A production-grade, MIT-licensed reference implementation: automatic failover with Patroni, RPO zero between two nodes, a witness that breaks the tie, a Docker lab you can run in five minutes, and Ubuntu scripts for three VMs.
Clone it, run the Docker lab, and break the cluster on purpose. Questions? Talk to us.
What ships in the repository
A practical method for diagnosing and improving production PostgreSQL: memory, WAL, checkpoints, queries, indexes, autovacuum, connections, RDS, and safe validation.
Read MoreA production-ready pg_dump backup script for Ubuntu: per-database custom-format dumps, cluster globals, integrity verification, safe retention, locking, cron, and a restore procedure that is actually tested.
Read MoreA read-only diagnostic script for Ubuntu that runs 27 checks across memory, autovacuum, indexes, connections, replication, backup and security — and prints the exact command that fixes each finding.
Read MoreSet up continuous WAL archiving and a guarded pg_basebackup script on Ubuntu, then perform a real point-in-time recovery: restore_command, recovery targets, replication slots, verification, and the failure modes that quietly break PITR.
Read MoreMove PGDATA on Debian or Ubuntu with a tested two-pass rsync workflow, controlled downtime, automatic configuration rollback, and a downloadable Bash script.
Read MoreVACUUM FULL needs room for a second copy of the table. Learn how to use an auxiliary tablespace on another volume, account for indexes and WAL, and return the compacted relation safely.
Read MoreA migration is not a server copy. Learn how to assess dependencies, choose between EC2, RDS and Aurora, build the landing zone, rehearse cutover and prove rollback.
Read MoreA practical database-focused approach to LGPD readiness: discover personal data, map access and data flows, identify exposure, and create an evidence-backed remediation roadmap.
Read MoreA production-ready AWS environment begins with identity, multiple accounts, audit logs, guardrails, budgets and tags—not with the first EC2 instance.
Read MoreHow to implement least privilege, encryption, masking, audit trails, backup protection and operational evidence for sensitive data.
Read MoreThe right PostgreSQL platform depends on operational control, compatibility, availability, read scaling and economics. Use this decision framework before sizing.
Read MoreA database-operational approach to retention, deletion, anonymization, backups, replicas and data-subject request evidence.
Read MoreA practical FinOps loop for attribution, rightsizing, storage, commitments and database tuning—ordered by evidence and operational risk.
Read MoreBuild a monitoring model that connects AWS infrastructure, PostgreSQL internals, query plans and application impact—and turns alerts into actions.
Read Moregenerate_series turns PostgreSQL into a practical time-series tool. Use it to expose missing data, build reports with zero-value days, create time buckets, and generate reliable test fixtures.
Read Moredate_trunc makes weekly and monthly reports simple, but using it in the wrong part of a query can turn an indexed lookup into a full scan. Learn safe grouping, time zones, and index-friendly filters.
Read MoreJSONB is flexible, but an unindexed JSONB filter can become a table scan fast. Learn operators, nested paths, GIN indexes, expression indexes, and when a normal column is the better design.
Read MoreEvery PostgreSQL monitoring tool shows you a chart. Very few tell you why a query got slower, open an incident automatically, and explain the fix in plain language. Here is an honest, feature-by-feature comparison.
Read MoreAutovacuum with default settings is tuned for a generic workload from 2005. On a write-heavy production table, those defaults let bloat win the race every time. Here is how to actually tune it, table by table.
Read MorePARALLEL SAFE is not a synonym for STABLE or IMMUTABLE — it is a separate, orthogonal declaration. Get it wrong and PostgreSQL silently runs your query on a single core even when a parallel plan would be four times faster.
Read MoreMost query performance problems are not bugs — they are patterns. SELECT *, missing indexes, OR in WHERE, implicit type casts, functions on indexed columns. Here are the rules that prevent 90% of them, and a complete EXPLAIN walkthrough.
Read MorePostgreSQL silently accumulates dead tuples after every UPDATE and DELETE. Autovacuum is supposed to clean them up, but when it falls behind, disk grows, queries slow down, and nothing errors. Here is how to see it before it hurts.
Read MoreMost monitoring tools show you a graph and send an email. PG Monitoring opens a tracked incident, deduplicates it, routes it by severity, broadcasts it over WebSocket, and auto-resolves it when the metric returns to normal. Here is the full lifecycle.
Read MoreCloudWatch tells you the CPU is at 90%. Performance Insights shows the top SQL. Neither tells you why replication lag is about to spike or which index will fix it. Here is what native AWS tooling misses — and how PG Monitoring closes the gap.
