You have written exquisite code that works in development. It is time to carry it into production for use by different people. That’s when thousands of questions start popping into your head: What if the web application breaks down mid-production? How will I know if my web application is in peak performance? Is there a technique I can use to understand production performance easily? Is there a way my team can address flaws that can cause genuine production problems?
This article will answer these questions and teach you a process that works well for moving applications to production.
Continuous profiling is the process of optimizing the performance of your code in production, at any time, on any scale. It involves continuously collecting performance data from the production environment and providing it to developers and operations teams for swift and deep analysis.
This is a rough sketch showing the continuous profiling feedback.
You need a continuous profiling architecture in place so that programmers can get line-level feedback on their code performance. When I say performance here, I mean you will get to see some limited resource of interest consumption rate. Resources can be wallclock time, memory, CPU time, disk I/O and so on.
If these resources get exhausted, it can lead to a bottleneck within the system. So, if you can identify and improve the part of your codebase that utilizes these resources, you will recover quickly from performance regressions; reduce costs; and improve scalability, programmers’ mental models, and user experience.
Even though you feel like you need to implement continuous profilers for each of the coding languages, the concepts are not too different. The continuous profiler gets profiles unpremeditatedly and periodically to ensure the overhead stays inconspicuous.
Profilers provide amazing benefits by helping developers like you solve performance problems cheaply and automatically using profiling reports that offer you important data on your application’s production behavior. This information allows you to understand and analyze important areas of code that are hotspots for you.
There are two major types of code profilers: sampling profilers and instrumenting profilers.
1. Sampling Profilers: Also referred to as statistical profilers, they work by estimating the “time spent” allocation in an application by getting various time-point samples.
2. Instrumenting Profilers: They work by upgrading the application codes and inserting calls into functions that calculate the number of times a process was called and the time spent inside a function. The overhead related to this performance analysis is often high because the profiler injects instrumentation directly into the application code.
gProfiler by Granulate is an open-source continuous profiler that you can install with minimal effort seamlessly without making code changes: it’s plug and play. Visibility into the production code is immediately facilitated, and gProfiler is provisioned to work continuously in the background.
So, analysis of performance issues is facilitated in real-time with minimal CPU usage. It also works towards optimizing the application’s cloud usage, making it a cost-effective solution.
It supports programming languages like Python, Java, Go, Scala, Clojure, and Kotlin applications.
Datadog’s continuous profiler can easily discover code lines that are utilizing more of your CPU or memory. It is provisioned with agents of Datadog that run on the host application. It can support applications programmed in different coding languages like Python, Java, and Go, but the profiling information types you get will be different depending on the language.
For example, Java applications are the only ones four which you are provided with profiling information for the time each method takes reading from and writing to files. However, the per function time used in the CPU is accessible in all programming languages.
Amazon CodeGuru Profiler helps programmers understand the behavior runtime of an application and find the lines of code that are costly. You can utilize it to diagnose issues with performance like high latency or low throughput by looking for opportunities to improve the CPU and memory usage. It helps you cut costs.
Thus, it can be constantly run in production to discover performance issues and provide machine learning-powered recommendations on how to know and optimize the most costly or resource-intensive lines of the code application. Amazon CodeGuru supports Java and Python applications.
Dynatrace Code Profiler uses their patented PurePath tech based on traces of code-level that spans an end-to-end transaction. It offers profiling of CPU and memory tools, allowing developers to dig deep to the method level to detect problems. It supports applications made in PHP, Java, .NET, Node.js, and Go.
We can see that continuous profilers are very integral to application production, and I hope this article has been able to answer many of the questions you had regarding continuous profiling. Thank you very much for reading.