max_parallel_workers

max_parallel_workers — Sets the maximum number of parallel workers that can be active at one time. Observed in PG10–19 Beta 3; its last measured boot default is 8 in PG19 Beta 3, with user context. This is a beta-snapshot fact and can change before PostgreSQL 19 GA.

Note

Fact — official short description: “Sets the maximum number of parallel workers that can be active at one time.”

Identity

Type , Valueinteger: Upstream pg_settings type
Context , Valueuser: Settable by an ordinary user
Unit , Value—: Raw unit
Range , Value0 – 1024: Raw limits in the last observed version
Enum values , Value—: — for non-enum types
Category , ValueResource Usage / Worker Processes: Upstream classification
Latest boot value , Value8: 8

Lifecycle

Fact	Value
First observed	PG10
Present in	PG10–19 Beta 3
Removed in	No
Introduction commit	`b460f5d66931` — Add max_parallel_workers GUC.
Commit date	2016-12-02
Discussion	—

Default history

Measured PG9.0–19 Beta 3 boot defaults
Versions	Raw `boot_val`	Unit	Human value
PG10–19 Beta 3	`8`	—	8

How it works

max_parallel_workers caps the cluster-wide number of workers simultaneously active for parallel query and maintenance. It is a pool ceiling beneath max_worker_processes.

Per-operation parameters request workers from this pool, but no slots are reserved. A plan can start with fewer workers than planned, and concurrent jobs can starve each other.

Increasing it expands potential CPU, memory, and I/O concurrency; it does not itself make plans parallel. Planner thresholds, safety checks, and max_parallel_workers_per_gather still govern query choices. Its user context permits session- or transaction-local changes; newly performed or newly planned work sees the value.

Tuning advice

Tip

Advice. These are workload-specific starting points and must be validated with measurements.

Workload	Guidance
OLTP	Set max_parallel_workers from a concurrency budget, not core count alone. Protect latency-sensitive OLTP from report and maintenance bursts, and verify actual Workers Planned versus Workers Launched.
OLAP	Analytical work can use a larger max_parallel_workers, but multiply per-node memory and I/O by concurrent statements. Benchmark throughput under realistic worker contention rather than one isolated query.
Small nodes	Keep max_parallel_workers conservative on a small host. More possible workers can reduce throughput through context switching and memory pressure even when a single query becomes faster.

Pigsty

Values use the fixed 8-vCPU, 32-GiB, 100-GiB SSD fixture and render the current Pigsty templates for PG19 Beta 3; this does not assert current Pigsty support for that historical or beta release.

Template	Effective value	Versus upstream boot	Source expression
OLTP	`4`	different	`{{ pg_max_parallel_workers }}`
OLAP	`7`	different	`{{ pg_max_parallel_workers }}`
CRIT	`4`	different	`{{ pg_max_parallel_workers }}`
TINY	`4`	different	`{{ pg_max_parallel_workers }}`

Caution

Advice — pending human review. Fact from the current Pigsty template projection: OLTP: PG10–19 Beta 3 = 4 (dcs); OLAP: PG10–19 Beta 3 = 7 (dcs); CRIT: PG10–19 Beta 3 = 4 (dcs); TINY: PG10–19 Beta 3 = 4 (dcs). Advice, pending human review — Editorial inference, pending maintainer review: The resolved pool gives OLAP more cluster-wide parallel capacity than OLTP/crit/tiny, consistent with a throughput-oriented profile.

Common pitfalls

Treating max_parallel_workers as reserved capacity rather than an upper bound shared with other work.
Ignoring that parallel plans multiply CPU, I/O, and work_mem-limited nodes.
Benchmarking one query without concurrent worker contention.
Assuming planned workers will always be launched at execution time.

max_worker_processes · max_parallel_workers_per_gather · max_parallel_maintenance_workers · parallel_setup_cost · parallel_leader_participation