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UNABLE_TO_LOCK_ROW and retried writes We have an Apex integration where inbound middleware retries a request whenever the first attempt fails partway through — for example after a row lock contention error such as UNABLE_TO_LOCK_ROW, or when our own Queueable retry logic re-enqueues a job. The Apex side currently performs a plain insert, so every retry after
Mocha allows for test retries via the this.retries(n) method or the --retries CLI flag. When a test fails and is retried, the framework re-executes the test function from the beginning to determine if the failure was transient. A challenge arises when tests involve external side effects, such as database inserts or API calls. Because Mocha does not provide a
Goal: Ensure that a write operation retried by the Supabase client does not result in duplicate rows in the underlying PostgreSQL table. The Supabase JavaScript SDK automatically retries failed network requests with exponential back‑off, but it does not deduplicate the corresponding INSERT or UPDATE statements. Developers must therefore rely on database‑leve
Goal Determine how Traefik’s retry middleware should interact with a backend that returns a 409 Conflict response to indicate a duplicate write, so that retries on non‑idempotent requests do not cause unintended resource duplication or infinite retry loops. The retry middleware currently forwards the original request unchanged and decides to retry based only
The PyPI warehouse endpoint accepts POST uploads for package distribution. Network retries caused by transient failures may re-transmit the same payload, and if the warehouse does not enforce idempotency keys, duplicate objects can be created. The twine utility provides a --skip-existing flag intended to prevent re-uploading packages already present, but its
Goal Model retry logic in an activity diagram while ensuring that write operations are not duplicated across attempts. Constraint The diagram must represent a retry mechanism that updates a counter or condition atomically with the write action, preserving idempotency. Unresolved Decision The UML 2.5 specification does not prescribe a standard pattern for ret
tRPC does not ship a retry layer; retry behavior is inherited from the query-client integration, which delegates to TanStack Query's mutation options. Under tRPC v10 that integration is @trpc/react-query , while v11 uses @trpc/tanstack-react-query . TanStack Query does not retry mutations by default, unlike queries, because re-issuing a write-shaped request
When designing a system to handle network timeouts or request retries in SurrealDB, ensuring that writes do not result in duplicate records is critical. There are two primary architectural patterns for achieving this: utilizing deterministic record IDs with UPSERT or wrapping operations in transaction blocks. Deterministic IDs provide inherent idempotency by
Worker Execution Constraints Cloudflare Workers are designed for edge execution, utilizing bindings to services like KV. Event-Driven Logic When Workers respond to events, ensuring idempotent behavior is crucial for reliable state management. What is the documented behavior regarding event reprocessing within the Worker execution environment? Does the bindin
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