Distributed tracing is a technique used to track and observe application requests as they move through distributed systems or microservice environments. Distributed tracing tracks these by collecting and aggregating data on a user’s interactions throughout the transaction process. This gives you insight into your application’s health and overall user experience. Developers can then use this collection of traces to troubleshoot areas where there are bugs, errors, or high latency.
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Now that you have an idea of what distributed tracing is, let’s dive deep into how it works. Unlike a monolithic application, microservice environments run on distributed backends making it more difficult to track a full request journey. Thankfully distributed tracing can follow a user’s actions each step of the way and monitor how it affects your application from the front end to the back end.
Distributed tracing starts by instrumenting your microservice architecture. You can use open-source tools such as OpenTelemetry to begin the instrumentation and telemetry collection process.
Next, developers need to implement code into your services to track trace data and tag unique identifiers to each transaction. The encoded trace context passes from one server to another across the entire application environment. The identifiers that attached themselves to the transaction journey give visibility into your customer experience.
Distributed tracing tools track each activity or segment after being triggered by an event as it travels through a server. As one span is collected, it then moves to the next one, and so on. These spans typically start with a parent span and move to child spans.
Your tool will put these actions in order and collect relevant metrics such as custom attributes, timestamps, and metadata. Usually, a distributed tracing tool will help you visualize this data in a flame graph or waterfall view format. These graphs help engineers interpret which parts of a distributed system are experiencing bottlenecks, slow-downs, or performance issues.
Lastly, you’ll need to combine your distributed tracing tool with an observability platform to gain end-to-end monitoring of your application. Including a platform like Instana will help you extract and process data so you can take the right next steps in solving any application error.
The complexity of modern architecture has made it difficult for monolithic legacy applications to serve the tools that host them. With this in mind, distributed tracing has become essential in attaining observability in cloud-native environments.
Here are some of the major benefits of distributed tracing:
Troubleshoot Problems Faster: Drastically reduce mean time to resolution (MTTR) and mean time to discovery (MTTD). Engineers can review distributed traces to find the root cause and location of application errors.
Boost Team Collaboration: In a typical microservice environment, specialized teams handle and develop different technologies. This can create confusion among teams if they don’t know where the error occurred and who is in charge of solving it. A trace link can help engineering teams visualize the data so they can alert the correct developer to fix the issue.
Flexible Integration & Implementation: Developers can implement distributed tracing into almost any cloud-native environment. The tools are compliant with a wide range of programming languages and applications.
Each of these benefits leads to improvements in application performance by giving you insight into how a single request is handled by your server. While there are many benefits to distributed tracing, there are also some challenges to be aware of.
Manual Instrumentation: Some distributed tracing platforms require developers to modify their code to start tracing user requests. The manual instrumentation process takes many manhours, leaves your application more vulnerable to bugs, and can conclude in missing traces.
Lack of Front-End Analysis: When purchasing a distributed tracing tool, it’s important to ensure you have end-to-end coverage. Without this ability, you will only have insight into the backend without the end user’s front-end experience. This can make it much more difficult to debug your application.
Sampling: Some distributed tracing tools use arbitrary sampling, which randomly chooses traces to sample and analyze. Because traces are picked at random, and there is no way to know which traces will have issues, it can lead to teams missing major errors that are present.
Although there are some difficulties that can arise when using a distributed system, the benefits almost always outweigh the cons. Combine your distributed tracing tool with Instana to help troubleshoot these challenges in real-time.
To understand the difference between distributed tracing and logging, we first need to cover what a log is. A log is a timestamp of an event occurring within an application system. Logging is monitoring these important events identified by logs to highlight unpredictable behaviors within your application. If an error occurs, it will trigger an automatic response and alert your DevOps team.
One of the major downfalls of logging alone is that it can’t provide a fully comprehensive look into application performance without traces.
Distributed tracing uses trace IDs to follow transactions through your system with context. This context allows you to find the exact location of where an error occurred in your system. This visibility into your microservice-based system reduces the response time to detection throughout the transaction landscape. With this in mind, many teams use distributed tracing and logging in tandem with each other to get a full picture of their modern application health.
Distributed tracing tools usually support instrumentation, data collection, and visualization of data into flame graphs. The most popular way to set up distributed tracing solutions is with open-source tools.
Below are some of the most popular open-source options available on the market:
OpenTelemetry: OpenTelemetry offers a collection of software development kits (SDKs), data collection software, vendor-neutral APIs, and tools for instrumentation. It is a combination of OpenCensus and OpenTracing. This popular performance monitoring framework for cloud environments is one of the most popular distributed tracing tools. OTel doesn’t include tools for analysis or visualizing data, but you can send telemetry data to third-party applications to conduct this research.
OpenCensus: OpenCensus was created by Google based on its internal tracing system. It was eventually made open-source and became available in multi-language libraries. It can collect and transfer data to backend platforms to help with debugging but, unfortunately, didn’t have an API available to embed the software into code. This is one of the main reasons OpenCensus and OpenTracing have been combined together by the Cloud Native Computing Foundation (CNCF) to create OpenTelemetry.
OpenTracing: OpenTracing is a vendor-agnostic API that assists developers in instrumenting code for distributed tracing. This open-source project is available in nine different languages, including Java, Python, and Ruby.
Zipkin: Zipkin is another open-source project created by Twitter. This distributed tracing system helps DevOps professionals with collecting important application data and troubleshooting latency issues in different service architectures. You can report data to Zipkin using Apache, Kafka, or HTTP.
Jaeger: Jaeger is the newest open-source project on this list and was created by Uber and integrates easily with OpenTracing. This tool is highly elastic, making it a great option for request tracing through a microservice environment. Zipkin and Jaeger both assist in the visualization of statistics but have limitations when it comes to sampling data.
While OpenCensus and OpenTracing were popular in the past, we recommend using OpenTelemetry, Zipkin, or Jaeger. Use these tools in combination with an APM or observability tool like Instana to get full clarity into what is happening within your application.
To understand the interaction between messages passed between your application and its components, you need tracing. With Instana AutoTrace, you’ll never miss any context or call because of our capabilities to capture every request and correlating traces from open-source APIs. Instana makes it easy through their Dynamic Graph. We optimize each trace between your application, service, and system architecture to give you full system coverage. To try out Instana with distributive tracing, sign up for our free two-week trial to access our features.
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