Django Queues

This is an implementation of message queues for Django.

The current implementation supports an in-memory queue and a Redis-backed pub/sub queue with custom Lua for atomic updates.

Requirements

django-queues requires Python 3.14 or later, as queue entry IDs use the standard-library UUIDv7 implementation to support ordering introduced in Python 3.14.

Choose a queue type

Choose the semantic queue type first; choose its memory or Redis backend second.

Choose	Use it for	Classes	Consumer model	Retention
Async queue	Work that runs later and whose progress or outcome must be inspectable	MemoryAsyncQueue, RedisAsyncQueue, and their stack/priority variants	An async HANDLER, normally run by manage.py runqueues	A durable lifecycle: queued, running, then succeeded, failed, or timeout until pruning
Event queue	Short-lived notifications delivered to one or more local listeners	MemoryEventQueue, RedisEventQueue	@queue_listener; Django starts the queue runtime once at process startup when at least one queue is configured	Consumed, retried, or expired; no durable outcome record

Async queues are the correct choice when a producer needs to determine a result, observe lifecycle progress, or retain completed work temporarily.

Event queues are for streaming data in "fan-out" fashion to one or more consumers, and no result needs to be retrieved afterwards.

Backend choices

Currently only two:

• Memory queues exist only while the application process runs. Configured MemoryAsyncQueue instances are local to the resolving process and thread; MemoryEventQueue is process-scoped.
• Redis queues are shared and persistent. Use them when producers and consumers run in different processes, containers, or hosts. Install support with pip install "django-queues[redis]".

FIFO, LIFO stack, and priority ordering are available for async queues. Event queues use their selected backend's transient delivery semantics.

Configuration

Queues are configured in the Django settings module, and use a simple and familiar configuration format like DATABASES and CACHES.

Async queues

An async queue is a good choice for task monitoring or collecting results from code that has been handed off to another thread, process, or computer.

An async queue used only by producers is configured as follows:

QUEUES = {
    "default": {
        "BACKEND": "django_queue.backends.redis.RedisAsyncQueueJson",
        "LOCATION": f"redis://localhost:6379/12",
        "maxsize": 64,
    },
}

This configures a Redis-backed FIFO async queue with JSON values. Redis-backed queues own their asynchronous connections; application code does not supply Redis client instances.

To implement a stack (FILO), use django_queue.backends.redis.RedisAsyncStackJson, or add "stack": True.

All aliases are validated and initialised when Django starts. Application code can retrieve a configured queue through queues["alias"]; initialisation only constructs queue services and never starts a worker. Queue aliases cannot contain *, ?, [ or ].

Configuration reference

The alias is the queue's stable application identity. It is the key in QUEUES, not a separate setting; queue_name is not a supported option.

Setting	Applies to	Meaning
BACKEND	All queues; required	Dotted class path for the queue backend. It selects both the semantic kind (AsyncQueue or EventQueue) and storage provider.
LOCATION	All queues	Backend location. Redis backends require a Redis URL such as redis://localhost:6379/12; memory backends ignore it and may omit it.
HANDLER	Async queues only	Dotted path to the async callable that handles entries. Its presence opts that alias into manage.py runqueues; it is not passed to the backend. Event queues reject it because they use listeners.
WORKER	Optional	Compatible concrete worker class or dotted class path. Omit it to use the backend's default; Redis and memory workers are provider-specific.
ENTRY_CLASS	Optional	QueueEntry subclass or dotted class path used for queue entries. It defaults to QueueEntry; extra fields must be JSON-serialisable.
TIMEOUT	All queues	For an async queue, the default execution budget for its handlers (600 seconds when unset). For an event queue, the unclaimed event lifetime (60 seconds when unset). An entry-specific timeout_seconds takes precedence.
RETENTION_TIMEOUT	Async queues only	Terminal-record retention in seconds. Defaults to 600; set to None to disable automatic cleanup. Event queues do not retain terminal records.

