10 Essential Distributed Tracing Best Practices for Microservices

If you are a SaaS provider making an application that deals with, say, a health registry or some personal information of the public, you realize how crucial it is to maintain their confidentiality.

It is these situations that demand a previous encryption of data followed by a prompt tracing mechanism that finds out the faults right at the moment or prior to its occurrence. And what better way to keep track of your application than tracing?

In the previous blog posts, we have already learned about distributed tracing and how it works. To refresh your memory, distributed tracing refers to the method of tracking application requests when you have an application program spread over a wide network of distributed interfaces.

A distributed framework is like a bundle of entwined threads, unfurling them is definitely not easy. Complex DevOps applications are the same way, making a single successful application requires tremendous underlay across various platforms and interfaces.

Our applications today are laid on such distributed platforms that it is impossible to root out any recurrent lag completely. Relying on dedicated monitoring platforms like Atatus for one-stop solutions to all these problems would be an easier fix.

Since we have already discussed about distributed tracing in detail, this blog will only contain some of the industry's best practices for making your tracing experience more comprehensive and complete. We have divided them into ten headers for easier understanding. Now let's dive in!

Table Of Contents:-

1. Define a clear trace context with unique identifiers
2. Instrument key components to capture relevant data
3. Use standardized tracing libraries
4. Setting sampling rates to control data volume
5. Define service boundaries with entry and exit points
6. Integrate tracing with logs and metrics
7. Visualizing to analyze latency causes
8. Defining thresholds and alert mechanisms
9. Encrypt trace data and store them properly
10. Deploy regular monitoring and compliance features

#1 Define a clear trace context with unique identifiers

When working with a garage of data, it is important to mark them distinguishingly as it is almost impossible to track them at a later date. This is where marking them specifically will be of great help. And to do this, you will need a unique trace identifier.

• The trace identifier is typically a string value that uniquely identifies a specific request or transaction. Commonly used formats for trace identifiers are UUID (Universally Unique Identifier) or a combination of a timestamp and request ID.
• Each request entering your system should be assigned a unique trace identifier.
• Ensure that the generated trace identifier is passed along with the request as it traverses through different components and services in your distributed system. This can be achieved by including the trace identifier in request headers, such as the "X-Trace-ID" header, or by adding it to the request payload.
• Each component or service that receives the request should propagate the trace identifier to downstream components.

#2 Instrument key components to capture relevant data

The goal is to capture data at critical points in your system where requests flow or important actions are performed. By instrumenting these key components, you can gather comprehensive tracing data and gain insights into the behavior and performance of your distributed system. So here are some key components that you must instrument.

i.) Entry Points - Instrument all the entry points of your system, such as APIs, web servers, or message queues, where requests or events enter your system. This includes capturing incoming request headers, payload data, and any other relevant metadata.

ii.) Service Interactions - Instrument the interactions between services or microservices. This involves capturing data as requests flow from one service to another, including outgoing request headers, timestamps, response codes, and any other contextual information that can aid in tracing the request path.

iii.) External Service Calls - If your system relies on external services or third-party APIs, instrument the calls made to these services. Capture data such as the service being called, request parameters, response times, and any errors or exceptions encountered.

iv.) Background Tasks - If your system performs background jobs or asynchronous tasks, instrument these processes. Capture data such as task execution times, dependencies on other services, and any errors or exceptions encountered during execution.

v.) Response and Error Handling - Instrument the components responsible for handling responses and errors. Capture data such as response codes, response times, error messages, and stack traces. This can provide insights into the performance and behavior of your system under different scenarios.

#3 Use standardized Distributed Tracing Libraries

Standardized tracing libraries provide a common API and instrumentation methodology across different programming languages and frameworks. They abstract away the complexities of tracing implementation, making it easier and more consistent to instrument your code. This ensures that the tracing data is captured uniformly across various components and services, enabling seamless interoperability.

