Metadata-Version: 2.4
Name: faststream-concurrent-aiokafka
Version: 0.7.0
Summary: Concurrent message-processing middleware for FastStream + aiokafka
Keywords: faststream,kafka,aiokafka,concurrency,middleware,messaging,asyncio,python
Author: Artur Shiriev
Author-email: Artur Shiriev <me@shiriev.ru>
License-Expression: MIT
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: Free Threading :: 2 - Beta
Classifier: Typing :: Typed
Classifier: Topic :: Software Development :: Libraries
Requires-Dist: faststream[kafka]>=0.7.1,<0.8
Requires-Python: >=3.11, <4
Project-URL: Homepage, https://modern-python.org
Project-URL: Repository, https://github.com/modern-python/faststream-concurrent-aiokafka
Project-URL: Issues, https://github.com/modern-python/faststream-concurrent-aiokafka/issues
Project-URL: Changelog, https://github.com/modern-python/faststream-concurrent-aiokafka/releases
Description-Content-Type: text/markdown

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Concurrent message processing middleware for [FastStream](https://faststream.ag2.ai/) with aiokafka.

By default FastStream processes Kafka messages sequentially — one message at a time per subscriber. This library turns each incoming message into an asyncio task so multiple messages are handled concurrently, while keeping offset commits correct and shutdown graceful.

## Features

- Concurrent message processing via asyncio tasks
- Configurable concurrency limit (semaphore-based)
- Batch offset committing per partition after each task completes
- Rebalance-safe: pending offsets are flushed on partition revocation via `ConsumerRebalanceListener`
- Fast shutdown: cancels in-flight tasks; uncommitted offsets are redelivered on restart (at-least-once)
- Signal handling owned by your lifespan / process manager — this lib does not register SIGTERM/SIGINT handlers
- Handler exceptions are logged but do not crash the consumer
- Health check helper to probe handler status from a `ContextRepo`

## Installation

```bash
pip install faststream-concurrent-aiokafka
```

## Quick Start

`ack_policy=AckPolicy.MANUAL` is **required** on every concurrent subscriber — the middleware enforces this at runtime.
Without it, FastStream would commit offsets before processing tasks complete, causing silent message loss on crash.
Subscribers on any other ack policy — `ACK_FIRST`, `ACK`, `REJECT_ON_ERROR`, `NACK_ON_ERROR` — are passed through without concurrent processing,
behaving exactly as they would if this middleware were not registered. That keeps a single broker-level registration safe across a mix of subscribers.

> **`AsgiFastStream` note**: its lifespan receives an app-level `ContextRepo` separate from `broker.context`. Pass `broker.context` explicitly instead of the injected argument.

```python
from contextlib import asynccontextmanager
from faststream import ContextRepo
from faststream.asgi import AsgiFastStream
from faststream.kafka import KafkaBroker
from faststream.middlewares import AckPolicy
from faststream_concurrent_aiokafka import (
    KafkaConcurrentProcessingMiddleware,
    initialize_concurrent_processing,
    stop_concurrent_processing,
)

broker = KafkaBroker(...)
# Register KCM on the broker before any other middleware (see DI note below)
broker.add_middleware(KafkaConcurrentProcessingMiddleware)


@asynccontextmanager
async def lifespan(_context: ContextRepo):
    await initialize_concurrent_processing(
        context=broker.context,
        concurrency_limit=20,  # max concurrent tasks (minimum: 1)
        commit_batch_size=100,  # commit after this many completed tasks
        commit_batch_timeout_sec=5.0,  # or after this many seconds
    )
    try:
        yield
    finally:
        await stop_concurrent_processing(broker.context)


app = AsgiFastStream(broker, lifespan=lifespan)


@broker.subscriber("my-topic", group_id="my-group", ack_policy=AckPolicy.MANUAL)
async def handle(msg: str) -> None: ...


# Any non-MANUAL policy (ACK_FIRST is FastStream's default) is passed through
# unchanged, not processed concurrently
@broker.subscriber("other-topic", group_id="other-group", ack_policy=AckPolicy.ACK_FIRST)
async def handle_other(msg: str) -> None: ...
```

## Core Concepts

### KafkaConcurrentProcessingMiddleware

A FastStream `BaseMiddleware` subclass. Add it to your broker to enable concurrent processing. It wraps each incoming message in an asyncio task submitted to `KafkaConcurrentHandler`.