Built-in backend options are deliberately small:

Option	Applies to	Meaning
maxsize	Memory and Redis raw-value operations	Maximum number of values accepted by add; 0 (the default) is unbounded.
stack	Redis queues and memory async queues	Use LIFO ordering instead of FIFO. Prefer the explicit RedisAsyncStack backend where one exists.
encoding	Redis queues	Python codec used for raw Redis values; defaults to UTF-8.

Custom backends may document additional options. Queue metadata (HANDLER, WORKER, ENTRY_CLASS, TIMEOUT, and RETENTION_TIMEOUT) is consumed by Django Queue and is never forwarded to a backend constructor.

Event queues

MemoryEventQueue and RedisEventQueue deliver short-lived events to local listeners instead of retaining async-work outcomes. Configure one explicitly, then register one or more listeners in application code:

# settings.py
QUEUES = {
    "events": {
        "BACKEND": "django_queue.backends.redis.RedisEventQueue",
        "LOCATION": "redis://localhost:6379/12",
    },
}


# myproject/listeners.py
from django_queue import queue_listener


@queue_listener("events")
async def send_notification(entry):
    await notify(entry.payload)
    return True

An eligible listener returning True consumes and removes the event. Returning False logs a rejection and also removes it; returning None lets the next listener see it. If every listener passes, or a filter/listener raises, the event is released for a short delayed retry. Events expire unconsumed after an entry-specific timeout_seconds, the queue TIMEOUT, or 60 seconds by default. They never acquire a task result or terminal entry record.

Django starts one process-local queue runtime once, at process startup, when QUEUES is non-empty. It owns one background thread and one asyncio loop, shared by every configured event queue's worker task and every observed async queue's Redis receiver task. An alias may set WORKER to an event-worker subclass compatible with the selected backend: memory event queues require a subclass of their memory-aware worker and Redis event queues require a subclass of their Redis-aware worker. Async-queue HANDLER metadata is invalid because listeners provide event dispatch. Memory event queues are local to that process. Redis workers use claims so processes compete for one active delivery, but ordering remains indeterminate across multiple listeners, processes, or retries. Use one listener in one process when strict ordering is required. An active Redis listener renews its claim while it runs; if its worker stops before settling the event, a later dispatcher recovers the expired claim for redelivery. The runtime retries an event dispatcher that stops from an infrastructure failure with bounded backoff.

Async queue handlers and extensions

Each alias may optionally choose the concrete worker and entry types it uses:

QUEUES = {
    "requests": {
        "BACKEND": "django_queue.backends.redis.RedisAsyncQueue",
        "LOCATION": "redis://redis:6379/12",
        "HANDLER": "myproject.queue_handlers.process_request",
        "WORKER": "myproject.workers.RequestWorker",
        "ENTRY_CLASS": "myproject.entries.RequestEntry",
    },
}

HANDLER must resolve to an asynchronous callable. runqueues imports each configured handler at startup, waits until that queue has work, then creates its configured worker and passes the handler to it. A queue without HANDLER remains producer-only until application code supplies another worker.

WORKER and ENTRY_CLASS each accept either a class object or a dotted import path. Each backend selects a provider-compatible default worker: memory async and event queues use memory-aware workers, while Redis async and event queues use Redis-aware workers that manage transport delivery internally. AsyncQueueWorker and EventQueueWorker are orchestration bases, not default workers for every backend. A configured async-queue worker must be compatible with its backend's selected worker type and use the normal queue-lookup and handler-mapping constructor. A queue constructs its worker with its own clock, so a subclass that overrides __init__ must accept a clock keyword and pass it to super().__init__, or accept **kwargs and forward them. Django validates and imports entry and worker types during queue configuration. A worker is constructed only when its queue first becomes active; an entry only when it is enqueued, restored, or updated.

RETENTION_TIMEOUT controls how long terminal entry records remain available. A running worker removes expired terminal records during its normal loop. prune(entry_id) and await aprune(entry_id) remove one terminal record immediately.

