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  • The purpose of your application is to serve your end-users well.

    It must be fast, responsive, easy to use, and reliable, among other desirable functionalities.

    But maintaining your software in a manner that keeps on delivering top-level performance is not that easy.

    When your code starts calling unnecessary functions, trips over itself, catches bugs, and goes into extra loops, it may result in inefficiencies. Your application could become sluggish, unresponsive, or behave erratically.

    And if you do not fix these issues, the overall application performance is suffered.

    Consequently, your customers may get irritated or stop using your application altogether due to poor performance and slow speed. It not only degrades your reputation but also costs you in terms of revenue and profits. Therefore, your code needs to be analyzed, reviewed, and debugged to achieve optimal performance. And the quick way to do it is by using a software profiling tool to monitor and debug your codes and eliminate performance-related bottlenecks.

    In this article, you’ll learn about software profiling and how it can help you. And then, I’ll walk you through some of the best profiling tools to debug your application and optimize its performance.

    What is Software Profiling?

    Software profiling is a dynamic code analysis where a program’s behavior is investigated using the data collected as the program runs. It aims to determine various program sections that you must optimize to increase the application speed, responsiveness and decrease its memory and resource consumption.

    A software profiler commonly measures the duration and frequency of function calls along with memory or time complexity associated with a program. There are also specific profilers available, like memory profilers.

    Profiling is generally performed by instrumenting the program’s source code. Profilers can use different techniques of profiling such as instrumented, event-based, statistical, or simulation methods.

    Why Does Software Profiling Matter?

    Software profiling is needed to determine the resource usage and execution time associated with a specific function. It helps optimize the program’s speed and, at the same time, ensure that it consumes minimal resources.

    Furthermore, it is done to track and optimize CPU usage and command execution time.

    Therefore, choosing the right software profiling tool is necessary to ensure you can debug the performance-related issues faster to improve its efficiency and provide a better end-user experience. Many profilers also come with detailed reports and interactive graphs and visualizations that help you find the exact root cause of the problems, making it easier to solve them.

    So, here’s a list of some of the best software profilers you can try, and tell us what worked for you the best.

    py-spy

    py-spy is an excellent sampling profiler for Python. This allows you to get a sneak peek at all the things your Python-based application spends time on.

    For this, you don’t have to modify your codes or restart the program altogether. py-spy involves low overhead and is developed in Rust to execute greater speed. It is not built to operate in the same process where your profiled python-based program runs. This implies that py-spy is highly safe and secure to use against the production Python-based codes.

    The tool enables you to record profiles, generate flame graphs to create interactive SVG files. You can view other options as well, like changing sampling rates, native C extensions for profiling, subprocesses, thread-ids, and more. You can get a live view of functions happening in your programs using the ‘top’ command and display the present call stack using the ‘dump’ command for every python thread.

    It supports every CPython interpreter version, such as 2.3 – 2.7 & 3.3 – 3.8. You can install py-spy from PyPI or GitHub.

    Pyroscope

    The open-source continuous profiling software of Pyroscope helps you debug all the performance issues within your application in minutes.

    You can start the server followed by the agent no matter what you use, Docker, Linux, or are looking for Ruby or Go docs, Pyroscope covers you. Even if you aim for ten seconds or ten months of software profiling data, their custom-designed storage engine makes fast queries.

    You don’t need to worry about overhead or application performance as they use sampling profiling technology that doesn’t affect the performance. Pyroscope stores your profiling data efficiently; hence, it is cost-effective for you even if you want to store different profiling data from various applications for years.

    It works on macOS, Linux, and Docker, and supports programs written in Python, Go, and Ruby.

    Bubbleprof

    Bubbleprof by Clinic.js provides a fresh and unique way of profiling your software written in Node.js. It uses a ‘bubble’ UI that helps everyone from experts to beginners determine asynchronous time spent in your app.

    It visualizes how your Node.js processes operate by observing its asynchronized operations, grouping them, calculating the delays, and mapping them.

    Bubbleprof determines operation timings by looking at the size of bubbles within a specific group of operations that can be your code, a node core, or a module. It also clubs the adjacent groups to decrease clutter.

    To calculate the delays as the operation flows from one group to another, Bubbleprof measures the arrow length that connects bubbles. In addition to this, it uses different colors in the measurement processes as well. Simultaneously, the inner colored lines represent a mixture of async operation types as the cause of delay.

    Pyinstrument

    Optimize your Python codes with Pyinstrument.

    It shows you why your Python code is slow and helps you diagnose the issues so you can have that blazing fast performance.

    To use Pyinstrument, you don’t have to write a Python script; just call Pyinstrument using the command line directly. Your script would run normally, and the tool would yield a colored summary of the areas where the application spent its time. It also comes with a Python API that makes the process even easier.

