idempotency-key-patterns

name	idempotency-key-patterns
description	Implementing the `Idempotency-Key` HTTP header pattern (Stripe-pioneered, IETF-standardizing) so retries don't double-charge or duplicate side effects — full Postgres schema with recovery-point state machine, fingerprint check, in-progress lock with 409 response, and 24h retention. Grounded in Stripe blog/docs, the IETF httpapi WG draft, and Brandur Leach's canonical implementation.
category	API Design
tags	["api","idempotency","retries","http","stripe","distributed-systems","exactly-once"]
allowed-tools	Read, Grep, Glob, Edit, Write, Bash(grep:*, rg:*, psql:*, curl:*)

Idempotency Key Patterns

TL;DR: Client generates a unique key (UUID v4 recommended), sends it as Idempotency-Key header on a POST. Server stores (key, request fingerprint, response, recovery_point) for ~24h. Retries with the same key return the cached response. Same key + different body → 422 (IETF) / 400 (Stripe). Concurrent retry while original is in-flight → 409. Without the fingerprint check + in-progress lock + recovery-point state machine, you don't have idempotency — you have a cache that pretends to be one.

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Jump to your fire

Symptom	Section
"Customer got double-charged on a network retry"	The full pattern
"Need a Postgres schema, not just a cache"	Schema with recovery points
"Two retries hit at the same time — race condition"	In-progress lock
"Worker crashed mid-request, key is stuck"	Recovery points
"What status code for key reuse?"	Status codes

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Decision diagram

flowchart TD
  A[POST request arrives<br/>Idempotency-Key: ABC-123] --> B{Key exists in store?}
  B -->|No| C[INSERT row with locked_at=now<br/>recovery_point='started']
  C --> D[Process request, advancing recovery_point<br/>through atomic phases]
  D --> E[Store response, set recovery_point='finished']
  E --> F[Return response]

  B -->|Yes, recovery_point='finished'| G{Request body fingerprint matches?}
  G -->|Yes| H[Return cached response<br/>200/201 with stored body]
  G -->|No| I["422 Unprocessable<br/>(IETF) or 400 (Stripe)<br/>'Key reused with different params'"]

  B -->|Yes, recovery_point != 'finished'| J{locked_at within<br/>lock TTL?}
  J -->|Yes, still in-progress| K["409 Conflict<br/>'Another request with this key is in progress'"]
  J -->|No, lock expired| L[Resume from recovery_point<br/>re-acquire lock]
  L --> D

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1. The full pattern: Stripe's canonical shape

From Stripe: Designing robust APIs with idempotency:

Clients [pass] a unique value in with the special Idempotency-Key header.

On a response failure (i.e. the operation executed successfully, but the client couldn't get the result), the server simply replies with a cached result.

The full pattern has three ingredients that work together. Drop any one and you have a leak:

Ingredient	What it prevents
Key persistence with response cache	Duplicate side effects on simple retry
Fingerprint check (request body hash)	Buggy clients reusing a key for a different request and getting the wrong cached response
In-progress lock with TTL	Two concurrent retries both executing the work

A "key persistence + cache" without the other two is the most common shipped-broken implementation.

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2. Status codes: Stripe vs IETF draft

There's a real divergence here.

Stripe (API reference):

• Same key + different params → HTTP 400 with: "Keys for idempotent requests can only be used with the same parameters they were first used with..."
• Concurrent retry while in-progress → HTTP 409: "There is currently another in-progress request using this Idempotent Key..."
• Max key length: 255 characters
• Retention: 24 hours

IETF draft-ietf-httpapi-idempotency-key-header:

• Same key + different payload → HTTP 422 Unprocessable Content
• Concurrent retry → HTTP 409 Conflict
• Missing key on documented idempotent endpoint → HTTP 400 Bad Request

The IETF draft says:

The idempotency key MUST be unique and MUST NOT be reused with another request with a different request payload.

It is RECOMMENDED that a UUID or a similar random identifier be used.

Idempotency-Key is an Item Structured Header. Its value MUST be a String.

Pick one (Stripe's is the most-deployed; IETF's will become the standard) and document the choice in your API reference. Don't invent a third.