### KafkaConcurrentHandler

The processing engine. Manages:
- An `asyncio.Semaphore` to enforce `concurrency_limit`
- In-flight task tracking via a `set[asyncio.Task]`; each task's done-callback releases the semaphore, removes the task from the set, and logs any non-cancellation exception at ERROR with a traceback
- FastStream control signals raised by a middleware registered *after* this one are absorbed before they can end the task, so they neither pin the message body via a traceback nor reach error reporters that wrap asyncio tasks. See [Limitations](#faststream-control-signals-from-a-middleware-registered-after-this-one) for which are honoured and which only log
- A `KafkaBatchCommitter` for offset commits
- A `ConsumerRebalanceListener` on every concurrent subscriber that flushes pending commits when partitions are revoked. `initialize_concurrent_processing` attaches it automatically, including to subscribers declared on routers; see [Rebalance handling](#how-it-works)

This library does **not** install signal handlers — shutdown is driven by your lifespan / process manager calling `stop_concurrent_processing`.

### KafkaBatchCommitter

Runs as a background asyncio task. A streaming loop absorbs `KafkaCommitTask` objects into per-partition pending state and commits each partition's contiguous-done prefix when total pending crosses `commit_batch_size`, when `commit_batch_timeout_sec` fires, or when `commit_all`/`close` sets the flush event. Cancelled tasks are treated as a hard boundary — the offset advance stops at the cancelled task so it gets redelivered on restart (at-least-once). If the committer's task dies, `CommitterIsDeadError` is raised to callers.

## API Reference

### `initialize_concurrent_processing(context, ...)`

Create and start the concurrent processing handler; store it in FastStream's context; attach a rebalance listener to every concurrent subscriber. Call it before the broker starts.

| Parameter | Default | Description |
|---|---|---|
| `context` | required | FastStream `ContextRepo` instance |
| `concurrency_limit` | `10` | Max concurrent asyncio tasks (minimum: 1) |
| `commit_batch_size` | `10` | Max messages per commit batch |
| `commit_batch_timeout_sec` | `10.0` | Max seconds before flushing a batch |
| `shutdown_timeout_sec` | `20.0` | Max seconds the batch committer waits for its background task to drain before forcing cancellation |
| `max_uncommitted_tasks` | `10000` | Max tasks accepted but not yet committed before the consume path blocks (backpressure). `None` disables the bound. |
| `rebalance_flush_timeout_sec` | `10.0` | Max seconds the attached rebalance listener waits for in-flight tasks when partitions are revoked |

Returns the `KafkaConcurrentHandler` instance.

> **Tuning `max_uncommitted_tasks`:** each uncommitted entry holds only commit metadata — a task reference, its `TopicPartition`, offset, and consumer reference — not the message payload, so the default of `10000` is on the order of a few MB. Lower it to tighten the memory bound during a commit or broker outage, at the cost of stalling consumption sooner. Keep it `>= commit_batch_size` so size-based batching can still trigger (below that, commits fall back to the timeout/flush path); set it to `None` to disable the bound and restore unbounded buffering.

### `stop_concurrent_processing(context)`

Cancel all in-flight tasks, flush completed offsets via the committer, then stop the handler. Uncommitted offsets (from cancelled tasks or anything queued past a cancelled offset) are redelivered on restart — at-least-once.

### `is_kafka_handler_healthy(context)`

Returns `True` if the `KafkaConcurrentHandler` stored in `context` is running and healthy, `False` otherwise (not initialized, stopped, or committer task dead). Useful for readiness/liveness probes.

### `KafkaConcurrentProcessingMiddleware`

FastStream middleware class. Register it via `broker.add_middleware(...)`. See Quick Start for usage examples.

> **Must be outermost.** `consume_scope` fires the handler as a background task and returns `None` immediately. Any middleware that wraps it on the outside will see that premature return and misfire — wrong timing, early cleanup, or missed exceptions. Middlewares added after it (i.e. inner in the chain) run correctly inside the background task.

#### DI framework compatibility (`modern-di-faststream` and similar)

DI frameworks like `modern-di-faststream` register a broker-level middleware that creates a REQUEST-scoped dependency container around each message. If that middleware is **outer** to `KafkaConcurrentProcessingMiddleware`, its scope closes as soon as `consume_scope` returns — before the background task runs — so any dependencies resolved inside the task (database sessions, repositories, …) are created from an already-closed container. Their finalizers never run, leaving connections unreturned to the pool.

