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FastAPI Backend Development Bootcamp · 课时

事务型发件箱模式

使用发件箱表和中继流程，保证状态变更与事件发布的原子性。

事务型发件箱模式 是 CoddyKit 上的免费 FastAPI Backend Development Bootcamp 课时。 这是第 3 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 FastAPI Backend Development Bootcamp 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

The Dual-Write Problem

In an event-driven FastAPI service, a single request often needs to do two things: persist state to your database and publish an event to Kafka or Pulsar.

The trap is that these are two separate systems with no shared transaction. If you commit the DB row then crash before publishing, downstream services never hear about it. If you publish first then the DB commit fails, you emit an event for state that does not exist.

  • db.commit() succeeds, producer.send() fails → lost event
  • producer.send() succeeds, db.commit() fails → phantom event

This is the dual-write problem, and no amount of try/except fully solves it.

async def create_order(db, producer, payload):
    order = Order(**payload)
    db.add(order)
    await db.commit()          # write #1: database
    await producer.send(
        "orders", order.as_event()
    )                          # write #2: broker (may fail!)
    return order

Why You Cannot Just Retry

A common first instinct is: "I'll just wrap the publish in a retry loop." But retries do not close the gap.

  • The process can be killed (OOM, deploy, k8s eviction) between commit and publish — no code runs to retry.
  • Retrying after a broker timeout can produce duplicates if the first send actually landed.
  • You cannot roll back a Kafka write once partially acknowledged.

The core issue is that the commit and the intent to publish are not atomic. We need to make the publish-intent part of the same database transaction as the state change. That is exactly what the Transactional Outbox pattern does.

The Outbox Idea

The Transactional Outbox pattern adds an outbox table in the same database as your business data. Instead of publishing to the broker inline, you INSERT a row describing the event into the outbox table — inside the same transaction that changes your state.

Because the order row and the outbox row are written in one transaction, they either both commit or both roll back. There is no window where one exists without the other.

A separate relay process later reads unpublished outbox rows and pushes them to Kafka/Pulsar, marking them sent. The broker write is decoupled from the request path.

  • Atomicity comes from the DB transaction, not from a distributed transaction.
  • Delivery becomes at-least-once — consumers must be idempotent.

Designing the Outbox Table

The outbox table needs enough metadata for the relay to publish correctly and for you to debug. A typical schema:

  • id — UUID primary key, also reused as the event id for idempotency
  • aggregate_type / aggregate_id — what the event is about (e.g. order / order id), often used as the partition key
  • event_type — e.g. OrderCreated
  • payload — JSON body of the event
  • created_at — ordering
  • published_at — NULL until the relay sends it

Keeping aggregate_id lets the relay set the Kafka partition key so all events for one order stay ordered.

from sqlalchemy import Column, String, DateTime, JSON, func
from sqlalchemy.dialects.postgresql import UUID
from .db import Base
import uuid

class Outbox(Base):
    __tablename__ = "outbox"
    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
    aggregate_type = Column(String, nullable=False)
    aggregate_id = Column(String, nullable=False)
    event_type = Column(String, nullable=False)
    payload = Column(JSON, nullable=False)
    created_at = Column(DateTime(timezone=True), server_default=func.now())
    published_at = Column(DateTime(timezone=True), nullable=True)

Writing State and Event Atomically

Here is the heart of the pattern. The order and the outbox row are added to the same session and committed together. Notice there is no broker call in the request handler at all.

If commit() fails, neither row exists. If it succeeds, both exist. The event will be delivered by the relay, guaranteed.

import uuid

async def create_order(db, payload):
    order = Order(**payload)
    db.add(order)

    event = Outbox(
        id=uuid.uuid4(),
        aggregate_type="order",
        aggregate_id=str(order.id),
        event_type="OrderCreated",
        payload={"order_id": str(order.id), **payload},
    )
    db.add(event)

    await db.commit()   # ONE transaction: state + event intent
    return order

The Relay: Polling Publisher

The simplest relay is a polling publisher: a background loop that repeatedly selects unpublished outbox rows, sends them to the broker, and marks them published.

  • Select rows where published_at IS NULL, ordered by created_at.
  • Publish each to Kafka/Pulsar using aggregate_id as the key.
  • Set published_at = now() and commit.

If the process crashes after publishing but before marking, the row gets re-sent on the next poll — hence at-least-once and the need for idempotent consumers. The event id is the deduplication key.

async def relay_once(db, producer, batch=100):
    rows = await db.fetch(
        "SELECT id, aggregate_id, event_type, payload "
        "FROM outbox WHERE published_at IS NULL "
        "ORDER BY created_at LIMIT $1",
        batch,
    )
    for r in rows:
        await producer.send(
            topic=r["event_type"],
            key=r["aggregate_id"].encode(),
            value=r["payload"],
            headers=[("event_id", str(r["id"]).encode())],
        )
        await db.execute(
            "UPDATE outbox SET published_at = now() WHERE id = $1",
            r["id"],
        )

Avoiding Double Processing with FOR UPDATE SKIP LOCKED

If you run more than one relay replica for throughput, two workers might grab the same outbox rows and double-publish. Postgres gives you a clean fix: SELECT ... FOR UPDATE SKIP LOCKED.