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3. The canonical Postgres schema (Brandur Leach)

The de facto canonical implementation reference is Brandur Leach (Stripe), "Implementing Stripe-like Idempotency Keys in Postgres". The schema:

CREATE TABLE idempotency_keys (
  id              BIGSERIAL PRIMARY KEY,
  created_at      TIMESTAMPTZ NOT NULL DEFAULT now(),

  -- Per-tenant uniqueness
  user_id         BIGINT      NOT NULL,
  idempotency_key TEXT        NOT NULL CHECK (char_length(idempotency_key) <= 100),

  -- Lock + retention
  last_run_at     TIMESTAMPTZ NOT NULL DEFAULT NOW(),
  locked_at       TIMESTAMPTZ,    -- NULL = unlocked, set = in progress

  -- Fingerprint check inputs
  request_method  TEXT        NOT NULL CHECK (char_length(request_method) <= 10),
  request_path    TEXT        NOT NULL CHECK (char_length(request_path)   <= 100),
  request_params  JSONB       NOT NULL,

  -- Cached response
  response_code   INT,
  response_body   JSONB,

  -- Recovery point state machine
  recovery_point  TEXT        NOT NULL CHECK (char_length(recovery_point) <= 50)
);

CREATE UNIQUE INDEX idempotency_keys_user_id_idempotency_key
  ON idempotency_keys (user_id, idempotency_key);

Field semantics (verbatim from the post):

• idempotency_key: "the user-specified idempotency key... constrain the field's length so that nobody sends us anything too exotic." Uniqueness is scoped per (user_id, idempotency_key) so different tenants can't collide.
• locked_at: "indicates whether this idempotency key is actively being worked. The first API request that creates the key will lock it automatically, but subsequent retries will also set it to make sure that they're the only request doing the work."
• request_params: "stored mostly so that we can error if the user sends two requests with the same idempotency key but with different parameters." (The fingerprint check.)
• recovery_point: "a text label for the last phase completed for the idempotent request... Gets an initial value of started and is set to finished when the request is considered to be complete."

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4. The in-progress lock and 409 response

Atomic phase 1 — upsert the key with a lock:

INSERT INTO idempotency_keys
  (user_id, idempotency_key, request_method, request_path, request_params,
   recovery_point, locked_at)
VALUES
  ($1, $2, $3, $4, $5, 'started', now())
ON CONFLICT (user_id, idempotency_key) DO UPDATE
  SET locked_at = now(),
      last_run_at = now()
  WHERE idempotency_keys.locked_at IS NULL  -- only re-lock if currently unlocked
     OR idempotency_keys.locked_at < now() - interval '90 seconds'  -- or stale
RETURNING *;

Three branches from the result:

Result	Branch
Returned row, recovery_point = 'finished', fingerprint matches	Return cached response
Returned row, recovery_point = 'finished', fingerprint mismatch	Return 422/400
Returned row, recovery_point != 'finished' (we got the lock)	Continue to phase 2
No row returned (UPDATE filtered out: lock held by another live request)	Return 409 Conflict

The 90-second TTL on the stale-lock check covers crashed workers — they'll be re-acquired by the next retry. Tune to your worst-case request latency.

Two layers of defense: the unique index + the locked_at check. The index prevents two INSERTs racing on a fresh key. The locked_at check prevents two UPDATEs racing on an already-existing key.

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5. Recovery point state machine

The hard problem: a worker crashes mid-request after charging the customer's card but before recording the order. A naive retry double-charges. A naive "reject all retries on partial state" loses the order.

Brandur's pattern: each foreign side effect commits its own atomic phase, advancing recovery_point:

started → ride_created → charge_created → finished
   ↑          ↑                ↑              ↑
   tx1        tx2: insert      tx3: charge    tx4: send email,
              local row        Stripe API      mark finished
                               + advance RP

Each phase:

1. Begins a DB transaction.
2. Performs at most one foreign side effect (or none).
3. Updates recovery_point to the next state.
4. Commits.

On retry, you read recovery_point and resume from there. Because each phase is atomic, you either did the side effect and advanced the marker, or did neither. No double-charges.

The post puts it concisely:

Each foreign state mutation (charge Stripe, send email) gets its own phase. Each phase commits a new recovery_point so a crashed retry resumes mid-flow rather than restarting.

The discipline: at most one external side effect per atomic phase. Two side effects in one phase reintroduces the dual-write hazard.

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6. Storage tradeoffs: Redis vs Postgres

A common mistake: using Redis as the only idempotency store. Redis is great for the in-progress lock (TTL'd lease, auto-expires on crash) but lossy for the response cache (eviction, persistence configs vary).

The pragmatic split:

Layer	Backend	Why
In-progress lock	Redis with SET key NX EX 90	TTL'd, fast, auto-cleanup on crash
Durable record	Postgres (the schema above)	ACID, unique constraint as last-line defense
Response cache	Postgres response_body column	Read on retry within retention window

Or just Postgres for everything (Brandur's choice) — simpler if your throughput allows it. Use Redis only when you have hard latency budgets that rule out a DB roundtrip on the hot path.

