The demand for speed and agility in software production has driven most industries to transition from monolithic to microservice architecture. Developers now build applications into segments of smaller and more manageable pieces. Hence, adding new product lines, functionalities, and capabilities is easier, yielding accessible and scalable products. However, it’s crucial to prioritize performance testing and monitoring for your microservice applications.
In this blog post, we’ll discuss how testing and monitoring work in a microservice architecture. Firstly, I’ll show you what monitoring and testing is all about. Secondly, I’ll lay out challenges for testing and monitoring your microservices. Finally, I’ll highlight the top existing monitoring and testing tools, and how and why they’re helpful for microservices.
Introduction to monitoring in microservices
Monitoring is simply a set of practices to understand your microservice environment’s performance and allow you to troubleshoot problems. It helps to identify underlying errors and recognize the application’s overall health. Because of the multiple independently divisible services within your microservice applications, it becomes tricky to pinpoint the exact cause of errors in the production environment after an application has been launched.
Monitoring your microservice architecture is critical to ensuring the health of your system. You can monitor and track the behavior of your microservices by using defined metrics and logs to gain insight into a system’s health. Therefore, you can obtain information on the performance of each service that makes up an application, understand whether API transactions are available and functioning correctly, identify and isolate transactions and endpoints that are problematic, and most importantly, enhance the end-user experience.
Monitoring solutions give you the ability to collect logs and other data from your application, providing value in and of themselves. You may also add observability tools that perform algorithms on the data collected by these monitoring solutions and give you deeper insight into your system’s performance. Observability provides information about the relationship between the various systems, whether they are cloud-based or on-premises, and between both internal systems and third-party applications by aggregating the logs collected from each independent system. Observability is only possible once monitoring is in place to collect the data on which the analysis is performed. This blog post focuses on the top monitoring tools available to collect this data.
Common challenges in monitoring microservices
There are potentially numerous interdependent services that are developed using different programming languages in a microservice environment. This will eventually increase the number of individual components you must track. That is to say, runtime failures are likely to occur for countless reasons (container failures, microservices, etc.), across a broad set of disparate services, and recovery might be difficult.
Top five microservice monitoring tools
Platforms and libraries are the two types of microservice monitoring solutions. Some systems may integrate the two, providing both a data collection platform and a library for instrumenting code.
Libraries: In the process of developing an application, libraries are incorporated. Popular language frameworks, such as Java, .NET, Go, and others, possess libraries that handle logging and monitoring tasks.
Platforms: Monitoring platforms focus on collecting and analyzing data retrieved from the applications, network platforms, and operating systems.
Here are a few examples of microservice monitoring tools.
Jaeger is an end-to-end distributed tracing tool designed to monitor and troubleshoot complex interactions in a microservice environment. You may use this monitoring tool to track the progress of a request or transaction as it moves across many services or through the monitored application.
Instrumentation, data pipeline, back-end storage, and web UI/visualization are the four components that Jaeger uses to record and track transactions throughout a microservice architecture. Together, these components can capture transactions into spans and then correlate those spans to form a trace. Jaeger is suitable for scaling as it supports the implementation of OpenTelemetry.
Graphite is an open-source, time-series data monitoring tool that tracks the performance of a system. It emphasizes collecting, storing, and displaying time-series data in real-time. Software professionals can use Graphite to display metrics and give deep insights into underlying system causes and how to troubleshoot them.
Graphite has introduced a network-based strategy for how organizations can monitor time-series data by ingesting and handling metrics data coming from external systems. Therefore, it saves the need for protocols like SNMP.
This tool offers a lot of integrations that are well supported, rendering many forms of graphical visualization. It also has a variety of useful utilities for working with time-series data. In addition, it possesses an integrated event tracker to monitor your system.
Graphite consists of the following components:
- Carbon: Listens to the time-series data
- Whisper: A database library for storing time-series data
- Graphite web app: A Django web app that uses the Cairo framework to render graphs on demand
Datadog is a cloud monitoring, security, and analytics platform that provides a comprehensive solution for servers, databases, and cloud-based applications. It integrates and automates infrastructure monitoring by collecting and visualizing data and managing traces, logs, and metrics. By using Datadog to monitor microservices, you can study how the services interact and quickly identify potential issues and troubleshoot them. IT and DevOps teams can use this monitoring and analytics tool to gather real-time insight and track events in their infrastructure and cloud services.
Datadog provides a whole lot of infrastructure monitoring and analysis capabilities. Some of these include the following features:
- Provides real-time interactive dashboards to monitor traces, metrics, logs, and more automatically
- Has support for over 450+ built-in integrations
- Provides a unified view of your servers, apps, metrics, and other services
- Uses security monitoring to highlight potential threats in your infrastructure
- Serves as excellent APM (application performance monitoring), reduces wait times, and eliminates errors
4. Prometheus (+ Grafana)
Prometheus is an open-source tool created by SoundCloud to offer monitoring and alerting solutions. It stores and retrieves time-series data. Prometheus gathers metrics data from endpoints at predetermined intervals, analyzes rule expressions, and displays the results, and it can send alerts when particular conditions are fulfilled. It works effectively with multidimensional data using key-value labels in memory in cached files.
Grafana is an open-source observability tool used to build dashboards that visualize data provided by Prometheus (traces, metrics, and logs), InfluxDB, and other data sources.
These tools are often paired together to offer a much more user-friendly view of your microservice metrics.
