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Configuration

Backend Configuration

Configure your task backend in Django settings using the TASKS dictionary.

Database Backend

INSTALLED_APPS = [
    # ...
    "django_vtasks",
    "django_vtasks.db",  # Required for Database backend
]

TASKS = {
    "default": {
        "BACKEND": "django_vtasks.backends.db.DatabaseTaskBackend",
    }
}

Database Compatibility

The Database backend relies on SELECT ... FOR UPDATE SKIP LOCKED for efficient parallel processing. SQLite and older MySQL versions don't support this, limiting them to one worker at a time.

Valkey Backend

INSTALLED_APPS = [
    # ...
    "django_vtasks",
]

TASKS = {
    "default": {
        "BACKEND": "django_vtasks.backends.valkey.ValkeyTaskBackend",
        "OPTIONS": {
            "BROKER_URL": "valkey://localhost:6379/0",
            # Optional: Timeout for blocking operations (default: 1.0)
            "BLOCKING_TIMEOUT": 1.0,
        }
    }
}

BLOCKING_TIMEOUT is the maximum wait time when queues are idle. Tasks that arrive are processed immediately regardless of this value.

  • Production: 1.0 second (default) - only 1 Redis request per second per worker when idle
  • Testing: Use 0.1 seconds for faster test execution

Shared Cache Connection

If you use a compatible cache backend like django-vcache, share connections to minimize resource usage:

CACHES = {
    "default": {
        "BACKEND": "django_vcache.backend.ValkeyCache",
        "LOCATION": "valkey://localhost:6379/1",
    },
}

TASKS = {
    "default": {
        "BACKEND": "django_vtasks.backends.valkey.ValkeyTaskBackend",
        "OPTIONS": {
            "cache_alias": "default",
        }
    }
}

Shared Connection Pool

For applications using valkey-py directly, share an existing connection pool:

import valkey.asyncio as valkey

MY_APP_VALKEY_POOL = valkey.ConnectionPool.from_url("valkey://localhost:6379/0")

TASKS = {
    "default": {
        "BACKEND": "django_vtasks.backends.valkey.ValkeyTaskBackend",
        "OPTIONS": {
            "CONNECTION_POOL": MY_APP_VALKEY_POOL,
            # Still required for synchronous operations like task.enqueue()
            "BROKER_URL": "valkey://localhost:6379/0",
        }
    }
}

Settings Reference

Setting Default Description
VTASKS_QUEUES ["default"] Queues the worker processes — a list of names, or a dict of name → options (worker_concurrency, batch). See Queue configuration
VTASKS_CONCURRENCY 20 Global per-worker concurrency: default per-queue limit and the shared pool for queues without their own worker_concurrency
VTASKS_RUN_SCHEDULER True Whether to run the scheduler when requested
VTASKS_SCHEDULE {} Periodic task schedules
VTASKS_COMPRESS_THRESHOLD 1024 Bytes threshold for Zstandard compression
VTASKS_DLQ_CAP 1000 Maximum failed tasks in Dead Letter Queue
VTASKS_VALKEY_PREFIX "vt" Prefix for Valkey keys (namespace isolation)
VTASKS_METRICS_PORT None Port for Prometheus metrics (standalone workers)
VTASKS_HEALTH_CHECK_FILE None Path to file touched for liveness probes
VTASKS_WORKER_ID None Custom worker ID (defaults to hostname:pid)
VTASKS_BACKEND "default" The alias in TASKS to use for the worker

Worker Command Arguments

Most arguments can also be set via environment variables:

Argument Environment Variable Django Setting
--concurrency VTASKS_CONCURRENCY VTASKS_CONCURRENCY
--backend VTASKS_BACKEND VTASKS_BACKEND
--id VTASKS_WORKER_ID VTASKS_WORKER_ID
--health-check-file VTASKS_HEALTH_CHECK_FILE VTASKS_HEALTH_CHECK_FILE
--metrics-port VTASKS_METRICS_PORT VTASKS_METRICS_PORT

Queue Configuration

VTASKS_QUEUES declares which queues a worker consumes. It accepts either a list of names or a dict mapping each name to a per-queue options dict:

All concurrency in vtasks is per-worker (per-process). A limit of N means up to N at once in each worker; the cluster-wide ceiling is N times your worker (pod) count.

VTASKS_CONCURRENCY = 50              # global pool / default per-queue limit, per worker

VTASKS_QUEUES = {
    "default": {},                              # shares the global pool
    "cold_storage": {"worker_concurrency": 3},  # its own cap: 3 at once per worker
    "emails": {"batch": {"count": 100, "timeout": 5.0}},
}

# the simple list form still works (all queues share the global pool):
# VTASKS_QUEUES = ["default", "cold_storage"]

Per-queue options (unknown keys raise ImproperlyConfigured so typos can't silently drop a cap):

  • worker_concurrency (int) — give this queue its own dedicated semaphore of that size, per worker. Queues without it share the global VTASKS_CONCURRENCY pool.
  • batch ({"count", "timeout"}) — process this queue in batches: collect up to count tasks, waiting at most timeout seconds, then hand them to the task as a list.

Per-queue concurrency

VTASKS_CONCURRENCY alone is a single global pool shared by every queue a worker consumes — ideal for cheap I/O-bound tasks. But a handful of heavy CPU/RAM-bound tasks (analytics, image processing, data exports) at that same concurrency can exhaust memory or a connection pool. A queue's own worker_concurrency isolates it:

  • A queue with worker_concurrency gets its own semaphore; queues without one keep sharing the global pool, so a saturated capped queue cannot starve the rest.
  • A worker's maximum concurrency is VTASKS_CONCURRENCY plus the sum of the per-queue overrides it consumes; the connection-isolation lane pool is sized to match.
  • Every limit is per-worker (per-process) — the right scope for bounding per-pod resources like memory or database connections. The fleet-wide ceiling is the limit times your worker count. The worker_ prefix in the key name is a deliberate reminder of this scope at the point where you set it.

Batch processing

Declare batch per queue (see example above). Tasks on a batch queue are collected and delivered as a list — see the Guide.

Periodic Task Configuration

Define scheduled tasks in VTASKS_SCHEDULE:

from django_vtasks.scheduler import crontab

VTASKS_SCHEDULE = {
    "daily_report": {
        "task": "myapp.tasks.generate_report",
        "schedule": crontab(hour=5, minute=0),
    },
    "hourly_cleanup": {
        "task": "myapp.tasks.cleanup",
        "schedule": 3600,  # Every hour (in seconds)
    },
}