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Parrot QA VS locust

Compare Parrot QA VS locust and see what are their differences

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Parrot QA logo Parrot QA

Automate your bug testing without writing a line of code.

locust logo locust

An open source load testing tool written in Python.
  • Parrot QA Landing page
    Landing page //
    2023-01-18
  • locust Landing page
    Landing page //
    2021-10-11

Parrot QA features and specs

  • Ease of Use
    Parrot QA provides a user-friendly interface that makes it easy for users, even non-technical ones, to create and run tests without requiring extensive programming knowledge.
  • Automated Testing
    The platform offers automated testing capabilities, allowing users to efficiently run tests and identify bugs without manual intervention, saving time and reducing human error.
  • Integration Capabilities
    Parrot QA integrates smoothly with various development tools and platforms, enhancing its usability within existing workflows and making it easier to incorporate into the software development life cycle.
  • Comprehensive Test Coverage
    It allows for testing of web applications across multiple browsers and devices, ensuring comprehensive coverage and helping to identify issues that could affect a diverse user base.

Possible disadvantages of Parrot QA

  • Cost
    For smaller teams or startups, the pricing of Parrot QA can be a concern as it might not fit into tight budgets, especially compared to some open-source alternatives.
  • Limited Customization
    While Parrot QA is designed to be straightforward, its customization options are limited compared to more advanced testing tools, which might be a drawback for users needing highly tailored test scenarios.
  • Learning Curve
    Despite its ease of use, there might still be a learning curve for users new to automated testing tools, requiring time to adapt and understand the best practices for using Parrot QA effectively.
  • Dependency on Internet Connection
    As a cloud-based solution, Parrot QA requires a reliable internet connection, which could be a limitation for users operating in areas with unstable connectivity.

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.

Analysis of locust

Overall verdict

  • Locust is a powerful and flexible tool for load testing, particularly advantageous for teams familiar with Python. Its scalability and ease of setup make it a strong choice for both small and large projects.

Why this product is good

  • Locust (locust.io) is considered a good tool for load testing due to its easy-to-use, scalable, and distributed nature. Written in Python, it allows developers to write simple or complex test scenarios in the same language. It enables the simulation of millions of users by distributing tasks across multiple machines, making it highly valuable for performance testing of websites and applications. The web-based user interface is another advantage, allowing real-time monitoring of test progress and results.

Recommended for

  • Development teams looking for a scalable load testing tool.
  • Organizations that prefer open-source solutions.
  • Projects requiring custom test scenarios in Python.
  • Teams needing real-time monitoring and distributed testing capabilities.

Parrot QA videos

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locust videos

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Locust review - GTA Online guides

More videos:

  • Review - GTA Online: Ocelot Locust Review
  • Review - GTA 5 - DLC Vehicle Customization - Ocelot Locust and Review

Category Popularity

0-100% (relative to Parrot QA and locust)

Parrot QA

locust

QA
100 100%
QA
0% 0
Monitoring Tools
0 0%
Monitoring Tools
100% 100
Development
100 100%
Development
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 more popular. It has been mentiond 64 times since March 2021. 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.

Parrot QA mentions (0)

We have not tracked any mentions of Parrot QA yet. Tracking of Parrot QA recommendations started around Mar 2021.

locust mentions (64)

  • Small-Scale Chaos Testing: The Missing Step Before Production
    Locust: While primarily a load testing tool, it can be used to simulate user behavior under stress. - Source: dev.to / 3 days ago
  • Log Spikes? No Sweat: How Top DevOps Teams Tame Bursty Workloads
    But you don’t have to operate at Netflix’s scale to benefit from the same mindset. Effective teams simulate log floods during load tests, which push traffic through staging environments while tracking how ingestion, indexing, and alerting respond to the increased load. Tools like Grafana’s k6 and Locust can simulate thousands of requests per second, while synthetic log generators mimic bursty error scenarios. - Source: dev.to / 3 months ago
  • Serving 200M requests per day with a CGI-bin
    I mean honestly - the "classic" Apache model of throwing things into the www root is very strong for rapid development. Hot code reloading is sometimes finicky, you can end up with unexpected hidden state and lose sanity over a stupid heisenbug. Trust me. IMO you don't need to compensate for bad configs if you're using a proper staging environment and push-button deployments (which is good practice regardless of... - Source: Hacker News / 3 months ago
  • 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 / 5 months 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 / 8 months ago
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What are some alternatives?

When comparing Parrot QA and locust, you can also consider the following products

RainforestQA - Insanely simple testing. Create tests for your website in plain English, then run them across all major browsers with a single click. Powered by human intelligence

Apache JMeter - Apache JMeter™.

Bitrise - Tens of thousands of agencies, startups and enterprise companies with mobile apps - including Runkeeper, Grindr, Duolingo and more - use Bitrise to automate their way to increased productivity & speed

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

Cypress.io - Slow, difficult and unreliable testing for anything that runs in a browser. Install Cypress in seconds and take the pain out of front-end testing.

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

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