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Data Engineering Knowledge Base

Query Performance

Caching, Joins, Materialized Views, and Vectorization

Overview

Query performance optimization reduces query latency and compute costs through caching, join optimization, materialized views, and vectorization.

Optimization Strategies

Strategy Overview

Optimization Impact

Performance Improvement

OptimizationSpeedupUse Case
Result caching70-90%Repeated queries
Broadcast join2-10xSmall x Large joins
Materialized views100-1000xExpensive aggregations
Vectorization2-10xAll analytics

Combined Impact: 100-1000x query improvement possible.

Query Performance Guides

DocumentDescriptionStatus
Caching StrategiesResult caching✅ Complete
Join StrategiesJoin optimization✅ Complete
Materialized ViewsPre-computed results✅ Complete
VectorizationCPU optimization✅ Complete

Quick Wins

Immediate Actions

  1. Enable caching: Cache frequently accessed results
  2. Use broadcast joins: For small dimension tables
  3. Create materialized views: For expensive aggregations
  4. Enable vectorization: Default in most platforms
  5. Monitor query performance: Track latency and throughput

Long-Term Strategy

  1. Implement caching layer: Redis or platform caching
  2. Optimize join strategies: Choose based on data sizes
  3. Schedule MV refreshes: Based on data velocity
  4. Use columnar formats: Parquet, ORC for analytics
  5. Monitor and optimize: Continuous performance tuning

Key Takeaways

  1. Caching: Store frequently accessed results
  2. Joins: Broadcast for small tables, shuffle for large
  3. Materialized views: Pre-compute expensive queries
  4. Vectorization: CPU optimization with SIMD
  5. Combined: 100-1000x query improvement
  6. Monitoring: Track cache hit rates, join performance
  7. Optimization: Continuous performance tuning
  8. Use When: All query workloads, performance issues

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