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Showing content from https://github.com/Jibola/django-asv below:

Jibola/django-asv: Benchmarks for Django using asv

This repository contains the benchmarks for measuring Django's performance over time.

The benchmarking process is carried out by the benchmarking tool airspeed velocity and the results can be viewed here

Conda is being used to run the benchmarks against different versions of python

If you already have conda or miniconda installed,you can run the benchmarks by using the commands

to run the benchmarks against the latest commit.

If you do not have conda or miniconda installed, change the contents of the file asv.conf.json as follows to use virutalenv to run the benchmarks

{
    "version": 1,
    "project": "Django",
    "project_url": "https://www.djangoproject.com/",
    "repo": "https://github.com/django/django.git",
    "branches": ["main"],
    "environment_type": "virtualenv",
    "show_commit_url": "http://github.com/django/django/commit/",
}

and run the benchmarks using the commands

Note: ASV prompts you to set a machine name on the first run, please do not set it to 'ubuntu-22.04', 'windows-2022' or 'macos-12' as the results for the machines with these names are currently being stored in the repository

Benchmarking results of differnt branches can be compared using the following method

asv run <commit1 SHA or branch1 name>
asv run <commit2 SHA or branch2 name>
asv compare <commit1 SHA or branch name> <commit2 SHA or branch name>

• Fork this repository and create a new branch

• Install pre-commit and run pre-commit install to install pre-commit hooks which will be used to format the code

• Create a new directory with the name benchmark_name under the appropriate category of benchmarks

• Add the files __init__.py and benchmark.py to the directory

• Add the directory to the list of INSTALLLED_APPS in settings.py

• Use the following format to write your benchmark in the file benchmark.py

      from ...utils import bench_setup()
  
      class BenchmarkClass:
  
          def setup():
              bench_setup()
              # if your benchmark makes use of models then use
              # bench_setup(migrate=True)
              ...
  
          def time_benchmark_name():
              ...

• Commit changes and create a pull request

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