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Eclipse Memory Analyzer VS locust

Compare Eclipse Memory Analyzer VS locust and see what are their differences

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Eclipse Memory Analyzer logo Eclipse Memory Analyzer

The Eclipse Foundation - home to a global community, the Eclipse IDE, Jakarta EE and over 350 open source projects, including runtimes, tools and frameworks.

locust logo locust

An open source load testing tool written in Python.
  • Eclipse Memory Analyzer Landing page
    Landing page //
    2022-06-15
  • locust Landing page
    Landing page //
    2021-10-11

Eclipse Memory Analyzer features and specs

  • Efficient Memory Leak Detection
    Eclipse Memory Analyzer is highly effective at detecting memory leaks and helping developers understand why a Java application is consuming excessive memory.
  • Comprehensive Heap Analysis
    It provides detailed insights into memory consumption, object retention, and references within heap dumps, which can help in optimizing application performance.
  • Standalone and Integrative
    Eclipse MAT can be used as a standalone tool or integrated into Eclipse IDE, providing flexibility based on user preference.
  • Automated Reports
    The tool can automatically generate reports that highlight potential memory issues, making it easier for developers to diagnose problems without deep manual inspection.
  • Open Source
    Being an open-source tool, it is freely available and benefits from community support, which can be advantageous for customization and troubleshooting.

Possible disadvantages of Eclipse Memory Analyzer

  • Steep Learning Curve
    The tool can be complex for new users to learn, as it requires understanding of Java memory management and heap dump analysis.
  • Performance Overheads
    Analyzing large heap dumps can be resource-intensive and time-consuming, potentially requiring significant computational power and memory.
  • Java-Specific
    The tool is designed specifically for Java applications, limiting its usability for developers working in other programming environments or languages.
  • GUI Limitations
    Some users find the graphical user interface to be less intuitive compared to other modern development tools, which can impact productivity.
  • Sparse Official Documentation
    While community support exists, the official documentation can be sparse and insufficient for solving complex issues or fully utilizing advanced features.

locust features and specs

  • Scalability
    Locust is designed to distribute the load tests across multiple machines, allowing for high scalability and the ability to simulate millions of users.
  • Python-based
    The tool is written in Python, which makes it highly flexible and suitable for those who are familiar with the language. You can write custom test scenarios easily.
  • Web-based UI
    Locust provides a user-friendly web-based interface that makes it easy to monitor and control the test execution in real-time.
  • Real-time monitoring
    During test execution, you get real-time statistics and charts that help in monitoring the performance and load.
  • Open-source
    Being an open-source tool, Locust allows for community contributions and is free to use, which helps in continuous improvement and support from the user base.

Possible disadvantages of locust

  • Setup Complexity
    Initial setup can be somewhat complex, especially for large scale or distributed tests. Requires experience with Python and potentially other infrastructure setups.
  • Resource Intensive
    Locust can be resource-intensive, requiring significant compute resources, particularly when simulating large numbers of users.
  • Steeper Learning Curve
    Despite its flexibility, the requirement to write test scenarios in Python may present a learning curve for users not familiar with programming.
  • Limited Protocol Support
    Primarily designed for HTTP/HTTPS protocols, Locust might not be suitable for load testing applications that use other protocols without additional customization.
  • Dependence on External Libraries
    While the use of Python offers flexibility, it also means that you might need to rely on external libraries and tools, which can introduce dependency management issues.

Eclipse Memory Analyzer videos

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Locust review - GTA Online guides

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Category Popularity

0-100% (relative to Eclipse Memory Analyzer and locust)

Eclipse Memory Analyzer

locust

Software Development
100 100%
Software Development
0% 0
Monitoring Tools
0 0%
Monitoring Tools
100% 100
Resource Profiling And Monitoring
100 100%
Resource Profiling And Monitoring
0% 0
Website Testing
0 0%
Website Testing
100% 100

User comments

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Social recommendations and mentions

Based on our record, locust seems to be a lot more popular than Eclipse Memory Analyzer. While we know about 61 links to locust, we've tracked only 1 mention of Eclipse Memory Analyzer. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Eclipse Memory Analyzer mentions (1)

  • Graph Data Fits in Memory
    Https://eclipse.dev/mat/ can handle very large graphs of objects using a similar approach. It also does implement some kind of paging, such that you do not have to load the complete graph into memory when running some of the graph algorithms. - Source: Hacker News / about 1 year ago

locust mentions (61)

  • 3 Types of Chaos Experiments and How To Run Them
    Use load testing tools like JMeter, Gatling, or Locust to simulate demand spikes and verify that your auto-scaling rules work as expected. This will ensure that your system can handle real-world traffic patterns. - Source: dev.to / about 1 month ago
  • Outgrowing Postgres: How to identify scale problems
    Use load testing tools like Apache JMeter, Gatling, or Locust to measure your application's throughput under various loads and compare it to historical data. - Source: dev.to / 4 months ago
  • Five Advanced Techniques to Improve Automated Testing by 50%
    You should also incorporate performance testing into your daily builds to catch performance regressions early. For this, you can use Locust for load testing. You can also implement performance budgets in your CI/CD pipeline. This will allow you to fail builds that don't meet performance criteria, ensuring performance doesn't degrade over time. - Source: dev.to / 5 months ago
  • Why your FastAPI (or Flask) App performs poorly with high loads
    These tests were done on GCP Cloud Run using a single processor, and 512M RAM machine, and we used Locust, an incredible tool (for Python, LoL). - Source: dev.to / 7 months ago
  • How MySQL Tuning Can Dramatically Improve WordPress Performance
    Our test duration was 2 days. To handle this longer testing period, we switched from BlazeMeter (max test duration of 20 minutes) to Locust, an open-source load-testing tool. - Source: dev.to / 11 months ago
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What are some alternatives?

When comparing Eclipse Memory Analyzer and locust, you can also consider the following products

VisualVM - VisualVM is a visual tool integrating several commandline JDK tools and lightweight profiling...

Apache JMeter - Apache JMeter™.

dotMemory - dotMemory allows users to analyze memory usage in a variety of .NET and .NET Core applications.

Loader.io - Loader.io is a simple cloud-based load testing service

JConsole - Provides information about performance and resource consumption for Java applications.

gatling.io - Gatling is an open-source load testing framework based on Scala, Akka and Netty

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