Outbox Pattern Implementation

Outbox Pattern Implementation

The outbox pattern is the answer to one specific failure mode: a service updates its database AND publishes a message, and only one of them succeeds. Without the pattern, the database and the broker silently disagree forever; with it, the message publish becomes part of the same transaction as the row write, and a separate relay propagates it.

The two components are unchanging:

1. Outbox table.   Application writes business rows AND a row to outbox in ONE transaction.
2. Relay.          Separate process reads outbox, publishes to broker, marks row as published (or deletes it).

The relay can be polling (read the table, publish, delete; simple, works anywhere, adds 100ms+ latency) or CDC (Debezium tails the WAL/binlog, publishes near-instantly; more infrastructure, sub-second). (Conduktor — Transactional Outbox, Debezium blog — Reliable Microservices Data Exchange)

The delivery guarantee is at-least-once, always — the relay can publish then crash before deleting; on restart it'll publish again. Consumers MUST be idempotent. (conduktor-outbox)

Jump to your fire:

• "We lost an event between DB and Kafka" → The dual-write hazard
• Outbox table schema → Canonical outbox schema
• Polling relay vs CDC → Choosing the relay
• Outbox table getting huge → Pruning strategy
• Consumer needs to dedupe → Consumer idempotency
• "Should we just use Kafka transactions?" → When NOT to use the outbox
• Multi-service inbox-on-the-other-side → Inbox pattern

When to use

• A service writes to its own DB and must reliably publish a corresponding event.
• You've seen the "DB committed but the event never went out" bug at least once.
• Migrating off a dual-write pattern (db.commit(); broker.publish();) that you've discovered isn't atomic.
• Building any event-driven microservice that owns its data.
• The application can't tolerate the message being lost OR published twice unless the consumer dedupes.

Core capabilities

The dual-write hazard

// THE BUG.
async function placeOrder(order) {
  await db.insert('orders', order);          // commit 1: DB
  await kafka.publish('orders.placed', order);  // commit 2: broker
  // What if the process dies between these two? Or the broker is down?
  // The DB has the order, but no event was emitted. Downstream services have no idea.
}

There is no two-phase commit between Postgres and Kafka that's actually used in production. The fix isn't a smarter try/catch; it's removing the second commit:

// THE FIX.
async function placeOrder(order) {
  await db.transaction(async (tx) => {
    await tx.insert('orders', order);
    await tx.insert('outbox', {                 // SAME transaction.
      aggregate_type: 'order',
      aggregate_id: order.id,
      event_type: 'orders.placed',
      payload: JSON.stringify(order),
    });
  });
  // Both rows commit together, or neither does. The relay handles the rest.
}

The application is now done. A separate process reads outbox and publishes to Kafka. If the process is down, events queue in the table; when it recovers, it catches up.

Canonical outbox schema

Debezium and the surrounding ecosystem have converged on this shape: (conduktor-outbox, Debezium — EventRouter SMT)

CREATE TABLE outbox (
  id              UUID         PRIMARY KEY DEFAULT gen_random_uuid(),
  aggregate_type  VARCHAR(255) NOT NULL,   -- → routes to Kafka topic (e.g. "order")
  aggregate_id    VARCHAR(255) NOT NULL,   -- → Kafka message key (e.g. "ord_42")
  event_type      VARCHAR(255) NOT NULL,   -- e.g. "orders.placed", "orders.canceled"
  payload         JSONB        NOT NULL,   -- event body
  created_at      TIMESTAMPTZ  NOT NULL DEFAULT NOW(),
  -- For polling relay only:
  published_at    TIMESTAMPTZ,             -- NULL = unpublished
  -- Optional headers:
  trace_id        VARCHAR(64)              -- for distributed tracing correlation
);

-- Polling relay needs an index on the unpublished tail.
CREATE INDEX idx_outbox_unpublished
  ON outbox (created_at) WHERE published_at IS NULL;

Debezium's EventRouter SMT reads the row and produces a Kafka message where:

