django-vcache 0.1.1

A specialized, lightweight Django cache backend for Valkey.

django-vcache

A very fast Django cache backend for Valkey (and Redis). This async + sync backend is designed to be resource-efficient.

It powers the GlitchTip open-source error tracking platform.

Why django-vcache?

• Zero "Sync-to-Async" Overhead: Native implementations for both Sync (get) and Async (aget) methods. No thread-switching wrappers.
• Connection Efficiency: Maintains at most two client instances (one Sync, one Async) per configured backend.
• Lazy Loading: Connections are established only when a command is issued, keeping startup time instant and memory usage low.
• Raw Access: Easily borrow the underlying valkey-py client for advanced operations (locking, pipelines, custom data structures) without spinning up new connections. Use the existing client with django-vtask.
• Opinionated and Fast - We use libvalkey and focus on simplicity and speed over every possible use case. Stop thinking about which parser class to use and write your fast application.

Installation

pip install django-vcache

Usage

Update your settings.py to configure the cache backend:

CACHES = {
    "default": {
        "BACKEND": "django_vcache.backend.ValkeyCache",
        "LOCATION": "valkey://your-valkey-host:6379/1",
        "OPTIONS": {
            "max_connections": 200,  # Example: limit the number of connections in the pool
            "connection_pool_timeout": 5, # Example: time to wait for a connection before raising an error
            "socket_connect_timeout": 5,  # Example: set a connection timeout
            "retry_on_timeout": True,     # Example: enable retry on timeout
        }
    },
}

The max_connections and connection_pool_timeout options enable sensible blocking behavior. When max_connections is reached, subsequent requests for a connection will wait for up to connection_pool_timeout seconds for a connection to become available before raising an error. It is recommended to set values for these options to prevent connection exhaustion.

You can then use Django's cache framework as usual:

from django.core.cache import cache

cache.set('my_key', 'my_value', 30)
value = cache.get('my_key')

To access the underlying raw valkey-py client instance, you can use the get_raw_client method:

# Get the synchronous client
sync_client = cache.get_raw_client()

# Get the asynchronous client
async_client = cache.get_raw_client(async_client=True)

Async usage

You must use an ASGI server to run the asynchronous client. For example, you can use granian or uvicorn. This is due to limitations in the valkey-py library. aget will not run reliably on a WSGI server.

Example equivalent of Django runserver: granian --interface asgi --host 0.0.0.0 --port 8000 sample.asgi:application --reload

Contributing

Development Environment

This project uses Docker for development. To get started:

1. Clone the repository.

2. Build and start the services:

   docker compose up -d --build
   

This will start a Valkey container and an app container with the Django sample project running on http://localhost:8000. The development server uses granian with auto-reload, so changes you make to the code will be reflected automatically.

Using Valkey Sentinel

To run the development environment with Valkey Sentinel enabled, use the override compose file:

docker compose -f compose.yml -f compose.sentinel.yml up -d --build

You will also need to configure your sample/settings.py to use the Sentinel URL. The recommended way is to set the VALKEY_URL environment variable before starting the services:

export VALKEY_URL="sentinel://localhost:26379/mymaster/1"

The application will then be available at http://localhost:8000.

Using Valkey Cluster

To use django-vcache with a Valkey Cluster, set the CLUSTER_MODE option to True in your cache configuration. The LOCATION should point to one of the cluster's nodes; valkey-py will automatically discover the rest of the cluster nodes.

CACHES = {
    "default": {
        "BACKEND": "django_vcache.backend.ValkeyCache",
        "LOCATION": "valkey://your-cluster-node-1:6379/1",
        "OPTIONS": {
            "CLUSTER_MODE": True,
            "socket_connect_timeout": 5,
            "retry_on_timeout": True,
        }
    },
}

Note that distributed locking (via cache.lock() and cache.alock()) is not supported when CLUSTER_MODE is enabled, as this functionality is not provided by the underlying valkey-py library in cluster environments. Attempting to use these methods will raise a NotImplementedError.

To run the development environment with Valkey Cluster enabled, use the override compose file and environment variables:

docker compose -f compose.yml -f compose.cluster.yml up -d --build \
    -e VALKEY_URL='valkey://valkey-1:6379/1' \
    -e VALKEY_CLUSTER_MODE='true'

The application will then be available at http://localhost:8000.

Running Tests

To run the test suite, execute the following command:

docker compose run --rm app bash -c "python sample/manage.py test"

Credits

Inspired by the excellent work of django-valkey and django-redis, but re-architected for strict resource efficiency and modern async/sync hybrid stacks.