Before moving PostgreSQL to a larger, smaller, faster, or differently configured host, review shared_buffers, query and maintenance memory, connection limits, worker capacity, WAL and checkpoint settings, autovacuum, and replication. Choose values from measurements of the current workload and the target machine—not from RAM or CPU size alone. A hardware move and a PostgreSQL major-version upgrade are separate projects: this checklist covers infrastructure tuning, while a major release also requires release-specific compatibility work.
The relevant settings are documented in the PostgreSQL 18 resource configuration, connection configuration, WAL configuration, and vacuum configuration references.
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First separate an infrastructure move from a PostgreSQL version upgrade
An infrastructure upgrade changes the host, storage, operating-system limits, network path, or topology while ideally keeping the same PostgreSQL major version. A minor PostgreSQL update follows the project’s normal maintenance process. A major-version upgrade can introduce user-visible incompatibilities, changed defaults, removed features, and extension requirements. Read the migration notes for both releases and follow the official cluster-upgrade guidance.
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Capture a baseline before changing anything
Record the current state and the workload it serves. The baseline should include:
- PostgreSQL major and minor version, extensions, database and largest-table sizes, and the current configuration (including settings overridden at database, role, or table level).
- RAM available to PostgreSQL after the operating system and other processes, CPU count and utilization, storage latency and throughput, and separate capacity for data, WAL, archives, and backups.
- Peak and average connections, active sessions, pool behavior, query mix, transaction rates, read/write ratio, and periods of batch or reporting activity.
- Replication mode, number of standbys or subscribers, archive destination and retention, replication-slot status, failover process, and recovery-point and recovery-time objectives.
- Representative query latency, CPU and memory pressure, I/O wait, WAL generation, checkpoint activity, autovacuum progress, table bloat indicators, and standby lag.
Take measurements during normal traffic and the busiest expected window. A host with twice the RAM may still be slower if storage latency, CPU frequency, noisy neighbors, or network limits differ.
Memory: tune related settings as one budget
Review shared_buffers, work_mem, maintenance_work_mem, and autovacuum_work_mem together. PostgreSQL also relies heavily on the operating-system file cache, so shared_buffers is not a reservation for all database memory.
| Setting | What it affects | How to review it before a move |
|---|---|---|
shared_buffers |
Shared cache used by PostgreSQL | PostgreSQL documents 25% of system RAM as a reasonable starting point for a dedicated server with at least 1 GB of RAM. Values above 40% are unlikely to improve performance in general because the operating-system cache is also needed. Treat both figures as guidance, not automatic targets. |
work_mem |
Memory available to an individual sort, hash, or similar operation | Estimate simultaneous operations, not just connections. One complex query can use several allocations, and many sessions can do so concurrently. |
maintenance_work_mem |
Memory for tasks such as index creation and maintenance | Budget it alongside scheduled maintenance and other processes; a large value can compete with query memory. |
autovacuum_work_mem |
Memory available to each autovacuum worker | Multiply the per-worker amount by possible workers and include other maintenance activity. |
Increasing RAM does not automatically mean increasing every memory setting. If the old host was swapping, first confirm that the target has adequate headroom and that the operating system is not reclaiming aggressively. If queries spill sorts or hashes to temporary files, investigate concurrency and query plans before making work_mem globally large. A safer approach is a conservative global value with targeted role- or session-level adjustments for known workloads.
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Memory parameters can interact with parallel query and background workers. Include parallel workers and maintenance jobs in the peak budget, and leave capacity for connection overhead, extensions, monitoring, the operating system, and failover processes.
Connections and worker capacity
max_connections controls the number of concurrent database connections. It is a start-time parameter, and raising it increases PostgreSQL’s resource allocation. A larger host therefore does not automatically justify a larger connection limit.
Decide how many sessions must really be active
Measure peak active sessions separately from idle sessions. If an application uses a connection pool, size the pool for useful concurrency and queue excess requests rather than opening one database connection per application thread. Check pool behavior during deploys, failovers, and traffic spikes; a pool that works at steady state can still create a connection storm during recovery.
Inventory all worker consumers
Account for parallel query workers, autovacuum workers, replication workers, background workers used by extensions, and logical-replication apply or synchronization workers. Worker-related limits must fit the available worker slots and the CPU and I/O capacity of the target. The logical-replication configuration documentation describes the separate limits that may need to rise together.
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WAL, checkpoints, and storage
WAL settings encode durability, archiving, replication, and recovery requirements. Review them against measured write volume and the target storage system rather than copying values from another server.
Set WAL capability for the deployment
Choose wal_level to support what the cluster actually does: local crash recovery, archiving and physical replication, or logical decoding and logical replication. Logical mode records additional information and can increase WAL volume. Lowering the level without checking archive, standby, and decoding requirements can silently break the recovery design.
Size WAL and checkpoints together
Review max_wal_size, checkpoint_timeout, and checkpoint_completion_target with WAL generation rate, storage throughput, archive health, standby lag, and recovery objectives. A larger shared_buffers commonly warrants a corresponding review—and often an increase—of max_wal_size, but the correct value depends on how quickly the workload generates WAL and how much storage is available.