5. AWS CloudWatch
Amazon CloudWatch is both a monitoring and observability platform for Amazon Web Services that provides an overall understanding of your Amazon, hybrid, and on-premises applications. That is to say, it gives you access to all your performance and operational details. It helps cloud developers, DevOps engineers, IT managers, and site reliability engineers to use Amazon Web Services. Some features of AWS CloudWatch include the following:
- It allows you to monitor your complete stack (i.e., applications, infrastructure, and services).
- AWS CloudWatch collects and tracks the activities within your infrastructure.
- It enables monitoring infrastructure resources like EC2 instances, RDS databases, VPCs, Lambda functions, and other services.
- CloudWatch gives you insight into your application performance and how to optimize it.
- You can use it to generate automatic trigger alarms, logs, and event data and help reduce the mean time to resolution.
- It also analyzes trends and AWS resources as well as monitors application performance and operational health.
Introduction to microservice testing
Multiple services are constantly in communication with each other in a robust microservice application. Because of the flexibility of these applications, team members can modify a service’s features without altering the overall application performance. As the application grows more extensive, keeping track of each new feature becomes complex. For instance, say you have six team members working on different areas of your systems and subsystems, deploying new features daily. Since you’re unaware of the changes implemented by other team members, tracking the combined functionality of numerous services can be tricky. Thus, testing individual microservices as well as the integration between microservices can help us eliminate these complexities by preventing a domino effect if an error is encountered in any particular system.
Common challenges in microservice testing
It can be challenging to isolate the source of a problem because a single user’s activity can set off a chain reaction within numerous microservices that communicate with one another. Some common errors that result from this include errors generated by a bug within the microservice source code, alteration in data distributed between microservices, and termination of an operation due to timeout. Furthermore, because automated testing is becoming more popular for microservices, test script and autotest authoring abilities are necessary.
Top five microservices testing tools
Postman is a popular API testing tool for generating or using APIs. It uses a graphical user interface to test HTTP requests and get multiple responses, which are then verified. Postman eliminates the need to set up any frameworks with its user-friendly dashboard. This tool is commonly used among DevOps engineers and developers for application testing and to handle the continuous integration (CI) and continuous delivery or deployment (CD) pipeline.
2. Apache JMeter
Apache JMeter is a 100% Java application with a graphical interface that uses the Swing graphical API. That is to say, any environment that accepts a Java virtual machine can run this microservice testing tool (e.g., Windows, Linux, Mac, etc.). Performance, functional, and load testing are all done with this open-source tool.
JMeter can analyze and measure application performance and strength under various load types. DevOps engineers, developers, and other testers can use JMeter for load, performance, spike, and unit testing. It supports multiple formats for text reporting (HTML, XML, and JSON). JMeter also supports different processes and protocols like FTP, JDBC, JMS, SOAP, HTTP, etc. It’s popular among developers for its data analysis and visualization features. Similarly, JMeter takes care of web services, functional testing, generic TCP connections, JDBC database connections, and OS-native processes.
Selenium is an open-source software that automates tests for web applications across different platforms and browsers. It enables you to create test scripts in various languages, including Java, Python, Ruby, and C#. DevOps engineers and testers use this tool to create high-performing web apps using simple test scripts to validate functionality.
Selenium consists of different testing tools that individually cater to different needs. They include the following:
- Selenium Integrated Development Environment (IDE)
- Selenium Remote Control (RC)
K6 is an open-source framework designed to test the load and performance of cloud-native services. Its design allows engineering teams to carry out performance testing efficiently and productively. This tool tests and gives you real-time insight into your system performance. It identifies potential bugs or threats to the application’s health. Using this tool, your team can create scalable applications that are durable and perform well.
K6 can execute heavy load tests with low resource use. You can use K6 to regularly conduct tests with a small load to verify your production system’s performance and availability. It possesses an extensible architecture and can be used to mimic traffic as part of your chaotic experiments, or you can use it to trigger traffic from your K6 tests.
5. Docker Compose
Docker Compose is a tool responsible for running multiple containers as a single service. The containers within these services still interact, although the services run in isolation. Compose allows you to configure your application’s services using a YAML file. With a single command, you can activate every service in your configuration. Compose operates in all contexts, including production, staging, development, testing, and continuous integration workflows.
Compose simplifies the creation and deconstruction of isolated testing environments for your test suite in a test environment. However, you can create and destroy these environments with a singular command by specifying the entire environment in a Compose file.
Here are some additional features of Docker Compose:
- It helps with app security. Isolating containers from one another reduces the risk landscape.
- It’s a single host deployment, which entails running everything on a single piece of hardware.
- Because it uses YAML scripts, the configuration is quick and straightforward.
- Docker Compose enables high productivity because it shortens the time required to complete activities.
Learn more about monitoring and testing
This blog post introduced microservice monitoring and testing and explained why they’re essential in achieving a robust system in a microservice environment. It highlights the top 10 microservices tools that handle monitoring and testing in a microservice approach.
In conclusion, testing and monitoring are primary requirements for your microservice applications. Although several microservices tools today possess these capabilities, you can choose the one that meets the expectations for your architecture.
This post was written by Anita Ihuman. Anita is a software developer with experience working with React (Next.js, Gatsby) and in the web development industry. She has proficient skills in technical blogging and public speaking, and she enjoys exchanging information. She loves contributing to open-source projects. Anita is community manager at layer5 and creates blog posts for the community blog and she’s a content creator for the Gnome Africa Blog.
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