7 Tools for Debugging and Troubleshooting Microservices
A variety of tools are available for troubleshooting and debugging microservices. With so many possible levels of abstraction and complexity, developers must go deeply into their logs, dependencies, and reporting. As the complexity of microservices architecture grows, many admins and developers can find themselves battling to manage and support a system that has overtaken them.
However, debugging and troubleshooting microservice tools are still necessary. Here are 7 tools that can help you out:
1. Helios
Helios intends to make it easy for developers to learn, debug, and test distributed systems throughout the development process. To that result, Helios delivers useful insights into the whole microservices architecture and each service by employing distributed tracing. Helios then uses this data to automate the development of microservices tests and also to offer insights into individual failures and inefficiencies.
Because it is difficult for developers to grasp how their code works with the other components of the system, project development frequently stalls. Meanwhile, even a small issue in a single microservice or API may quickly bring a big distributed app to a halt.
Helios’ goal is to assist developers to understand how their code works with the other parts of the apps. Helios gathers global tracing data from the application and contextualizes it for the developers using OpenTelemetry.. This implies that they could repeat precisely how their code works with massive apps, for instance, to more readily detect and recreate bugs.
2. Rookout
Rookout is a game-changing development tool for cloud native debugging & data collecting in real-time. Rookout was designed for production settings and contemporary architectural debugging, including microservices applications. Non-Breaking Breakpoints in Rookout allow you to capture any form of data on the fly with no additional code, redeployment, or restart.
They can quickly collect data for efficient troubleshooting in both staging and production. Adding debugging lines at various points in the code allows them to observe where code is being touched and then swiftly locate the real issues.
With Rookout’s on-the-fly non-breaking breakpoints, you can collect variables and entire stack trace data in seconds rather than days, and from live systems. They may now isolate the source of a problem without altering or pushing new code. This can be done even without delaying or halting the stack.
When writing a simple code for Rookout, you will run into situations where the AWS Lambda will be inaccessible after its execution. If the AWS Lambda is not actively operating, Rookout will not enable you to examine its details.
Rookout eliminates the need to wait for troubleshooting by providing non-breaking breakpoints during real-time, in production and staging. Rapidly adding log lines has resulted in fuller logs, which has contributed even more fuel to the debugging process. The ultimate outcome? The average time spent troubleshooting microservices has decreased from hours to a few minutes.
However, cold start times for an AWS Lambda function with a Python runtime, which includes the Rookout service, are around 2.5 seconds longer.
3. Lightstep
Lightstep is a relatively recent debugging tool. It is a ready-to-use tool that offers incident-related data, logs, and events in real-time. This simplifies problem analysis and solutions.
Lightstep has a single dashboard that shows metrics and spans the data while highlighting data deviations that affect performance.
It detects the crucial route in trace data automatically, allowing for a more efficient and effective root-cause failure investigation.
Moreover, they enable you to transition from a wider context of the distributed system to specific services with ease.
They give crucial context while troubleshooting a dynamic and complex environment. This is because they allow developers to examine the underlying relationships between distinct cloud-based microservices. The ability to integrate tracing data with code-level, situation-specific debug data provides developers with even more insights into application behavior, propelling observability to the next stage of evolution: understandability. This is the capacity to not only see the application’s internal state but also completely comprehend its structure and operation.
Because Lightstep is newer, a lot of the features are still in the early stages of development. The search functionality occasionally falls short, and some users have complained about poor performance. However, since the Lightstep team produces new updates on a regular basis, they are continuously assessing where they might improve.
Other issues include slow data processing and the need for improvement in Satellite deployment documentation.
4. Honeycomb
Honeycomb is a distributed system for event observation and correlation. It differs from other tools. This is because it abandons the single-request-tracing strategy in favor of a more free-form style of gathering and querying data throughout layers and dimensions.
Honeycomb consumes “events,” which have JSON structures. It saves these events to the backend for subsequent retrieval. Users may run queries using the web app, to obtain insight into their (typically production) systems.
Honeycomb collects data at every level, such as the load balancer, microservices, and databases, tagging the data, and allowing the user to mix-and-match and run ad-hoc queries on the data later on. Honeycomb uses this method, since tracing leaves you with the overhanging question of whose queries are representative and so worth looking at in the first place. Once the data is present in Honeycomb, the user may connect data from multiple levels and combine and assign functions to them. They can also compare the performance of different systems over time.
Honeycomb does not have SLO support for non-enterprise versions and has fewer integrations with other SaaS companies.
It is less useful for traditional monitoring use cases. While Honeycomb has a dashboard and trigger features, they are currently quite rudimentary and inadequate for actively monitoring the health of complicated services.
5. Lightrun
Lightrun is the world’s first platform for continuous debugging and observability. It is simple and safe for developers to add logs, metrics, and traces to production and staging environments in real-time. Developers and I&O leaders benefit from 100% code-level observability and quicker resolution of production issues. This allows developers to collect data from the app in real-time. They eliminate the iterative, non-agile technique that you currently use for debugging apps.
Debugging microservices is possible with this tool. You may add logs, performance data, and breakpoints in real-time. This doesn’t need to hotfix issues or replicate the error locally, making debugging microservices much easier.
A data pollution bug causes poor data. That isn’t a big deal in and of itself. The issue is that this data may spread across microservices and into the database. An excellent example is “undefined,” which pollutes databases all over as it spreads from faulty JavaScript code and wiggles its way into them. After the fact, they are often debugged by inserting a stacked log into the location where the data is written or sent. Use a condition there just to ensure that the data is invalid and to identify the violation. This may be accomplished using code and continuous observability tools like Lightrun.
6. Datadog
Datadog is a monitoring and SaaS-based data analytics solution for IT and DevOps teams. They examine infrastructure and cloud service performance metrics, and event tracking for servers, databases, and tools.
Datadog is used by organizations of all sizes and companies. This allows digitalization and cloud solutions, drives collaboration among development, operations, security, and business teams, and accelerates application time to market. In addition, Datadog makes it faster to reach problem resolution, secures the infrastructure and applications to understand user behavior, and track key business metrics.
Datadog correlates metrics from SaaS, cloud providers, and services, which include Web Servers, StatsID, SQL, and NoSQL databases.
Using real-time dashboards, Datadog can simply analyze, notify, and graph massive data. They can limit performance metrics so that you may concentrate on what is important. They also facilitate team communication by allowing you to change and comment on annotations for the production data.
However, it is hard to find training for Datadog and the learning curve is quite steep for many people. Besides, there are a lot of instructions that are outdated and not Windows-friendly.
7. New Relic
New Relic is a cloud-based observability tool designed to assist you in developing flawless software. It presently has three main components: Full-stack observability, Applied Intelligence, and Telemetry Data Platform.
Full-Stack Observability visualizes, analyzes, and troubleshoots your whole software stack in a single linked experience. You stop jumping between programs and attempting to piece together fragmented data to figure out what went wrong.
Applied Intelligence enables you to recognize, comprehend, and address situations more quickly. It provides AIOps solutions that decrease alert noise, allowing you to identify insights in data that would otherwise go missing.
Furthermore, Telemetry Data Platform allows you to ingest, display, and alert on all of your metrics, logs, and traces from just about any source in a single location.
Conclusion
Debugging microservices will always be difficult for many developers due to the inherent complexity of the endeavor. As businesses develop, they will employ more microservices than before, and the amount of microservices used by each organization is also expected to increase significantly.
Hence, investing in extra tools, seminars, and training can hugely benefit developers who are already dealing with troubleshooting their microservices’ architecture.