  • FOR UPDATE locks the selected rows for the transaction.
  • SKIP LOCKED makes other workers skip already-locked rows instead of blocking.

Each worker claims a disjoint batch, publishes, marks them, and commits — releasing the locks. This turns the outbox into a safe concurrent work queue without an external queue system.

SELECT id, aggregate_id, event_type, payload
FROM outbox
WHERE published_at IS NULL
ORDER BY created_at
LIMIT 100
FOR UPDATE SKIP LOCKED;

Idempotent Consumers Are Mandatory

Because the outbox guarantees at-least-once delivery, every consumer must tolerate seeing the same event more than once. The standard technique is a processed-events table keyed by the event id.

Before applying an event, attempt to insert its id. If it already exists, you have seen it — skip. Do the dedup insert and the business effect in the same transaction so a crash cannot leave them out of sync.

def handle_event(conn, event_id, body):
    cur = conn.cursor()
    try:
        cur.execute(
            "INSERT INTO processed_events(event_id) VALUES (%s)",
            (event_id,),
        )
    except UniqueViolation:
        conn.rollback()      # duplicate -> already handled
        return
    apply_business_change(cur, body)
    conn.commit()            # dedup + effect: one transaction

Change Data Capture as a Relay

Polling is simple but adds latency and DB load. A higher-performance alternative reads Postgres's write-ahead log (WAL) directly using Change Data Capture — typically Debezium.

  • Debezium tails the WAL and emits a Kafka message for every INSERT into the outbox table.
  • No published_at column or polling loop needed — the log is the queue.
  • Its Outbox Event Router SMT maps outbox columns to topic, key, and payload.

Trade-off: CDC is operationally heavier (connector, replication slot) but gives near-real-time delivery and offloads the relay entirely from your FastAPI app.

Ordering and Partition Keys

Events for the same aggregate usually must arrive in order — OrderCreated before OrderShipped. Both the outbox and the broker preserve this if you are careful:

  • Order the relay select by created_at (or a monotonic sequence).
  • Use aggregate_id as the Kafka partition key so all of one order's events land on the same partition, which Kafka delivers in order.

Events for different aggregates can interleave freely — you only need per-aggregate ordering, which is what partition-by-key buys you. A pure global ordering across all orders is rarely required and kills parallelism.

# Stable hash -> partition keeps one aggregate on one partition
def partition_for(aggregate_id: str, num_partitions: int) -> int:
    h = 0
    for ch in aggregate_id:
        h = (h * 31 + ord(ch)) & 0xFFFFFFFF
    return h % num_partitions

if __name__ == "__main__":
    ids = ["order-1", "order-1", "order-2", "order-3"]
    for a in ids:
        print(a, "->", partition_for(a, 6))

Operating the Outbox in Production

A few practices keep the outbox healthy at scale:

  • Prune published rows — periodically delete rows where published_at is older than a retention window, or move them to an archive, so the table stays small and the partial index stays fast.
  • Partial index on unpublished rows: CREATE INDEX ... ON outbox (created_at) WHERE published_at IS NULL; keeps the relay's hot query cheap.
  • Monitor lag — alert on the count or age of unpublished rows; a growing backlog means the relay is down or the broker is unreachable.
  • Idempotent producer — enable Kafka enable.idempotence=true to suppress relay-side resend duplicates at the broker level.

Quick Check: Why the Outbox Works

Test your understanding of why the Transactional Outbox pattern actually solves the dual-write problem.

Recap

You learned how to publish events reliably from a FastAPI service:

  • The dual-write problem: committing to the DB and publishing to Kafka/Pulsar are separate, non-atomic actions that can desync on a crash.
  • The Transactional Outbox writes an event row into an outbox table in the same transaction as the state change, making them atomic.
  • A relay (polling publisher or Debezium CDC) later sends outbox rows to the broker and marks them published.
  • Use FOR UPDATE SKIP LOCKED for safe concurrent relays, aggregate_id as the partition key for ordering, and a processed-events table for idempotent, at-least-once consumers.
  • Operate it with pruning, a partial index, and backlog monitoring.

常见问题解答

「事务型发件箱模式」课时是免费的吗？

是的 — 「事务型发件箱模式」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 FastAPI Backend Development Bootcamp 课程的其余内容，请升级到 CoddyKit PRO。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

「事务型发件箱模式」这节课中我会学到什么？

使用发件箱表和中继流程，保证状态变更与事件发布的原子性。 你通过在浏览器中直接运行的动手代码来练习 FastAPI Backend Development Bootcamp，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 FastAPI Backend Development Bootcamp 需要有经验吗？

无需任何先前经验。CoddyKit 上的 FastAPI Backend Development Bootcamp 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 3 节课，共 4 节。

「事务型发件箱模式」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 FastAPI Backend Development Bootcamp 课中编写并运行代码吗？

能。每节 FastAPI Backend Development Bootcamp 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 异步生产与消费 Kafka 事件
  2. 模式注册表与 Avro 契约演进
  3. 事务型发件箱模式
  4. 幂等消费者与恰好一次语义
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