django-db-connection-pool 1.2.6

Database connection pool component library for Django

django-db-connection-pool

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MySQL & Oracle & PostgreSQL & JDBC (Oracle, OceanBase) connection pool components for Django, Be based on SQLAlchemy. Works fine in multiprocessing and multithreading django project.

• 中文版

Quickstart

Installation

Install with pip with all engines:

$ pip install django-db-connection-pool[all]

or select specific engines:

$ pip install django-db-connection-pool[mysql,oracle,postgresql,jdbc]

or one of mysql,oracle,postgresql,jdbc

$ pip install django-db-connection-pool[oracle]

Update settings.DATABASES

MySQL

change django.db.backends.mysql to dj_db_conn_pool.backends.mysql:

DATABASES = {
    'default': {
        'ENGINE': 'dj_db_conn_pool.backends.mysql'
    }
}

Oracle

change django.db.backends.oracle to dj_db_conn_pool.backends.oracle:

DATABASES = {
    'default': {
        'ENGINE': 'dj_db_conn_pool.backends.oracle'
    }
}

PostgreSQL

change django.db.backends.postgresql to dj_db_conn_pool.backends.postgresql:

DATABASES = {
    'default': {
        'ENGINE': 'dj_db_conn_pool.backends.postgresql'
    }
}

Pool options(optional)

you can provide additional options to pass to SQLAlchemy's pool creation, key's name is POOL_OPTIONS:

DATABASES = {
    'default': {
        'POOL_OPTIONS': {
            'POOL_SIZE': 10,
            'MAX_OVERFLOW': 10,
            'RECYCLE': 24 * 60 * 60
        }
    }
}

django-db-connection-pool has more configuration options here: PoolContainer.pool_default_params

Here's the explanation of these options(from SQLAlchemy's Doc):

• pool_size: The size of the pool to be maintained, defaults to 5. This is the largest number of connections that will be kept persistently in the pool. Note that the pool begins with no connections; once this number of connections is requested, that number of connections will remain. pool_size can be set to 0 to indicate no size limit; to disable pooling, use a :class:~sqlalchemy.pool.NullPool instead.

• max_overflow: The maximum overflow size of the pool. When the number of checked-out connections reaches the size set in pool_size, additional connections will be returned up to this limit. When those additional connections are returned to the pool, they are disconnected and discarded. It follows then that the total number of simultaneous connections the pool will allow is pool_size + max_overflow, and the total number of "sleeping" connections the pool will allow is pool_size. max_overflow can be set to -1 to indicate no overflow limit; no limit will be placed on the total number of concurrent connections. Defaults to 10.

• recycle: If set to a value other than -1, number of seconds between connection recycling, which means upon checkout, if this timeout is surpassed the connection will be closed and replaced with a newly opened connection. Defaults to -1.

Or, you can use dj_db_conn_pool.setup to change default arguments(for each pool's creation), before using database pool:

import dj_db_conn_pool

dj_db_conn_pool.setup(pool_size=100, max_overflow=50)

multiprocessing environment

In a multiprocessing environment, such as uWSGI, each process will have its own dj_db_conn_pool.core:pool_container object, It means that each process has an independent connection pool, for example: The POOL_OPTIONS configuration of database db1 is{ 'POOL_SIZE': 10, 'MAX_OVERFLOW': 20 }, If uWSGI starts 8 worker processes, then the total connection pool size of db1 is 8 * 10, The maximum number of connections will not exceed 8 * 10 + 8 * 20

JDBC

Thanks to JPype, django-db-connection-pool can connect to database by jdbc

Usage

Set Java runtime environment

export JAVA_HOME=$PATH_TO_JRE;
export CLASSPATH=$PATH_RO_JDBC_DRIVER_JAR

Update settings.DATABASES

Oracle

change django.db.backends.oracle to dj_db_conn_pool.backends.jdbc.oracle:

DATABASES = {
    'default': {
        'ENGINE': 'dj_db_conn_pool.backends.jdbc.oracle'
    }
}

OceanBase

use dj_db_conn_pool.backends.jdbc.oceanbase:

DATABASES = {
    'default': {
        'ENGINE': 'dj_db_conn_pool.backends.jdbc.oceanbase'
    }
}

Performing raw SQL queries

Just like django's built-in backends, all JDBC backends support named parameters in raw SQL queries, you can execute raw sql queries like this:

from django.db import connections

with connections["default"].cursor() as cursor:
    cursor.execute('select name, phone from users where name = %(name)s', params={"name": "Altair"})
    result = cursor.fetchall()

Acknowledgments

• Thanks to all friends who provided PR and suggestions !
• Thanks to JetBrains for providing development tools for django-db-connection-pool !