Queues, Streams, Eventing
Consumer-side dedup: the cheapest path to exactly-once processing
You added a dedup check to your payment consumer — a quick SELECT before charging. Duplicates dropped from dozens per week to zero. Then, three weeks later, a DB connection pool exhaustion caused the check to fail silently and the charge ran twice again. The check was outside the transaction. One line in the wrong place.
The naive dedup pattern and why it fails
The first instinct is a two-step check:
SELECT 1 FROM processed WHERE msg_id = 'msg-7a3f';
-- if found: skip
-- if not found: call Stripe, then INSERT into processedThis fails under concurrent delivery. Two consumers receive the same message simultaneously (possible during a rebalance or after a visibility timeout). Both run the SELECT at the same moment, both see “not found”, both call Stripe. Race condition. Two charges.
The correct pattern: INSERT-first, single transaction
The fix: put the dedup INSERT and the side effect in one atomic DB transaction, and INSERT first:
BEGIN;
INSERT INTO processed (msg_id, created_at)
VALUES ('msg-7a3f', now());
-- on UNIQUE violation: ROLLBACK and skip
-- if insert succeeded: do the side effect
UPDATE orders SET status = 'paid' WHERE id = 'O-123';
COMMIT;On a unique-constraint violation, the transaction rolls back — the side effect never runs. On commit, both the record and the side effect are written together. No crash window between them.
The key property: if the consumer crashes after the DB transaction commits but before it acks the broker, the broker redelivers. The next consumer tries to INSERT msg-7a3f again, hits the unique constraint, rolls back, and acks the broker. The side effect was already done once; the duplicate is silently discarded.
External APIs: the Stripe Idempotency-Key
The transaction trick only works when the side effect is a DB write inside the same transaction. What about external API calls — Stripe, SES, Twilio? You cannot include an HTTP call in a Postgres transaction.
The pattern for external APIs: pass an Idempotency-Key header derived from the message ID.
POST /v1/charges
Idempotency-Key: msg-7a3fStripe stores the key and the first response for 24 hours. If you call Stripe again with the same key (because the broker redelivered), Stripe returns the cached response without charging the card again. PayPal, Square, and most payment APIs follow the same convention.
When the external API supports idempotency keys, the pattern is:
- INSERT a pending row:
INSERT INTO stripe_intents (msg_id, status='pending')— in a transaction. This is the intent log. - Call the external API with
Idempotency-Key = msg_id. - On success: UPDATE the row to
status='completed', charge_id=....
If the consumer crashes between steps 2 and 3, redelivery re-calls Stripe with the same key (Stripe returns the cached charge_id), then completes the UPDATE. No double charge.
Why must the dedup INSERT be in the same transaction as the side effect?
A consumer uses Stripe's Idempotency-Key but no local DB dedup. The Stripe call succeeds, then the consumer crashes before acking. On redelivery, what happens?
Order the steps of a correct idempotent consumer wrapping an external payment API:
- 1 Receive msg-7a3f from broker
- 2 BEGIN DB transaction
- 3 INSERT INTO payment_intents (msg_id, status='pending') — unique on msg_id
- 4 COMMIT the pending row
- 5 Call Stripe with Idempotency-Key=msg-7a3f
- 6 UPDATE payment_intents SET status='done', charge_id=ch_abc123
- 7 Ack the broker — message removed from queue
- 01What is the crash window that makes SELECT-then-act unsafe for dedup?
- 02If the consumer crashes after the DB COMMIT but before acking the broker, what happens on redelivery?
- 03What is the Stripe Idempotency-Key TTL and what happens after it expires?
