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Data Engineering Knowledge Base

Storage Optimization

File Size, Compression, and Data Layout

Overview

Storage optimization reduces storage costs and improves query performance through proper file sizing, compression, compaction, and data layout strategies.

Optimization Strategies

Strategy Overview

Optimization Impact

Cost Savings

OptimizationStorage SavingsQuery ImprovementEffort
File size optimization0%10-100xMedium
Compression (ZSTD)30-50%0-20% slowerLow
Compaction0%10-50xMedium
Partition pruning0%10-100xLow
Z-Ordering0%2-10xMedium
Data skipping0%2-10xLow

Combined Impact: 100-1000x query improvement possible.

Strategy Selection

Decision Tree

Storage Optimization Guides

DocumentDescriptionStatus
Small Files ProblemImpact and solutions✅ Complete
Compaction StrategiesFile merging strategies✅ Complete
Compression CodecsCodec comparison✅ Complete
Data SkippingPredicate pushdown✅ Complete
Partition PruningPartition optimization✅ Complete
Z-Ordering ClusteringMulti-dimensional clustering✅ Complete

Quick Wins

Immediate Actions

  1. Check file sizes: Ensure 256MB-1GB files
  2. Enable compression: Use ZSTD for most data
  3. Partition by date: Most effective pattern
  4. Collect statistics: Enable data skipping
  5. Monitor metrics: Track optimization effectiveness

Long-Term Strategy

  1. Implement compaction: Continuous optimization
  2. Z-Order critical tables: Multi-dimensional queries
  3. Lifecycle policies: Tier hot/warm/cold data
  4. Automation: Automatic optimization triggers
  5. Monitoring: Continuous metrics tracking

Key Takeaways

  1. File size: 256MB-1GB optimal for most formats
  2. Compression: ZSTD best balance (30-50% savings)
  3. Compaction: Essential for streaming ingestion
  4. Partitioning: Date partitioning most effective
  5. Z-Ordering: Multi-dimensional query optimization
  6. Data skipping: Statistics and bloom filters
  7. Combined: 100-1000x query improvement possible
  8. Use When: All data platforms, query performance issues

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