Apache JMeter
Caffeine
Apache JMeter | Caffeine | |
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25 | 43 | |
7,935 | 15,227 | |
0.9% | - | |
9.3 | 9.7 | |
7 days ago | 2 days ago | |
Java | Java | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Apache JMeter
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Ask HN: What are you using for load testing?
Usually, I would let organic users be my load test. However, I am working on a project that has an anticipated load on a new-to-my-team stack, so I'm looking into ways to load test.
I've seen tools like k6 (https://k6.io/), Artillery (https://www.artillery.io), and JMeter (https://jmeter.apache.org/).
I've been using Artillery, but it's hard to visualize the results.
What do you use?
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What is Load Testing? Complete Tutorial With Best Practices
Apache JMeter: This tool is an open-source application built on Java, designed specifically to test load functionality and performance. Developed by the Apache Software Foundation, JMeter is versatile, able to simulate loads across a wide range of services and protocols such as HTTP, HTTPS, JDBC, LDAP, and SOAP. With an extensible core that can be tailored with plugins, it provides the flexibility needed for different testing scenarios. Its intuitive GUI makes it easy for testers to design test plans and visualize the results in various ways.
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Lambda to S3: Better Reliability in High-Volume Scenarios
I'll use Apache JMeter to do this experiment:
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ssd + cpu vs nvme + premium cpu - benchmark results for wordpress
Thanks for the tip. Hows that compare to this tool? https://jmeter.apache.org/
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Mastering API Stress Testing With JMeter’s HTTP(S) Test Script Recorder And Postman Proxy
Apache JMeter: Download and install JMeter from the official website (https://jmeter.apache.org/). Java Development Kit (JDK): JMeter requires Java, so ensure you have the latest JDK installed on your system. Postman: Install Postman from the official website (https://www.postman.com/downloads/).
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GC, hands off my data!
The test scenario consists of querying for descriptions of different offers. During the test, I will collect data on memory and GC parameters using jConsole. I will run the test scenario using jMeter, which additionally will allow me to measure response times.
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Tell HN: Postman just wiped all my stuff
FYI some of our people internally use Jmeter. https://jmeter.apache.org/
It's not flashy so it probably wont get the standard "we are going to milk you for data" plan
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What's new in Apache JMeter 5.6?
Issue #5682Pull request #717 - Open Model Thread Group: avoid skipping rows from CSV Data Set Config
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Best Practices in Testing GraphQL APIs
Performance and load testing are essential parts of GraphQL API testing. It ensures APIs can handle expected traffic volumes and respond within acceptable timeframes. You can use tools like Apache JMeter or Gatling to generate realistic loads and evaluate the API's performance under different scenarios. Techniques like batched queries and caching can help mitigate this issue.
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2x m5a.xlarge EC2 servers reach 90% CPU usage and more or less freeze for 5 minutes when 100 users access at the exact same time.
JMeter https://jmeter.apache.org
Caffeine
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Otter, Fastest Go in-memory cache based on S3-FIFO algorithm
/u/someplaceguy,
Those LIRS traces, along with many others, available at this page [1]. I did a cursory review using their traces using Caffeine's and the author's simulators to avoid bias or a mistaken implementation. In their target workloads Caffeine was on par or better [2]. I have not seen anything novel in this or their previous works and find their claims to be easily disproven, so I have not implement this policy in Caffeine simulator yet.
[1]: https://github.com/ben-manes/caffeine/wiki/Simulator
[2]: https://github.com/1a1a11a/libCacheSim/discussions/20
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Google/guava: Google core libraries for Java
That, and also when caffeine came out it replaced one of the major uses (caching) of guava.
https://github.com/ben-manes/caffeine
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GC, hands off my data!
I decided to start with an overview of what open-source options are currently available. When it comes to the implementation of the on-heap cache mechanism, the options are numerous – there is well known: guava, ehcache, caffeine and many other solutions. However, when I began researching cache mechanisms offering the possibility of storing data outside GC control, I found out that there are very few solutions left. Out of the popular ones, only Terracotta is supported. It seems that this is a very niche solution and we do not have many options to choose from. In terms of less-known projects, I came across Chronicle-Map, MapDB and OHC. I chose the last one because it was created as part of the Cassandra project, which I had some experience with and was curious about how this component worked:
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Spring Cache with Caffeine
Visit the official Caffeine git project and documentation here for more information if you are interested in the subject.
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Helidon Níma is the first Java microservices framework based on virtual threads
not to distract from your valid points but, when used properly, Caffeine + Reactor can work together really nicely [1].
[1] https://github.com/ben-manes/caffeine/tree/master/examples/c...
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FIFO-Reinsertion is better than LRU [pdf]
Yes, I think that is my main concern in that often research papers do not disclose the weaknesses of their approaches and the opposing tradeoffs. There is no silver bullet.
The stress workload that I use is to chain corda-large [1], 5x loop [2], corda-large at a cache size of 512 entries and 6M requests. This shifts from a strongly LRU-biased pattern to an MRU one, and then back again. My solution to this was to use hill climbing by sampling the hit rate to adaptively size of the admission window (aka your FIFO) to reconfigure the cache region sizes. You already have similar code in your CACHEUS implementation which built on that idea to apply it to a multi-agent policy.
Caffeine adjusts the frequency comparison for admission slightly to allow ~1% of losing warm candidates to enter the main region. This is to protect against hash flooding attack (HashDoS) [3]. That isn't intended to improve or correct the policy's decision making so should be unrelated to your observations, but an important change for real-world usage.
I believe LIRS2 [4] adaptively sizes their LIR region, but I do not recall the details as a complex algorithm. It did very well across different workloads when I tried it out and the authors were able to make a few performance fixes based on my feedback. Unfortunately I find LIRS algorithms to be too difficult to maintain for an industry setting because while excellent, the implementation logic is not intuitive which makes it frustrating to debug.
[1] https://github.com/ben-manes/caffeine/blob/master/simulator/...
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Guava 32.0 (released today) and the @Beta annotation
A lot of Guava's most popular libraries graduated to the JDK. Also Caffeine is the evolution of our c.g.common.cache library. So you need Guava less than you used to. Hooray!
- Monitoring Guava Cache Statistics
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Apache Baremaps: online maps toolkit
Unfortunately, I don't gather statistics on the demonstration server. I believe that the in-memory caffeine cache (https://github.com/ben-manes/caffeine) saved me.
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Similar probabilistic algorithms like Hyperloglog?
Caffeine is a Java cache that uses a 4-bit count-min sketch to estimate the popularity of an entry over a sample period. This is used by an admission filter (TinyLFU) to determine whether the new arrival is more valuable than the LRU victim. This is combined with hill climbing to optimize how much space is allocated for frequency vs recency. That results in an adaptive eviction policy that is space and time efficient, and achieves very high hit rates.
What are some alternatives?
Karate - Test Automation Made Simple
Ehcache - Ehcache 3.x line
TestNG - TestNG testing framework
Hazelcast - Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.
Cucumber - Cucumber for the JVM
cache2k - Lightweight, high performance Java caching
WireMock - A tool for mocking HTTP services
Apache Geode - Apache Geode
REST Assured - Java DSL for easy testing of REST services
Guava - Google core libraries for Java
JUnit - A programmer-oriented testing framework for Java.
scaffeine - Thin Scala wrapper for Caffeine (https://github.com/ben-manes/caffeine)