Consumer-side dedup is the cheapest path to effectively exactly-once processing: maintain a processed-messages table with a UNIQUE constraint on message ID, BEGIN a transaction, INSERT the dedup row first, perform the side effect, COMMIT. A UNIQUE violation on redelivery rolls back the entire transaction so the side effect never re-runs. For external APIs that live outside the DB transaction, derive an Idempotency-Key from the message ID and pass it with every call — Stripe, PayPal, and Square all support this convention and will cache the first response for at least 24 hours, making retries safely idempotent across the broker boundary.
appears again in202
- Why GraphQL gets N+1junior
- DataLoader mechanics: tick-boundary batchingmiddle
- Batch function contracts: ordering, shapes, errorsmiddle
- Federation and lookahead: batching beyond DataLoadermiddle
- Query complexity defences: depth, cost, persisted queriesmiddle
- Senior GraphQL API: scheduling contract, tenant isolation, observabilitysenior
- Why idempotency: making retries safejunior
- Server-side state machine: four states of an idempotency keymiddle
- Outbox and inbox: effectively-once across the dual-write boundarymiddle
- Concurrency and cache architecture for idempotency at scalesenior
- Observability, production failures, and global-scale designsenior
- The event loop: one thread, three queuesjunior
- Tasks, microtasks, and scheduler.yield()middle
- Microtask starvation, Long Tasks, and LoAFsenior
- Node.js event loop: phases, nextTick, and loop lagsenior
- React, Vue, and INP observability in productionsenior
- The render pipeline: six stages from bytes to pixelsjunior
- Stage costs and the renderer process modelmiddle
- Invalidation, dirty bits, and containmiddle
- Compositor layers: promotion, overlap, and GPU memorymiddle
- DevTools flame strip and the frame lifecyclemiddle
- Layout thrash: forced synchronous layoutsenior
- BeginMainFrame, compositor-driven animations, and GPU memorysenior
- Production observability: LoAF, INP, and the full attack surfacesenior
- What V8 is and why performance varies 100×junior
- V8''''s four-tier JIT pipeline and profile-guided tieringmiddle
- Hidden classes, transition trees, and memory layoutmiddle
- Inline caches, IC states, and deoptimizationmiddle
- Orinoco GC: parallel scavenger, concurrent marking, and write barriersmiddle
- TurboFan''''s speculative engine and the deopt-loop trapsenior
- V8 in production: isolates, pointer compression, and real failuressenior
- What workers are and why they existjunior
- Web worker mechanics: dedicated, shared, and OffscreenCanvasmiddle
- Structured clone and transferablesmiddle
- Service worker lifecycle and cache strategiesmiddle
- SharedArrayBuffer, Atomics, and cross-origin isolationsenior
- Service worker edge cases: version skew, durability, and navigation trapssenior
- Worker pools, Comlink, and production observabilitysenior
- What the reconciler does: render vs commitjunior
- The fiber object and the double-buffer treemiddle
- Render phase purity and commit phase sub-stepsmiddle
- Reconciliation: diffing heuristics and the key trapmiddle
- Priority lanes, time-slicing, and useTransitionmiddle
- Bailout, memoisation, and tearingsenior
- React Profiler, the Compiler, and production observabilitysenior
- Rendering strategies: SSG, SSR, ISR, streaming, and hydrationjunior
- SSG, SSR, ISR, streaming, and RSC — how each worksmiddle
- Hydration cost: selective, progressive, islands, resumabilitymiddle
- Hydration mismatch: causes, detection, and the determinism rulesenior
- RSC, per-route strategy, and production observabilitysenior
- Core Web Vitals: what LCP, INP, and CLS measurejunior
- CLS: why layout shifts happen and how to stop themmiddle
- Metric tradeoffs, RUM attribution, and the CI+field loopsenior
- The full picture: URL to LCP to INP as a relay racejunior
- Eight layers traced: from the service worker to the second navigationmiddle
- Five canonical breaks: where production reliably diessenior
- The three-track method: reading traces and building a monitored systemsenior
- What is a cache stampede and why it makes things worsejunior
- Lock and single-flight: bounding concurrent rebuildsmiddle
- XFetch: coordination-free probabilistic early expirationmiddle
- Stale-while-revalidate and CDN request coalescingmiddle
- Detecting stampedes and designing TTL for productionmiddle
