Performance
Code splitting: route-level, component-level, vendor splitting
A dashboard app ships 900 KB of JS to every page because all routes share one bundle. The homepage needs React, a chart library, a date picker, and the admin panel — even though the homepage never shows a chart, uses the admin panel, or renders the date picker. Three routes of unneeded code travel to the user on every visit to every page.
Route-level splitting
The most impactful change. Each route gets its own JavaScript chunk; navigating to /settings does not download /editor’s bundle. Every modern bundler supports this out of the box.
- Webpack —
splitChunksconfiguration, or React Router 6 withlazy(). - Vite — automatic per-route splitting when using a router plugin.
- Next.js — automatic per-page chunks in both Pages and App Router. Every file in
/pagesor/(routes)is a separate chunk automatically. - Remix — route modules are automatically code-split; parallel data loading is built-in.
The senior pattern: every route has its own chunk. If the bundle analyzer shows that 40% of the homepage bundle is from /admin routes, that is route splitting not working.
Component-level splitting
Components that are not on the critical path — modals, sidebars, drawers, admin tools, complex charts — should load only when they are needed. The primitive is dynamic import().
// React
const DatePicker = React.lazy(() => import('./DatePicker'));
// Render only inside a <Suspense> boundary
// Vue
const DatePicker = defineAsyncComponent(() => import('./DatePicker.vue'));The rule of thumb: any component over 30 KB that is not rendered on first paint should be lazy-loaded. Common candidates: rich text editors, data visualisation libraries, map components, video players, admin panels.
A lazy-loaded component defers its download until the first render. When the user triggers it (opens a modal, navigates to a sub-route), the chunk downloads and parses on the spot. To avoid the visible delay on that first interaction, prefetch the chunk when the user hovers or when the trigger is nearby:
<link rel="prefetch" href="/chunk-datepicker.js" />Or use React.lazy with import(/* webpackPrefetch: true */ './DatePicker').
Vendor splitting
Third-party libraries (React, React DOM, charting libraries, utility packages) change infrequently compared to your own application code. If they are bundled with your app code, every new deploy invalidates the user’s cached copy of both — the user re-downloads React even though React did not change.
Vendor splitting puts third-party libraries in a separate chunk with its own content hash. The user caches vendor.abc123.js indefinitely. A deploy that only changes your app code invalidates app.def456.js but the vendor chunk stays cached. Second visits after a deploy download only the small changed chunk.
How to configure: Webpack’s splitChunks.cacheGroups, Vite’s manualChunks, Next.js’s automatic framework chunk (framework-*.js in _next/static/chunks/).
| Strategy | Mechanism | Best for |
|---|---|---|
| Route-level | Each route in its own chunk via bundler or router | SaaS apps with distinct routes sharing little code |
| Component-level | dynamic import() + React.lazy / defineAsyncComponent | Modals, drawers, charts, editors not on critical path |
| Vendor splitting | Third-party libs in separate content-hashed chunk | Apps that deploy frequently; libraries change rarely |
The critical path boundary
Not all lazy loading is free. Deferring a component means the first time the user opens it, the chunk downloads and parses in the foreground. This improves LCP (smaller initial bundle) but can hurt INP for that specific interaction.
The rule: keep on the critical path anything the user sees within the first 2-3 seconds of a page load. Defer everything else. If a component is below the fold, behind a click, or on a route the user visits second, it belongs in a lazy chunk.
Why this works
Why does vendor splitting help even if the vendor chunk is large? Cache headers. Static assets are served with Cache-Control: public, max-age=31536000, immutable because the filename includes a content hash. Once the user has downloaded vendor.abc123.js, it never downloads again unless the hash changes. Only your app code changes on every deploy.
Order the steps of bringing an existing 1.2 MB bundle under a 200 KB per-route budget:
- 1 Open bundle analyzer and sort modules by size
- 2 Identify which modules are not used on the current route
- 3 Set up route-level splitting via the framework router
- 4 Lazy-load large components not on the critical first-paint path
- 5 Move third-party libraries to a vendor chunk with a content hash
- 6 Re-run bundle analyzer and verify each route is under budget
- 7 Add a CI size-limit gate to enforce the budget on future PRs
After lazy-loading a 100 KB chart component, LCP improves but a user reports a visible delay when they first click 'Open Chart'. What is the cause and the fix?