• The topic is derived from aggregate_type (order → topic outbox.event.order).
• The Kafka message key is aggregate_id (so all events for one aggregate hit the same partition, preserving order).
• The Kafka message value is payload.
• Headers can carry event_type, trace_id, etc. (debezium-eventrouter)

Choosing the relay

Polling-publisher:

-- Relay's main loop, atomic with FOR UPDATE SKIP LOCKED so multiple relay instances coexist.
WITH next AS (
  SELECT id, aggregate_type, aggregate_id, event_type, payload
  FROM outbox
  WHERE published_at IS NULL
  ORDER BY created_at
  LIMIT 100
  FOR UPDATE SKIP LOCKED
)
UPDATE outbox SET published_at = NOW()
WHERE id IN (SELECT id FROM next)
RETURNING *;
-- Then publish each row to Kafka. If publish fails, the txn rolls back; row reappears unpublished.

Pros: simple, works on any DB, no extra infrastructure, easy to reason about.
Cons: polling adds 100ms–seconds of latency; ordering across aggregates depends on poll ordering.
Use when: you don't have a Debezium-shaped infra footprint, latency tolerance is generous.

CDC (Debezium) relay:

# Debezium PostgreSQL connector + Outbox SMT
{
  "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
  "database.hostname": "postgres",
  "database.dbname": "orders",
  "table.include.list": "public.outbox",
  "transforms": "outbox",
  "transforms.outbox.type": "io.debezium.transforms.outbox.EventRouter",
  "transforms.outbox.route.topic.replacement": "outbox.event.${routedByValue}",
  "transforms.outbox.table.expand.json.payload": "true"
}

Pros: sub-second latency, zero polling overhead, ordering preserved by WAL/binlog order.
Cons: you now run Debezium + Kafka Connect; logical replication slot to manage; one bad consumer
      can stall the slot and grow Postgres WAL.
Use when: you already have Kafka Connect, latency matters, throughput is high.

Pruning strategy

The outbox table grows. Don't let it become a multi-billion-row monster:

-- Approach 1: drop after publish (simplest, polling relay only).
DELETE FROM outbox WHERE id = $1;   -- after successful publish

-- Approach 2: keep for a few hours then drop, supports replay / debugging.
DELETE FROM outbox
WHERE published_at IS NOT NULL AND published_at < NOW() - INTERVAL '24 hours';

-- Approach 3 (recommended for high-volume): time-partitioning.
CREATE TABLE outbox (
  ... ,
  created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
) PARTITION BY RANGE (created_at);

CREATE TABLE outbox_2026_05 PARTITION OF outbox
  FOR VALUES FROM ('2026-05-01') TO ('2026-06-01');

-- Drop a whole month in O(1).
DROP TABLE outbox_2026_01;

Conduktor's recommendation for high volume: "Drop entire partitions: instant, no row-by-row delete." (conduktor-outbox) Combine with pg_partman for automatic monthly partition creation/drop.

For Debezium, the row can be deleted immediately after the WAL captures it — Debezium has already consumed the event. Some teams keep a short retention window (1–24h) for debugging.

Consumer idempotency

At-least-once delivery means the consumer will see the same event twice some day:

// Idempotency at the consumer side.
async function handleOrderPlaced(event) {
  const inserted = await db.queryOne(
    `INSERT INTO processed_events (event_id, source) VALUES ($1, 'orders.outbox')
     ON CONFLICT DO NOTHING RETURNING event_id`,
    [event.id]
  );
  if (!inserted) return { skipped: 'duplicate' };

  // First time. Do the work in the same transaction.
  await db.update('inventory', { product_id: event.product_id }, {
    reserved: db.raw('reserved + ?', [event.quantity])
  });
  // Both rows commit together; if the side-effect fails, the dedup row rolls back, retry safe.
}

This is the same pattern as webhook-receiver-design, background-job-queue-design, and the dual-writes pattern in zero-downtime-database-migration. The DB unique constraint IS your idempotency primitive. Don't use Redis for this.

