Credential rotation is a compliance requirement (PCI DSS, SOC 2, HIPAA) that most teams treat as a downtime event. Here is how to rotate postgres passwords in a Node.js application without restarting a single process or dropping a single connection.
Keyword search returns exact matches but misses intent. pgvector brings vector similarity search into PostgreSQL, letting you find documents by meaning instead of string overlap. Here is the schema, indexing, and query patterns you need in production.
Your Node.js validation library catches 90% of bad data. The remaining 10% quietly corrupts your database and breaks downstream consumers. Here is how to push business rules into PostgreSQL constraints so bad data never reaches disk.
Your Postgres CPU is at 60%, users are complaining about slow pages, and EXPLAIN ANALYZE on the query in psql shows a fast index scan. The real problem is the other 99% of your queries. Here is how to use pg_stat_statements, auto_explain, and planner cost tuning to find the actual bottlenecks and fix them systematically.
A migration breaks production at 3 AM. The rollback script does not exist, the data is half-transformed, and every minute of downtime costs thousands. Here is the three-step plan: writing rollbacks alongside forward migrations, testing them in CI, and automating the detection that triggers them before customers notice.
A single slow query can exhaust your entire Postgres connection pool in under a minute, cascade to every endpoint, and take your service down. Here is how to set per-query timeouts with statement_timeout, cancel hung queries with AbortSignal, and handle the 57014 error before it becomes a pager alert.
Adding Redis to your infrastructure stack just for session storage adds operational complexity, memory pressure, and a new failure mode. Here is a production-grade session store built on PostgreSQL with row-level locking, lazy cleanup, and session rotation that handles 10,000 concurrent users without breaking a sweat.
Your pager goes off at 2 a.m. Postgres CPU is pegged, queries are timing out, and you have no idea where to look. Here are the eight SQL queries that should be in every production runbook, with the exact thresholds that tell you it is time to act.
Your application computes the same derived values over and over: full names from first and last, price with tax, JSON field extracts. Move that logic into Postgres generated columns and eliminate the code path entirely. This post covers the DDL, the stored vs virtual trade-off, index strategies, and the migration pattern that does not require downtime.
A misconfigured database connection timeout turns a brief network blip into a 10-minute outage. Here is how to set connectionTimeoutMillis, idleTimeoutMillis, statement_timeout, and TCP keepalives so your Node.js app fails fast instead of hanging silently.
You are still writing correlated subqueries that iterate row by row, or joining aggregated subqueries that scan the same table twice. Postgres LATERAL JOINs solve both problems in a single pass: the top-N-per-group pattern, the nearest-neighbor search, and the set-returning-function explode, all with an index-friendly execution plan.
Your seed.sql is three months out of date, your integration tests pass on your machine but fail on CI because the assumed data does not exist, and nobody knows how to add a new entity to the test dataset without breaking six other tests. Here is the factory-based seeding pattern that makes test data declarative, reproducible, and easy to maintain.
Your single PostgreSQL database handles 1M users just fine. At 10M users writes start queuing, the replication lag creeps up, and every query feels like it is running through molasses. Here is how to shard PostgreSQL at the application layer with Node.js, including shard key selection, query routing, cross-shard operations, and the rebalancing strategy that will save your weekend.
Someone changed a customer email and support cannot see the old value. Here is a trigger-based audit log in Postgres that captures every insert, update, and delete, with zero changes to your application code and less than 2% query overhead.
You write to Postgres, then write to Elasticsearch, then invalidate Redis, then hope nothing failed halfway through. CDC streams the WAL to every downstream consumer in order, with exactly-once semantics, and eliminates the dual-write pattern that corrupts data under load.
Your normalized write schema makes every list page join five tables. CQRS within a single Postgres database builds a read-optimized copy that serves queries in milliseconds, kept fresh by triggers and a lightweight queue. No event store, no message broker, no second system.
Row-by-row INSERT is the slowest way to move data into PostgreSQL. Here is the COPY command, how to use it from Node.js, the error-handling sharp edges, and the benchmark that proves 10x faster bulk loads.
