Cut troubleshooting time by 70%. Detect risks early, optimize queries continuously, and keep PostgreSQL healthy around the clock.
Health Score
94%
+2% vs yesterday
Queries/sec
1,247
Normal
Replication Lag
12ms
Excellent
AI Copilot insight
"3 heavy queries detected on 'orders'. Suggested index: created_at."
Preventive Bug
Avoided before production
500+
Monitored Instances
50M+
Queries Analyzed
99.9%
Guaranteed Uptime
70%
Incident Reduction
One platform for live observability, AI guidance, bug prevention, and reliable automation.
Track health score, performance counters, waits, and critical alerts in real time.
Analyze incidents, get query tuning guidance, and speed up root-cause analysis with context-aware recommendations.
Detect known PostgreSQL bugs and risky patterns before they impact production.
Monitor lag, slots, conflicts, and topology health across primary/standby environments.
Audit SSL usage, permissions, superusers, and hardening settings continuously.
Receive configuration and index recommendations with impact and operational risk context.
Run the pg_monitor agent on your server with a single command. No PostgreSQL extensions or superuser required.
Paste the agent token into the dashboard and metrics start flowing in seconds.
Dashboards, alerts, AI Copilot, and automation ready to use — zero configuration.
DBAs and DevOps teams operating PostgreSQL with fewer incidents.
"PG Monitoring reduced our investigation time by 70%. The AI Copilot made tuning workflows much faster."
Carlos Mendes
Senior DBA - Fintech
"We prevented three severe incidents in one month thanks to anomaly and bug detection."
Ana Paula Souza
CTO - E-commerce
"Grafana and automation were straightforward to adopt. We now have reliable operational playbooks."
Roberto Lima
DevOps Lead - SaaS
A technical assessment that turns scattered signals into a prioritized plan for performance, availability, security, recovery, and capacity.
Explore the PostgreSQL AssessmentThe assessment includes
Start small and scale with confidence.
Ideal for small teams
Built for scaling operations
For critical infrastructure
In-depth articles on performance, tuning, replication, anomaly detection and more.
A 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.
A 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.
Set 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.
Move PGDATA on Debian or Ubuntu with a tested two-pass rsync workflow, controlled downtime, automatic configuration rollback, and a downloadable Bash script.
VACUUM 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.
A 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.
A practical database-focused approach to LGPD readiness: discover personal data, map access and data flows, identify exposure, and create an evidence-backed remediation roadmap.
A production-ready AWS environment begins with identity, multiple accounts, audit logs, guardrails, budgets and tags—not with the first EC2 instance.
How to implement least privilege, encryption, masking, audit trails, backup protection and operational evidence for sensitive data.
The right PostgreSQL platform depends on operational control, compatibility, availability, read scaling and economics. Use this decision framework before sizing.
A database-operational approach to retention, deletion, anonymization, backups, replicas and data-subject request evidence.
A practical FinOps loop for attribution, rightsizing, storage, commitments and database tuning—ordered by evidence and operational risk.
Build a monitoring model that connects AWS infrastructure, PostgreSQL internals, query plans and application impact—and turns alerts into actions.
generate_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.
date_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.
JSONB 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.
Every 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.
Autovacuum 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.
PARALLEL 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.
Most 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.
PostgreSQL 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.
Most 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.
CloudWatch 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.
A 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.
PostgreSQL ROW_NUMBER() examples for PARTITION BY, latest row per group, pagination, deduplication, and top-N queries. Learn when to use it instead of RANK() or DISTINCT ON.
PostgreSQL ranking functions compared side by side: ROW_NUMBER(), RANK(), and DENSE_RANK(). See exactly how ties, gaps, leaderboards, and top-N queries behave.
PostgreSQL LAG() and LEAD() examples for PARTITION BY, period-over-period growth, sequence gaps, previous and next values, and time-series deltas.
Learn to read PostgreSQL EXPLAIN ANALYZE output with examples of estimated versus actual rows, scans, joins, sorts, BUFFERS, and query-plan fixes.
B-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.
A 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.
PostgreSQL logical replication explained with PUBLICATION and SUBSCRIPTION examples, cross-version upgrades, selective table replication, monitoring, and operational pitfalls.
Replication 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.
When 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.
Logical vs physical backups, why mysqldump is not enough at scale, and how to stand up GTID-based replication — the essentials every MySQL operator needs.
Netezza 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.
A 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.
Most PostgreSQL monitoring tools only show "replication is working." PG Monitoring identifies lag bottlenecks, predicts failures, and tells you exactly which standby needs attention.
Find missing, redundant, and high-risk PostgreSQL indexes from real workload evidence. PG Monitoring quantifies impact, considers write overhead, and keeps DDL reviewable.
Static configuration recommendations are dangerous. PG Monitoring classifies your workload in real-time and suggests parameters that match how you actually use your database.
Slow 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.
Other 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.
Generic AI knows SQL syntax. PG Monitoring Copilot knows YOUR database - schema, query patterns, historical issues, and suggests fixes based on your actual workload.
Table bloat silently kills performance. PG Monitoring tracks bloat per table, identifies vacuum inefficiencies, and provides targeted autovacuum tuning.
Connection errors are a symptom, not the cause. PG Monitoring tracks pool exhaustion patterns, identifies which queries hold connections longest, and suggests optimal pool sizing.
Manual security audits are error-prone. PG Monitoring continuously checks 50+ security parameters, tracks configuration drift, and generates compliance reports automatically.
Managing multiple PostgreSQL instances is chaos. PG Monitoring provides unified dashboards, cross-instance correlation, and organization-level insights that scale.
Start your free trial today. Setup takes minutes.
Copilot suggestions should be reviewed before production changes.