Read MoreA function call in a WHERE clause or a LATERAL join can be nearly free — or it can turn into a hidden row-by-row loop the planner cannot see through. The difference comes down to LANGUAGE, volatility, and whether Postgres can inline the body.
Read MorePostgreSQL ROW_NUMBER() practical guide for PARTITION BY, latest row per group, pagination, deduplication, and top-N queries. Learn when to use it instead of RANK() or DISTINCT ON.
Read MorePostgreSQL ranking functions compared side by side: ROW_NUMBER(), RANK(), and DENSE_RANK(). See exactly how ties, gaps, leaderboards, and top-N queries behave.
Read MorePostgreSQL LAG() and LEAD() examples for PARTITION BY, period-over-period growth, sequence gaps, previous and next values, and time-series deltas.
Read MoreLearn to read PostgreSQL EXPLAIN ANALYZE output with examples of estimated versus actual rows, scans, joins, sorts, BUFFERS, and query-plan fixes.
Read MoreB-tree is the default for a reason, but it is the wrong tool for full-text search, JSONB and huge append-only tables. Here is how to pick — and size — the right index type.
Read MoreA practical walkthrough of setting up a physical streaming replica: primary configuration, pg_basebackup, replication slots, and how to verify the standby is actually caught up.
Read MorePostgreSQL logical replication explained with PUBLICATION and SUBSCRIPTION examples, cross-version upgrades, selective table replication, monitoring, and operational pitfalls.
Read MoreReplication slots stop the primary from discarding WAL a standby still needs. They prevent broken replicas — and, when forgotten, they are the single most common cause of a full pg_wal partition.
Read MoreWhen the database is slow, the answer is often outside the database. A focused toolkit of Linux commands to diagnose CPU, memory, disk I/O and connections on a database server.
Read MoreLogical vs physical backups, why mysqldump is not enough at scale, and how to stand up GTID-based replication — the essentials every MySQL operator needs.
Read MoreNetezza performance lives and dies by one decision: the distribution key. Plus how zone maps replace indexes, why GROOM matters, and the fastest way to load data with nzload.
Read MoreA practical approach to debugging IBM DataStage parallel jobs: how to read the Director log, find the slow stage, fix partitioning skew, and stop warnings from hiding real errors.
Read MoreMost PostgreSQL monitoring tools only show "replication is working." PG Monitoring identifies lag bottlenecks, predicts failures, and tells you exactly which standby needs attention.
Read MoreFind missing, redundant, and high-risk PostgreSQL indexes from real workload evidence. PG Monitoring quantifies impact, considers write overhead, and keeps DDL reviewable.
Read MoreStatic configuration recommendations are dangerous. PG Monitoring classifies your workload in real-time and suggests parameters that match how you actually use your database.
Read MoreSlow query logs are too late. pg_stat_statements is incomplete. PG Monitoring captures every query pattern, tracks evolution over time, and alerts before users complain.
Read MoreOther tools alert when your database is already on fire. PG Monitoring predicts issues hours in advance using ML-powered trend analysis on your actual metrics.
Read MoreGeneric AI knows SQL syntax. PG Monitoring Copilot knows YOUR database - schema, query patterns, historical issues, and suggests fixes based on your actual workload.
Read MoreTable bloat silently kills performance. PG Monitoring tracks bloat per table, identifies vacuum inefficiencies, and provides targeted autovacuum tuning.
Read MoreConnection errors are a symptom, not the cause. PG Monitoring tracks pool exhaustion patterns, identifies which queries hold connections longest, and suggests optimal pool sizing.
Read MoreManual security audits are error-prone. PG Monitoring continuously checks 50+ security parameters, tracks configuration drift, and generates compliance reports automatically.
Read MoreManaging multiple PostgreSQL instances is chaos. PG Monitoring provides unified dashboards, cross-instance correlation, and organization-level insights that scale.
Read MoreTraditional PostgreSQL monitoring tools like pg_stat_statements, pgBadger, and pgWatch2 provide basic visibility but miss the bigger picture. They show you what happened, but not why. They alert when problems occur, but can't predict them.
PG Monitoring combines real-time metrics, AI-powered analysis, and predictive anomaly detection to give you complete database intelligence. From replication lag prediction to automated index recommendations, we solve the problems that keep DBAs awake at night.
Our blog explores real-world PostgreSQL challenges and demonstrates how PG Monitoring outperforms traditional tools in replication monitoring, query performance optimization, security auditing, and multi-instance management.