Java Performance Testing
- Java performance testing framework
- Top 7 Java Performance Software Testing Tools :: Software Testing
- Performance Testing | Java Servlet | Java (Programming Language)
Setup and Teardown can be set for three levels: - Trial: before/after each fork - Iteration: before/after each iteration - Invocation: before/after measured method invocation. Javadoc for this option starts with WARNING: HERE BE DRAGONS! so unless you want to meet Game of Thrones dragons don't use it In order to fully understand @Scope, you need to take a look into an actual example. Let's suppose we want to measure multiplication in 4 scenarios (1*1, 1*31, 31*1, 31*31). We also want to start each fork with 0 as a result. After each iteration, we want to do garbage collection. Here is how our benchmark should look like: If you still don't understand please run the benchmark and analyze console output in order to see what's going on and when. 8. Demo Uff... finally, demo time! After this long introduction, we can do something interesting. Let's say we want to measure how fast different implementations of methods summing 20000000 longs work. It's 1 + 2 + 3 +... + 20000000. We have 5 contenders (full credit for problem definition goes to Modern Java in Action book which I can recommend).
Java performance testing framework
BlazeMeter BlazeMeter is a paid tool that allows developers to quickly spin up performance tests for web and mobile applications as well as microservices and APIs. It integrates with popular open-source tools (like the aforementioned JMeter and Selenium) and has a UI that allows for easy and replicable load testing. While it's a premium product, it's evidently popular for enterprise companies who don't want to commit development resources and money to creating reliable load testing solutions. Creating a performant Java application starts with the architect, and ends with the developer making those choices work. Whether that's technical solutions like cache configuration, or optimizing classes to reduce memory consumption, developers need tools that can help identify performance issues during development, and during production. For the purposes of this exercise, we're looking at one development stage performance tuning tool, and one tool that is primarily used during production. Stagemonitor Stagemonitor is an open source application performance monitoring tool.
Variety of platforms Tests results gathered from a single platform are not relevant enough. Even if there is a really good benchmark, it is recommended to run it on multiple platforms, to collect and compare the results before drawing any conclusions. The diversity of hardware architecture implementations (e. Intel, AMD, Sparc) in regards to intrinsics (e. compare-and-swap or other hardware concurrency primitives), CPU and memory, could make a difference. Microbenchmark (component level) Testing at the component level means that you focus on a specific part of the code (e. measuring how fast a class method runs) ignoring everything else. In most of the cases such performance tests could become useless because microbenchmarks aggressively optimize pieces of code (e. class method) in a way that real situation optimizations might not occur. This principle is sometimes called testing under the microscope. For example, during a microbenchmark test in Java HotSpot Virtual Machine there might be optimizations specific to Just-In-Time C2 Compiler but in reality these optimizations won't happen because the application does not reach that phase, it might run with Just-In-Tim C1 Compiler.
So, what's the answer for Java development teams that want to minimize those performance problems? One, ensure you're doing formalized performance testing. This is typically done via a third party performance monitoring tool or if you are really feeling ambitious, custom-made for your project. Two, identify strategies for each stage of the application lifecycle. This is critical as the sooner you are able to identify issues, the sooner you can address, and the less expensive those performance issues become for your team. For those interested in formalizing their performance testing processes, the next section will give you a good overview of what's available for performance testing tools. After that, we'll look at a few of the Java performance tuning and tracing tools that can help Java development teams get additional insight without formal tests. The Java performance testing landscape for developers isn't nearly as barren as it used to be. Part of that is the adoption of DevOps strategies that shift testing further left, and part of it is the maturation and adoption of prevalent technologies like JMeter.
When running microbenchmark tests try to launch the Java Virtual Machine multiple times and discard first launches to avoid OS caching effects. Another advice is to trigger the tests enough times to get statistically relevant results (e. 20-30 times). Microbenchmarking is in general useful for testing standalone components (e. g a sorting algorithm, to add/remove elements to/from lists), but not in a way that involves slicing a big application into small pieces and testing every piece. For big applications I would recommend macrobenchmark testing, it provides more accurate results. Macrobenchmark (system level) In some cases microbenchmarking the application does not help too much, it does not say anything about the overall throughput or the response time of the application. That is why in such cases we have to focus on macrobenchmarks, to write real programs, to develop realistic loads plus environment configurations in order to measure the performance. The dataset test must be similar to the one used in real cases, otherwise a "fake" dataset will create different optimization paths in the code and will end up with performance measurements that are not realistic.
