributed systems come in.

The simplest definition of observability is the ability to monitor the performance and measure activities of a system by its outputs. Outputs refer to a log of recorded actions of the system. These actions include information such as system activity, the system in which the activity occurred, timestamp and much more. Observability is one of the key components of service reliability engineering which helps monitor and improve app performance.

Aside from troubleshooting, there are additional benefits of observability patterns for distributed systems. They help you to catch potential problems before they happen. That way you can take steps to prevent failures. Not only that, observability patterns for distributed systems provide information vital for business decision-making. According to Jeff Rumburg Co-founder and Managing Partner, MetricNet “effective performance measurement is not just a necessity, but a prerequisite for effective decision making.”

Observability Patterns for Distributed Systems

Observability patterns for distributed systems refer to the specific techniques to monitor and gain insights into your systems. They are essentially a more effective way to aggregate and analyze activities across all of your systems. It provides you with in-depth information for maximum visibility into your system.

Log Aggregation

Log aggregation is the most basic pattern of the observability patterns for distributed systems. Logs are key to debugging a distributed system. You can think of logs as a timestamped record of events that occur when applications run. Logs can take many formats such as plaintext logs which are simply freeform text documents. They can also be in the form of structured output in JSON notation. Logs contain information such as errors, warnings and debug information. It is the easiest of the observability patterns for distributed systems to implement. Most application libraries have tools to create logs.

Best Practices for Implementing Logging

It is important to note that log data isn’t used exclusively for troubleshooting. Logs contain valuable information that is useful in business intelligence. Jason Rappaport, president, and CEO of Innovative says "IT metrics shouldn’t exist in a vacuum, isolated from business metrics. IT should be used to move the needle on specific business outcomes that align with strategic goals,” he explains. “Utilization metrics aligned with systems and tools that are intended to support overall business goals provide the greatest insight into the success of IT within the organization.”

That said, the best practice is to ensure your logs capture metrics relevant to IT and metrics relevant to the business. The most effective way to accomplish this is to process the logs as events via stream processing. With stream processing, each log record becomes an event/action. These events can then be fed into a stream processing tool such as Kafka. That way, the events can feed both troubleshooting processes and business intelligence tools.

Application Metrics

One of the key observability patterns for distributed systems is metrics. Metrics transform log data into information useful for determining the state and behavior of a system. They represent numeric measurements over specific time intervals. Metrics provide information such as availability, system load, CPU or memory usage, and the number of errors. According to CIO, application availability is one of the seven most important metrics that matter to CIOs. Metrics allow you to answer questions such as:

  • System Metrics: How fast is the system running? What is the utilization of a host or process?
  • Resource Metrics: How much memory does my app use? Do I have enough disk space for this operation?
  • Business Metrics: How many errors do the APIs generate? How much time does it take to process an API request? How many API requests take place over a day, week or month, etc.?

Collecting this data about your business will allow you to get insight into what needs optimization and what is functioning well. This is one of the observability patterns for distributed systems that help you identify potential issues before they arise. Not only that, this data is useful in feeding information into business intelligence tools to help leaders drive business strategy.

Pros and Cons of Metrics

Metrics is one of the observability patterns for distributed systems that can be challenging to implement. The problem with metrics is an issue caused by cardinality. Metrics are stored as a key-value pair of information known as tags. Cardinality is the permutation of these values. For example, if measuring CPU utilization of four hosts, you would have a cardinality value of four.

  1. CPU - Host 1
  2. CPU - Host 2
  3. CPU - Host 3
  4. CPU - Host 4

Adding additional hosts to this metric increases cardinality which increases resource usage. Generating metrics is tightly coupled with RAM resources which affect pricing in a cloud-based architecture.

Despite this, the benefits far outweigh the cons. The level of useful information the metrics provide and how they can be leveraged for multiple purposes makes the cost worth it. Also, metrics are stored in a time-series database. This makes querying the data more efficient.


Tracing is one of the most useful observability patterns for distributed systems. It refers to looking at the end-to-end steps a request takes through the system. This provides insight into the path the request took along with the structure of the request. Tracing uncovers the many services involved in processing a request. It helps find errors that may occur along the path of a request. How does tracing work?

The basic element of a trace is a specific point in the execution paths. The path might include function calls, threads across the app’s framework, libraries, runtime environments, and middleware. When a request reaches one of the preset points in the execution path, an event record is generated. The request travels through each execution point, generating metadata about the request. This information helps you determine information such as:

  • How long does it take to get from point A to point B?
  • Was there increased latency from point A to point B?
  • What path along the route took the longest?

Pros and Cons of Tracing

This distributed pattern for observability is relatively easy to implement when building a new system. However, that is rarely the case as companies may not realize the need for it until after releasing the app. Thus, retrofitting the application to incorporate the process is usually what ends up happening. As mentioned, this approach requires the collection of data at specific points along the request path. Developers would need to revisit the code to add these checkpoints.

The benefit of this approach, however, is that it provides a more granular view of what takes place for each request. It provides information that is valuable in proactively identifying potential issues.

Best Practices for Implementing Tracing

The value in tracing comes in evaluating the full path of a request. The best practice is to ensure the trace includes the entire request. Make sure your traces report on RED metrics. RED is an acronym that stands for requests, errors, and duration. Next, set up alerts to be notified of these types of issues. Lastly, report on custom business metrics. This is where you’ll get even greater benefit from this pattern. Custom metrics allow you to hone in on specific areas that are useful for your business.

Distributed systems present complex issues in acquiring in-depth and insightful information. The size of the systems and volume of data these systems use require sophisticated processes to gather this information. Observability patterns for distributed systems are an effective approach to solving the complexities of gathering this information.

Challenges With Observability Patterns for Distributed Systems

Before distributed systems, locating problems was relatively simple. With one or two systems, there was usually just a single log file that had all the information you needed. However, the rise of cloud computing has increased the complexity of system architectures. It is not uncommon for companies to have hundreds of systems spread across multiple environments. Not only that, the volume and scale at which these systems operate generate tons of information. Gathering and combining useful information from these massive logs can be a challenge.

If your business is looking for help implementing these observability patterns for distributed systems, reach out to us. We would love to help you implement a solution that fits best with your project.

Published on
March 23, 2023

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