    You have the option to profile web requests in Flask and Django as well, for which they have maintained elaborative documentation. Here, please note that Pyinstrument offers statistical profiling that records call stack every 1 ms instead of tracking every function call made by your program.

    It is advantageous as statistical profilers involve lower overhead compared to tracing profilers. As it records the whole stack, tracking expensive function calls becomes effortless. In addition to this, Pyinstrument also hides (by default) library frames, allowing you to focus on applications or modules responsible for affecting the performance.

    Debugging performance issues is made easier because Pyinstrument records time spent using ‘wall-clock’ time. The tool tracks all the program’s time to read files, download data, communicate to a database, etc.

    Xdebug

    To improve your code’s performance issues and make your development experience a little more fun, Xdebug comes with wide-ranging capabilities for profiling and debugging.

    It is actually a PHP extension that allows you to find the bottlenecks in your PHP application and analyze its performance using external visualization tools to generate performance graphs.

    Xdebug creates a detailed output showing the application’s path to reaching an error, including the parameters it passed to a given function. This is done to track the errors. To help a developer understand things clearly, it generates color-coded information along with structured views.

    It comes with a remote debugger as well that you can use to connect Xdebug with a running code, IDE, or browser to see code breakpoints and execute the codes line-by-line. Another feature it offers is code coverage that shows how much your program’s code was executed, and it also helps you with unit tests.

    SPX

    Simple Profiling eXtension (SPX) is a profiling extension designed for PHP. It has some unique properties, setting it apart from other profiling extensions. It is completely FREE to use and confined to just your infrastructure, which means there’s no risk of data leaks.

    SPX’s simplicity makes it very easy to use: all you need is to set a command line or environment variable to profile a script. Or, you can also switch on the radio button on a web page to profiling the script. As a result, you don’t have to instrument your code manually.

    It also supports a running command-line script – Ctrl-C. In addition to this, this process also eliminates the need for using a command-line launcher or dedicated browser extension. SPX supports multi metrics of around 22, including various time and memory metrics, objects, files in use, I/O, etc.

    It can gather data without leaving the context. Its web UI allows configuring/enable profiling for the browser session currently in use and lists all the profiled script details and reports. The web UI lets you select a specific report for deeper analysis and features some interactive visualizations such as Flamegraph, flat profile, and timeline that can scale to function calls in millions.

    Prefix

    Prefix by Stackify is an easy-to-install and lightweight code profiler that many developers love. It helps you eliminate the bottlenecks in your application performance to optimize it and improve the user experience.

    Prefix’s superior tracing and profiling capabilities allow you to quickly find hidden exceptions, slow SQL queries, and more. It provides your developers with the real power of APM (application performance monitoring). For this, Prefix validates code performance the way it’s written and allows you to push better-performing codes to test.

    In this way, it receives fewer support tickets from the production side and helps development managers achieve the goals sooner. Discover all the underperforming queries, unknown bottlenecks, and ORM-generated queries.

    You can also track each SQL call parameter, download the timings, and view the affected records. Prefix makes it simpler to spot N+1 patterns as well. Forget about sorting through all those messy logs; bring them together to locate issues easily.

    Prefix lets you find the context of a suspicious log within a query request directly and jump from one log to a trace for debugging effortlessly. Prefix sheds light on poor-performing dependencies, which is useful for finding hidden exceptions and working with legacy code or framework sections. These dependencies can be web services, 3rd-party services, cache services, and others.

    Prefix works on Windows and Mac and supports .Net, Ruby, Java, PHP, Python, and Node.js.

    Scalene

    Scalene is a high-precision, high-performing GPU, CPU, and memory profiler for Python-based programs. It offers several advantages over other profilers, such as running orders of faster magnitudes and delivering more in-depth information.

    Scalene is incredibly fast and utilizes sampling rather than instrumentation. It does not even rely on the tracing facilities of Python. In addition to this, its overhead is usually below 10-20%. This tool performs software profiling at the line level and points to those lines of codes responsible for your program’s execution time.

    These details are more valuable than those at function-level profiling. Scalene separates the time spent purely in Python from that of native code that includes libraries. As most Python programmers won’t optimize native code performance, developers can focus their efforts on optimizing codes that you can actually improve.

    It highlights hotspots in red that make it easier for you to spot CPU time/memory allocation and easily separate system time to find I/O issues. Scalene can report GPU time, profiles memory usage and tracks CPU usage. Scalene can also identify possible memory leaks, profile copying volume, and generate reduced profiles for code lines consuming greater than 1% of CPU.

    VisualVM

    The all-in-one troubleshooting tool for Java, VisualVM, is designed to be used for both the production and development phases. It is a visual software that integrates lightweight profiling functionalities and command-line JDK tools.