Custom queue backends that support identified entry dispatch must implement has_pending(), returning whether dequeue() can immediately return an entry. They must also implement aprune() and _aprune_expired(): pruning rejects non-terminal entries, removes the durable record, and publishes an observer-only terminated snapshot. Workers publish an entry's initial lifecycle snapshot when they first observe it. Custom backends that emit Django's entry_enqueued signal must call send_entry_enqueued() after durable enqueue; that signal is separate from lifecycle observation. Built-in backends expose queue_name, their stable entry namespace.

Usage

Within an application, data is added to the queue by using the add method:

Example

from django_queue import queue
...
   queue.add({"some": "object", "with": "values"})
...

Priority queues require slightly different handling in that a priority should be set to determine the order in which messages are consumed and when added should be done as a (priority, value) tuple:

from django_queue import queue
...
   queue.add((10, {"some": "object", "with": "values"}))
...

Multiple values can be added in the one add() call if required.

Instants

ClockTime is how this package names a point in time: an immutable value holding whole seconds and microseconds since the Unix epoch. It exists so an instant and a duration cannot be confused — a duration is expressed as a plain count of seconds.

from datetime import UTC, datetime

from django_queue import ClockTime

moment = datetime(2026, 8, 3, 23, 33, 20, 250_000, tzinfo=UTC)

instant = ClockTime.from_timestamp(1785800000.25)  # a count of seconds
instant = ClockTime.from_timeval(1785800000, 250_000)  # a Redis TIME pair
instant = ClockTime.from_datetime(moment)  # a timezone-aware datetime

instant.to_timestamp()  # 1785800000.25, the durable form
instant.to_datetime()  # an aware UTC datetime, for calendar work

Instants compare and order chronologically. Subtracting one from another gives the seconds between them, and adding or subtracting a count of seconds gives another instant, with the duration on either side:

elapsed = finished - started  # a float count of seconds
later = started + 600.0  # a ClockTime
same = 600.0 + started  # the order of operands does not matter
started + finished  # TypeError: adding two instants means nothing

Construction rejects anything that cannot describe an instant. A component of the wrong type raises TypeError — including a bool, which is an integer in Python but not a moment. A microsecond component outside [0, 1000000), a naive datetime, a count of seconds that is NaN or infinite, or a time before the epoch raises ValueError. The epoch is a floor on arithmetic too: shifting back past it fails rather than yielding a negative time.

An instant does not convert to a number implicitly. float(instant) raises, and so does json.dumps on one, so a caller that wants a number asks for it.

In a Redis-backed environment the base instant used is that returned by the Redis server's TIME command, so that multiple consumers across systems (such as a horizontally scaled Django application) are effectively synced with the Redis server when it comes to internal handling of event times and durations.

Identified lifecycle records

The lifecycle-record API is appropriate when a producer needs to poll the outcome of work processed later. Payloads and handler results must be JSON-serialisable. The queue generates the UUIDv7 identifier and owns all lifecycle timestamps, taking them from its own clock — Redis-aligned for a Redis queue, local time otherwise. That clock is available as queue.clock, so anything recording times alongside a queue's entries can share its basis.

queued_at, dispatched_at and finished_at are ClockTime values, stored as a float count of seconds since the epoch. Nothing parses a string or resolves a timezone to read one, and a stored instant is directly usable as a Redis sorted-set score.

Because those instants share one basis, an entry can report elapsed time directly:

entry.queued_for  # seconds it waited before a worker picked it up
entry.ran_for  # seconds its handler took

Each is a count of seconds carrying its microseconds, not a whole number of them — a handler that ran for 137 microseconds reports 0.000137, which matters because most work finishes in well under a second.

Both are derived from the instants rather than stored, so they cannot disagree with them, and both are None until the instants describing them exist — an entry still waiting has not waited zero seconds. They are also None if the instants contradict, which a clock recalibrating backwards can cause: a negative elapsed time is meaningless rather than merely small.

