Open-Source Distributed Tracing Solutions
As microservice architectures become more widely adopted by organizations,
Distributed Tracing is essential to understand the performance of these services. By collecting performance data from each service, teams can gain insight into the interactions between services, the path a request takes, and the performance of each service. With this data, teams can identify and address issues quickly and accurately, and take steps to ensure their applications are functioning optimally.
What is Distributed Tracing?
Distributed Tracing allows for the collection of performance data from each service in an application. This data can be used to identify bottlenecks, latency, and errors that may occur when requests travel from one service to another. This data can also be used to create a “service map” of the application, which allows for an understanding of how different services are interacting with each other.
For distributed tracing to be effective, it must be consistent and accurate. Because the performance of each service is dependent on the other services it interacts with, any variation in tracing data can lead to inaccurate results. To ensure accuracy, distributed tracing must be implemented across all services in an application, including any third-party services.
The illustration above shows a distributed multi-service architecture running on AWS that utilizes various services like API Gateway, Lambda, DynamoDB, DynamoDB Streams, and SNS Topic Subscriptions. It is important to log in at different locations to understand what’s happening within the system.
Distributed tracing can be used to understand the performance of a specific service within the larger distributed application. For example, in the illustration above, distributed tracing can be used to track how the Lambda function processes user data received from the API Gateway.
To fully understand tracing, it is crucial to know how a trace is created. A trace can be broken down into “Spans,” which represent a single operation within a trace (such as an HTTP call or a DB query). These spans are typically associated with individual URIs or services that participate in the larger request context, such as authentication.
As shown in the above diagram, a trace context is passed across each service (process/span) in your distributed architecture to track a user request across multiple services. Thus, you can see how a user request performs across several spans without maintaining multi-page dashboards.
Open-Source Distributed Tracing Solutions:
Open-source distributed tracing solutions have become increasingly popular in recent years, offering organizations the flexibility to customize the solution to fit their needs.
In this article, we will take a look at some of the top open-source distributed tracing solutions currently in the market.
One of the most popular open-source distributed tracing solutions is Jaeger.
Jaeger is a distributed tracing system that collects and visualizes data from services in a distributed system.
It allows users to trace requests from end to end, observe latency and performance issues, and troubleshoot errors.
Its intuitive interface makes it easy to track the overall performance of the system, as well as individual components of the distributed system, allowing users to gain valuable insights into how their distributed system is performing.
Furthermore, Jaeger is highly scalable and can be deployed across multiple services, making it a great tool for users who need to observe the performance of their distributed systems.
Jaeger works by assigning a unique identifier to each request that propagates through the system. This identifier is known as a trace ID and is used to track the path the request takes across the system. As requests pass through each service, Jaeger records details such as latency, time, and status.
Jaeger also provides a graphical visualization of the system’s performance over time. This helps developers identify bottlenecks and errors quickly and improve the overall performance of their applications.
Another popular open-source distributed tracing solution is Zipkin.
Zipkin allows users to store and query traces from multiple services and helps them analyze the performance of their distributed systems.
It provides a web-based UI that makes it easy to visualize data and identify performance issues.
Zipkin offers a wide range of features, such as metrics, annotations, and trace de-duplication, which make it a great tool for developers who need to monitor and debug their distributed systems.
It records timing data for each call made between services and stores them in a data store such as Apache Cassandra. The data can then be used to gain insight into the system performance, and to trace the flow of requests throughout the system. It can also be used to identify points of latency or to measure the time it takes to complete a request.
In addition, it can be used to detect anomalies and identify potential bottlenecks. Lastly, Zipkin is highly extensible, allowing users to easily integrate it with their existing systems.
Finally, there is OpenTracing.
OpenTracing is an open standard that provides instrumentation and APIs for distributed tracing. It allows users to collect performance data from a wide range of sources and gain insights into application performance.
It allows developers to trace requests that span multiple applications and services. Open Tracing enables developers to collect detailed performance data with minimal overhead.
With OpenTracing, developers can create detailed traces of a request's journey throughout an application. Every span in a trace can contain data such as the application's name, the start and end times of the span, and the span's operation name. This information can be used to build an overall performance picture of an application and pinpoint potential bottlenecks.
OpenTracing also allows for distributed transaction logging, which can be used to troubleshoot errors and investigate performance issues. By tracing individual requests, developers can determine where errors are occurring, how long each request is taking, and how errors can be addressed. The data gathered from distributed tracing can also be used to identify issues with service-level agreements and optimize performance.
Which solution to select and what considerations to take into account?
These open-source solutions provide users with a variety of features and customization options. For example, Jaeger and Zipkin offer an intuitive UI for tracking requests, while OpenTracing provides users with an open standard for instrumentation. Additionally, Jaeger and Zipkin offer support for a wide range of programming languages, making them suitable for a variety of applications.
Organizations can choose the best solution for their needs depending on the features and customization options they require. For instance, if they need an intuitive UI for tracking requests, Jaeger and Zipkin may be the best solutions. On the other hand, if they require an open standard for instrumentation, then OpenTracing may be more suitable.
Additionally, users should consider the programming language support offered by the solutions to ensure it is compatible with their application. Organizations should also consider the cost and maintenance involved in selecting a distributed tracing solution. Some open-source solutions may require additional costs for hosting or support services, while others may require users to manage the infrastructure and maintenance of the solution.
Additionally, the complexity of managing the solution should be taken into account when selecting the most suitable option. Finally, users should determine if the solution is easily scalable, as this may be essential for growing applications. By considering the features, customization options, cost, and maintenance requirements, organizations can select the best open-source distributed tracing solution for their needs.
Organizations should also consider the security of the open-source distributed tracing solution. Open-source solutions are generally less secure than commercial solutions due to the lack of dedicated support and maintenance. It is important to ensure the security of the solution by looking into the security features provided by the solution and any additional measures required to ensure the data is secure.
Additionally, users should ensure the solution complies with any industry-specific data security regulations. Moreover, users should consider the level of technical expertise required to use the solution. Open-source solutions may require users to have the extensive technical knowledge to be able to install, configure, and manage the solution. Therefore, it is important to ensure the technical requirements of the solution are met by the organization's IT team. Additionally, users should consider the availability of documentation, tutorials, and support services to help them use the solution.