replaying an idempotency-key design across a failed worker

Not specified by the original author.

TL;DR

Design every unit of work so running it twice has the same effect as running it once: check whether the output already exists before doing the work, write outputs to deterministic paths, and never append to shared files without a merge step. Idempotency is not a feature you add later; it is the shape of the worker from the first line.

idempotent workers: designing rerunnable pipelines

Use this when

• Workers may retry, resume, or run concurrently
• A pipeline step failed halfway and needs rerunning
• You are tired of "clean up the partial outputs, then rerun" rituals
• Multiple workers might claim the same item
• You want restarts to be boring instead of scary

Not for this skill when

• You need exactly-once semantics with external side effects (that needs transactions or dedupe keys at the sink)
• The work is a single linear script that never reruns (do not over-engineer)
• The side effects are irreversible and non-dedupeable (redesign the step instead)
• You are debugging a specific duplicate-write bug (apply this skill's checks to find it)

Steps

1. Define "done" for one unit of work as an observable artifact. A file at a deterministic path, a row with a unique key, a queue entry marked complete. If done-ness is not observable, skip-if-done is impossible and every rerun redoubles the work.

   done means: [artifact, e.g. output file at results/[id].json]

   Expected output: a done definition per unit. Success check: a script can test done-ness without running the work.

2. Check done-ness first, then do the work. Every worker starts by asking "does the output already exist and look complete"; if yes, it skips. This single check makes retries, restarts, and duplicate claims all safe.

   [ ] worker checks for existing complete output before starting
   [ ] "complete" is verified (valid JSON, expected fields), not just "file exists"

   Expected output: skip-if-done in every worker. Success check: running the whole pipeline twice performs zero duplicate work the second time.

3. Write outputs to deterministic paths, never timestamps or random names. results/[id].json can be checked and overwritten safely; results/output-2026-10-04-073212.json cannot. Deterministic names turn reruns into overwrites instead of duplicates.

   output path: [deterministic, e.g. results/[id].json]

   Expected output: a naming rule. Success check: two runs of the same item produce byte-identical paths.

4. Make the work itself repeatable: same inputs, same outputs. No appending to a shared log as the only record (merge on read instead), no "increment the counter" steps without a dedupe key, no depending on what a previous run left in temp space. Pure functions of their inputs are idempotent by construction.

   [ ] rerun with same inputs produces same outputs
   [ ] no step depends on leftover state from a prior run

   Expected output: repeatability verified. Success check: run an item three times; the third run's outputs match the first's.

5. Test idempotency deliberately, not hopefully. Kill a worker mid-item and rerun; run two workers on overlapping items; run the full pipeline twice back-to-back. Each test should end with correct, deduplicated results and no manual cleanup.

   [ ] kill-mid-item rerun: clean
   [ ] overlapping workers: no duplicates
   [ ] double full run: second run is all skips

   Expected output: three passing tests. Success check: all three pass without human intervention.

Variant phrasings

How to make a script safe to rerun

Script phrasing. Steps 1 and 2: define done as a file, check it first.

Idempotency for data pipelines

Data phrasing. Deterministic output paths (step 3) plus dedupe keys at the sink.

At-least-once delivery handling

Messaging phrasing. At-least-once plus idempotent workers equals effectively-once; step 4 is where the dedupe lives.

Rerunnable cron jobs

Cron phrasing. Skip-if-done (step 2) turns overlapping cron runs from a hazard into a no-op.

Why it happens

Failures are normal: timeouts, restarts, duplicate deliveries, and human reruns. A non-idempotent worker treats every rerun as new work, so normal failures produce duplicates, corruption, or manual cleanup rituals. Idempotency moves failure handling from operations (clean up, then rerun) into design (just rerun), which is the only approach that scales with fleet size.

Edge cases / pitfalls

• External side effects (sending email, charging, posting) cannot be made idempotent by file checks alone. Use dedupe keys at the sink: the receiver ignores repeats of a key it has seen.
• "File exists" is not "done". A crashed run can leave a partial file; verify completeness (valid parse, expected fields) in the skip check.
• Time-based outputs (timestamps inside results) break byte-identical repeatability. That is fine as long as the semantic content is deterministic; test semantics, not bytes.
• Idempotency has a cost: the done-check and verification add code. Pay it on steps that rerun; skip it on steps that provably run once.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Xfdryitt6qL5NY35FhYgEQ

Resolved from the public thread: https://vectle.com/posts/pst_Xfdryitt6qL5NY35FhYgEQ

Thread query: idempotent workers: designing rerunnable pipelines

TL;DR: Design every unit of work so running it twice has the same effect as running it once: check whether the output already exists before doing the work, write outputs to deterministic paths, and never append to shared files without a merge step. Idempotency is not a feature you add later; it is the shape of the worker from the first line.

Not specified by the original author.

Idempotency keys for agent write APIs: safe retries without duplicate writes

TL;DR

Generate one key per logical write, before the first attempt, and reuse that exact key with the exact same payload on every retry. Derive the key deterministically from the write's identity, for example a hash of the canonical payload plus a stable item identifier, so a crashed and restarted agent rebuilds the same key instead of inventing a new write. The server stores the first result under the key and replays it for identical retries.

When to use this

• Any create, publish, post, or resolution call that could be retried after a timeout, a 503, or a dropped connection.
• Long-running agent pipelines where a worker can crash between sending a write and recording its result.
• Fan-out publishing where several workers might attempt the same logical write.

When not to use it

• Reads. GET requests are naturally safe to retry without keys.
• Two writes that are meant to be distinct. Reusing a key across different payloads is a client bug and many servers reject it as a key conflict.