A daily-deploying Next.js app has one large bundle with React, app code, and libraries mixed. After vendor splitting, users on a second visit after a deploy report the page loads faster. Why?
A product engineer wants to add a rich text editor (180 KB) to the homepage. What is the senior recommendation?
- 01Explain route-level, component-level, and vendor splitting. When is each the right choice?
- 02A lazy-loaded component causes visible delay on first interaction. What is the fix?
Code splitting is the most impactful bundle optimisation: route-level ensures each page pays only for its own code, component-level defers large non-critical components, vendor splitting preserves cached library chunks across frequent deploys. The primitive for the latter two is dynamic import(), which every modern bundler and framework wraps into React.lazy, defineAsyncComponent, or equivalent. All three strategies stack. The next lesson covers tree shaking and compression — complementary techniques that reduce bytes within each chunk.
appears again in159
- The journey of a request: seven stops from socket to responsejunior
- Accept and parse: from kernel queue to a typed requestmiddle
- Routing and middleware: choosing what runs, and in what ordermiddle
- Handler and response: from business logic to bytes on the wiremiddle
- Streaming and backpressure: when the client reads slower than you writesenior
- Timeouts and tail latency: budgets, deadlines, and the fan-out trapsenior
- Middleware and DI: the two patterns that shape every backendjunior
- Writing middleware: signatures, next(), and the three framework modelsmiddle
- Inversion of control: how dependencies reach a classmiddle
- DI scopes and lifecycles: singleton, request, transientmiddle
- DI as a testing seam: fakes, mocks, and the boundary that matterssenior
- DI containers in production: resolution graphs, circular deps, and when not tosenior
- Blocking vs non-blocking I/O: two ways to waitjunior
- The event loop: one thread, ordered phasesmiddle
- What blocks the loop: CPU work and sync callsmiddle
- Offloading CPU work: worker threads and the libuv poolmiddle
- Backpressure and bounded concurrencysenior
- Throughput under load: tail latency and saturationsenior
- Why pool: the cost of creating a connectionjunior
- Pool sizing: why bigger is not fastermiddle
- Acquisition and timeouts: the wait queue is the real latency dialmiddle
- Retry strategies: backoff, jitter, and thundering herdmiddle
- Observability, production failures, and global-scale designsenior
- Tasks, microtasks, and scheduler.yield()middle
- Timer accuracy, throttling, and idle workmiddle
- Node.js event loop: phases, nextTick, and loop lagsenior
- Rendering strategies: SSG, SSR, ISR, streaming, and hydrationjunior
- SSG, SSR, ISR, streaming, and RSC — how each worksmiddle
- Hydration cost: selective, progressive, islands, resumabilitymiddle
- Core Web Vitals: what LCP, INP, and CLS measurejunior
- LCP: four phases, one dominant costmiddle
- INP: input delay, processing, presentationmiddle
- Lab vs field: why the two disagree and how to use eachmiddle
- 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 an index is and how it speeds up queriesjunior
- The leading-column rule and composite index designmiddle
- Partial, expression, and covering indexesmiddle
- Index types: GIN, GiST, BRIN, Hash, Bloom, and HOT updatesmiddle
- Index-only scans, the Visibility Map, and INCLUDEsenior
- Production failure modes and the index audit playbooksenior
- Index design exercise: full-text search strategysenior
- EXPLAIN and execution plans: what the planner decides and whyjunior
- Scan types: Seq, Index, Bitmap, Index-Onlymiddle
- Join algorithms and the row-estimate cascademiddle
- pg_statistic, ANALYZE, and production observabilitymiddle
- Extended statistics: fixing correlated-column estimate failuressenior
- Plan cache, cost-constant tuning, and planner internalssenior
- Production failure modes and plan stabilitysenior
- 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
- 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
- Migration failure taxonomy and production disciplinesenior
- Shard-key selection: hash, range, list, and directory strategiesmiddle
- Co-location and Citus: the invariant that makes sharding usablemiddle