Top 7 Java Performance Software Testing Tools :: Software Testing
InfoQ Homepage Presentations Performance Testing in Java Summary Ix-chel Ruiz and Andres Almiray talk about the tools in the Java space that can help us get better measurements and results, like JMeter (perhaps one of the most well known) and JMH (probably the next likely candidate), and some techniques that should make engaging in performance testing a rewarding experience. Bio Ix-chel Ruiz has developed software application & tools since 2000. Her research interests include dynamic languages, testing and client-side technologies. Systems Administration (*nix on the top), Data Modeling and IA are among her career passions. Andres Almiray is a Java/Groovy developer and a Java Champion with more than 17 years of experience in software design and development. About the conference Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in their teams.
State Scope: A state object can be reused across multiple calls to your benchmark method. JMH provides different scopes that the state object can be reused in. These are: Thread: each thread running the benchmark will create its own instance of the state object. Group: each thread group running the benchmark will create its own instance of the state object. Benchmark: all threads running the benchmark share the same state object. Since this is a single-threaded benchmark (even though it uses multiple threads inside each benchmark), the scope is not all that important, but we'll be using the benchmark scope in this example. Setup and Teardown: State objects can provide methods that are used by JMH either during the setup of the state object, before it's passed as argument to a benchmark, or after the benchmark is run. To specify such methods, annotate them with the @Setup and the @Teardown annotations. The @Setup and @Teardown annotations can receive an argument that controls when to run the corresponding methods.
The higher the Throughput is, the better is the server performance. In this test, the throughput of Google server is 1, 491. 193/minute. It means Google server can handle 1, 491. 193 requests per minute. This value is quite high so we can conclude that Google server has good performance The deviation is shown in red - it indicates the deviation from the average. The smaller the better. Let compare the performance of Google server to other web servers. This is the performance test result of website (You can choose other websites) The throughput of a website under test is 867. 326/minutes. It means this server handle 867. 326 requests per minute, lower than Google. The deviation is 2689, much higher than Google (577). So we can determine the performance of this website is less than a Google server. NOTE: The above values depend on several factors like current server load at Google, your internet speed, your CPU power etc. Hence, it's very unlikely that you will get the same results as above.
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Performance Testing | Java Servlet | Java (Programming Language)
You may understand the testing capacity along with the number of users it can handle simultaneously. This tool was written in JAVA and is available in English and French languages. NeoLoad System requirements include Microsoft windows, Solaris as well as Linux. 3. CLIF: It is a load testing platform. CLIF is a flexible as well as modular distributed load injection framework. It can address any target function that is accessible from a Java program (DNS, HTTP, TCP/IP). This application provides 3 user interfaces (Eclipse GUI, Swing or command line) for deploying, controlling or monitoring a set of resource consumption probes along with distributed load injectors. An Eclipse wizard ensures programming support for new set of protocols. Its requirements include Java 1. 5 or higher, with enhanced support for Windows XP, Linux, MacOSX/PPC. 4. ContiPerf: It is a lightweight test utility that enables the user to easily provide JUnit 4 test cases as performance application for consistent performance testing.
I'll describe only the four most common pitfalls. For a more complete list please refer to other sources (see the further reading section at the bottom). a) Dead code elimination JVM is smart enough to detect that certain code is never used. That's why the methods you measure should always return something. Alternatively, you can use JMH consume method which guarantees that consumed code will never be buried by JVM. b) Constant folding If JVM realizes the result of the computation is the same no matter what, it can cleverly optimize it. That's why you should act against your IDE suggestion and don't make any fields final. c) Loop optimizations You need to be very careful benchmarking unit operations within loops and dividing measurements by the number of iterations. JVM optimizes the loop, so the cost of the loop is smaller than the sum of the costs of its parts measured in isolation. It's a bit tricky (I have misunderstood how it works at first) so I suggest to take a closer look at JMH example 11 and 34. d) Warmup You need warmup iterations because of JVM and JIT warmup.