Why Database Compaction Needs a Size-Tiered Strategy

Lucas and Luna dive into the trade-offs between size-tiered and leveled compaction in LSM-tree databases like Cassandra and RocksDB. They explain why choosing the wrong compaction strategy for your workload can lead to write amplification, read stalls, and disk space bloat. Using a real-world example from an ad-tech company with a 10-to-1 write-to-read ratio, they show how switching from leveled to size-tiered compaction reduced write amplification by 60 percent and cut P99 write latency from 120 milliseconds to 45 milliseconds. The episode covers the key tuning knobs: compaction triggers, fanout factors, and tombstone thresholds. If you're running a write-heavy workload on an LSM-based database, this episode gives you a concrete framework for choosing your compaction strategy. No abstract theory—just practical engineering decisions that affect your latency and storage costs. #DatabaseCompaction #LSMTrees #SizeTieredCompaction #LeveledCompaction #WriteAmplification #Cassandra #RocksDB #DatabaseEngineering #WriteHeavyWorkloads #LatencyOptimization #DataStorage #TechPodcast #FexingoBusiness #BusinessPodcast #DatabaseTech #NoSQL #SQL #DataEngineering Keep every episode free: buymeacoffee.com/fexingo