- The hot-shard failure mode: detection, isolation, and durable policymiddle
- 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
- Bits on the wirejunior
- Latency mathmiddle
- Bufferbloat and congestionsenior
- The physical frontiersenior
- Sequence numbers and connection statemiddle
- Flow control and congestion controlmiddle
- BBR, production observability, and beyond TCPsenior
- CDN: putting content next doorjunior
- Anycast and GeoDNS: routing to the nearest edgemiddle
- Tiered cache and Cache-Controlmiddle
- Vary header and cache keysmiddle
- Stale-while-revalidate and cache stampedesenior
- Edge workers and edge-side compositionsenior
- CDN operations and observabilitysenior
- WebSocket: the HTTP upgrade handshakejunior
- WebSocket vs SSE vs long-polling: choosing the right transportmiddle
- 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
- Balancing algorithms: round-robin to power-of-two-choicesmiddle
- L4 vs L7 load balancing and client-IP preservationmiddle
- Health checks, connection draining, and slow startmiddle
- Retry storms, circuit breakers, and load sheddingsenior
- Resilient LB architecture: anycast, zone-aware routing, and observabilitysenior
- Why QUIC and not TCP+TLSjunior
- QUIC streams and head-of-line blockingjunior
- Integrated handshake and 1-RTTmiddle
- Connection IDs and network migrationmiddle
- Loss detection and congestion controlmiddle
- 0-RTT resumption and packet encryptionsenior
- Deployment tradeoffs and CPU costsenior
- 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
- Critical render path and Core Web Vitalsmiddle
- 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
- Metrics and cardinality: the cost model of a time-series databasemiddle
- Logs and volume: the cost model of structured loggingmiddle
- Traces and sampling: the cost model of distributed tracingmiddle
- Join keys and exemplars: making the three signals composemiddle
- Observability 2.0: wide events and the cost shiftsenior
- Failure modes and engineering practice: cardinality budgets, PII, and samplingsenior
- Why structured logs exist: the diary vs the spreadsheetjunior
- The production log schema: fields every line must carrymiddle
- Log levels and alert routingmiddle
- Sampling strategies and log costmiddle
- PII redaction and log injectionsenior
- Trace context propagation in logssenior
- OTel Logs Data Model and audit logs as a subsystemsenior
- OTel signals, Semantic Conventions, and the OTLP wire formatmiddle
- Auto-instrumentation and manual spans: the 80/20 of OTelmiddle
- The OTel Collector: receivers, processors, exporters, and deployment patternsmiddle
- Sampling strategies: head, tail, and parent-basedmiddle
- Vendor neutrality, eBPF instrumentation, the Operator, and browser/serverless OTelsenior
- Operating the OTel Collector: reliability, version skew, failure modes, and governancesenior
- RED and USE: two checklists, one triage disciplinejunior
- Instrumenting RED in Prometheus: counters, histograms, and cardinality disciplinemiddle
- USE on Linux: CPU, memory, disk, network, and PSImiddle
- Golden signals, dashboard layout, and service mesh auto-REDmiddle
- Cardinality as a cost driver: labels, PII, exemplars, and samplingmiddle
- Native histograms, SLO tie-in, and production failure patternsmiddle
- Choosing SLIs and SLO targets: ratios, not feelingsmiddle
- Multi-window multi-burn-rate alerting: why AND beats ORmiddle
- Error budget policy, latency SLOs, and composite journeysmiddle
- Iceberg SLIs, composite SLO math, and SLA vs SLOsenior
- Flame graphs: reading the picture that shows where time goesjunior
- Sampling vs instrumentation profiling: why 99 Hz wins in productionmiddle
- Profile types: CPU, memory, off-CPU, mutex — which one to reach formiddle
- Continuous profiling: always-on flame graphs with eBPF and trace-id correlationmiddle
- How flame graphs are built from samples, and the production workflows that use themmiddle
- Linux perf, eBPF internals, PGO, and the limits of samplingsenior
- Profiling in production: security, war stories, OTel profiles, and the infrastructure designsenior
- The debugging funnel: SLO → RED → trace → profilejunior
- OTel architecture: one SDK, four signals, one wire formatmiddle
- Cost discipline: keeping observability under 5% of infra spendmiddle
- Scale, security, and the ROI of observable systemssenior