Inbox pattern

The receiver-side mirror. Same shape, opposite direction:

CREATE TABLE inbox (
  message_id   VARCHAR(255) PRIMARY KEY,   -- broker's message ID, dedup key
  source       VARCHAR(255) NOT NULL,
  payload      JSONB NOT NULL,
  received_at  TIMESTAMPTZ NOT NULL DEFAULT NOW(),
  processed_at TIMESTAMPTZ
);

async function consume(msg) {
  await db.transaction(async (tx) => {
    const inserted = await tx.queryOne(
      `INSERT INTO inbox (message_id, source, payload) VALUES ($1, $2, $3)
       ON CONFLICT DO NOTHING RETURNING message_id`,
      [msg.id, msg.source, msg.payload]
    );
    if (!inserted) return;   // already processed
    await applySideEffects(tx, msg);
    await tx.update('inbox', { message_id: msg.id }, { processed_at: new Date() });
  });
}

Outbox + inbox together is the canonical pattern for reliable cross-service messaging without distributed transactions.

When NOT to use the outbox pattern

• Kafka EOS within Kafka. If your write IS to Kafka and your downstream IS Kafka (no DB in the middle), Kafka transactions / idempotent producer give you exactly-once within Kafka. Outbox solves DB-to-Kafka, not Kafka-to-Kafka.
• Read-only services. If you don't write, you don't dual-write.
• Sagas / orchestrated workflows. Use Temporal or a saga framework — different abstraction (workflow steps with retries are first-class). See background-job-queue-design.
• Event sourcing. Your event log IS the source of truth; there's no separate "DB row" to keep in sync.
• Simple "fire and forget" events with low business cost if lost. Outbox is overhead; pick it for important state changes.

Multi-aggregate transactions

// Multiple aggregates in one transaction → multiple outbox rows.
await db.transaction(async (tx) => {
  await tx.insert('orders', order);
  await tx.update('inventory', { product_id }, { reserved: db.raw('reserved + ?', [qty]) });
  await tx.insert('outbox', { aggregate_type: 'order', aggregate_id: order.id, event_type: 'orders.placed', payload: JSON.stringify(order) });
  await tx.insert('outbox', { aggregate_type: 'inventory', aggregate_id: product_id, event_type: 'inventory.reserved', payload: JSON.stringify({ product_id, qty }) });
});
// Both events publish to their own topics with their own keys. Order preserved per aggregate.

Tracing across the boundary

Pass trace_id in the outbox row so consumers can join their span back to the producer:

await tx.insert('outbox', {
  ..., trace_id: currentSpan().spanContext().traceId
});

Consumer extracts and creates a follower span. See opentelemetry-instrumentation.

Anti-patterns

Publishing first, writing second

Symptom: Sometimes events fire for orders that don't exist. Diagnosis: kafka.publish(...) succeeded, then db.insert(...) failed and the transaction rolled back. Fix: Outbox pattern. Never publish before the DB commit succeeds.

db.commit(); broker.publish();

Symptom: Eventually the broker is unreachable for a few seconds, the DB row exists but the event was never published. Downstream is permanently inconsistent. Diagnosis: Two non-transactional steps; no recovery if the second fails. Fix: Outbox + relay. The publish-failure becomes a retry, not a missing event.

Polling without SKIP LOCKED

Symptom: Two relay instances try to claim the same rows; deadlocks under load. Diagnosis: FOR UPDATE blocks; without SKIP LOCKED they queue. Fix: FOR UPDATE SKIP LOCKED so each instance grabs a different chunk.

Outbox table without index on unpublished

Symptom: Polling relay does a sequential scan over millions of historical rows on every poll. Diagnosis: Index on (created_at) exists, but doesn't filter by published_at IS NULL. Fix: Partial index: CREATE INDEX ... WHERE published_at IS NULL. The unpublished set stays small.

Consumer that isn't idempotent

Symptom: Duplicate side effects (charged twice, two emails) when the relay or broker retries. Diagnosis: Consumer trusts that each delivery is unique. At-least-once means it isn't. Fix: processed_events table with unique constraint on (event_id, source); insert-then-handle pattern.