Your Postgres cluster has never been tested with a real restore. When the accidental DELETE hits at 2 PM on a Friday, pg_dump alone will not save you. Here is the WAL archiving, PITR, and backup-validation pipeline that turns a worst-case scenario into a five-minute recovery.
Your query runs in 2 ms in psql but spikes to 800 ms in production. The ORM is using prepared statements, and Postgres just switched to a generic plan that assumes the wrong data distribution. Here is how plan instability works, how to catch it, and how to fix it without turning off prepared statements entirely.
Your tags table has 12 million rows and every product query needs a three-way join. Postgres arrays can collapse that to a single row lookup, but misuse them and you trade a join for a sequential scan. Here is the decision framework, the GIN indexing strategy, and the queries that separate a fast array from a slow one.
Two users click "Book" at the same millisecond and your application check passes for both. Here is how Postgres EXCLUDE constraints eliminate the race condition at the database level, with the DDL, the GiST index, and the Node.js error handling you need to ship it.
Your index scan is fast, but your query is still slow. The heap is the bottleneck. Here is how Postgres INCLUDE indexes create true index-only scans and cut read latency by 60% or more without rewriting a single query.
Your "latest status per device" query takes 800 ms with window functions and self-joins. Postgres DISTINCT ON solves it in 8 ms with a single index scan. Here is the syntax, the index strategy, and the gotchas that make the difference between a fast dashboard and a slow one.
Your concurrent transactions pass tests individually but corrupt data in production. Read Committed, Repeatable Read, and Serializable behave differently in Postgres than any other database. Here is how dirty reads, lost updates, phantom reads, and write skew actually manifest in application code, and the isolation level that fixes each one.
Your UPDATE query latency doubled overnight while transaction volume stayed flat. The culprit is often not the query plan but how Postgres stores your updated rows. Here is how HOT updates, fillfactor tuning, and index bloat detection turn a 200 ms write into a 2 ms write without adding hardware.
A production API that randomly hangs for thirty seconds and then recovers is often a connection pool inching toward exhaustion. Here is how to detect the leak, trace it to the query that never releases, and build a wrapper that prevents it from happening again.
Your dashboard queries aggregate millions of rows and timeout after 30 seconds. A materialized view fixes the read speed, but REFRESH MATERIALIZED VIEW locks the table and blocks every reader. Here is how to keep the cache warm without the downtime, using concurrent refresh, transactional swaps, and incremental triggers.
You need exactly-once invoice generation across five workers and immediately reach for Redis Redlock. Postgres has had a simpler answer for years. Here is how advisory locks work, three production patterns, and the one pool config that silently breaks them.
Your team added Elasticsearch for product search and now fights sync lag, mapping conflicts, and another database to operate. Postgres has had production-grade full-text search for years. Here is how to use it, when it is enough, and the exact migration path from LIKE queries to ranked search.
The daily report cron ran twice last Tuesday, missed Wednesday entirely, and silently failed on Thursday until a customer complained. Here is the small Postgres-backed pattern that makes scheduled tasks observable, overlap-safe, and idempotent. With working TypeScript.
Your time-series table has a billion rows and a 4 GB B-tree index on timestamp. Insertions slow down, autovacuum chokes, and disk space disappears. BRIN indexes cover the same queries in 40 MB by exploiting the natural order of append-only data. Here is when they work, when they do not, and the exact DDL to deploy them safely.
Your product team wants an audit trail, replayable history, and the ability to rebuild read models without running migrations on a 500GB table. Here is how to implement event sourcing in PostgreSQL without Kafka, schema registries, or six months of migration pain: just an append-only table, a projection function, and the replay logic that makes it useful.
Your users table has 10 million rows but only 200,000 are active. Every lookup scans the deleted ones too. Here is how partial indexes shrink your indexes by 90%, speed up hot queries, and the three mistakes that make them silently stop working.