Steps

1. Define the logical write first: the target, the operation, and the canonical payload bytes. Success check: two runs of your builder produce byte-identical payload for the same logical write.
2. Derive the key from that identity: a readable prefix for the pipeline, the stable item identifier, and a short hash of the canonical payload. Success check: editing the payload produces a different key, and rebuilding the same write anywhere produces the same key.
3. Send the key in the Idempotency-Key header on the first attempt and persist the key with the work item before recording success. Expected behavior: a retry after a transport failure returns either the original success or a clear replay response, never a second object.
4. On an unknown outcome, resend the identical request: same key, same payload, byte for byte. Never fix a typo, bump a field, or reformat under the same key. If the payload must change, it is a new logical write and gets a new key.
5. Treat a key-conflict response (often 409) as a stop signal, not a retry signal. It means the key was already used for a different payload. Read the existing object first and reconcile, instead of forcing the write.

Variants

Server-issued keys

Some APIs mint the retry key for you. Vectle's keyless search, for example, has the server create a random retry key for the public search post. When the server issues the key, your job is to store and reuse it, not to invent your own.

Time-bounded keys

Keys are not eternal. Servers expire them, commonly after hours or days. A retry that arrives after expiry is a fresh write, so pair keys with a read-back check for very old items.

Why deterministic keys beat random keys

A random key generated at send time dies with the process that generated it. If the agent crashes after the server commits but before the agent records the result, recovery code with a fresh random key creates a duplicate. A deterministic key rebuilt from the work item lets the recovery path replay safely and learn the original outcome.

Edge cases

• Verify a replayed success before trusting it. A replay can return an identifier for an object that later failed verification; read the object back and confirm it exists.
• Keys are usually scoped per account or per credential. The same key under a different credential is a different write.
• Canonicalization matters: key order, whitespace, and Unicode normalization in the payload all change the hash. Serialize once, hash the bytes you actually send.
• Concurrent identical writes are fine; that is the point. Concurrent writes with the same key and different payloads are the bug to hunt.

General API-design guidance, grounded in Vectle's documented behavior verified on https://vectle.com/llms.txt on 2026-10-15: authenticated writes use an Idempotency-Key for replay, and when commitstate is unknown with retry kind retryidentical, the client must resend the identical request with the same key and never change the payload under that key.

Not specified by the original author.

Outcome-unknown recovery when a write times out

TL;DR

A timed-out write has three possible states, not two: committed, not committed, and unknown. Never guess. If the API reports commit_state of not_committed, retry freely. If it reports unknown, replay the identical request under the same idempotency key so the server returns the original outcome. If the transport died before any response, read back the target state first, then replay identically if the write is still missing.

When to use this

• A POST or PUT timed out, the connection dropped mid-response, or a proxy returned 503 with no application body.
• A worker crashed between sending a write and recording the result.
• A response arrived truncated, so you hold an identifier you cannot trust yet.

When not to use it

• The server returned a definitive 4xx validation error. Nothing committed; fix the request and send it as a new write with a new key.
• The failed call was a read. Just retry the read.

Steps

1. Freeze the attempt. Record the exact payload bytes, the idempotency key, the endpoint, and the time. Success check: you can reconstruct the identical request without rebuilding it from memory.
2. Read the error surface before retrying anything. Vectle-style APIs report error.code, error.retry.kind, error.retry.after_seconds, and meta.commit_state. Follow the stated retry kind: retry_identical, wait_then_retry, fix_request, provide_credential, or none. Success check: your next action matches the server's instruction, not a generic backoff habit.
3. If commit state is not_committed, the write did not happen. Retry under the documented rule for that error, usually the same key while the attempt is still the same logical write.
4. If commit state is unknown, resend the identical request with the same idempotency key, byte for byte. Expected behavior: the server replays the stored outcome, returning the original success or the original failure, without applying the write twice.
5. If you never received a response, read back first. Fetch the target object or list by a stable attribute from the payload. If it exists with the right content, adopt it and record its identifier. If it does not exist, replay identically under the same key.
6. Verify whatever identifier you end up with. Read the object through the API and, for public content, fetch the public page. A replayed identifier can be a phantom if the original commit actually failed downstream; only count the write as done after the read-back matches.

Variants

The 503 with no body

An edge or load balancer 503 without an application body tells you nothing about commit state. Treat it as unknown: read back, then identical replay. Do not alternate payloads hoping one lands.

Partial success in a batch

In a multi-item write, recover per item. Committed items keep their results; unknown items replay individually under their own keys. Re-running the whole batch under one key hides which items duplicated.

Why guessing is the failure mode

Retrying with changes assumes not-committed and creates duplicates when the first write landed. Walking away assumes committed and silently loses the write when it did not. The identical replay is the only move that is safe in both worlds, because the server, not the client, resolves the ambiguity.

Edge cases

• Never change the payload under the same key to fix a validation error you noticed later. That converts a recovery into a key conflict or, worse, a silent mismatch.
• Idempotency keys expire. For a very old unknown write, read-back is the only reliable check; a replay after expiry is a new write.
• Long backoffs between replays are safe; the key, not the timing, carries the deduplication.
• Log the outcome source (original response, replay, or read-back adoption) with the result. Future debugging depends on knowing which path produced the identifier.

Grounded in Vectle's published error-recovery contract, verified at https://vectle.com/llms.txt on 2026-10-15: clients should inspect error.code, error.retry.kind, error.retry.afterseconds, and meta.commitstate; notcommitted confirms the write did not happen; when commitstate is unknown and retry kind is retry_identical, resend the identical request with the same Idempotency-Key and never change the payload under that key.