Locust vs Cicada Shell Comparison | StackShare | StackShare

Cicada vs Locust

Overview

Locust

Stacks174

Followers317

Votes51

GitHub Stars27.0K

Forks3.1K

Cicada Shell

Stacks3

Followers12

Votes0

GitHub Stars995

Forks50

Cicada vs Locust: What are the differences?

What is Cicada? A Unix shell written in Rust. It is a simple bash-like Unix shell written in Rust.

What is Locust? Define user behaviour with Python code, and swarm your system with millions of simultaneous users. Locust is an easy-to-use, distributed, user load testing tool. Intended for load testing web sites (or other systems) and figuring out how many concurrent users a system can handle.

Cicada and Locust are primarily classified as "Shells" and "Load and Performance Testing" tools respectively.

Cicada and Locust are both open source tools. It seems that Locust with 10.6K GitHub stars and 1.53K forks on GitHub has more adoption than Cicada with 696 GitHub stars and 32 GitHub forks.

Advice on Locust, Cicada Shell

Vrashab

QA at Altair

Jun 23, 2020

Needs adviceon

Gatling

Locust

Flood IO

I have to run a multi-user load test and have test scripts developed in Gatling and Locust.

I am planning to run the tests with Flood IO, as it allows us to create a custom grid. They support Gatling. Did anyone try Locust tests? I would prefer not to use multiple infra providers for running these tests!

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Comments

Detailed Comparison

Locust	Cicada Shell
Locust is an easy-to-use, distributed, user load testing tool. Intended for load testing web sites (or other systems) and figuring out how many concurrent users a system can handle.	It is a simple bash-like Unix shell written in Rust.
Define user behaviour in code;Distributed & scalable;Proven & battle tested	simple; powerful
Statistics
GitHub Stars 27.0K	GitHub Stars 995
GitHub Forks 3.1K	GitHub Forks 50
Stacks 174	Stacks 3
Followers 317	Followers 12
Votes 51	Votes 0
Pros & Cons
Pros 15 Hackable 11 Supports distributed 7 Open source 6 Easy to setup 6 Easy to use Cons 1 Bad design	No community feedback yet
Integrations
Python	Linux Rust macOS

What are some alternatives to Locust, Cicada Shell?

k6

It is a developer centric open source load testing tool for testing the performance of your backend infrastructure. It’s built with Go and JavaScript to integrate well into your development workflow.

Gatling

Gatling is a highly capable load testing tool. It is designed for ease of use, maintainability and high performance. Out of the box, Gatling comes with excellent support of the HTTP protocol that makes it a tool of choice for load testing any HTTP server. As the core engine is actually protocol agnostic, it is perfectly possible to implement support for other protocols. For example, Gatling currently also ships JMS support.

Loader.io

Loader.io is a free load testing service that allows you to stress test your web-apps/apis with thousands of concurrent connections.

BlazeMeter

Simulate any user scenario for webapps, websites, mobile apps or web services. 100% Apache JMeter compatible. Scalable from 1 to 1,000,000+ concurrent users.<br>

Apache JMeter

It is open source software, a 100% pure Java application designed to load test functional behavior and measure performance. It was originally designed for testing Web Applications but has since expanded to other test functions.

GNU Bash

The Bourne Again SHell is an sh-compatible shell that incorporates useful features from the Korn shell (ksh) and C shell (csh). It is intended to conform to the IEEE POSIX P1003.2/ISO 9945.2 Shell and Tools standard.

RedLine13

It is a load testing platform that brings the low cost power of the cloud to JMeter and other open source load testing tools.

AWS Device Farm

Run tests across a large selection of physical devices in parallel from various manufacturers with varying hardware, OS versions and form factors.

Flood IO

Performance testing with Flood increases customer satisfaction and confidence in your production apps and reduces business risk.

IPython

It provides a rich architecture for interactive computing with a powerful interactive shell, a kernel for Jupyter. It has a support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing.