How to optimize E2E performance: queries, APIs, caching, UI?

Long Answer

Optimizing end-to-end performance starts with evidence, not hunches. I map bottlenecks across the stack—from database queries to API performance, the caching strategy, and front-end rendering—then fix the biggest offenders.

1) Discover & Budget
Define SLIs/SLOs (TTFB, LCP, p95 latency, errors) and set per-page/API budgets. Enable tracing (OpenTelemetry) so one user action links UI timings to API spans and database calls. This reveals N+1 patterns, over-chatty endpoints, and expensive queries. Load-test with prod-like concurrency and throttling.

2) Database queries & storage
Shape reads/writes: covering indexes, composite keys by selectivity, fetch only needed columns. Kill N+1 with batched loaders or server-side joins. Inspect query plans; add partial indexes or materialized views for hot aggregations. For write spikes, queue and batch. When a service needs a projection, maintain a read model (CQRS) to avoid heavy joins. Cache carefully at the DB edge (Redis) with TTLs and cache invalidation via change streams.

3) API performance
Design narrow endpoints; collapse waterfalls with BFF. Use HTTP/2+, keep-alive, Brotli/Zstd. Paginate/filter on server. Cut payloads with field selection and JSON streaming; prefer 304s via ETags. Make writes idempotent. Profile serialization; precompute DTOs on hot paths. Push invariants to edge caches when safe.

4) Caching strategy (CDN → app → DB)
Adopt layered caching: CDN for static/cacheable HTML/APIs, application cache for computed fragments, client-side caches for replays. Key by tenant/locale/role. Prefer stale-while-revalidate to keep p95 low. Invalidate by event, not guesses. For personalization, cache templates plus per-user deltas. Log hit/miss to catch regressions.

5) Front-end rendering
Ship less, do less. Code-split by route/component; lazy-load below-the-fold. Use image transforms, modern formats, correct sizes. Minify, tree-shake, dedupe deps; prefer native APIs. Choose CSR for interactivity, SSR/SSG for first paint, islands to avoid big rehydrations. Defer non-critical scripts; move some data fetching server-side. Track Core Web Vitals.

6) Concurrency & resilience
Protect upstreams with pools, circuit breakers, timeouts, and cancellation. Use queues/streams to absorb spikes.

7) Observability & guardrails
Make performance observable: RED/USE dashboards, slow-query logs, and trace exemplars for worst p95s. Automate CI checks (Lighthouse, k6). Every change has a hypothesis and rollback; dark-launch or A/B when risky.

8) Governance & culture
Publish playbooks for database queries, API performance, caching strategy, and front-end rendering. Add linters (bundle size, N+1 detectors), PR templates with before/after metrics, and cache-invalidation runbooks. Treat performance as a product with owners and SLAs; balance speed with cost so wins persist.

Result: a snappy system where data, APIs, caches, and UI are tuned together—validated by traces, not vibes.

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Table

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Common Mistakes

Teams chase micro-optimizations without profiling, missing the real bottleneck. They add indexes blindly and slow writes, or denormalize everywhere and create painful invalidations. APIs over-fetch, paginate poorly, and force client waterfalls; the “fix” becomes more endpoints, not better shapes. Caches ship with long TTLs and no event invalidation, so users see stale data—or everything is uncacheable out of fear. Front-end bundles balloon and eager hydration crushes LCP; images stay unoptimized; third-party tags load synchronously. Observability is thin: no tracing to link UI pain to a query, no budgets in CI, and no rollback when p95 spikes. Timeouts are missing so slow downstreams stall threads; ORM lazy loads and default serializers live on hot paths. Finally, nobody owns performance; without clear owners and SLAs, regressions linger. Teams also forget locale/timezone costs and run heavy jobs at peak, starving pools. Cache keys ignore tenant/role, causing leaks or chronic misses.

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Sample Answers (Junior / Mid / Senior)

Junior:
“I start by measuring. I log slow database queries and use EXPLAIN to add indexes. For API performance, I paginate and trim fields. I add a simple caching strategy: CDN for static, ETag/304 for APIs. For front-end rendering, I split bundles and lazy-load images. I watch p95 latency after each change.”

Mid:
“I add tracing so a page view links to API spans and SQL. I remove N+1, add projections, and ship a BFF to collapse waterfalls. Edge/CDN caches plus Redis hold hot fragments with event invalidation. For front-end rendering, I move first fetch to SSR and use image transforms; Core Web Vitals are our budget. Canaries verify wins.”

Senior:
“I set SLOs/budgets, then prioritize by impact. Storage gets covering indexes and read models; API performance uses narrow shapes, HTTP/2+, and idempotent writes. Caching strategy is layered: CDN → app → DB with stale-while-revalidate. Front-end rendering uses route-level code-split and islands. Guardrails: timeouts, circuit breakers, A/B, and rollback; we track p95 and cost.”

