What does this guide solve?
PostgreSQL performance is not driven by CPU alone. Memory, working set, storage latency, WAL writes, autovacuum, checkpoints and query/index design all matter.
Operating system
PostgreSQL supports a broad range of platforms; Linux is one of the most common production choices. Enterprise Linux distributions simplify package, security and automation operations.
Windows is also possible; existing automation, monitoring, backup and team skills should guide the decision.
CPU and memory
High single-core performance and sufficient cores matter for OLTP; parallel analytical queries can benefit from more cores.
More memory can improve OS page cache, shared buffers and active-data-set caching, but settings should be measured against the real workload.
Storage and WAL
Low-latency SSD/NVMe is valuable for WAL and random read/write activity. Separating WAL and data storage can help selected workloads.
RAID design, safe write cache, fsync behavior and virtualization storage latency are critical for durability and performance.
Security
SCRAM-SHA-256 authentication, TLS, pg_hba.conf policy, least-privilege roles and network segmentation form a strong baseline.
Row Level Security, auditing, secret management and OS hardening should be added according to risk.
HA and backup
Streaming replication and controlled failover can provide HA, but HA does not replace backup.
Base backups, WAL archiving and point-in-time recovery should be tested against RPO/RTO targets.
Frequently Asked Questions
Is Linux required for PostgreSQL?
No. PostgreSQL runs on many platforms; Linux is common in production but workload and team requirements matter.
Does PostgreSQL require NVMe?
No, but NVMe can provide substantial benefit for latency- and IOPS-sensitive OLTP workloads.
Does a replica replace backup?
No. Replicas can reproduce logical errors, accidental deletes or ransomware impact; independent backup is still required.
Evaluate Your Database Infrastructure
We can review workload, security, hardware, HA and backup requirements together.
Architecture and operating model
PostgreSQL memory combines shared buffers, OS cache, connections and query work. work_mem is not a total-server budget; operators and concurrent queries can multiply usage. Monitor WAL, checkpoints, vacuum and replication slots separately. pg_hba.conf, role permissions and TLS govern different access layers.
- 1Connections and queries
- 2Memory/plans/locks
- 3WAL and data files
- 4Vacuum/replica/recovery
Design parameters
- Memory multiplier
- work_mem can multiply across query operators and parallel work; budget alongside shared_buffers and OS cache.
- WAL and vacuum
- Correlate checkpoints, replication slots and long transactions with growth; do not manually delete WAL.
- Workload and query mix
- Measure read/write ratio, connection count, transaction duration and expensive queries. Database size alone does not explain hardware needs.
- Memory and storage
- Examine working set, log writes, temporary space and checkpoint behavior separately. Reliable write guarantees matter alongside speed.
- Recovery chain
- Test recovery of the starting backup, required logs, configuration and keys together.
- Access and change
- Separate application, administrator and schema-change permissions. Control production index and parameter changes.
Worked example
Record memory, temporary files, transaction duration and autovacuum during reporting load. Connected replicas are not necessarily current. Examine slot-related WAL retention and never manually delete pg_wal files to regain space.
Example commands: replace lab values and confirm permissions and software versions before use.
SELECT application_name, state, sync_state, write_lag, flush_lag, replay_lag
FROM pg_stat_replication;Troubleshooting
| Observation | Likely cause / distinction | Verification |
|---|---|---|
| WAL storage keeps growing | Lagging slots or archive failure may retain WAL. | Check slot, archive and transaction statistics together. |
| CPU is low but queries are slow | Locks, I/O or client waits may dominate. | Inspect the execution plan and wait type for the same query. |
| Replica is connected but stale | Sending, replay or transaction waits can create lag. | Compare the last applied operation with source commit time. |
| Storage keeps growing | Log retention, open transactions, replication slots or maintenance may be involved. | Inspect supported product statistics; do not delete logs manually. |
Acceptance checks
- Measure plans, WAL growth and replica state alongside application load.
- Test representative transactions and queries using realistic data distributions.
- Restore to a separate target and validate application records.
- Reject unauthorized reads and schema changes using a controlled identity.
- Verify one authorized writer and the correct connection target after failover.
- Measure locks, log volume and latency during maintenance.
Related concepts
Transaction boundary
A transaction groups operations that an application treats as jointly successful or failed. Database atomicity does not automatically include email delivery or changes in other services. Define the commit point and retry behavior. Evaluate idempotency to prevent processing the same request twice. Test connection loss before and after commit separately: a client-side error does not always mean the transaction failed.
Transaction logs and recovery chains
WAL, redo and transaction-log mechanisms record changes in order for durability and recovery, with product-specific details. Data copies and logs must belong to compatible timelines and chains. Having log files alone does not prove point-in-time recovery: a valid base backup and the required uninterrupted log range are also needed. Measure archival lag, log volume and replay time. Do not delete logs arbitrarily to regain space; use supported retention and cleanup methods.
Indexes and execution plans
An index can reduce scanned data for suitable queries, but adds write, maintenance and storage costs. Evaluate column order, selectivity, filters and sorting together. Slow queries are not always caused by missing indexes: estimates, locks, storage latency or excessive data transfer may dominate. Inspect representative execution plans and actual row counts. Measure read improvement alongside write overhead, and avoid uncontrolled production maintenance.
Locks, waits and deadlocks
Concurrent operations use locks or versioning to preserve integrity. Long transactions can block others; a deadlock forms a cycle of mutual waits. The database may terminate one participant, requiring safe application retries. Examine blocked and blocking queries together. Arbitrarily reducing isolation can change consistency guarantees. Test shorter transactions, consistent access order and suitable indexes under the same workload.
Durability and power loss
Survival of committed data depends on write guarantees across the database, OS, filesystem, controller and disks. Do not disable safety settings for speed without understanding caches and flush behavior. Power-loss-protected storage helps but does not prove correctness of the entire chain. Run failure and recovery tests in a controlled lab, not production. Validate application records and supported consistency checks rather than merely opening a sample file.
Replication scope
Replication transfers data or state changes to another copy. Synchronous transfer can introduce latency and connectivity dependence; asynchronous transfer can lag behind. A current replica is not necessarily protected from incorrect changes: deletion and corruption can propagate too. Compare lag with the application’s last committed operation, not merely connection status. Define which copy receives write authority after the source is lost and how the old node is reconciled when it returns. Report replication lag and the actual recoverable point separately.