Introduction to continuous profiling
Developers encounter problems and inefficiencies in their production applications regularly. Typically, they have to spend a lot of time examining logs and metrics to address these issues, which might not be very informative.
However, there’s a more modern approach that doesn’t just give actionable insights into performance issues but also identifies where and why problems occur in codebases. This approach is known as continuous profiling.
In this post, we’ll learn about continuous profiling, how it works, types of profilers, the benefits of continuous profiling, and much more.
What is continuous profiling?
Continuous profiling is a real-time monitoring technique used to analyze production applications to identify and resolve performance issues that impact application user experience. It involves gathering data on execution-related factors like CPU usage, disk I/O consumption, and duration of function calls to point out impediments and areas for improvement.
With continuous profiling, developers get more information about executed functions. Engineers, on the other hand, are able to collect CPU utilizations and note measuring usage for performance improvements.
What is a continuous profiler?
Professionals use a code development support tool called profilers to implement continuous profiling.
By embedding this into a program’s environment during execution, developers can identify codes that need improvement and optimize them.
In doing so, they ensure that their applications meet performance criteria and scale efficiently. Additionally, this process helps developers to understand the performance of their code better and make informed decisions about future optimizations.
Ultimately, the ability to identify bottlenecks and optimize code can result in an improved user experience and increased productivity.
Types of profilers
A sampling profiler, also called a statistical profiler, collects different time-point samples and analyzes the functions currently being executed at a particular time.
The result is usually the number of times a specific source code is executed while the application runs. Professionals use this result to analyze the application’s performance and identify potential bottlenecks.
This type of profiler generates more feedback data than sampling profilers.
How does this work? Consequently, developers insert additional code at key checkpoints within an application to gather performance data.
This method allows them to measure function, call duration, record routine sequences, and capture logging. However, the downside is that source codes are likely to be corrupted since they need to be modified to get the required result.
Continuous profiling operates by routinely sampling the execution of a program and gathering information on the condition of the program at each sample point.
This process typically involves the following steps:
Instrumentation — The first step is integrating these profiling tools into the application’s runtime environment to collect performance data. Various techniques—such as code instrumentation, runtime hooking, and sampling—can be used for this.
Data collection — After instrumenting the application, the profiling tool gathers data on various aspects of its execution, such as CPU usage, memory usage, network activity, and disk I/O.
Data analysis — After collecting data, developers examine it to detect patterns and trends that may suggest performance issues. To conduct this analysis, they use different techniques, including statistical analysis, data visualization, and machine learning algorithms.
Performance optimization — Based on the results of the data analysis, developers can make changes to the application’s code or configuration to optimize performance. This may involve optimizing algorithms, reducing memory usage, or improving I/O performance.
Continuous monitoring — After performance optimizations are made, the profiling tool continues to monitor the application to ensure that the changes have had the desired effect and to identify any new performance issues that may arise.
Data visualization in continuous profiling
The most common way profiling tools visualize collected data is in the form of flame graphs.
A flame graph is a visual representation of stack traces collected through sampling in your application to display profiling data in an intuitive and easy-to-understand way.
The timed and color-coded horizontal bar illustrates the amount of time spent in a function or method call, making it simple to identify bottlenecks, particularly in complex and dynamic applications.
Below is an example of a flame graph.
Here, the Y axis represents the stack depth, and the width of each rectangle is proportional to the total time in the code paths. Basically, the widest tower is the point where the highest latency occurs.
Choosing a continuous profiling tool
Developers use a variety of tools to ensure that applications are performing optimally. Some of the popular tools include:
Datadog Continuous Profiler
Datadog’s continuous profiler collects profiles from production servers, ensuring you always have the necessary data to resolve performance issues.
It’s environment agnostic and works with any stack, including Java, Python, Node, .NET, and Ruby. They have a low overhead profiler that services lines of code as well as CPU bound.
Datadog profilers are closely integrated with other services on the platform. This allows you to use the profiler alongside other programs of your choice based on the performance issue your team is trying to solve.
They also have a custom dashboard that tracks profiling trends and is available across all languages and code bases.
Amazon CodeGuru Profiler
Amazon CodeGuru profiler uses machine learning to collect runtime performance data from your production applications. This service provides actionable insights into the functions that take the most time to execute or consume the most CPU capacity.
With years of experience in performance monitoring, Amazon CodeGuru profiler can analyze your profiles automatically and suggest ways to improve code performance.
CodeGuru supports Python and Java applications. You can also use this profiler to troubleshoot latency and CPU issues within your application.
To get started, you’ll need to set up a CodeGuru profiler, after which you may explore the different data visualizations provided.
Google Cloud Profiler
Google Cloud profiler examines code performance on your servers and uses this information to suggest how to make your application faster and more efficient.
Amazon developers designed this software for applications running on Google Cloud and other cloud platforms. It also has a low overhead that’s perfect for production environments.
This profiler supports common languages; collects multiple profile types; and works with Node.js, Go, Python, and Java applications.
Dynatrace Continuous Profiler
Dynatrace is a full-stack monitoring and performance optimization tool that provides real-time monitoring and analytics for cloud-based applications.
Its continuous CPU profiling allows you to perform code-level CPU analysis. With this, you can break down data; identify the CPU with the highest consumption level; and filter them through code execution, network, and disk I/O consumption.
Dynatrace also has a memory dump analysis and process crashes options that allow you to detect application crashes on Linux and Windows environments. It helps to analyze the main reason for these crashes and supports various programming languages, including .NET, PHP, Node.js, Java, and Golang applications.
Benefits of continuous profiling
Continuous profiling offers several benefits for software development and application performance optimization. Here are some of the main benefits:
Early detection of performance issues — Continuous profiling enables developers to detect performance issues early in the development cycle. By identifying these performance issues early, developers can address them before they become more complex and costly to fix.
Reduced downtime — By spotting possible problems before they become severe, continuous profiling helps minimize application downtime. As a result, developers can increase application availability and dependability by taking proactive measures to address performance issues.
Better resource utilization — With continuous profiling, developers can quickly identify problems with resource use. For example, they can detect high CPU or memory usage, enabling applications to scale more efficiently.
Improved application performance — Continuous profiling usually optimizes application performances by identifying slow or inefficient areas of the code. By optimizing these areas, applications can therefore run faster and more efficiently.
Continuous profiling vs. traditional profiling
Continuous profiling has gained popularity in recent years due to its numerous advantages over traditional profiling. One of the key benefits of continuous profiling is its ability to identify and address performance issues quickly. Unlike traditional profiling, which collects data at specific intervals, continuous profiling constantly monitors an application’s performance in real time. This provides developers with an accurate and detailed picture of the application’s behavior.
However, traditional profiling, on the other hand, may miss critical performance issues that occur between sampling intervals, leading to delays in identifying and addressing problems.
By integrating continuous profiling into production environments and building pipelines, developers can automate the process of performance monitoring and analysis. This leads to increased efficiency, a high-quality user experience, and faster feedback.
Profiling should not be treated as a one-off event that needs to be done only when there’s a problem in production applications. By taking a proactive approach to solving performance issues rather than a reactive one, the development team can always anticipate these issues and are prepared to overcome them.
This is why continuous profiling has emerged as a powerful tool for developers to improve the performance and efficiency of their applications. Adopting this approach can help developers stay ahead of the curve and deliver high-quality software at scale.