Dead tuples can occupy up to 80% of disk space, and slow queries can kill page load speed. Default PostgreSQL settings are not designed for high-load projects. Our team of engineers with 5+ years of PostgreSQL production experience has serviced over 50 projects and ensures 99.9% uptime. We offer end-to-end database administration: audit, tuning, monitoring, and support. Contact us for a free audit — we will evaluate your project within 2 business days.
PostgreSQL is not "install and forget." By default, it is configured conservatively to run on the widest range of hardware. Without regular maintenance, tables bloat from dead tuples, indexes fragment, bloat consumes gigabytes, and slow queries drag down the entire application. System administration is a set of recurring tasks and constant monitoring. Our experience shows that up to 30% of disk space can be reclaimed after bloat optimization without downtime.
Initial Installation Audit
The first thing we do when connecting to a new database is gather diagnostic data:
SELECT version(); SHOW config_file; SHOW data_directory; SELECT datname, pg_size_pretty(pg_database_size(datname)) AS size FROM pg_database ORDER BY pg_database_size(datname) DESC; SELECT schemaname, relname, pg_size_pretty(pg_total_relation_size(relid)) AS total, pg_size_pretty(pg_relation_size(relid)) AS table, pg_size_pretty(pg_indexes_size(relid)) AS indexes FROM pg_catalog.pg_statio_user_tables ORDER BY pg_total_relation_size(relid) DESC LIMIT 10; SELECT relname, n_dead_tup, n_live_tup, round(n_dead_tup::numeric / nullif(n_live_tup + n_dead_tup, 0) * 100, 1) AS dead_pct, last_autovacuum, last_autoanalyze FROM pg_stat_user_tables ORDER BY n_dead_tup DESC LIMIT 20; Such an audit reveals bottlenecks: tables with high bloat, unused indexes, suboptimal autovacuum settings. In one project, we reduced the database size by 40% after removing duplicate indexes and tuning VACUUM.
VACUUM and ANALYZE
Autovacuum runs in the background, but for high-load tables its default settings are often insufficient. We tune autovacuum under load using per-table parameters:
ALTER TABLE orders SET ( autovacuum_vacuum_scale_factor = 0.01, autovacuum_analyze_scale_factor = 0.005, autovacuum_vacuum_cost_delay = 2 ); To fight bloat without a maintenance window, we use pg_repack — it is 100 times faster than VACUUM FULL and does not block writes.
How to Tune autovacuum for High Load?
For tables with intensive UPDATE/DELETE, a more aggressive autovacuum is required. We recommend reducing scale_factor to 0.01 and increasing cost_limit to 2000 to clean dead tuples more frequently. Also, enabling parallel VACUUM via max_parallel_workers_maintenance helps reduce bloat by 50-70% without manual intervention.
Why Trust PostgreSQL Administration to Professionals?
Incorrect VACUUM tuning can lead to XID wraparound — a complete database halt. Suboptimal indexes slow writes by 2–3 times. Violation of access rights can lead to data leaks. We provide comprehensive protection: tune autovacuum under load, remove dead indexes, grant minimal privileges, archive WAL. All with a guarantee and 24/7 monitoring.
Index Management
Unused indexes consume space and slow down INSERT/UPDATE. We regularly check them using pg_stat_user_indexes. For index creation on production, we always use CREATE INDEX CONCURRENTLY — this avoids blocking writes.
Backup
| Backup Type | Point-in-Time Recovery | Size | Restore Speed |
|---|---|---|---|
| pg_dump (logical) | No (only at dump time) | Smaller | Medium (data-dependent) |
| pg_basebackup (physical) | Yes (PITR) | Larger (entire cluster) | Fast (file copy) |
We combine both methods: daily logical backup and continuous WAL archiving for PITR. Backups are tested by restoring once a month.
Replication and High Availability
We set up streaming replication in synchronous or asynchronous mode. We monitor replication lag via pg_stat_replication. For connection pooling, we use PgBouncer — it effectively reduces load on PostgreSQL with thousands of concurrent connections.
How We Ensure Non-Stop Database Operation?
We use comprehensive monitoring: replication lag, WAL fill, CPU temperature, disk load, query response time. Upon deviation from the norm — instant alerts in Telegram/Slack. In case of an accident — SLA 15 minutes. Our engineers are certified and have experience recovering from failures of any complexity.
Typical Configuration Mistakes and Their Solutions
| Mistake | Consequences | Solution |
|---|---|---|
| Too high max_connections | Memory exhaustion | Reduce to 200-400, use PgBouncer |
| Disabled autovacuum | Bloat, XID wraparound | Enable and tune |
| shared_buffers > 25% RAM | Low performance | Set to 15-25% RAM |
| No monitoring | Sudden failures | Implement Prometheus + Grafana |
Query Optimization
We analyze slow queries via pg_stat_statements and EXPLAIN (ANALYZE, BUFFERS). Typical issues: N+1 queries, missing indexes, incorrect JOINs. For example, replacing a sequential scan with an index scan speeds up queries 10-100 times. We use covering indexes and partial indexes for frequently filtered data.
What's Included
- PostgreSQL configuration audit (version, parameters, extensions)
- Query and index optimization (recommendations for 2–10x speedup)
- Replication setup (streaming, logical) and high availability
- Backup (logical + physical backup, PITR)
- Monitoring and alerting (Prometheus, Grafana, Zabbix)
- Version upgrades and planned migration
- Documentation and administrator training
- 24/7 technical support (SLA up to 15 minutes)
For more details on VACUUM, see the official documentation. Request a consultation — we will help tune PostgreSQL for your project.