Using standardized tracing libraries provides us with the following benefits:-

• Consistent instrumentation
• Integration with tracing backends
• Vendor-neutral approach
• Cross-platform compatibility
• Apace with evolving technologies

i.) Distributed Tracing libraries provide built-in support for exporting tracing data to backends such as Jaeger, Zipkin, or other distributed tracing systems. This simplifies the setup and configuration process, allowing you to focus on capturing and analyzing tracing data rather than dealing with the intricacies of backend integration.

ii.) The vendor-neutral approach allows you to switch tracing backends or monitoring tools without significant code changes.

iii.) You can implement distributed tracing consistently in different components of your distributed system, regardless of the programming language or technology stack used, as it boasts of cross-platform compatibility.

iv.) Standardized tracing libraries regularly update their libraries to incorporate new features, improvements, and bug fixes based on community feedback and emerging industry trends.

#4 Setting Sampling Rates to control data volume

A sampling rate may be a good idea if you wish to control the data volume and have a set of data that is relevant, eliminating the common less important data to keep space and overhead to a minimum.

There are some common sampling types which you can use in these cases:-

i.) Random Sampling: Random sampling involves randomly selecting a subset of requests to be traced. This approach helps distribute the tracing load across requests and ensures a representative sample.

For example, you can configure a sampling rate of 10%, which means only 10% of incoming requests will be traced. Random sampling provides a good balance between capturing useful data and reducing the overall tracing overhead.

ii.) Probabilistic Sampling: Probabilistic sampling involves assigning a probability to each request to decide whether it should be traced or not.

For instance, you can assign a higher probability to critical requests or requests with specific attributes that require closer scrutiny. Probabilistic sampling allows you to focus on specific types of requests while reducing the tracing load for less critical or high-volume requests.

iii.) Adaptive Sampling: Adaptive sampling adjusts the sampling rate dynamically based on certain criteria or conditions.

For example, you can dynamically increase the sampling rate during peak traffic periods or when specific performance metrics exceed predefined thresholds. Adaptive sampling ensures that you capture more detailed traces during critical periods while reducing the tracing volume during normal operation.

iv.) Hierarchical Sampling: In distributed systems with multiple layers or tiers, you can employ hierarchical sampling. With this approach, you can configure different sampling rates at different levels of the system.

For example, you can set a higher sampling rate at the entry points of the system to capture the initial requests and gradually decrease the sampling rate as the requests flow through the system. This helps in prioritizing tracing at critical points while reducing the overall tracing load.

v.) Business or Operational Rules: Consider specific business or operational rules to guide your sampling rates.

For example, you may decide to trace all requests related to financial transactions or requests that have encountered errors. By aligning the sampling rates with specific business or operational requirements, you ensure that the traced data focuses on the areas that matter most to your system and stakeholders.

It's important to note that the sampling rate should be carefully chosen to strike a balance between capturing sufficient data for analysis and minimizing the impact on system performance and resource utilization. You may need to experiment and iterate to find an optimal sampling rate that meets your specific needs.

#5 Define Service Boundaries with entry and exit points

A service boundary encapsulates the functionality and responsibility of a component or a group of related components. It helps to delineate the scope of each service and understand its interactions with other services. And for that, you can start with these steps:-

Analyze how requests or events flow through your system. Identify the paths they take from the entry point to the exit point, passing through different components.

Then identify the entry points where requests or events enter your system. These are the first components or services that receive external requests. Common entry points include APIs, web servers, message queues, event brokers, or any other interfaces that receive input from external sources.

Determine the exit points where requests or events leave your system. These are the components or services that generate responses or trigger events to be consumed by external systems or downstream processes. Exit points can include APIs, message queues, event emitters, or any other interfaces that emit output to external entities.

Ensure that the trace context, including the unique trace identifier, is propagated from the entry point to the exit point within each service. This allows for correlation and continuity of tracing data as the request flows through different services.

#6 Integrate tracing with Logs and Metrics

Tracing, logs and metrics are triangular entities of application performance monitoring. There is a whole blog dedicated to discussing why adapting them might be a brilliant choice to have a healthy system at your side. I would suggest you to read them when you get time.

Here's the link: Logging, Traces and Metrics. What's the difference?

