Best Benchmarking Libraries

sohaibfarooqi/hierarchical-data

Benchmarking different methodologies in retrieving hierarchical data using Flask, SQLAlchemy and Postgres

alan-turing-institute/AnnotateChange

1910<> Python

A simple flask application to collect annotations for the Turing Change Point Dataset, a benchmark dataset for change point detection algorithms

sumitsk20/backend_server_benchmark

100<> Python

Backend Server and frameworks comparison.

go-outside-labs/flask

80<> Python

👾 𝗙𝗹𝗮𝘀𝗸: 𝗯𝗼𝗶𝗹𝗲𝗿𝗽𝗹𝗮𝘁𝗲𝘀 𝗮𝗻𝗱 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀.

mitar/meteor-web-rendering-framework-benchmark

83<> JavaScript

Benchmark for web rendering frameworks available in Meteor.

cv-stuttgart/springwebsite

50<> Python

Website code of Spring benchmark

DrewJay/WASM-benchmark

40<> JavaScript

Benchmarking tool comparing WASM and JS performance in the browser.

thibaudcolas/bakerydemo-gold-benchmark

34<> Python

Reference Wagtail site implementation for energy usage benchmarking.

tushar5526/python-framework-benchmarks

32<> Python

Quickly Benchmark different python frameworks on your machine!

dnutiu/benchmark-scoreboard

30<> Python

A simple Flask app which displays a scoreboard for benchmark results.

archatas/primary_keys_benchmark

22<> Python

Primary Keys Benchmark for Django Projects

c-w/python-loadtests

20<> Python

Performance benchmarks and setup-complexity comparison for Sanic, Flask, Connexion and Django

AgRenaud/benchmark-py-api

10<> Python

This project provides code to install different configurations for web applications written in python as well as script to run a benchmark on those configuration.

flix477/django-criterion

10<> Python

Django benchmarking and queries analysis tool

Sukhrobjon/CS-1.2-TweetGeneratorSG

00<> Python

A project based course that looks under the hood at data structures and algorithms to see how they work. In addition to implementing these structures in an application; students will build them from scratch, analyze their complexity, and benchmark their performance to gain an understanding of their tradeoffs and when to use them in practice. Students will write scripts, functions, and library modules to use text processing tools like regular expressions, construct and sample probability distributions to create a Markov language model and gain insight into how grammar works and natural language processing techniques.