Analyzing Backend and API Performance Issues as a Startup or Scale-Up
In the competitive SaaS landscape, it is common to start with an MVP to quickly test ideas and get to market. However, when the idea proves to be right and the user base rapidly expands, the quickly developed software designed for a limited number of concurrent users can struggle to handle the increased load.
I've seen this many times in my career. Young products struggling with ever-increasing performance problems, and limited resources spent fixing performance problems instead of delivering new features. It doesn't have to be this way. With proper preparation and minimal cost, a lot of effort can be saved.
This series of articles explores the challenges faced by products dealing with backend and API performance issues, and provides actionable strategies for overcoming them.
Gather the data
One of the first steps in solving performance problems is getting the right data. It is much better to set up the data collection processes before the issues arise than to do it when the need arises. Understanding the different tools that can be used to gather the data is critical to choosing the right strategy.
Monitoring tools: Monitoring plays a critical role in tracking the health of your application. It involves collecting data about hosts, virtual machines (VMs), application health, metrics and logs. This information is essential for tracking performance issues and proactively identifying bottlenecks before users start complaining. Production data allows you to monitor the actual user load your application is experiencing. In addition to providing performance data, this monitoring is invaluable for troubleshooting different types of bugs.
Code Instrumentation: Instrumentation is the process of automatically adding functionality to existing code. Use code instrumentation to gather additional data not provided by existing tools - measure the execution times of methods, database queries, and external calls. This can shed light on potential performance bottlenecks.
Distributed tracing: For distributed systems (such as microservices, microliths, or multiple monoliths), always implement distributed tracing. It is a fundamental tool that allows you to observe how requests propagate through your system, helping you to identify problems with slow processes or incorrect service dependencies. In addition, distributed tracing is the most useful tool for early detection of anomalies and service failures.
Load testing tools: Use load testing tools to reproduce high loads on test environments. By simulating high loads and combining the data with data from other tools, you can uncover IO and CPU bottlenecks that contribute to performance problems.
Profiler: When you need in-depth analysis, profilers provide detailed insight into the behavior of your application. Keep in mind that while the heavy instrumentation is useful, it can also introduce additional overhead that can lead to changes in application behavior. Working with large amounts of detailed data and complex features means that profilers require a great deal of expertise.
Application Performance Monitoring (APM) Tools: APM suites provide a comprehensive set of tools for understanding the root causes of performance problems. While more expensive, they can simplify performance monitoring and can be especially useful for teams with limited development resources.
Understand the issue
Okay, so we have the data in place. Now it’s time to tackle the issues. To effectively start analyzing the data at first limit your scope, focus only on API endpoints performance. The information you seek can be retrieved from instrumentation, load tests, distributed tracing, and HTTP server logs.
Identify endpoints with longer execution times, significant deviations, or overall slow performance. These indicators can guide you towards different types of performance issues. After spotting the suspicious endpoints, start looking into details. Check method execution times, database queries and remote integrations.
At the same time take a look at the culprit code and ask yourself what may be an issue there. This will help you approach the performance issues with ease, and limit the time needed to develop solutions.
Common Backend Performance Issues and Solutions
In the next article, We will explore common backend performance issues in-depth, providing valuable tips and strategies to trace and solve them. Stay tuned for practical guidance on optimizing your backend performance and enhancing the user experience.
Craftspire’s Performance Monitoring Stack
At Craftspire we are experts in the development of Spring Boot based Java and Kotlin web applications. If you are looking for some technology stack for similar projects then this is a list of tools that have proven it’s worth to us over the years.
Monitoring: ELK stack from Elastic. It provides a wide range of monitoring solutions, including system and VM monitoring - Metricbeat / Dockbeat), log collection - Filebeat, and various vendor integrations. (https://www.elastic.co)
Code Instrumentation: Spring AOP's PerformanceMonitorInterceptor, along with Hibernate's Query Performance tool, can help identify slow methods and queries. (https://docs.spring.io)
Distributed tracing: OpenZipkin, a Docker-enabled distributed tracing platform, seamlessly integrates with Spring Boot microservices using Spring Cloud. It can be configured to use Elastic as its trace storage database, making it compatible with the ELK stack. (https://zipkin.io)
Load Testing: JMeter, a powerful tool for testing servers under distributed load. It's suitable for integration into CI/CD pipelines and provides continuous performance reporting under high load conditions. (https://jmeter.apache.org)
Profiler: JProfiler, a comprehensive Java profiler that excels at tackling complex performance issues. It helps optimize code, address multithreading and IO/CPU waits, improve database performance, and resolve deadlocks. (https://www.ej-technologies.com)
APMs: Elastic APM, an excellent tool that integrates seamlessly with the ELK stack. It provides feature-rich performance monitoring and is accessible directly from Kibana. Best of all, it's available as both a free on-premises installation and an inexpensive cloud-native solution, making it a cost-effective choice. (https://www.elastic.co)
The bottom line
By setting up the right API performance monitoring tools early on, startups and scale-ups can ensure a seamless user experience and efficient use of resources.
Remember to collect the right performance data through monitoring from the start, use performance tracing tools, and understand the root cause of issues through detailed analysis.
Stay tuned for our next blog post, where we'll dive deeper into common backend performance issues and provide practical strategies for overcoming them.