3 min read
EXPLAIN ANALYZE BUFFERS: Reading PostgreSQL Plans Like a Production Trace
EXPLAIN ANALYZE is not just a plan tree. With BUFFERS and real row counts, it becomes a production trace for estimates, I/O, loops, spills, and wasted work.
Database Field Notes
Notes for the problems that show up after launch: bad plans, awkward migrations, index debt, vacuum pressure, replica lag, and the small decisions that keep PostgreSQL, MySQL, MariaDB, and SQL Server easier to operate.
Featured Guides
Slow query optimization
Find the queries dragging your Postgres down, read their plans, and fix them in order of impact.
Read the guide →Index optimization
Which indexes to add, which to drop, and how to spot redundant index debt before it costs you writes.
Read the guide →Vacuum guide
How autovacuum works, when it falls behind, and how to keep bloat from eating your storage and your buffer cache.
Read the guide →Connection pooling
Sizing pools, PgBouncer modes, and how to stop idle-in-transaction sessions from exhausting max_connections.
Read the guide →Replication monitoring
Track lag, WAL throughput, and replication slots before they turn into stale reads or a full disk.
Read the guide →Browse by Topic
Backups and Durability
9 articlesBackups, restores, PITR, archive gaps, and recovery drills for PostgreSQL.
Cloud Providers
5 articlesAWS RDS, Azure Database for PostgreSQL, Google Cloud SQL, AlloyDB, and managed PostgreSQL monitoring notes.
Connections and Pooling
10 articlesConnection storms, pool saturation, PgBouncer behavior, and max_connections pressure.
CPU, Memory, and I/O
17 articlesCPU, memory, disk, temp files, checkpoints, buffers, and workload pressure.
Indexes
37 articlesIndex design, index debt, reindexing, constraint indexes, and evidence-driven DDL.
Locks and Transactions
22 articlesLock waits, deadlocks, blocking chains, idle transactions, and transaction hygiene.
MariaDB
56 articlesGalera clustering, InnoDB and Aria internals, replication, optimizer behavior, and MariaDB operations notes.
MySQL
107 articlesInnoDB internals, replication, locking, query performance, and MySQL operations notes.
pgvector and RAG
15 articlesHNSW, IVFFlat, recall, embedding search, and production RAG performance notes.
Replication and WAL
28 articlesReplica lag, WAL growth, failover readiness, hot standby behavior, and replication slots.
Slow Queries
38 articlesQuery plans, EXPLAIN ANALYZE, planner regressions, pagination, joins, and statistics.
SQL Server
47 articlesWait statistics, tempdb, Query Store, Always On availability groups, and SQL Server operations notes.
Vacuum and Bloat
16 articlesAutovacuum, dead tuples, bloat, wraparound, MVCC, freeze age, and visibility.
Latest Field Notes
3 min read
PostgreSQL Connection Saturation: Performance Problems That Look Like Slow SQL
Not every PostgreSQL performance incident is a bad query. Too many connections, idle transactions, pool starvation, and client backpressure can make normal SQL look slow.
3 min read
Autovacuum and Query Performance: When Dead Tuples Become User Latency
Autovacuum is not just cleanup. When it falls behind, dead tuples, bloated indexes, stale statistics, and transaction age turn into slow queries and operational risk.
3 min read
Vector Search Observability: The Dashboard I Want Before Scaling RAG
Vector search observability needs recall, result count, filter selectivity, freshness lag, tenant skew, index health, reranker cost, and answer grounding - not just latency.
3 min read
Vector Deletes, Freshness, and Permissions: The Hidden RAG Incident
A RAG system is unsafe if deleted documents, permission changes, and stale embeddings can still appear in answers. Freshness is part of correctness.
3 min read
Vector Database Cost Model: Why Your RAG Bill Explodes
Vector cost is not just storage. Dimensions, top_k, filters, rerankers, embedding refreshes, payload size, index rebuilds, and tenant skew all compound.
3 min read
Pinecone vs Qdrant vs Weaviate vs pgvector: A Production Decision Framework
The best vector database depends on ownership boundaries, filter semantics, recall targets, migration paths, cost, and operational maturity - not benchmark screenshots.
7 min read
TimescaleDB in Production: When Time-Series Postgres Starts Hurting
TimescaleDB helps when the pain is truly time-series pain. Chunk interval, compression windows, retention, late data, and aggregate refresh policies decide whether it stays boring.
3 min read
Re-Embedding Without Breaking Production Search
Embedding model upgrades are migrations. Versioned embeddings, dual indexes, shadow queries, backfills, and rollback decide whether search quality survives.
3 min read
Rerankers in RAG: The Expensive Fix That Only Works After Retrieval
Rerankers can improve answer quality, but they cannot recover evidence that retrieval never found. Use them after recall, cost, and latency are understood.