And thus, here I will be providing you with only bullet points of the same:

• Detailed insights into the behavior of individual requests
• Holistic performance evaluation
• Effective troubleshooting an droot-cause analysis
• Finding latency hotspots and targeted solutions
• A store of value for later use or to formulate performance thresholds
• Improved access and understanding among developers and system admins.

Here are some commonly used approaches for achieving this integration:

1. Correlation IDs: Assign unique correlation IDs or trace IDs to link logs, metrics, and traces associated with a specific request or transaction. Including this ID in logs and metrics enables easy correlation with corresponding traces.
2. Log Context Propagation: Propagate trace context, including correlation IDs, in log entries generated by each component as a trace spans across multiple services. This allows for associating log events with specific traces, facilitating easier troubleshooting and analysis.
3. Log Injection: Inject trace-related information, such as trace IDs or span IDs, directly into log statements using log frameworks or libraries. This approach allows trace context to be included within log messages, simplifying the correlation between logs and traces.
4. Metrics Tagging: Include trace-related tags or labels when recording metrics. Tags can consist of trace IDs, span names, or other trace-specific metadata. This tagging approach enables filtering and aggregation of metrics based on specific traces, providing insights into the performance of different components within your distributed system.
5. Observability Platforms: Utilize observability platforms like Atatus that offer built-in integration between distributed tracing, logs, and metrics. These platforms provide unified views and correlation capabilities, allowing for seamless analysis and troubleshooting across all observability data sources.
6. Standardized Formats: Adopt standardized formats or protocols like OpenTelemetry or OpenTracing, which promote consistent integration between tracing, logs, and metrics. These standards provide guidelines and APIs for the propagation and correlation of observability data, ensuring compatibility and interoperability across various components of your system.

#7 Visualizing to analyze latency causes

To tackle the problem of latency in your system, you can counter some of the issues listed below. These are mostly the first set of solutions that you must be implying while trying to solve the exact reason for lethargy and slowness in your programs.

• Define service level objectives for each metric
• Identify latency hotspots from trace data
• Analyze trace data to find dependencies and interactions between components
• Optimize database and network calls as they are common sources of latency
• See if parallelizing or executing tasks asynchronously is possible
• Identify resource-intensive operations and optimize them
• Leverage caching mechanisms if available
• Apply load testing and performance monitoring

#8 Defining thresholds and alert mechanisms

Set your service-level objectives first, which are specific goals or targets for key performance metrics such as response time, error rates, or throughput. The threshold limits for alerting should align with these SLOs.

For example, if your SLO for response time is 200 milliseconds, you might set an alert threshold at 250 milliseconds to indicate a potential degradation.

Establish performance baselines by collecting historical data on some of the common parameters. This will allow us to set proper threshold limits for our current application too.

Consider the expectations of your users or customers. Understand their tolerance for delays, errors, or performance fluctuations.

Identify critical transactions or workflows that directly impact the user experience or revenue generation. Set lower threshold limits for alerting on these high-impact areas. For example, if a payment processing service experiences errors or slowdowns, it may have a more severe impact than other non-critical services.

Certain applications or industries may have stricter requirements or regulations. For example, healthcare systems may have stricter alerting thresholds to ensure patient safety.

Dont forget to strike a balance between setting threshold limits that trigger meaningful alerts and avoiding excessive false positives. Fine-tune the thresholds to reduce noise and minimize unnecessary alerting due to temporary spikes or transient conditions.

#9 Encrypt trace data and store them properly

Trace data often contains sensitive information, such as user identifiers, authentication tokens, or business data.

Encrypting the trace data ensures that this sensitive information is protected from unauthorized access. It prevents potential data breaches and safeguards the confidentiality of the trace data.

Many industries are subject to data privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union or the California Consumer Privacy Act (CCPA) in the United States. Encrypting trace data helps organizations comply with these regulations, as it provides an additional layer of security for sensitive information.

During the transmission of trace data between different components or systems, encrypting the data provides a secure mechanism for data transfer.

Trace data is often stored for analysis, troubleshooting, or compliance purposes. Encrypting the trace data at rest ensures that it remains secure even if the storage media or backup systems are compromised. It adds an extra layer of protection to the trace data when it is stored or backed up.