**Fix**: call `broker.add_middleware(KafkaConcurrentProcessingMiddleware)` **before** `setup_di(...)` (or any equivalent DI bootstrap call). FastStream stacks broker middlewares so the **first** registered is outermost; adding KCM first makes it wrap the DI middleware, so the DI middleware runs *inside* KCM's background task and can manage the scope lifetime correctly.

```python
broker = KafkaBroker(...)
broker.add_middleware(KafkaConcurrentProcessingMiddleware)  # registered first → outermost
modern_di_faststream.setup_di(app, container=container)  # registered after → inner to KCM
```

## How It Works

1. **Message dispatch**: On each incoming message, `consume_scope` calls `handle_task()`, which acquires a semaphore slot then fires the handler coroutine as a background `asyncio.Task`.

2. **Concurrency control**: The semaphore blocks new tasks when `concurrency_limit` is reached. The slot is released via a done-callback when the task finishes or fails.

3. **Offset committing**: Each dispatched task is paired with its Kafka offset and consumer reference and enqueued in `KafkaBatchCommitter`. Once the task completes, the committer groups offsets by partition and calls `consumer.commit(partitions_to_offsets)` with `offset + 1` (Kafka's "next offset to fetch" convention).

4. **Rebalance handling**: `initialize_concurrent_processing` attaches a `ConsumerRebalanceListener` to every subscriber it processes concurrently: `AckPolicy.MANUAL`, not `batch=True`, subscribed by topic or pattern, on any Kafka broker of the FastStream application in the context, including subscribers from included routers. It must run before the broker starts (in the lifespan, as above), because FastStream hands the listener to aiokafka when the consumer subscribes; if the broker has already started, or the context holds no FastStream application, it logs an ERROR and attaches nothing. A `listener=` you pass yourself is kept and called after the flush. For setups where it cannot attach, pass `listener=ConsumerRebalanceListener.from_context(broker.context)` to the subscriber. When Kafka revokes a partition, the listener calls `committer.commit_all()` to flush pending offsets before the partition is reassigned. The flush waits for in-flight tasks up to `flush_timeout_sec` (default 10 s) so a slow handler cannot stall the rebalance past `max.poll.interval.ms`; on timeout, the remaining in-flight messages are redelivered after reassignment (at-least-once). A future optimization may scope the wait to only the revoked partitions.

5. **Shutdown**: `stop_concurrent_processing` cancels every in-flight asyncio task, then awaits `committer.close()`. The committer treats cancelled tasks as a hard offset boundary — cancelled-and-after offsets stay uncommitted and get redelivered on restart. Total wall-clock is sub-second in normal conditions and bounded by `shutdown_timeout_sec` only as a safety net for stuck network commits.

## Limitations

### FastStream control signals from a middleware registered *after* this one

A middleware you register **after** `KafkaConcurrentProcessingMiddleware` runs
*inside* the coroutine this library dispatches as a background task, so a
FastStream control signal it raises never reaches FastStream. Every such signal
is absorbed and the message's offset is committed; what differs is whether the
library could act on it.

| raised by an inner middleware | effect | logged |
|---|---|---|
| `AckMessage` | offset commits — this *is* the ack | DEBUG |
| `RejectMessage` | offset commits — for Kafka `reject()` is `ack()` | DEBUG |
| `SkipMessage` | offset commits, processing moves on | DEBUG |
| `NackMessage` | **not honoured** — offset commits instead of being redelivered | ERROR |
| `StopConsume` | **not honoured** — the subscriber keeps consuming | ERROR |
| `StopApplication` | **not honoured** — the application keeps running | ERROR |

The three marked *not honoured* have never worked from a concurrently dispatched
handler. Before 0.6.4 they failed silently — and `StopApplication`, which
subclasses `SystemExit`, tore down the event loop outright, losing every
in-flight offset. They now log a loud ERROR naming the signal instead. If you
depend on any of them, raise it from a middleware registered **before**
`KafkaConcurrentProcessingMiddleware` (which runs outside the dispatched task),
or from outside the message-processing path entirely.