Asynchronous queue API and heartbeat

The a-prefixed lifecycle operations are the primary API in asynchronous code: aenqueue, afind, alist, adequeue, and ahas_pending. All must be awaited when called.

Lifecycle transitions are worker-internal operations, not producer APIs. AsyncQueue backends also expose aprune for explicit retained-entry removal. Built-in queues also expose aadd, aget, apoll, apeek, asize, and aclear for raw queue values. Await these from an ASGI view, a handler, or another coroutine:

entry_id = await queue.aenqueue({"request_id": 42})
entry = await queue.afind(entry_id)

The corresponding synchronous methods remain for synchronous Django code. They must not be called from a running event loop; use the a-prefixed operation instead. A custom backend implements the asynchronous methods, while the base class supplies the synchronous wrappers. len(queue), bool(queue), and is_empty() are likewise synchronous-only; use asize() or ais_empty() in an event loop. Synchronous wrapper calls release their bridge-loop resources after each operation, so a custom backend's aclose() must be idempotent.

runqueues disposes its queues on its owning event loop. Other async hosts must await aclose_queues() before closing that loop; close_queues() only serves synchronous-wrapper resources and cannot close a different loop's Redis client.

For a Redis backend, a synchronous queue operation uses a fresh bridge-loop connection and Redis TIME calibration before closing it. Prefer the async API in asynchronous or high-volume producer code, where the loop-local client and clock are reused.

Long-running handlers may call heartbeat() after genuine progress to restart their current execution budget as they approach its deadline:

from django_queue import heartbeat


async def process_request(entry):
    await store_progress(entry.payload)
    heartbeat()
    return {"processed": True}

Heartbeat extends only the local execution budget. It is neither a lease renewal nor an ownership or delivery guarantee; a later claim-and-recovery backend may add those guarantees. It is not a keepalive to call on a timer or in a loop: doing so disables the protection the budget provides.

from django_queue import queue

entry_id = queue.enqueue({"request_id": 42})
entry = queue.find(entry_id)

assert entry.status == "queued"

Lifecycle observation

Use queue_observer for best-effort, passive async-queue monitoring. A subscription receives immutable entry snapshots from an async-queue worker; it cannot affect async-queue execution.

from django_queue import queue_observer


def update_dashboard(entry):
    print(entry.id, entry.status)


subscription = queue_observer("default", update_dashboard)
subscription.unsubscribe()  # stop future local delivery

queue_observer also works as a decorator, useful for registering an observer at import time without triggering any backend I/O immediately:

from django_queue import queue_observer


@queue_observer("default")
def update_dashboard(entry):
    print(entry.id, entry.status)

A decorator-registered observer records its registration immediately but activates — fetching retained snapshots and beginning delivery — only once the process-wide queue runtime starts. update_dashboard._queue_observer_subscription is usable immediately, before or after activation, to unsubscribe.

Memory queues notify only within the same Django process. Redis queues use best-effort Pub/Sub: a disconnected observer can miss transitions. Register a new observer when a new retained-state bootstrap is needed. Observer callback failures are logged and do not affect queue processing. Each observed queue's local delivery queue holds up to 128 snapshots; later snapshots are dropped when it is full, with one warning logged for that queue's process-local lifetime.

When a worker receives an entry, it first publishes that entry's persisted queued snapshot, then publishes running and its terminal state after each state is stored. A running worker also scans retained entries once per second and publishes snapshots it has not previously seen, using the queue-owned UUIDv7 IDs as its cursor. This makes entries changed outside the worker's own dispatch path observable; when the entry is later dispatched, the cursor avoids republishing its queued snapshot. An entry awaiting a worker remains available in the retained snapshots delivered at subscription.

Retention cleanup and explicit pruning remove a terminal record and publish one final immutable entry-shaped snapshot to its observers with status == "terminated". This final snapshot is never persisted as a retained record, although terminated is the final lifecycle state after any completed state. Dashboards can use it to remove the entry from their projection. A later find() or afind() for the removed ID raises QueueEntryNotFoundError.