Encryption can be combined with access control mechanisms to control who can access the trace data. Only authorized individuals or systems with the appropriate decryption keys or credentials can access and view the trace data. This helps prevent unauthorized access and ensures that trace data is accessed only by trusted entities.

#10 Deploy regular monitoring and compliance features

Distributed systems are deep systems, in terms of their complexity. There are multiple layers, and each outsourced service might even have a dependency that you don't know about.

This enormity creates problems that are humanly insolvable.

Identifying and singling out one request transaction that faulted from this myriad of requests would not be easy. It would be like finding a needle in the hay. More so, because most of your services might have been outsourced. You don't know where it went wrong or what made it go wrong. Most of the time, even teams that made this service might themselves be inaccessible (just the worst-case scenario, though!)

This is where distributed tracing fill in these gaps. They easily comprehend your system's working and tell exactly what went wrong. Making it all the more important to deploy one for regular monitoring so your system or application never experiences a free fall!

Simplify Distributed Tracing with Atatus

Atatus offers a powerful feature called distributed tracing, which enables you to effectively monitor and trace the flow of requests across your distributed systems.

By incorporating Atatus APM agent into your application code, you can instrument it to capture data and trace information as requests propagate through the application.

As a request enters your application, Atatus assigns a unique trace ID to it. This trace ID serves as a distinct identifier that allows you to correlate and track the request as it progresses through different services and components.

As the request traverses your application, Atatus generates spans, representing individual operations or events within the trace. These spans capture essential details like timing information, metadata, and contextual information about specific operations such as database queries, HTTP requests, or function invocations.

Atatus ensures the propagation of trace context across various services and components. This means that the trace ID and other relevant span information are seamlessly carried along as the request flows through the system, enabling you to comprehend the request's path and dependencies.

The traces collected by Atatus can be visualized and analyzed within the Atatus APM console. The console provides a graphical representation of the request flow, allowing you to observe timing aspects and dependencies between spans. This visualization assists in identifying bottlenecks, performance issues, and dependencies within your distributed systems.

Distributed tracing in Atatus greatly simplifies troubleshooting and performance optimization. By analyzing the traces, you can pinpoint areas of latency, errors, or inefficiencies, empowering you to make informed decisions and improve the overall performance and reliability of your distributed systems.

Wrap-Up

This blog was a long list of all the best practices you could follow while adapting distributed tracing.

We have discussed about distributed tracing and how it works in the previous blogs. You may check out them at your own leisure. Anyways, to sum up what this blog was all about, here are a few pointers:

1. Having a unique trace identifier for each request is a good idea.
2. Instrument some of the key components like entry/exit points, APIs, external service calls etc.
3. Using standardized tracing libraries is always a better choice.
4. Try out the different sampling methods mentioned above to control data volume.
5. Analyze the flow of your requests and its interaction with other system components.
6. Integrate tracing with logs and metrics.
7. Cash in on the visual dashboard for identifying latency hotspots.
8. Set up alert mechanisms so that you are never late on
9. Encrypt any data, be it while storing or in transit.
10. Rely on dedicated monitoring platforms like Atatus for one-stop solutions to all these problems.

Enhance Application Visibility with Atatus Distributed Tracing

Distributed tracing offers significant advantages for monitoring and optimizing distributed systems. By providing end-to-end visibility into the path of requests as they traverse different services and components, it allows for a comprehensive understanding of system behavior.

Analyze the time taken at each step of a request's journey through your distributed system. By pinpointing areas of high latency, you can identify specific services or components that may be causing delays.

Atatus Distributed tracing facilitates effective debugging and root cause analysis. When a request encounters an error or exception, you can trace its path through the system and examine the corresponding spans. This allows you to identify the exact point of failure and understand the context surrounding the error, making it easier to diagnose and resolve issues.

Gain insights into the resource utilization of different components and services. This information can aid in capacity planning and scaling efforts, ensuring that you allocate resources effectively and efficiently to handle increasing demand and maintain optimal system performance.

Try your 14-day free trial of Atatus!