Rationale and the rejected alternatives:
[ADR-0002](https://github.com/modern-python/faststream-concurrent-aiokafka/blob/main/docs/adr/0002-control-signals-not-honoured.md).

### Calling `msg.ack()` / `msg.nack()` / `msg.reject()` directly

**These raise `RuntimeError` on the concurrent path.** Offset control belongs to
`KafkaBatchCommitter`; reaching around it silently loses data, so the middleware
refuses the call rather than letting it through.

`KafkaAckableMessage.ack()` issues a bare `consumer.commit()` with no offsets,
committing the consumer's *current fetch position* — past every in-flight task on
every assigned partition — so those messages are never processed and never
redelivered. `reject()` is an ack for Kafka and carries the same hazard under an
opposite-sounding name. `nack()` issues `consumer.seek(...)`, rewinding the
partition underneath tasks already processing it.

There is no supported way to request redelivery under concurrent processing: the
offset commits even when your handler raises. See
[ADR-0002](https://github.com/modern-python/faststream-concurrent-aiokafka/blob/main/docs/adr/0002-control-signals-not-honoured.md).

Subscribers that pass through — a `FakeConsumer` under `TestKafkaBroker`, or any
non-`MANUAL` ack policy — are unaffected, because this library is not managing
their offsets. The guards are installed only on the concurrent dispatch path, so
a passed-through subscriber never sees them.

**Still unguarded:** reaching through the message to the raw consumer, as in
`msg.consumer.commit()` or `msg.consumer.seek(...)`. The consumer is one shared
object across every message and partition, so it cannot be guarded per message.
Do not do it.

### Other

- **Batch subscribers (`batch=True`) are unsupported** — a `batch=True`
  subscriber declaring `AckPolicy.MANUAL` is rejected with an explicit
  `RuntimeError`. The concurrent path is one message → one task → one offset. A
  `batch=True` subscriber on any other ack policy simply passes through, since
  the middleware does not manage it at all.
- **`ack_policy=AckPolicy.MANUAL` is required** on subscribers you want processed
  concurrently. Every other policy passes through untouched, exactly as if the
  middleware were not registered — that is what makes a single broker-level
  `add_middleware` call safe across a mix of subscribers. `ACK_FIRST` leaves its
  offsets to aiokafka's `enable_auto_commit`; `ACK`, `REJECT_ON_ERROR` and
  `NACK_ON_ERROR` are acknowledged by FastStream's own
  `AcknowledgementMiddleware` as soon as this middleware returns. That ack is
  safe on the pass-through path — each FastStream subscriber builds its own
  `AIOKafkaConsumer`, so it touches only that subscriber's partitions and cannot
  commit past another subscriber's in-flight work, and with no background task
  "consumed" and "processed" are the same moment. It would *not* be safe if such
  a subscriber were dispatched, which is precisely why it is not.

## Migration from < 0.x

Previously, `stop_concurrent_processing` waited up to `2 × shutdown_timeout_sec` for in-flight tasks to drain to completion. The new behavior cancels them immediately. The at-least-once contract is unchanged — uncommitted offsets are redelivered on restart, the same way they always were when the handler crashed mid-task.

| What changed | Old | New |
|---|---|---|
| In-flight tasks on stop | drained to completion | **cancelled** |
| `KafkaConcurrentHandler.wait_for_subtasks()` | public method | removed |
| `shutdown_timeout_sec` | applied separately to handler and committer | applied to committer only |
| Signal handler installation | installed automatically | removed — own them via your lifespan / process manager |

If your handlers do non-idempotent work that's expensive to repeat, ensure your handlers are wrapped in `try/finally` so cleanup runs on `CancelledError`, or pin to the previous version of this library. To trigger shutdown on SIGTERM/SIGINT, your lifespan or main entry point must catch the signal and call `stop_concurrent_processing(broker.context)` — under uvicorn / AsgiFastStream this happens automatically through the lifespan `finally` block.

## Requirements

- Python >= 3.11
- `faststream[kafka]`

## 📦 [PyPI](https://pypi.org/project/faststream-concurrent-aiokafka)

## 📝 [License](https://github.com/modern-python/faststream-concurrent-aiokafka/blob/main/LICENSE)

## Part of `modern-python`

Browse the full list of templates and libraries in
[`modern-python`](https://github.com/modern-python) — see the org profile for the categorized index.
