Caching
Stale-while-revalidate and CDN request coalescing
A lock-based cache gives 10,000 waiting requests one of two things: the rebuilt value (after waiting 400 ms) or a fallback. Stale-while-revalidate gives all 10,000 waiting requests the old value immediately — and queues exactly one background refresh. Zero waiting, zero DB spike.
RFC 5861: the standard
RFC 5861 (2010) defines two Cache-Control extensions:
stale-while-revalidate=N— serve the stale (expired) cached response for up to N seconds while initiating a background revalidation. The user sees a response without waiting.stale-if-error=N— serve the stale cached response for up to N seconds if revalidation fails (5xx, timeout). Keeps the site available during origin failures.
Example header:
Cache-Control: max-age=60, stale-while-revalidate=30, stale-if-error=3600This means: fresh for 60 s, serve stale for an additional 30 s while refreshing in the background, serve stale for up to 1 hour if the origin errors.
What happens at TTL expiry
With SWR enabled on a cache (CDN or application-level):
T=60.0s— TTL fires. Cache key is now “stale but within stale-while-revalidate window”.- Requests 1–N all arrive at T=60.001s.
- All N requests immediately receive the stale value. No waiting.
- Exactly one background refresh is queued (the cache picks the first request, or uses a separate background goroutine/task).
T=60.4s— background refresh completes. New value stored.- Future requests get the fresh value.
DB load at the boundary: 1 query, not N.
| Mitigation | User wait at TTL boundary | DB queries at boundary |
|---|---|---|
| None (naïve TTL) | None — but DB falls over | N concurrent |
| Lock only | Up to rebuild p99 (waiters queue) | 1 (serialised) |
| Single-flight | Up to rebuild p99 (subscribers wait) | 1 per node |
| XFetch | None — cache never expires under traffic | ~1 (early rebuild) |
| SWR | None — stale served immediately | 1 (background) |
The tradeoff: bounded staleness
SWR explicitly accepts that readers will see stale data for up to the stale-while-revalidate duration after the max-age expires. This is fine for:
- Content pages, news feeds, product listings
- Homepage hero banners, navigation menus
- Any data where a 30–300 s lag is invisible to users
It is wrong for:
- Account balance, vote counts, anything that affects business decisions in real time
- Anything where two users must see consistent state simultaneously
CDN-level: request coalescing
CDNs extend SWR with request coalescing (Cloudflare) or request collapsing (Fastly). When a cache miss arrives at the edge:
- The edge enters a “stitching” state — it has issued one upstream fetch and is waiting for the response.
- Any additional requests for the same path while in “stitching” state do not generate additional upstream fetches.
- All waiting requests receive the response simultaneously when the single upstream fetch completes.
A viral content event with 10 million viewers hitting one URL produces one origin fetch, not 10 million. Both Cloudflare and Fastly publish that request coalescing turns sudden-traffic incidents into low-impact events at the origin.
Framework-level: Next.js ISR
Next.js Incremental Static Regeneration (ISR) is SWR at the framework level. A page configured with revalidate: 60 is served from cache for 60 seconds; the first request after the revalidate window triggers a background regeneration while the stale page continues serving. The shape is identical to RFC 5861 — the framework just implements it without requiring HTTP Cache-Control headers.
Why this works
Apollo’s GraphQL caching uses SWR semantics for normalised cache entries. A query result is served from the normalised cache while a background refetch reconciles any out-of-date fields. The same principle extends to gRPC response caching and even DNS TTLs — the pattern “serve stale, refresh in background” is universal wherever TTL-based caches exist.
At T=60.001 s, 2,000 requests arrive for a key with TTL=60 s and stale-while-revalidate=30 s. How many of them wait for the rebuild to complete?
Which use case is UNSUITABLE for stale-while-revalidate?
Cloudflare request coalescing fires at a CDN edge during a viral traffic event. 50,000 simultaneous requests arrive for the same URL that just expired. How many upstream origin requests are made?
- 01What does the HTTP header Cache-Control: max-age=60, stale-while-revalidate=30, stale-if-error=3600 mean in practice?
- 02How does Next.js ISR implement the same guarantee as RFC 5861 stale-while-revalidate?
Stale-while-revalidate (RFC 5861) eliminates waiter queues at TTL boundaries by returning the stale cached value immediately to all requests and triggering exactly one background refresh. The user latency at the boundary drops to zero; DB load drops to one rebuild query. CDN-level request coalescing extends the same principle globally: the edge issues one origin fetch per cache miss event regardless of concurrent request count. The tradeoff is explicit bounded staleness — acceptable for content, unsuitable for strongly consistent business data. Compose SWR at the CDN edge with XFetch or a distributed lock at the application cache layer for defence-in-depth at every tier.
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 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
- At-most-once, at-least-once, exactly-once: the three delivery contractsjunior
- The three failure legs — where duplicates and losses actually happenmiddle
- Consumer-side dedup: the cheapest path to exactly-once processingmiddle
- Kafka exactly-once semantics: idempotent producer and transactionsmiddle
- SQS visibility timeout, DLQ, and the outbox patternmiddle
- Exactly-once in production: impossibility proof, hybrid patterns, and real incidentssenior
- 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