All configured async-queue workers and Redis observer receivers share one process-wide background thread and asyncio loop (QueueRuntime), started once when the process comes up. A Redis-backed alias's receiver task begins as soon as that alias has its first observer registration, and is shared by every later registration for the same alias — no matter how many threads or queue instances register against it. It blocks in Pub/Sub while idle rather than polling, consumes no CPU while it waits, and does not keep Django alive during shutdown. If it exits because Redis fails, it logs the failure and clears its receiver task; existing observer registrations stay in place but stop receiving live snapshots until a later registration for that alias starts a fresh receiver task.

An entry normally transitions through queued, running, and one completed status: succeeded, failed, or timeout. Each completed status transitions to terminated when pruning removes its retained record. Failed entries expose only an exception type and safe message; the worker logs the traceback for diagnosis. A fourth completed status, cancelled, exists on the backend contract but no worker path produces it: a handler that finishes during shutdown is recorded by what it returned, and one that overruns is recorded as timeout. It is reserved for a deliberate per-entry cancellation the queue does not yet offer.

failed may also be recorded directly from queued when dispatch cannot begin, for example after queue-side validation or transport failure. Such an entry has finished_at but no dispatched_at.

Asynchronous worker

An application or management command explicitly owns the worker task. It must not be started from a request handler or Django app initialisation hook.

import asyncio

from django_queue import AsyncQueueWorker, queues


async def process_request(entry):
    return {"processed": entry.payload["request_id"]}


worker = queues["default"].create_worker("default", process_request)
asyncio.run(worker.run())

The worker dispatches one entry at a time and runs until cancelled. On cancellation it stops accepting new entries, gives an active handler its configured grace period, then cancels it if needed.

Redis queues use leased claims for at-least-once delivery. A worker claims an entry, renews its lease while dispatching, and atomically settles its terminal entry outcome only while it still owns that claim. Expired claims return the same entry ID to pending work, so a process failure can cause the handler to execute more than once. Handlers that make external changes must therefore be idempotent. Queue backends without claim-lease support retain best-effort delivery.

Claim, renewal, acknowledgement, recovery, and settlement are Redis delivery operations, owned by the Redis worker and its private queue provider rather than the public queue API. Other transports may use a different native model. Redis keys, scripts, timestamps, and record layout are not public contract. Redis Cluster is not supported by the Redis delivery implementation.

If a terminal outcome cannot be persisted because of an infrastructure failure, the worker logs the failure and continues. When it can still read a running entry, it makes one best-effort attempt to record a safe QueuePersistenceError failure outcome. If it cannot confirm either terminal outcome, the worker raises QueuePersistenceError rather than accepting further entries.

Loss of claim ownership is different: the worker stops handling that entry without recording an outcome, then continues serving later work. Recovery or the worker that acquired the claim owns the retry and its terminal outcome.

Execution budgets

Every dispatch runs under a budget: a count of seconds after which the worker stops waiting, cancels the handler, and records the entry as timeout. The worker then moves to the next entry, so one handler that never returns cannot starve an alias. A budget is always in force — an unbounded handler is the defect the budget exists to remove — so there is no value meaning unlimited.

The budget is resolved per dispatch, taking the first of these that is set:

1. the worker's timeout_seconds override, which applies to every alias it serves
2. the entry's own timeout_seconds, set when it was enqueued
3. the alias's TIMEOUT setting
4. 600 seconds

QUEUES = {
    "default": {
        "BACKEND": "django_queue.backends.redis.RedisAsyncQueueJson",
        "LOCATION": "redis://localhost:6379/12",
        "TIMEOUT": 30,
    },
}

TIMEOUT is a finite positive number of seconds, validated when settings are initialised rather than at first dispatch, so a bad value fails at startup. The same rule applies wherever a budget is supplied — the setting, the enqueue keyword, and the worker override all reject a non-number with TypeError and a zero, negative, infinite or NaN value with ValueError, at the point it is supplied. There is no value meaning unlimited, infinity included.

