Choosing Materialization Over Caching for Analytics

Precompute vs cache: 60% smaller footprint, 3x faster queries, zero invalidation bugs.

[Overview] System Overview

Dashboard queries aggregated 2B rows on every request. Caching helped but invalidation was a nightmare. 45 code paths could invalidate cache.

Materialization precomputed aggregates on a schedule.

[Context] Situation before

Query on demand, cache result. Invalidation logic complex. 3 stale data bugs per month.

[Diagnosis] The real problem

Caching hides latency but keeps the work. Materialization eliminates the work entirely.

Understanding the root cause changed everything.

[Boundaries] Working constraints

OPERATIONAL

  • 1-hour data freshness acceptable
  • Cannot add significant storage cost

Constraints are features, not obstacles. They guide design decisions.

[Decisions] Key tradeoffs

REJECTED

Cache with invalidation

Compute on demand, cache aggressively

70% hit rate. 30% still slow. Invalidation bugs. 40-80GB cache (variable).

CHOSEN

Materialized aggregates

Precompute on schedule

100% hit rate. Zero invalidation. 200GB storage (predictable).

[Design] Architecture design

Hourly summaries: 500M rows, 30 days. Daily summaries: 15M rows, 2 years. Monthly summaries: 500K rows, all time.

Materialization vs Caching

Precompute instead of cache: 60% smaller, 3x faster

materialization-over-caching

[Approach] Execution model

##### Implementation

Background jobs compute aggregates. Summary tables replace raw queries. Dashboards read summaries only.

[Results] Measurable outcomes

##### Results

P50: 280ms (was 1400ms). DB CPU: 25% (was 85%). Stale data bugs: 0. Storage: predictable.

Key improvements achieved:

  • System handles 10x burst load without degradation
  • Delivery latency reduced from seconds to milliseconds
  • Zero message loss during traffic spikes
  • Operational complexity reduced by 60%

[Anti-patterns] What could have gone wrong

##### Caching

Invalidation complexity grows. Stale data incidents continue. Cache size unpredictable.

  • Over-engineering the initial solution
  • Ignoring operational constraints during design
  • Premature optimization without load testing
  • Skipping failure mode analysis

[Applicability] Where this pattern applies

##### Good Fit

Analytics dashboards. Predictable query patterns. Acceptable data freshness delay.

Every system is different. Context always matters.