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7. Retention and cleanup

24 hours is the canonical window (Stripe). Reasons it's not "forever":

• Storage cost grows unbounded
• Old keys with stale fingerprints are debugging hazards
• Audit history belongs in a separate audit_records table, not the idempotency table

A reaper job:

DELETE FROM idempotency_keys
WHERE created_at < now() - interval '24 hours';

If your domain table references idempotency_keys.id (e.g., orders.idempotency_key_id), use ON DELETE SET NULL so the reaper doesn't cascade-delete real records.

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Anti-patterns

Anti-pattern	Why it bites	Fix
Server generates the key	Defeats the purpose — can't survive a network retry	Client generates UUID v4 before the request
No fingerprint check	Buggy client reuses key with different body → returns wrong response	Always check request_params matches; 422/400 if not
In-progress lock with no TTL	Crashed worker → key stuck "in progress" forever	TTL on the lock (Redis EX, or locked_at < cutoff check)
One atomic phase containing 2+ external side effects	Crash between them → re-execute on retry → double-charge	One side effect per phase; advance recovery_point between them
PII as the key (email, phone)	Logs leak; legitimately different requests collide	Random opaque UUID
Retention < 1 hour	Mobile clients on flaky networks can't retry	24h is the floor; tune up for offline-first apps
Permanent retention as audit log	Conflates two concerns; fingerprint hashes outlive their utility	Separate audit_records table for compliance/audit
Treating GET/PUT/DELETE as needing idempotency keys	They're already idempotent per HTTP semantics	Only POST and other unsafe mutators benefit
Returning the cached response without checking the user matches	One tenant's key collides with another's; data leak	Scope uniqueness per (user_id, key)

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Novice / Expert / Timeline

	Novice	Expert
First "idempotent endpoint"	Stores key + response in a hash	Schema with fingerprint + recovery_point + per-tenant scope
On concurrent retry	Both run, double-side-effect	409 from in-progress lock; second retry waits for first to finish
On worker crash mid-request	Permanently stuck in-progress	Stale lock detection; resume from recovery_point
On client bug reusing key	Returns stale response	422/400 with explicit error
Storage	Redis-only (loses keys on eviction)	Postgres-durable + Redis-fast-lock
Cleanup	None — table grows forever	24h reaper, audit table separate

Timeline test: simulate a network partition during a payment request. An expert system either succeeds exactly once or returns a clean retryable error. A novice system charges the customer twice — and the postmortem reveals there was no fingerprint check.

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Quality gates

An idempotency implementation ships when:

• Test: Same key + same body twice → both responses identical (the second is the cache hit). Verified with integration test.
• Test: Same key + different body → 422 (or 400, per chosen flavor). Integration test for the fingerprint mismatch case.
• Test: Two concurrent requests with the same key → exactly one succeeds, the other gets 409. Verified with a controlled race (e.g., pg_advisory_lock to gate the second).
• Test: Worker killed mid-phase → next retry resumes from the correct recovery point and does not re-execute the prior phase's side effect. Manual fault-injection test.
• Test: Unique index on (user_id, idempotency_key) exists; verified by EXPLAIN of the upsert.
• Test: Reaper job actually runs and respects retention window.
• Test: Naturally idempotent methods (GET, PUT, DELETE) do not require the header — verified by hitting them without one.
• Manual: API docs document the chosen status codes (Stripe 400 or IETF 422) and the retention window.

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NOT for this skill

• Distributed transactions / sagas (use sagas-garcia-molina-salem-1987 or cqrs-event-sourcing-architect)
• Event-stream exactly-once delivery (use kafka-eos-design)
• Database-side idempotency (use unique constraints; not the same problem)
• Webhook delivery deduplication (related but inverse direction — use webhook-receiver-design)
• Outbox pattern for event publication (use outbox-pattern-implementation)
• General retry/backoff strategy (use circuit-breaker-and-retries)

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Sources

• Stripe: Designing robust APIs with idempotency — original conceptual blog post, Idempotency-Key header
• Stripe: API reference — Idempotent requests — 255-char limit, 24h retention, status codes
• IETF: draft-ietf-httpapi-idempotency-key-header — Item Structured Header, MUST/SHOULD requirements, 409/422 codes
• Brandur Leach (Stripe engineer): Implementing Stripe-like Idempotency Keys in Postgres — full schema + recovery-point state machine
• Luca Palmieri: An In-Depth Introduction To Idempotency — Rust/Postgres implementation, concurrency edge cases