The query works fine in development with 1,000 rows. In production with 50 million rows it locks up the database for 3 minutes. One missing WHERE clause, one implicit type cast, one function wrapping an indexed column — and PostgreSQL ignores your index entirely.
You''ve been running backups for 18 months. The disk dies. You go to restore. The backup files are empty. Or corrupted. Or the backup job failed silently on month 4 and you''ve been running without a backup ever since. Untested backups are not backups.
You add ON DELETE CASCADE to a foreign key. You delete a test organization. It cascades to users, which cascades to sessions, orders, invoices, activity_logs — 10,000 rows gone in milliseconds. No warning, no undo. Cascade deletes are powerful and dangerous.
Debezium captures database changes at the source: WAL logs for PostgreSQL, binlog for MySQL. Stream changes to Kafka, Redis, Elasticsearch, or vector DBs instantly.
Master connection pooling with PgBouncer and pgpool-II. Learn transaction vs session mode, pool sizing math, Prisma connection pooling, serverless connection pooling, and monitoring.
Connection pool exhaustion is one of the most common and sneakiest production failures. Your app works perfectly at low load, then at 100 concurrent users it freezes completely. No errors — just hanging requests. Here''s the full diagnosis and fix.
Drizzle ORM combines type safety with performance. Learn why teams switch from Prisma: smaller bundle size, edge compatibility, prepared statements, and 3x query speed.
You shard by user ID. 80% of writes go to 20% of shards because your top customers are assigned to the same shards. Or you shard by date and all writes go to the current month''s shard. Uneven distribution turns a scaling solution into a bottleneck.
You need to export 10 million rows. You paginate with OFFSET, fetching 1,000 rows at a time. The first batch takes 50ms. By batch 5,000 the offset is 5 million rows and each batch takes 30 seconds. The total job takes 6 hours and gets slower as it goes.
Audit logs are critical for compliance and debugging. But an audit_logs table that grows without bounds will fill your disk, slow every query that touches it, and eventually crash your database. Here''s how to keep your logs without letting them kill production.
You deploy a migration that runs ALTER TABLE on a 40-million row table. PostgreSQL rewrites the entire table. Your app is stuck waiting for the lock. Users see 503s for 8 minutes. Schema changes on large tables require a completely different approach.
Month 1 — queries are fast. Month 6 — users notice slowness. Month 12 — the dashboard times out. The data grew but the indexes didn''t. Finding and adding the right index is often a 10-minute fix that makes queries 1000x faster.
Design multi-tenant applications at scale. Compare database-per-tenant, schema-per-tenant, and row-level security approaches. Implement tenant context middleware, enforce isolation, automate onboarding, and ensure data never leaks across tenants.
The N+1 query problem is responsible for more "why is my app slow?" investigations than almost anything else. It hides perfectly in development, then silently kills your database at scale. Here''s exactly what it is, how to detect it, and every way to fix it.
Page 1 loads in 10ms. Page 100 loads in 500ms. Page 1000 loads in 5 seconds. OFFSET pagination makes the database skip rows by reading them all first. Cursor-based pagination fixes this — same performance on page 1 and page 10,000.
One database cannot excel at everything. Learn when to use PostgreSQL, Redis, Elasticsearch, ClickHouse, and vector databases—and how to sync them without chaos.
pgai extends PostgreSQL with AI capabilities: auto-embedding, semantic search, and LLM function calls—all in SQL. No external vector database required.
Master PostgreSQL indexing strategies including B-Tree for general queries, GIN for JSONB/arrays, BRIN for time-series, partial indexes, covering indexes, and how to identify unused indexes with pg_stat_user_indexes.
Learn when sharding becomes necessary, compare hash vs range vs list partitioning, explore Citus for horizontal scaling, and understand the costs of distributed queries and shard key selection.
User saves their profile. Page reloads. Shows old data. They save again — same thing. The write went to the primary. The read came from the replica. The replica is 2 seconds behind. Read-after-write consistency is the hardest problem with read replicas.
The disk dies at 2 AM. You have backups. But the restore takes 9 hours because nobody tested it, the database is 800GB, the download from S3 is throttled, and pg_restore runs single-threaded by default. You could have restored in 45 minutes with the right setup.
Your query runs in 2ms in development with 1,000 rows. In production with 10 million rows, the same query takes 8 seconds. The database does a full table scan on every single request. Here''s how to identify missing indexes, write efficient queries, and build a database that stays fast as data grows.
Sessions table. Events table. Audit log. Each row is small. But with 100,000 active users writing events every minute, it''s 5 million rows per day. No one added a purge job. Six months later the disk is full and the database crashes.