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Evaluation Criteria

Interviewers look for end-to-end thinking backed by measurement, not folklore. Strong answers:

• Define SLIs/SLOs and budgets; tie user outcomes (LCP, TTFB) to server metrics (p95, error rate).
  
• Show mastery of database queries: indexes, query plans, N+1 removal, and when to add read models/materialized views.
  
• Improve API performance with narrow shapes, BFF aggregation, HTTP/2+, pagination, field selection, and idempotent writes.
  
• Articulate a layered caching strategy (CDN → app → DB), event-based invalidation, and stale-while-revalidate.
  
• Optimize front-end rendering: code-split, lazy-load, image transforms, SSR/SSG/islands, and third-party control.
  
• Include resilience: timeouts, circuit breakers, pools; plus observability (tracing, slow-query logs, CI perf tests).
  
• Provide rollout safety: canaries, A/B, rollback; and consider cost.
  Weak answers recite tips without trade-offs, skip measurement, or optimize one layer while ignoring the rest of the stack. The best candidates map risks to owners, keep playbooks for cache invalidation and perf regressions.
  
  

Preparation Tips

Build a small app and optimize it methodically. Add tracing (OpenTelemetry) to connect a click → API span → SQL query. Create SLIs/SLOs and budgets for LCP/TTFB and p95 latency. Break N+1 with batching; craft two indexes and justify them with EXPLAIN plans. Refactor an endpoint into a BFF that collapses three calls; add pagination and field selection. Implement a caching strategy: CDN for static, Redis for hot fragments, stale-while-revalidate for HTML/API. On front-end rendering, code-split by route, lazy-load media, and add image transforms; measure Core Web Vitals. Add timeouts and circuit breakers; simulate failures. Write a CI perf gate (Lighthouse, k6) that fails on budget breaches. Practice cache invalidation: publish an event on write and verify stale entries vanish. Try SSR/SSG vs islands and compare TTFB and hydration. Add canary + rollback and track cost. Rehearse a stakeholder story linking user KPIs to engineering levers.

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Real-world Context

A SaaS dashboard struggled at peak: traces showed 60% time in database queries due to N+1 on metrics. We batched loads, added two covering indexes, and built a read model for weekly aggregates; p95 query fell 72%.
An e-commerce API spent 30% of time in fan-out; a BFF collapsed four calls, payloads were trimmed with field selection, and API performance improved p95 by 45%. CDN plus Redis with event invalidation absorbed spikes; origin CPU dropped 40%.
On front-end rendering, a news site shipped 1.8 MB of JS and eagerly hydrated. Route-level splitting, image transforms, and islands cut JS by 55% and improved LCP from 4.2s→2.1s on Slow 4G. The layered caching strategy added stale-while-revalidate for HTML, smoothing deploy traffic. Timeouts and circuit breakers stopped incidental outages from cascading. Observability tied wins to budgets, so gains persisted. A billing service moved nightly jobs off peak, added queues; p95 stabilized under load. With ownership and SLAs per area, regressions were quarantined quickly and MTTR halved.

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Key Takeaways

• Measure first; set budgets and trace across layers.
  
• Fix database queries (indexes, N+1, projections).
  
• Improve API performance (BFF, shapes, pagination, HTTP/2+).
  
• Layer your caching strategy with event invalidation and SWR.
  
• Streamline front-end rendering (split, lazy, images, SSR/SSG/islands).
  
• Guard with timeouts/circuit breakers; prove wins with CI perf gates.

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Practice Exercise

Scenario: Your product page fails Core Web Vitals and has rising p95 API latency. Leadership wants a two-week plan to improve end-to-end performance without risky rewrites.

Tasks:

1. Discover: Define SLIs/SLOs (LCP, TTFB, p95 API, error rate). Add tracing so a page view links to API spans and database queries. Capture a 24-hour baseline.
   
2. Database queries: EXPLAIN hot queries; add two covering indexes and remove one N+1 with batching. Propose a read model for the heaviest aggregation.
   
3. API performance: Create a BFF to collapse three client calls. Enforce pagination and field selection; enable ETags to drive 304s. Make writes idempotent.
   
4. Caching strategy: Add CDN caching for static and cacheable HTML/APIs with stale-while-revalidate. Introduce Redis for hot fragments; publish invalidation events on writes.
   
5. Front-end rendering: Code-split routes, lazy-load below-the-fold, and apply image transforms. Defer non-critical scripts; move the first data fetch to SSR if it lowers TTFB.
   
6. Resilience & rollout: Add timeouts at each hop, circuit breakers on upstreams, and a canary rollout with rollback.
   
7. Prove it: Add CI perf gates (Lighthouse, k6). Report before/after: LCP, p95 API, hit rate, origin CPU.
   Deliverable: A one-pager with the plan, owners, and budgets, plus graphs showing improved LCP and p95 after changes. Add rollbacks per step and a follow-up list (trace worst p95 weekly). Aim for LCP ≤2.5s (Slow 4G) and p95 API ≤300 ms at peak.