The batch job runs fine locally and explodes in production with ERROR: 40P01 deadlock detected. Here is how to make Postgres tell you exactly which queries fought, how to reproduce the race in a test script, and the three lock-ordering rules that eliminate deadlocks without guesswork.
Two API requests update the same row. One silently disappears. Here is the compare-and-swap pattern that fixes it without adding pessimistic lock contention to your database.
You added read replicas to scale reads. Then users started seeing 404s for records they just created. Here is the request-scoped routing pattern that fixes replication lag without giving up the performance win.
Offset pagination looks fine on page one and falls apart on page two hundred. Here is the exact SQL and Node.js code to replace it with cursor-based pagination that stays fast, avoids duplicate rows, and survives concurrent writes.
Most teams reach for Redis, Sidekiq, or BullMQ the moment they need background jobs. You probably do not need any of it. Here is the 80 lines of Postgres-only code that gives you a multi-worker, retry-safe job queue, and the test that proves it does not double-process under load.
JSONB without an index is a sequential scan in disguise. GIN indexes are powerful but big; expression indexes are precise but single-purpose. Here is the decision framework, the four query patterns and which index each needs, and the production-grade configuration.
Hierarchical data (org charts, comment threads, file trees) looks unfriendly in SQL until you discover recursive CTEs. One query, no application loops, no N+1. Here is the pattern, the four common shapes, and the performance considerations that decide whether it scales.
Most teams reach for Redis pub/sub or a message broker before their actual traffic warrants it. Postgres has had pub/sub built in since 2010. Here is the working pattern, the limits to watch, and how to migrate to a real broker once you outgrow it.
A 2 TB events table is hard to manage and impossible to clean. Time-based partitioning turns it into 30 small tables you can drop on a cron. Here is the working pattern with declarative partitioning, automated partition management, and the three traps that catch teams new to it.
The UUID-vs-bigint debate is religious in some teams and absent in others. The numbers behind it are surprisingly clean: random UUIDs cost 30%+ more disk and a measurable insert-time penalty, but UUID v7 (time-ordered) closes most of the gap. Here is the data, the patterns each fits, and the “use both” design that ends the argument.
Postgres has two replication systems and most teams cannot articulate the difference. Streaming gives you a hot standby identical to the primary; logical lets you replicate selected tables to a different schema or major version. Here is the decision tree, the operational gotchas of each, and a realistic answer for which one you actually need.
Most developers learn SQL aggregations, hit a wall, and pull data into the application to compute running totals or per-group ranks. Window functions are the SQL feature that replaces that round trip with one query, and most teams use less than 10% of what they can do.
Renaming a column on a 50-million-row table looks like a one-line SQL change and is actually a six-step deploy spread across two PRs. Here is the pattern (expand, migrate, contract) applied to renames, type changes, and NOT NULL backfills, with the locks each step takes and the rollback at every stage.
A 4 GB table somehow uses 80 GB on disk and queries are slow. Autovacuum is on, autovacuum is running, autovacuum is not actually freeing space, and the reason is one setting most teams have never heard of. Here is what bloat is, why long-running transactions kill VACUUM, and the four queries you need to run before reaching for `pg_repack`.
In a multi-tenant SaaS, every query needs a `WHERE tenant_id = ?` and one missing one is a data breach. RLS moves that filter into the database where you cannot forget it. Here is the pattern that works in practice, including the connection-pool gotcha that breaks it.
Postgres falls over not because of slow queries but because of too many connections. Most teams reach for pgbouncer and copy a config they do not understand. Here is the actual job each setting does, the three pool modes ranked by what they break, and the rule for sizing pool_size that holds at any traffic level.
JSONB columns let you ship features without a migration, which is exactly why they end up holding half your domain model. Here is the rule for when JSONB is the right call, the four queries that decide whether to promote a key to a column, and the GIN-index pattern that keeps it fast.
Most developers run EXPLAIN ANALYZE, see a wall of nested operators and millisecond numbers, and either guess or scroll past. Here is the line-by-line read of a real plan, what every node means, and the four numbers that decide whether you need an index or a rewrite.