Distributed SQL Tuning and Optimization in YugabyteDB
summary
Workshop shows how YugabyteDB stores tables as primary-key indexes, splits data into tablets by hash or range, and replicates them (commonly replication factor 3). Hands‑on uses Docker, psql, EXPLAIN ANALYZE, pg_stat/statements and a small script to count seek/next calls. Demonstrations prove expression pushdown reduces RPCs, partial/covering indexes and index-only scans cut cross-node lookups, and follower reads or colocated indexes lower geo latency. Exercises build tables, indexes and joins, measure surprising differences, and validate that the optimizations actually speed queries.
AI-written summary · about
chapters (13)
- 0:00Workshop introduction
- 4:33Distributed database theory
- 10:24Sharding and storage internals
- 23:58Table and index creation
- 35:25Query execution plans
- 41:17Instrumentation and monitoring
- 54:57Execution plan analysis
- 1:11:24Pushdown optimizations
- 1:20:02Secondary and partial indexes
- 1:34:18Index order and covering indexes
- 1:45:38Join methods and hints
- 1:55:40Geo-distribution and latency
- 2:10:45Bulk loading and troubleshooting
related talks
26mUnderstanding the Query Execution in YugabyteDB
29mData Modeling for Distributed SQL
25mYugabyteDB Technical Overview
47mAnatomy of a Distributed SQL Database - YugabyteDB
27mIntroduction to YugabyteDB: Design and Architecture
30mBuilding Scalable Spring Applications with YugabyteDB
26mEfforts Toward Adoption and Uncovering Hidden Features
26mHow to Assess Your Cloud Native Workloads for Migration to a Distributed SQL Database