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Verify physical layout and archive operations
Measure latency and throughput separately for data files, WAL, and backups when they use different volumes. Confirm free space, filesystem limits, archive-command success, retention, and monitoring. A fast data volume does not compensate for a saturated WAL device or a failing archive destination.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep autovacuum resourced and operational
Autovacuum is enabled by default, but it also requires track_counts. Confirm both settings before and after the move. Then review autovacuum_max_workers, available worker slots, nap time, vacuum and analyze thresholds, and per-worker memory.
Match maintenance concurrency to table churn
Worker count should reflect how quickly tables accumulate dead tuples and how much I/O the target can sustain while serving queries. More CPU or faster storage may permit more workers, but adding workers can also create I/O contention. Compare vacuum duration, dead-tuple growth, index size, and query latency before raising concurrency.
Check table-level overrides
Global settings may not describe the effective policy. Inspect storage-parameter overrides on high-churn, append-heavy, partitioned, and very large tables. A table-level threshold or scale factor can explain why one table is cleaned promptly while another accumulates bloat even though both use the same global configuration.
Replication and upgrade-path checks
Physical replication and archiving
Confirm that the target can sustain the required WAL senders, replication slots, archive throughput, and standby catch-up rate. Test slot retention and alerting: an inactive slot can retain WAL until the primary runs out of space. Verify failover steps, promotion behavior, and the archive needed for point-in-time recovery.
Logical replication
Logical replication requires wal_level = logical. Size replication slots, WAL senders, logical-replication workers, synchronization workers, parallel-apply capacity where used, and the background-worker budget for subscriptions, extensions, and parallel queries. Confirm that the target’s worker limits and memory budget can support the largest expected synchronization event, not only steady-state apply.
Major-version migration
For a major upgrade, use the pg_upgrade utility from the new PostgreSQL installation and run pg_upgrade --check before the production cutover. The check identifies compatibility issues and manual adjustments for that procedure; it does not replace release-note review or application testing. Follow the exact source and target-version instructions in the official pg_upgrade documentation. Preserve the configuration and authentication files required by the new cluster, then explicitly review settings whose defaults or valid values changed between releases.
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- Inventory and baseline. Export effective settings, version and extension information, role and table overrides, workload metrics, replication state, and recovery objectives.
- Validate capacity. Confirm usable RAM, CPU, storage latency and throughput, WAL and archive space, filesystem limits, network bandwidth, and operating-system settings.
- Recreate conservatively. Start with measured values for memory, connections, WAL, checkpoints, vacuum, and workers. Keep a versioned copy of the old and new configuration.
- Move or restore a representative copy. Exercise normal queries, batch jobs, index builds, vacuum, backups, restores, replication, and failover in a staging environment that resembles production.
- Check migration compatibility. If changing major versions, read both releases’ migration notes, run
pg_upgrade --checkwith the new utility, and test extensions and authentication. - Cut over with observability. Watch connection counts, memory pressure, temporary-file use, CPU and I/O wait, checkpoint frequency, WAL rate, archive failures, vacuum progress, replication lag, and query latency.
- Change one class of setting at a time. For example, adjust a targeted memory setting, observe a full workload cycle, then evaluate WAL or vacuum changes. Record the reason, expected effect, rollback value, and observed result.
- Exercise recovery. Perform a restore or recovery drill and verify that the measured recovery time and recovery point meet the stated objectives.
Symptoms that indicate a bad tuning change
- More RAM but higher swap or out-of-memory events: reduce aggregate per-session and per-worker allocations, check connection bursts, and reserve memory for the operating system and extensions.
- Fewer checkpoints but unacceptable recovery time: reassess
checkpoint_timeoutand WAL volume against the recovery objective instead of optimizing only checkpoint count. - Higher connection limit but worse latency: inspect active concurrency and pool limits; excess runnable sessions can consume CPU and memory without increasing throughput.
- Replication or archiving falls behind: check WAL generation, storage and network throughput, slot retention, worker limits, and archive-command errors before lowering durability-related settings.
- Vacuum still cannot keep up: identify the highest-churn tables, inspect table-level overrides, and compare worker I/O contention with dead-tuple growth.
How to compare a current and target environment
If the move involves a managed PostgreSQL service or a different hosting model, compare equivalent workload and operational characteristics rather than headline CPU or RAM. Use the same questions for every candidate:
| Comparison axis | What to verify |
|---|---|
| Memory | RAM left for PostgreSQL after the operating system and provider processes; limits on shared and per-session memory. |
| Compute | CPU capacity, burst behavior, and practical parallel-query and maintenance concurrency. |
| Storage | Latency, throughput, capacity, and whether data, WAL, and backups can be separated. |
| Connections | Supported connection count, pooling options, and behavior during failover or maintenance. |
| Durability and replication | Archiving, physical or logical replication, point-in-time recovery, slot handling, and retention controls. |
| Operations | Maintenance windows, failover process, monitoring, supported PostgreSQL versions, and migration assistance. |
| Cost | Published price for the exact region and configuration, checked on the provider’s own pricing page; no generic price is transferable between services. |
The result should be a configuration justified by observed workload, recovery requirements, and operational limits. PostgreSQL’s documented 25% shared_buffers starting point and 40% caution are useful guardrails, not a universal tuning recipe.
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