Skip to content

connect-web/performance-hs-db

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

29 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

performance-hs-db

the objective of this repostory is to test the performance of database schema design & queries.

requirements

Setup

  1. Create fork of primary repo
  2. Download a clone of the forked repo git clone <your github repo path url>
  3. Open a terminal to your cloned repo
  4. follow linux sections for further setup

Linux

Install make, venv, pip

in WSL or debian based distributions

sudo apt install make
sudo apt install python3-venv
sudo apt install python3-pip

What is Make

use the make command with an action when developing on linux. think of actions as a list of predefined commands to help simplify common development routines. view avaiable actions make help to see all avaiable actions

What is python pip

pip is the package repository for python, and allows us to download (opensource) packages.

What is venv

venv or virtual environment allow us to create a virtual environment for python, this way we do not have conflicting packages with linux or other code running on your device.

Repository structure

example structure

├── src
│   ├── __init__.py
│   ├── database.py
│   ├── datamodel.py
│   ├── interface.py
│   ├── example
│   │   ├── main.py
│   │   └── mysql
│   │       ├── Dockerfile
│   │       └── docker-entrypoint-initdb.d
│   │           ├── 00_init.sql
│   │           └── 01_custom.sql
├── tests/
│   ├──__init__.py
│   ├── conftest.py
│   ├──test_example.py
  • in src/ you'll find most of the code
  • in tests/ you'll find the code for the tests
  • src/database you'll find the database, engine en session factory, used to connect to the database
  • src/datamodel you'll find the datamodel that you will receive and are expected to return in the benchmark
  • src/interface you'll find the abstract base class with the functions that you need to implement and that will be tested.
  • src/example/ this is an example benchmark implementation, here you'll find the code that you need to modify for your benchmark implementation.
  • src/example/main.py this is an example benchmark implementation, of the interface.
  • src/example/mysql/ this is the directory with all the code for the mysql database used in your benchmark
  • src/example/mysql/Dockerfile the dockerfile used in the example implementation, you should not change this
  • src/example/mysql/docker-entrypoint-initdb.d/ the init directory is run on initialization of the database, sql files will be run in order that you see in the directory
  • src/example/mysql/docker-entrypoint-initdb.d/00_init.sql the first sql file that will be run during initialization of the database, this will create the database schema playerdata you should not change this.
  • src/example/mysql/docker-entrypoint-initdb.d/01_custom.sql, the second sql file that will be run, please modify this file with a unique schema that you think is more performant.

setting up your own benchmark implementation

  1. copy the src/example to your desired contributions name cp -r src/example src/<name>
  2. create your schema in src/<>/mysql/01_custom.sql
  3. implement your schema with our interface BenchmarkABC
    • please consider only using the sqlalchemy query builder or raw sql with parameters.
  4. battle for insert speed, query speed, database size

running your database

this will first remove any running database if you had one, than spin up a new database

make docker-restart

testing your database

this will first remove any running database if you had one, than spin up a new database, to finally run pytest -s tests/

make docker-test

running a benchmark

this will first remove any running database if you had one, than spin up a new database, to finally run python3 performance_test/main.py

make docker-benchmark

About

testing database schema's for Mysql

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 85.5%
  • Jupyter Notebook 10.1%
  • Makefile 4.3%
  • Dockerfile 0.1%