A single piece of work that legitimately takes longer carries its own budget:

entry_id = queue.enqueue({"request_id": 42}, timeout_seconds=120)

The budget expires on the event loop's monotonic clock, while the entry's timestamps are read from the queue's own clock. They are deliberately independent: the budget decides when to stop, and the entry records what happened. A timed-out entry's ran_for is a wall-clock measurement and will not equal the budget that expired.

Custom entry-capable backends must implement the worker-internal _amark_timed_out(entry_id) alongside the other terminal transitions, moving a running entry to timeout and setting finished_at.

The shutdown grace period is separate from the budget: it bounds how long a cancelled worker waits for an active handler, and its expiry is also recorded as timeout.

A handler that raises TimeoutError of its own — from asyncio.wait_for, an HTTP client, or a database driver — is recorded failed with that error, not timeout. Only the budget actually running out means the handler never answered.

Entry priority

priority is an int on QueueEntry, defaulting to 0; a higher value dispatches first. It is only consulted by the priority-variant backends (MemoryAsyncPriorityQueue, RedisAsyncPriorityQueue, RedisAsyncPriorityQueueJson) on the identified-entry path — enqueue/aenqueue accept it as a keyword:

entry_id = queue.enqueue({"request_id": 42}, priority=10)

Non-priority backends and event queues accept the keyword but ignore it, dispatching in their own existing order regardless. Equal-priority entries dispatch in arrival order on every priority backend.

QueueEntry itself accepts any int — it does not know which backend an entry is destined for, and a non-priority backend must be free to ignore the value entirely, so it never rejects one on a priority backend's behalf. The Redis priority backend (RedisAsyncPriorityQueue, RedisAsyncPriorityQueueJson) packs priority and an arrival-order sequence number into one ZSET score, which is only exact up to a double's 53-bit integer range; that backend rejects a priority beyond ±100,000 with ValueError when the entry is actually pushed to its tracked pending store, keeping every score comfortably inside the exact range. The in-memory priority backend (MemoryAsyncPriorityQueue) has no such bound — Python integers are arbitrary precision.

Worker observability

Each AsyncQueueWorker has a generated UUIDv7 identity and exposes a frozen, process-local snapshot. It reports the current run state, registered queue aliases, active queue name and entry ID, total dispatches, and confirmed persisted terminal outcomes:

from django_queue import WorkerSnapshot

snapshot: WorkerSnapshot = worker.snapshot
health = {
    "worker_id": str(snapshot.worker_id),
    "running": snapshot.running,
    "queue": snapshot.active_queue_name,
    "succeeded": snapshot.succeeded_count,
}

The worker emits INFO lifecycle records with queue_worker_event set to started, dispatch_started, terminal_recorded, or stopped. Their structured fields are prefixed with queue_worker_ and include the same worker ID, running state, registered queue aliases, active queue name and entry ID, start time, dispatch count, and outcome counters as the snapshot. Counters advance only after the corresponding terminal entry state has been confirmed in the backend. timed_out_count is counted separately from cancelled_count, so a handler abandoned on its budget is distinguishable from one the queue was told to cancel.

started_at comes from the worker's clock, which its queue supplies when it creates the worker, so a worker's recorded time and the entries it dispatches share one basis and elapsed time across them is meaningful. A worker built directly defaults to local time and accepts a clock argument. Like every instant the package reports it is a ClockTime, rendered in structured log records as a count of seconds rather than an ISO string.

running_for reports how long the worker has been running, measured on that same clock, and stops advancing once the worker leaves its dispatch loop so it reports how long it ran. Structured records carry it, and a terminal outcome record also carries the entry's queued_for and ran_for.

