Why this matters
As a Data Architect, you design systems that must deliver fast reads at scale. Caching cuts latency, reduces database and service load, and stabilizes costs. You will decide when to cache, what to cache, how long to keep it, and how to keep data fresh enough for the business.
- Speed up user-facing APIs (e.g., product details, profile data)
- Protect databases during traffic spikes and batch windows
- Reduce compute costs for repeat analytics and feature-store lookups
- Keep SLAs predictable under load
Who this is for and prerequisites
Who this is for: Data Architects, Data Engineers, Platform Engineers, and Backend Engineers planning or reviewing caching in data and analytics platforms.
Prerequisites: Basic understanding of data stores (SQL/NoSQL), API read/write flows, and consistency concepts (eventual vs. strong).
Concept explained simply
A cache is a fast, smaller storage that keeps copies of data you read often, so you avoid slow or expensive recomputation and round-trips. You trade some freshness and memory for big gains in speed and cost.
Mental model
- Working set: The small fraction of data accessed frequently. Keep this in fast storage.
- Hit vs. miss: On a hit, you serve from cache (fast). On a miss, you fetch from the source, then optionally fill the cache.
- Freshness vs. cost: Shorter time-to-live (TTL) is fresher but causes more misses; longer TTL is cheaper but risks staleness.
Core patterns (quick definitions)
- Cache-aside (lazy loading): App tries cache first. On miss, load from source and populate cache.
- Read-through: Cache abstraction fetches from source on miss automatically.
- Write-through: Writes go to cache and source synchronously.
- Write-back (write-behind): Write to cache first; source is updated asynchronously.
- CDN/edge caching: Cache static or semi-static content near users.
Eviction and freshness
- TTL: Expire entries after a duration.
- LRU/LFU/FIFO: Evict least-recently-used, least-frequently-used, or first-in-first-out when memory is tight.
- Jitter: Add small randomness to TTL to avoid synchronized expirations.
- Negative caching: Cache not-found results briefly to prevent repeated misses.
Consistency choices
- Read-heavy, tolerate slight staleness: Cache-aside + TTL is usually best.
- Strong consistency needs: Consider write-through or immediate invalidation on changes.
- High write rates: Weigh write-through overhead vs. write-back complexity and risk.
Worked examples
Example 1: Product details API
Goal: Sub-50 ms reads under peak traffic without overloading the catalog database.
- Pattern: Cache-aside
- Key: product:{id}
- TTL: 5–15 minutes + 10% jitter
- Eviction: LRU when memory pressure
- Invalidation: On product update, delete product:{id} (fast-follow with repopulation on next read)
Why: Most reads repeat. Slight staleness is acceptable for descriptions and images.
Example 2: Metrics dashboard tiles
Goal: Keep dashboards snappy while nightly ETL recomputes aggregates.
- Pattern: Read-through or cache-aside
- Key: tile:{org}:{metric}:{date}
- TTL: Until next ETL completes (derived TTL); also invalidate upon ETL success event
- Negative cache: If a tile is not ready, cache a placeholder for 30–60 seconds to reduce thundering herds
Why: Tiles don’t change within a day; strong freshness after ETL is important.
Example 3: Feature store lookups for ML inference
Goal: Low-latency features during online inference.
- Pattern: Cache-aside, sometimes write-through for precomputed features
- Key: feature:{entity_id}:{feature_name}
- TTL: Based on update cadence of the feature (e.g., 1–5 minutes for fast-moving signals)
- Stampede control: Soft TTL with background refresh for hot keys
Why: Latency directly impacts user experience and model throughput.
Key metrics to watch
- Cache hit ratio: hits / (hits + misses). Track globally and per-keyspace.
- Tail latency (p95/p99): Should drop with effective caching.
- Evictions and memory use: Ensure policy aligns with working set.
- Origin load reduction: Fewer calls to primary stores = cost and reliability wins.
Design choices checklist
- Pattern: cache-aside, read-through, write-through, or write-back?
- Key design: stable, deterministic, and scoped (include version or tenant as needed)
- TTL policy: base TTL + jitter; conditions to invalidate early
- Eviction: LRU/LFU and memory limits sized to working set
- Stampede protection: request coalescing, soft TTL, backoff
- Consistency: how “stale” is acceptable and for how long
- Warm-up strategy: prefill hot keys after deploys or ETL
Exercises (hands-on)
Note: The quick test is available to everyone; only logged-in users get saved progress.
Requirements: 5 ms cache latency target, prices update several times per day, correctness matters within 10 minutes, traffic spikes at noon.
- Decide key format
- Choose TTL and jitter
- Define invalidation triggers
- Describe miss handling and stampede prevention
Requirements: Endpoint recomputes top 100 items every 2 minutes; at the minute mark traffic surges; computation takes 800 ms.
- Choose a refresh strategy
- Describe request coalescing
- Define soft TTL and background refresh flow
Self-check checklist
- Did you specify pattern, key design, TTL, and eviction?
- Did you handle invalidation and data updates?
- Did you include stampede protection and jitter?
- Is your plan measurable (hit ratio, latency, origin load)?
Common mistakes and how to self-check
- Same TTL for everything: Self-check: Align TTL with data change rates; add jitter.
- Forgetting invalidation: Self-check: List all update paths; ensure each triggers a delete or refresh.
- Overly long keys or missing namespaces: Self-check: Keep keys short and namespaced by tenant/version.
- Ignoring stampedes: Self-check: Add soft TTL + background refresh or a per-key lock.
- Caching sensitive or personal data without scoping: Self-check: Include user/tenant in key; set shorter TTL; follow policies.
- Relying only on average latency: Self-check: Track p95/p99 and eviction counts.
Practical projects
- Implement cache-aside for a read-heavy endpoint with TTL, jitter, and invalidation on updates.
- Add a soft TTL and background refresh worker for the top 10 hottest keys.
- Instrument metrics: hit ratio per keyspace, p95 latency, evictions, and origin QPS.
- Create a load test scenario to verify stability during synchronized expirations.
Learning path
- Start: Apply cache-aside to a single endpoint with a 5–15 minute TTL.
- Next: Add per-key jitter and measure hit ratio improvements.
- Then: Introduce invalidation on write events for high-importance entities.
- Advanced: Implement request coalescing and soft TTL for hot keys.
- Capstone: Design a multi-tier cache (edge + application) with measurable SLAs.
Mini challenge
Your analytics API returns a customer’s last 12 months of orders. Query is expensive; updates happen hourly. Propose a key schema, TTL policy, and an invalidation plan. Add one guard against cache stampede. Keep your answer under 6 bullet points.
Next steps
- Pick one production or sandbox endpoint and add cache-aside with safe TTLs.
- Instrument hit ratio and p95 latency; review after 24–48 hours.
- Refine TTLs and add jitter; introduce soft TTL for the hottest keys.