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Graph/restart-induced-retry-storm

AntiPattern

Restart-Induced Retry Storm

restart-induced-retry-storm

Retry and restart mechanisms recreate “fresh” state after 429/rate-limit events, so budgets, cooldowns, and learned traffic signals are reset and the proxy/system keeps hammering or re-expanding workloads, causing OOM and sustained throttling.

Node Details

Public graph metadata

Updated6/29/2026

Connections

30 linked nodes

MATCHESSolution

handle a scenario when the code is fine but backend went offline — prefetching data with usePrefetchQuery. Tension: prefetch works.

MATCHESSolution

It will be 10 seconds after the previous request completes. — If `refetchInterval` is set to 10 seconds in `useQuery`. Tension: Understanding the behavior would help me to pick correct `refetchInterval` value. Outcome: The timer re-starts every time when data is put into the cache.

MATCHESSolution

Manage the refreshing state more robustly — ensuring it only turns off after all async operations are complete. Tension: remove the arbitrary timeout. Outcome: ensure no state updates occur after the component begins to unmount.

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Wrap updates that suspend with startTransition

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post-start failures. Tension: it skipped durable user transcript append, synthetic assistant error append, and session-store terminalization.

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Disable retry — If the issue persists. Tension: it could be because the sdk is getting an error and appear to be hanging while it's retrying. Outcome: You could try disabling the retry and check if there is an error.

MATCHESSolution

Rate limiting - This enables you to control the number of incidents Sentry receives per project over a given period of time — Spike protection is dynamic and adjusts more to your personal needs. Tension: some redundant events won't be measured and counted against quota. Outcome: Combination of both could provide a great way to preserve your quota until the end of the month.

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any time you have an I/O bound process with lots of idle time — waiting for user input, waiting for a TLS handshake. Tension: wrapping it in an async task can let other code run.

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Run netstat after the runner executes its first job and then later, after a long period of inactivity. — Windows machine. Tension: it to poll correctly without having to restart it every so often. Outcome: Make sure that your Windows machine does not go to sleep or hibernation mode. Also make sure that your network supports long running connections.

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making sure you pace your requests to stay below those limits — API rate limits. Tension: allowing room for your normal operations, too. Outcome: handle any 429 responses gracefully to retry.

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bound it with a "progress ceiling" — a process emitting bytes but making NO semantic progress. Tension: a process emitting bytes but making NO semantic progress (livelock) would be shielded forever. Outcome: re-arms recovery after a long no-progress window even while bytes flow.

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implementing this retry behavior all over the place — axiosInstance.js. Tension: handle the retry logic. Outcome: you could try using a response interceptor to abstract and handle the retry logic.

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could re-call just the endpoint. Tension: To get away from implementing this retry behavior all over the place.

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A reload can be triggered utilizing the "dynamic configuration update feature" (KIP-226) via Kafka Admin API. — After reconfiguration of trust- or keystore. Tension: Do Kafka rereads the stores on each new connection ? Outcome: kafka-configs.sh --command-config /commando.conf --bootstrap-server $(hostname):9092 --entity-type brokers --entity-name 1 --alter --add-config listener.name.internal.ssl.keystore.type=JKS.

MATCHESSolution

Add request throttling/rate limiting (e.g., using express-rate-limit or infrastructure-level rate limiting) to cap OTP requests per time window per client/IP. Optionally require additional protections such as CAPTCHA and authentication/API keys for the OTP generation route.

MATCHESSolution

Use virtual threads for blocking DB/IO work to improve scalability under the same small pool, or switch to a reactive database driver/event-loop model to avoid blocking carrier threads; adjust concurrency/throttling accordingly (semaphore/backpressure) rather than assuming the current JDBC + thread pool design remains optimal.

MATCHESSolution

You can disable infinite retries using: — spring.kafka.producer.retries=1. Outcome: Handle the Send Failure via `Callback`.

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I am seeing an increase in latencies in requests that do succeed — I have a logic of the sort :. Tension: none of them enforce a timeout on the total duration of the request. Outcome: Then properly close the connection in a finally / try-resource block.

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after the system restarts. Tension: wouldn't it be a waste of DB space if I'm storing millions of tasks that I only ever need to process once. Outcome: store the last offset in cache and then continue processing data from the last checkpoint.

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decorate a connection pool with a priority queue to avoid overloading the connection pool — When the connection pool usage goes above some critical level. Tension: start throttling requests and maybe even drop low priority requests from the queue all together.

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Tension: it was being looped infinitely, crashed and restarted every time. Outcome: fixes the issue.

MATCHESSolution

Restart=on-failure # don't restart on clean exit — `Restart=always` + `RestartSec=3` means 20 restarts per minute with no systemd-level circuit breaker. Tension: systemd kills the service if it crashes 5x in 5min. Outcome: lets the OS stop the cascade.

MATCHESSolution

Fixed 30s retry on 429 keeps hammering. — when clearly rate-limited. Tension: keeps hammering. Outcome: Exponential backoff + hard rejection during cooldown.

MATCHESSolution

Write token usage, window start timestamp, consecutive 429 count, and cooldown state to a file (`state.json`) on every mutation. — without this, every restart is a full reset. Tension: every restart is a full reset. Outcome: Restore on startup, expiring the window if its timestamp is stale.

MATCHESSolution

Updated retrieval to use recency, normalized momentum, optional vector similarity, trigram score, live-state bias weights, short-term exclusion, escaped ILIKE parameters, and multiplicative tier boost. — Corrective pass for TypeScript memory subsystem retrieval and daemon resilience. Tension: retrieval scoring omitted recency decay, normalized momentum, live-state retrieval bias, and multiplicative tier boost. Outcome: Added daemon exponential reconnect/backoff with status reporting. Corrected spec constants, extractor error fallback handling, non-padding embedding normalization, partitioned migration DDL, non-null event entity roles, seed data migration, and idempotent markdown memory seed insertion.

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Add search to the anonymous allowlist, implement per-tool per-minute anonymous rate buckets, 500-character query caps, unique-query/node-enumeration fingerprinting, and cooldown after repeated over-limit hits.

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CancellationTokenSource pattern allows cancelling mid-reload — In `OnReloadStart`. Tension: when switching weapons or firing. Outcome: cancel any existing reload, create a new CTS, set `isReloading = true`, then `await ReloadAsync(token)`.

MATCHESSolution

Stopped the active runner — background extraction does not consume Gemini quota. Tension: continuing futile retries. Outcome: waits until the Pacific reset instead of continuing futile retries.

MATCHESSolution

Fix the integral controller's anti-windup guards — The budget nudge-up fired on `tokensUsedInWindow > 0 && consecutive429s === 0`. Tension: a freshly-restarted proxy with one request satisfies this immediately. Outcome: Gate budget increases on: - Minimum traffic duration (e.g. 1 hour of history) - Minimum budget consumption (e.g. 5%+ used) - No 429s in recent window.

MATCHESSolution

`OOMPolicy=continue` — via `systemctl daemon-reload` with NO service restart. Tension: a CHILD OOM-kill only fails that subprocess; the service keeps running.

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