The Transactional Outbox Pattern
Guarantee atomic state changes and event publishing using an outbox table and relay process.
The Transactional Outbox Pattern is a free FastAPI Backend Development Bootcamp lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the FastAPI Backend Development Bootcamp learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
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 eventproducer.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 orderWhy 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 idempotencyaggregate_type/aggregate_id— what the event is about (e.g.order/ order id), often used as the partition keyevent_type— e.g.OrderCreatedpayload— JSON body of the eventcreated_at— orderingpublished_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 orderThe 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 bycreated_at. - Publish each to Kafka/Pulsar using
aggregate_idas 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 UPDATElocks the selected rows for the transaction.SKIP LOCKEDmakes 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 transactionChange 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_atcolumn 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_idas 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_atis 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=trueto 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 LOCKEDfor safe concurrent relays,aggregate_idas 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.
Frequently asked questions
Is the “The Transactional Outbox Pattern” lesson free?
Yes — the full text of “The Transactional Outbox Pattern” is free to read here on the web, and the FastAPI Backend Development Bootcamp course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the FastAPI Backend Development Bootcamp course, upgrade to CoddyKit PRO.
What will I learn in “The Transactional Outbox Pattern”?
Guarantee atomic state changes and event publishing using an outbox table and relay process. You practise FastAPI Backend Development Bootcamp with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start FastAPI Backend Development Bootcamp?
No prior experience is required. FastAPI Backend Development Bootcamp on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “The Transactional Outbox Pattern” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this FastAPI Backend Development Bootcamp lesson?
Yes. Every FastAPI Backend Development Bootcamp lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.