Letting the outbox grow forever

Symptom: Postgres tablespace blows up; backups take 4 hours. Diagnosis: No prune. Fix: Time-partition + drop old partitions; or DELETE old rows on a schedule. (conduktor-outbox)

CDC slot starved by a stuck consumer

Symptom: Postgres WAL grows unbounded; replica lag climbs; eventually disk full. Diagnosis: Debezium's logical replication slot has fallen behind because the consumer is stuck or down. WAL can't be pruned past the slot's position. Fix: Monitor pg_replication_slots.confirmed_flush_lsn; alert on slot lag. If a consumer is dead, drop the slot and re-bootstrap.

Outbox row written outside the transaction

Symptom: Looks fine in code review; rare but real cases where the DB row exists and outbox doesn't (or vice versa). Diagnosis: Outbox insert is outside db.transaction(...) — separate connection, separate commit. Fix: Pass the transaction object to all writes. CI grep for outbox writes outside db.transaction.

Quality gates

• Test: chaos test — kill the relay mid-publish; assert events eventually propagate, no duplicates.
• Test: kill the producer service mid-transaction; assert no partial state (DB row without outbox row, or vice versa).
• Test: consumer is idempotent — replay 100 events twice, assert side effects unchanged (DB-level dedup table verified).
• Outbox table schema matches the canonical shape (id, aggregate_type, aggregate_id, event_type, payload, created_at).
• Outbox INSERTs are always inside the same transaction as the business write. CI grep / static analysis enforces.
• Polling relay (if used) uses FOR UPDATE SKIP LOCKED and a partial index on unpublished rows.
• CDC relay (if used) monitors pg_replication_slots lag; alerts on slot stall.
• Outbox table has a prune strategy: time-partitioning + scheduled DROP TABLE, or scheduled DELETE of published rows older than N hours.
• Consumer dedup table has unique constraint on (event_id, source); idempotency proven by replay test.
• OTel tracing: trace_id passed in outbox row; consumer joins follower span (see opentelemetry-instrumentation).
• Lag metric exported: outbox_unpublished_count, outbox_oldest_unpublished_age_seconds. Alert at thresholds (grafana-dashboard-builder).
• Documented per-aggregate-type topic mapping; new event types have a documented producer + consumer + lifecycle.

NOT for

• Receiver-side webhook handling — adjacent shape, but signed third-party deliveries are different. → webhook-receiver-design.
• Sagas / Temporal workflows — outbox is durable single-event publish; sagas are durable multi-step orchestration. → background-job-queue-design for queue/workflow choice.
• Event sourcing — log is the truth; there's no separate state to sync.
• Kafka producer tuning generally (acks=all, idempotent producer, EOS) — adjacent topic, different scope.
• Inbox pattern is covered briefly here but warrants a dedicated treatment for high-volume consumers.
• DB migrations / schema evolution of the outbox table itself — → zero-downtime-database-migration.

Sources

• Debezium — Reliable Microservices Data Exchange With the Outbox Pattern (canonical writeup; aggregate_type / aggregate_id / payload schema). debezium.io/blog/2019/02/19/reliable-microservices-data-exchange-with-the-outbox-pattern/
• Debezium — Outbox Event Router (SMT) (EventRouter routing by aggregate_type to topic, key from aggregate_id). debezium.io/documentation/reference/transformations/outbox-event-router.html
• Conduktor — Transactional Outbox: Database-Kafka Consistency (polling vs CDC comparison; partition-based pruning; at-least-once explanation). conduktor.io/blog/transactional-outbox-pattern-database-kafka
• Streamkap — The Outbox Pattern Explained (sub-second CDC latency, ordering guarantees from WAL). streamkap.com/resources-and-guides/outbox-pattern-explained
• Microservices.io — Pattern: Transactional outbox. microservices.io/patterns/data/transactional-outbox.html
• SeatGeek Engineering — The Transactional Outbox Pattern: Transforming Real-Time Data Distribution at SeatGeek. chairnerd.seatgeek.com/transactional-outbox-pattern/