Diagnose Pub/Sub Backlog and Duplicate Effects

Understand the likely causes

• Consumers can be slower than arrivals, lose acknowledgment deadlines, fail after committing, or retry malformed events indefinitely. Publishing retries can also produce duplicates. Exactly-once delivery has supported subscription/regional scope and does not make an arbitrary database transaction atomic with acknowledgment. Ordering, retention, dead-letter policy, permissions, and flow control each affect progress differently.

1. Identify the exact subscription and event identity

Record topic/subscription, delivery type, region/endpoint assumptions, consumer identity, message/application event identifiers, and timing from existing authorized evidence. Separate the same message redelivered from two unique publishes representing one business event. Do not publish test traffic or inspect a configured account from this guide. A synthetic record can establish the questions without creating a workload.

2. Compare arrival, processing, and retry rates

Inspect the defined time window and units for backlog, age, processing latency, errors, and downstream saturation. Retries consume work too, so unique-event rate alone can understate load. An acknowledgment deadline change may delay a symptom without improving capacity. Flow-control and concurrency settings should protect consumer memory and dependencies rather than create an unbounded hidden queue.

3. Check a synthetic effect ledger

The complete offline program parses three invented delivery records and compares delivery count with unique event count. It reports one duplicate opportunity and a unique-event total. The in-memory dictionary is deliberately not a distributed deduplication implementation; a real design must preserve state across restarts and coordinate the effect with its processed marker. Conflicting payloads require review rather than silent overwrite.

import json

records = json.loads('[{"event":"a","amount":10},{"event":"a","amount":10},{"event":"b","amount":20}]')
ledger = {}
for record in records:
    key = record["event"]
    if key in ledger and ledger[key] != record["amount"]: raise ValueError("conflicting synthetic event")
    ledger[key] = record["amount"]
print(f"deliveries={len(records)} unique={len(ledger)} duplicate_opportunities={len(records) - len(ledger)} total={sum(ledger.values())}")

deliveries=3 unique=2 duplicate_opportunities=1 total=30

4. Review the acknowledgment and commit boundary

Acknowledging before required work is durable can lose application work. Committing before an acknowledgment is known successful can lead to a repeated attempt. Define atomic/idempotent processing appropriate to the data store and business operation. A transport guarantee does not remove publishing duplicates or turn a separately committed effect into one transaction. Confirm the selected delivery feature’s current scope.

5. Define bounded retries, review, and replay

Handle invalid schemas and poison events through a documented retry/dead-letter review process with the required identities and permissions. Ordering keys can constrain progress; they are not validation. Replay and retention expiry need an idempotency horizon matching the business requirement. Do not purge, seek, acknowledge, change policies, or scale consumers against a real subscription as an unplanned diagnostic action.

Keep it from recurring

• Preserve event identity, schema/version, bounded flow control, retry policy, and durable idempotency design. Document delivery mode and regional assumptions, acknowledgment boundary, dead-letter ownership, and replay behavior. Link operational alerts to an approved runbook and never treat a process-local set as a persistent exactly-once guarantee.