Reading a running worker's snapshot samples the queue clock to measure running_for, so on a Redis-backed queue a snapshot read takes the clock's lock and may trigger its periodic recalibration. A stopped worker reads its recorded stop instant instead and touches no clock. Read snapshots from the worker's event loop for a consistent observation; they do not coordinate cross-thread reads. A shutdown can interrupt the worker's acknowledgement of an in-flight terminal write, so the final snapshot records only terminal outcomes the worker observed before it stopped.

Snapshots and log records are local to the worker process. Collect logs or add an exporter in application infrastructure to aggregate multiple runqueues or web processes; this package does not provide distributed liveness or metrics.

External runqueues worker

For production, run queue processing as a separate Django process and use a shared backend such as Redis. Declare an asynchronous handler on each queue that the process should dispatch:

# settings.py
QUEUES = {
    "requests": {
        "BACKEND": "django_queue.backends.redis.RedisAsyncQueueJson",
        "LOCATION": "redis://redis:6379/12",
        "HANDLER": "myproject.queue_handlers.process_request",
    },
}


# myproject/queue_handlers.py
async def process_request(entry):
    return {"processed": entry.payload["request_id"]}

Start it as its own service or container command:

python manage.py runqueues

runqueues validates every configured HANDLER and WORKER, exiting non-zero on a configuration error, then waits to create each configured worker until that alias has pending entry work. It reports the configured handler count at startup and each alias as its worker begins. Once started, a worker runs until it receives SIGINT or SIGTERM; shutdown cooperatively stops all active workers. Queue definitions without HANDLER remain available to application code but are not dispatched; when no handlers are configured, the command reports this and exits successfully. A worker failure is logged while the remaining queues stay watched; the command exits non-zero only when no configured queue is left.

With all queues, the get(), peek(), and poll() methods return the object. Priority queue backends honour priority on both APIs: the raw value API via add()'s (priority, value) tuple, and identified entries via enqueue()/aenqueue()'s priority keyword — see Entry priority.

API reference

All queues expose raw-value operations. stack, capacity, and queue_name describe the selected backend; len(queue) and bool(queue) are synchronous conveniences.

Raw-value operation	Meaning
add / aadd	Add one or more raw values. Priority queues accept (priority, value) values.
get / aget	Remove and return the next raw value.
poll / apoll	Wait for and remove the next raw value. Redis priority queues accept timeout and retries.
peek / apeek	Return the next raw value without removing it.
size / asize, is_empty / ais_empty	Inspect raw-value availability.
clear / aclear, close / aclose	Clear raw values or release queue resources.

AsyncQueue lifecycle API

Only AsyncQueue implementations retain lifecycle records. Their synchronous methods have a-prefixed async counterparts.

Operation	Meaning
enqueue / aenqueue	Create a durable queued record and return its UUIDv7 ID.
find / afind	Return one retained record by ID.
dequeue / adequeue	Remove the next pending record from delivery while retaining its record.
has_pending / ahas_pending	Report whether delivery work is available.
list / alist	Return retained records for administration or observer bootstrap.
prune / aprune	Remove one retained terminal record and publish its observer-only terminated state.

Lifecycle transitions are worker-internal. enqueue emits Django's entry_enqueued signal after durable storage; lifecycle observers receive records when workers first observe them and as their state changes.

EventQueue delivery API

EventQueue uses enqueue / aenqueue to create a transient event, find / afind to inspect one live event, and dequeue / adequeue for direct consumption. It deliberately has no list or prune lifecycle API: event records are consumed or expire without a terminal outcome, and listeners receive them through @queue_listener.

Exceptions

• InvalidQueueBackendError: invalid Django QUEUES configuration.
• QueueFullException and QueueEmptyException: raw-value capacity or availability errors.
• QueueEntryNotFoundError: find or prune requested a retained record that no longer exists.
• QueueEntryMissingError: an internal worker/provider recovery condition: a previously claimed record disappeared unexpectedly.
• QueueEncodingException and QueueValueError: invalid stored values.