- Metastable failure, fencing tokens, and production postmortemssenior
- What a relation is: tables, rows, keys, and constraintsjunior
- Constraints, keys, and Postgres data typesmiddle
- Normal forms, denormalization, and why schemas stickmiddle
- JSONB, arrays, and when a side table winsmiddle
- Heap storage, TOAST, and column alignmentsenior
- Schema integrity: deferral, versioning, and production failure modessenior
- Relational vs document, wide-column, graph, and key-valuesenior
- Index-only scans, the Visibility Map, and INCLUDEsenior
- Production failure modes and the index audit playbooksenior
- pg_statistic, ANALYZE, and production observabilitymiddle
- Production failure modes and plan stabilitysenior
- MVCC: why readers and writers never wait for each otherjunior
- Row versions and snapshots: the on-disk mechanicsmiddle
- HOT updates and isolation levels: what you gain and what you paymiddle
- Vacuum and bloat: keeping the storage tax boundedmiddle
- CLOG, XID wraparound, and MultiXact: deep visibility internalssenior
- SSI internals and production autovacuum tuningsenior
- Real-world MVCC failures, deployment patterns, and distributed snapshotssenior
- Connection pools: amortising the cost of a Postgres backendjunior
- PgBouncer session, transaction, and statement modesmiddle
- Pool sizing: the (cores × 2) + spindles formula and the two-layer stackmiddle
- Pool exhaustion and idle-in-transaction: the 3 AM failure modemiddle
- Migrating to transaction mode: rollout playbook and PgBouncer 1.21 prepared statementsmiddle
- The Postgres process model and why raising max_connections degrades throughputsenior
- Pooler landscape 2026, serverless connection storms, and the full failure-mode taxonomysenior
- What a schema migration is and why it replaces ad-hoc DDLjunior
- ADD COLUMN: instant in PG 11+ vs rewrite in older Postgresjunior
- The lock-queue failure mode: why instant DDL can freeze the databasemiddle
- Safe DDL patterns: NOT VALID, CONCURRENTLY, and unsafe-op fixesmiddle
- Expand-contract: zero-downtime for breaking schema changesmiddle
- Advisory locks, migration tools, and deploy coordinationsenior
- Migration failure taxonomy and production disciplinesenior
- Why sharding exists: the single-Postgres ceilingjunior
- Shard-key selection: hash, range, list, and directory strategiesmiddle
- Partitioning vs sharding: same word, two different thingsmiddle
- Co-location and Citus: the invariant that makes sharding usablemiddle
- The hot-shard failure mode: detection, isolation, and durable policymiddle
- Schema-based sharding and multi-tenancy alternativessenior
- Online resharding, 2PC, and the operational cost of shardingsenior
- The seven acts: from CREATE TABLE to Citusjunior
- Acts 1–3 in depth: schema, indexes, and planner statisticsmiddle
- Acts 4–6 in depth: MVCC bloat, connection pooling, and safe migrationsmiddle
- Act 7 in depth: sharding, co-location, and the seven-tier tradeoff cascademiddle
- Observability, anti-patterns, and production triagesenior
- Raft roles, terms, and why majority quorums prevent split brainjunior
- How Raft replicates a log entry and decides it is safe to commitmiddle
- Raft leader election: timeouts, voting rules, and the four safety propertiesmiddle
- Raft in the real world: partitions, slow disks, and client routingmiddle
- Raft extensions: pre-vote, learners, snapshots, and linearizable readssenior
- Raft in production: membership changes, Multi-Raft, and observabilitysenior
- Where data fetching happens — and why it decides LCPjunior
- Fetch waterfalls — diagnosis and the Promise.all curemiddle
- React Server Components and Suspense streamingmiddle
- Client-side cache: TanStack Query, SWR, and stale-while-revalidatemiddle
- LCP, prefetch, and race conditions in interactive fetchingmiddle
- Senior internals: RSC payload, caching layers, and production failure modessenior
- The IP envelopejunior
- Reading the IP headermiddle
- The three-way handshakejunior
- Sequence numbers and connection statemiddle
- DNS: what it does and why it existsjunior
- The resolver walk: referrals, record types, and gluemiddle
- TTL, caching, and DNS propagationmiddle
- What TLS does and why it existsjunior
- The 1-RTT handshake: key shares and ECDHEmiddle
- Session resumption and 0-RTTmiddle
- Key schedule, SNI, ALPN, and extensionssenior
- 0-RTT defenses, ECH, hybrid PQ, and production TLSsenior
