「retries」の検索結果

Use when building functions that must survive process crashes, retry automatically on failure, run on a schedule, react to events, or maintain state across infrastructure failures — e.g., webhook handlers that drop events, flaky cron jobs, background jobs that fail mid-execution, or workflows that need to resume where they left off. Covers Inngest function configuration, triggers (events, cron, invoke), step execution and memoization, idempotency, cancellation, error handling, retries, logging, and observability.

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Implement idempotency keys and handling to ensure operations can be safely retried without duplicate effects. Use when building payment systems, APIs with retries, or distributed transactions.

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Use when defining or debugging Spring Boot 4 database transaction boundaries, rollback, propagation, optimistic-lock retries, or commit-bound events. For duplicate HTTP commands use idempotency-patterns; for reactive transactions use webflux-reactive-patterns.

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Use when adding retries, backoff, or concurrency protection to Spring Boot 3 / Spring Framework 6 services. Covers Spring Retry or Resilience4j selection, proxy behavior, and transaction limits.

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Use when making retried HTTP commands or message processing safe against duplicate effects, including database-backed request keys, payload conflicts and concurrent retries. Do not apply caching as a substitute for business idempotency.

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Use when implementing Kafka, RabbitMQ, Pulsar, or JMS producers and consumers in Spring Boot 4. Covers modular starters, event contracts, idempotency, retries, dead letters, and outbox delivery.

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Use when building or changing a Spring Cloud Gateway on Spring Boot 3. Covers route design, authentication, header hygiene, rate limiting, retries, timeouts, observability, and testing.

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Use when defining or debugging Spring Boot 3 database transaction boundaries, rollback, propagation, optimistic-lock retries, or commit-bound events. For duplicate HTTP commands use idempotency-patterns; for reactive transactions use webflux-reactive-patterns.

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Use when implementing Kafka, RabbitMQ, Pulsar, or JMS producers and consumers in Spring Boot 3. Covers event contracts, idempotency, retries, dead-letter handling, ordering, and outbox delivery.

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Before removing code that looks redundant, wasteful, or "wrong" based on log observations or code review instinct, grep the test suite for tests whose names describe the exact behavior you're about to remove. A test whose title literally describes the "weird" thing (e.g. "calls play() even when player reports playing", "retries even when first call succeeded", "skips cache even when fresh") is a load-bearing workaround, and the production log you're reacting to is probably the workaround working as designed — not a bug. Use when: (1) Tempted to remove a function call that "looks redundant" based on a production log, (2) About to "simplify" a conditional that seems to always take the same branch, (3) Reviewing code and thinking "why would anyone write this?", (4) A log line shows a state that seems impossible or contradictory (e.g. playing=true but positionMs=0 for seconds), (5) CI surfaces a failing test whose name describes exactly what your fix removed.

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Diagnose malformed calls, retries, and unusable results after an agent selects an MCP tool.

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Cloudflare Workflows for durable long-running execution. Use for multi-step workflows, retries, state persistence, or encountering NonRetryableError, execution failed errors.

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Select, integrate, and operate multi-model video-generation gateways — hosted inference aggregators such as fal.ai, Replicate, and WaveSpeed that expose many third-party video (and video-adjacent audio) models behind one account, one API surface, and one bill. Use when deciding whether to route video generation through a gateway versus a direct model-provider API; when comparing gateways on catalog, async/queue job design, webhooks, pricing, schema discovery, and version pinning; and when operating gateway workloads in production — spend controls, retries, cross-gateway failover, content- safety differences, data retention, and commercial-rights passthrough of the underlying model. Not for direct model-provider APIs, local/self-hosted inference, model training/fine-tuning, or image-only gateway use.

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Produce and review production image-generation and image-editing workflows with Amazon Nova Canvas on Amazon Bedrock, including native InvokeModel payloads, safe authentication, validation, retries, cost controls, provenance, and lifecycle migration checks. Use when a task names Nova Canvas, amazon.nova-canvas-v1:0, Bedrock image generation, Canvas inpainting/outpainting/background removal, image conditioning, color guidance, image variation, virtual try-on, or Canvas fine-tuning.

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Generate, edit, and iterate still images with Runway's official API, especially native Gen-4 Image and Gen-4 Image Turbo reference workflows. Use when implementing Runway text-to-image, reference-driven image generation or natural-language image edits, task polling, secure artifact handling, production retries, cost controls, or Runway image API QA. Do not use for Runway video generation or third-party gateway APIs.

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Build and operate production image generation and natural-language image editing with ByteDance Seedream through first-party Volcengine Ark (China) or BytePlus ModelArk (global), including model and region selection, multi-reference and grouped outputs, streaming, secure artifact handling, retries, cost controls, prompting, safety, and rights review.

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Production-grade Python and AI engineering standards. Use for ANY Python work — FastAPI services, LangChain/LangGraph agents, RAG pipelines, LLM API integrations, document processing, or data scripts — whether writing new code, reviewing, refactoring, or debugging. Enforces typed Pydantic boundaries, structured errors and logging, LLM call timeouts/retries/output validation, secret management, async correctness, and tested code.

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Apply when designing commands, lifecycle steps, or processing loops that run amid crashes, restarts, and retries. Converge to the same end state regardless of partial prior runs.

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Configures Envoy, the high-performance C++ edge and service proxy, as an API gateway, load balancer or service mesh sidecar with L4/L7 routing, retries, circuit breaking, rate limiting, TLS and observability. Use when a user asks to set up Envoy, write an envoy.yaml, route traffic to backends, add health checks or rate limits, do a canary split, read Envoy admin stats, or understand the proxy behind Istio and Envoy Gateway.

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Runs background jobs in Node.js with BullMQ, a job queue library backed by Redis (or PostgreSQL since v6): delayed and prioritized jobs, retries with backoff, rate limiting, cron-style job schedulers, parent-child flows and concurrency control. Use when a user asks to add a background worker, queue emails or webhooks, schedule recurring jobs, retry failed jobs, rate limit an API client, or migrate from Bull or from BullMQ 5 repeatable jobs.

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Refuses "flaky" as a diagnosis. Turns an intermittent failure into a reproduction by isolating it from the environment, naming the variable that controls it, forcing that variable, and measuring before and after with enough runs to count. Separates real defects from environment contention with evidence, and never retries to green or widens a threshold. Use when a test, performance check, or deploy step fails intermittently or passes on rerun.

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Design safe queue consumers and retries. Use when a mid-level developer needs reliable background processing.

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Runs a multi-step procedure as a Python DAG, so ordering, branching and retries are enforced by the runner rather than described in prose a model can generate past. Use for "run these steps in order and retry the flaky one until the check passes", "build a pipeline that fetches, validates, then skips the upload when nothing changed", "make sure these steps cannot be skipped", "resume from where it broke instead of redoing the expensive early stages", "run these independent calls at once and merge the results", or any procedure of 3+ steps with branches, input contracts, or side effects that must not block the critical path. Primitives are depends_on, when=, validate=, retry_until=, detached= and journal_path=. Not for a single sequential call, for steps needing reasoning between them that no predicate captures, or for async and distributed work. To audit whether one verification check can actually go red, use gating. To fan work out across many subagents, use a dynamic workflow.

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Use when adding or changing a scheduled data or ML pipeline — Dagster assets, Prefect flows, Airflow DAGs or a cron'd entrypoint — idempotent partitioned stages, retries, backfills, quality gates, and testing the pipeline without the scheduler

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