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Performance & Scale

Patterns and techniques for scaling data systems to millions of users

Overview​

As applications grow, data systems must scale to handle increased load while maintaining acceptable latency. This section covers proven patterns: caching layers, read replicas, sharding, and pre-computation.

Core Patterns​

  • Caching Patterns - Write-through, write-behind, cache-aside for sub-ms latency
  • Read Replicas - Distribute reads across replicas, writes to primary
  • Sharding - Partition data across nodes to scale horizontally
  • Materialized Views - Precomputed query results for instant access
  • Search Offloading - Complex searches via Elasticsearch, analytics via data warehouse

Scaling Challenges​

  1. Read Bottleneck: Caching + read replicas
  2. Write Bottleneck: Sharding + async processing
  3. Query Complexity: Materialized views + denormalization
  4. Data Consistency: Eventual consistency + cache invalidation
  5. Operational Complexity: Monitoring, failover, rebalancing

Next Steps​

  1. Caching Patterns - reduce database load
  2. Read Replicas - distribute read traffic
  3. Sharding - scale writes horizontally
  4. Materialized Views - pre-aggregate data
  5. Search Offloading - optimize complex queries