    VisualVM monitors applications that run on Java 1.4+ and troubleshoots them using several technologies such as JMX, jvmstat, Attach API, and Serviceability Agent. This tool is a perfect fit for different requirements of quality engineers, system admins, and end-users.

    It detects remotely and locally running Java-based applications automatically and lists them. The tool also allows you to define the programs manually using the JMX connection. For every process, it shows typical runtime data such as PID, arguments passed, JDK home, main class, JVM flags, JVM version, and system and argument properties.

    VisualVM monitors CPU usage, heap, and metaspace or permanent generation memory, running threads, and loaded classes in an application. It displays all the running threads in a timeline with aggregated Sleeping, Running, Park, Monitor, and Wait times.

    Both instrumentation and sampling profilers can be performed using VisualVM for memory management and application performance. It displays thread dumps to provide quick insights into processes. It also views and creates .hprof snapshots on demand to help you uncover heap usage inefficiencies and debug memory leaks.

    Furthermore, VisualVM can read basic data on a crashed Java-based process along with its environment. You can analyze your apps offline; it can save app runtime environment and configuration with taken heap dumps, thread dumps, and profiling snapshots that you can process offline at a later stage.

    It works on Windows, Linux, and Unix.

    Orbit Profiler

    Visualize your C/C++ application and find performance issues quickly using Orbit Profiler. This is a debugging tool and standalone profiler that aims to help developers view and comprehend the complex app’s execution flow.

    It provides a sharp view of everything happening inside the app so you can quickly eliminate the performance bottlenecks and restore your application’s high performance.

    Orbit Profiler can work on any C or C++ app efficiently, provided it can access the PDB file. Next, it will start profiling once you complete downloading its program. The tool jumps to the target process, hooks itself to selected functions, and performs profiling.

    It can even work on your optimized final or shipping builds. Apart from dynamic instrumentation, Orbit Profiler offers ‘always on’ sampling capabilities as well, which is fast, available all the time, and robust.

    It works on Windows and Linux.

    Uber JVM Profiler

    Laced with advanced profiling capabilities, Uber JVM Profiler is another good option for your Java-based applications.

    It offers a Java Agent that collects several stack traces and metrics for Spark/Hadoop JVM processes in a distributed manner, for instance, memory/CPU/IO metrics.

    The tool can trace java arguments and methods on user codes without changing them. You can also use it to trace the call latency of HDFS name nodes for every Spark apps and find issues. It can even trace the Spark app’s HDFS file paths to find out hot files and perform further optimization.

    Uber JVM Profiler was created originally to profile Spark apps that generally include many machines or processes for one application. Hence, people can correlate metrics easily for these machines or processes.

    However, the tool works as a typical java Agent, and you can use it for any of your JVM processes. Its features include:

    • Debugging memory usage of Spark app executors, such as java heap memory, native memory, non-heap memory, buffer pool, and memory pool
    • Debugging CPU usage and Garbage collection time
    • Debugging java class methods for their frequency and time or Duration Profiling
    • Argument Profiling (debugging and tracing java class method call and its argument value)
    • Stacktrack Profiling & generating Flamegraphs for CPU time
    • Debugging I/O metrics and JVM Thread metrics

    Tracy

    Tracy is a useful tool to help developers debug PHP programs easily. It has a friendly design and advanced features such as CLI support, debugging AJAX calls, and more.

    It can find and correct errors quickly, dump variables, log errors, visualize memory consumption, and determine the execution time of queries or scripts. Using color coding and highlighting issues in red with clear explanations helps you visualize exceptions and errors easily and understand them.

    Tracy comes with logging functionality and environment autodetection. It stores data into log files and displays server error messages to a visitor during downtimes. Tracy can also integrate with Drupal 7, OpenCart, WordPress, and more.

    vprof

    vprof is a visual profiler for Python applications. It provides rich, interactive visualizations for your Python program’s different characteristics, such as memory usage and running time.

    It is available under a BSD license and supports Python 3.4 and above.

    Conclusion

    Application performance is a crucial factor in fulfilling the expectation of end-users. And if performance issues occur, you must be ready to diagnose the issue before impacting the end-user experience.

    Hence, keep optimizing your applications and fix the issues immediately to continue delivering super-fast application performance to the users using the tools I’ve mentioned in this article.

    Here is a quick comparison table showing the above profilers and what it is mostly used for.

    Name Languages
    py-spy Python
    Pyroscope Python, Ruby, Go
    Bubbleprof Node.js
    Pyinstrument Python
    Xdebug PHP
    SPX PHP
    Prefix Python, .NET, Java, Node.js, Ruby, PHP
    Scalene Python
    VisualVM Java
    Orbit Profiler C, C++
    Uber JVM Profiler Java
    Tracy PHP
    vprof Python