- WebSocket: the HTTP upgrade handshakejunior
- WebSocket frame format: opcodes, masking, fragmentationmiddle
- WebSocket backpressure: when clients can''''t keep upmiddle
- Reconnection: jittered backoff, thundering herd, message resumptionsenior
- WebSocket at scale: HTTP/2 multiplexing, permessage-deflate, C10Msenior
- WebSocket in production: proxies, security, and distributed architecturesenior
- What reverse proxies dojunior
- Health checks, connection draining, and slow startmiddle
- Session affinity, consistent hashing, and the right fixmiddle
- Retry storms, circuit breakers, and load sheddingsenior
- Resilient LB architecture: anycast, zone-aware routing, and observabilitysenior
- Why QUIC and not TCP+TLSjunior
- Connection IDs and network migrationmiddle
- 0-RTT resumption and packet encryptionsenior
- DDoS: what it is and why it worksjunior
- Amplification attacks and state exhaustionmiddle
- Rate limiting: algorithms and architecturemiddle
- WAFs, firewalls, mTLS, and HSTSmiddle
- DNS cache poisoning and BGP hijackingsenior
- Defense-in-depth architecture and attack economicssenior
- The twelve layers: one URL, seven actorsjunior
- DNS, TCP, TLS in sequence: where the milliseconds gomiddle
- Proxy intercepts and security gates: rate limiters, WAF, mTLSmiddle
- Alternate paths: QUIC 0-RTT, WebSocket upgrade, connection migrationmiddle
- Observability: distributed traces, USE/RED, and samplingsenior
- Resilience: cascading retries, circuit breakers, and error budgetssenior
- What the three signals are: logs, metrics, and tracesjunior
- Why structured logs exist: the diary vs the spreadsheetjunior
- The production log schema: fields every line must carrymiddle
- PII redaction and log injectionsenior
- OTel Logs Data Model and audit logs as a subsystemsenior
- What is OpenTelemetry: API, SDK, Collector, OTLPjunior
- OTel signals, Semantic Conventions, and the OTLP wire formatmiddle
- The OTel Collector: receivers, processors, exporters, and deployment patternsmiddle
- Vendor neutrality, eBPF instrumentation, the Operator, and browser/serverless OTelsenior
- Operating the OTel Collector: reliability, version skew, failure modes, and governancesenior
- SLI, SLO, and the error budget: reliability by the numbersjunior
- Error budget policy, latency SLOs, and composite journeysmiddle
- Production SLO failures, self-observability, security, and the big picturesenior
- What is trace propagation and why broken propagation is worse than nonejunior
- traceparent and tracestate: the W3C header format in fullmiddle
- Baggage and async boundaries: carrying context across queues and callbacksmiddle
- Async context per language, service mesh, B3 migration, and securitysenior
- Production propagation failures, span links, and platform designsenior
- The debugging funnel: SLO → RED → trace → profilejunior
- OTel architecture: one SDK, four signals, one wire formatmiddle
- The incident loop: from pager to postmortem to preventionmiddle
- Scale, security, and the ROI of observable systemssenior
- Cache lines, struct layout, and false sharingmiddle
- SIMD, SoA vs AoS, and memory bandwidthmiddle
- Cache-oblivious algorithms, PGO, and production failuressenior
- GC in production: observability, security, edge cases, and fleet governancesenior
- Batching: amortize fixed cost per operationjunior
- The batching window: size and wait timemiddle
- Batching in Kafka and Postgresmiddle
- io_uring and observability of batchingmiddle
- From Nagle to io_uring: evolution of batchingmiddle
- Backpressure, failure isolation, and batch security in productionsenior
- CI enforcement and RUM: making budgets stickmiddle
- V8 JIT pipeline, HTTP priorities, and bundle securitysenior
- The performance loop: discipline, not a projectjunior
- Classify and fix: matching bottleneck families to remediesmiddle
- Observability stack and CI gates: catching regressions before they shipmiddle
- Incident to enforcement: SLO burn to verified fix in 35 minutesmiddle
- Culture, economics, and org-scale performancesenior
- What OAuth is and why passwords are not the answerjunior
- Authorization code flow with PKCEmiddle
- ID token validation and JWKS cache managementmiddle
- Refresh token rotation and scope-based least privilegemiddle
- Sender-constrained tokens: DPoP and mTLSsenior
- OAuth in production: audience attacks, observability, and real failuressenior