For a modest-to-moderate background-work system already using PostgreSQL, a durable jobs table and bounded claims with FOR UPDATE SKIP LOCKED are a practical starting point. PostgreSQL coordinates which rows workers claim; your application or queue library must define retries, leases, ordering scope, fairness, and what happens after repeated failure. Because a worker can fail after performing an external action but before recording success, handlers should tolerate repeat execution.
How do PostgreSQL workers claim jobs concurrently?
A common pattern selects eligible jobs in a short transaction, locks them, skips rows another worker has locked, and marks the selected rows as running. PostgreSQL’s documentation describes SKIP LOCKED as suitable for avoiding contention among consumers of a queue-like table, while warning that it returns an inconsistent view of the data. It is a coordination technique, not a promise that every job will be selected promptly: PostgreSQL 17 SELECT documentation.
WITH picked AS (
SELECT id
FROM jobs
WHERE state = 'ready'
AND run_at <= now()
ORDER BY priority DESC, run_at, id
FOR UPDATE SKIP LOCKED
LIMIT 20
)
UPDATE jobs AS j
SET state = 'running',
claimed_at = now(),
attempts = attempts + 1
FROM picked
WHERE j.id = picked.id
RETURNING j.*;This is an illustrative query shape, not a universal schema or performance result. The eligibility predicate, ordering, batch size, index, transaction behavior, and recovery design must fit the workload. In particular, commit the claim promptly before doing slow external work; holding row locks during a long handler needlessly extends contention.
What does the lock guarantee?
FOR UPDATE locks the selected rows against concurrent updates. With SKIP LOCKED, a worker does not wait for a row lock already held by another transaction; it moves on to other available rows. This does not eliminate table-level locking and does not guarantee immediate selection of any particular job.
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Advisory locks can coordinate application-defined resources or keys, but they are an application protocol: PostgreSQL does not make every code path honor the same convention. Session-level advisory locks remain until explicitly released or the session ends; transaction-level advisory locks end with the transaction. See PostgreSQL 17 explicit locking documentation.
How should retries, failures, and duplicate effects work?
Retry behavior is queue policy, not behavior supplied automatically by SKIP LOCKED. A useful job record or related state should support an attempt count, next eligible time, terminal-failure policy, error details, and a way to inspect or re-drive jobs that stop retrying. Backoff and maximum attempts need explicit application or library rules.
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Do not equate exclusive row claiming with exactly-once effects. A process can complete an API call, email, or other external action and then crash before recording the job as complete. The job may be attempted again. pg-boss documents its own delivery semantics as at least once and advises handlers to tolerate repeat execution: pg-boss introduction. That is a description of pg-boss, not a universal guarantee for every PostgreSQL queue.
- Make handlers idempotent where possible, so repeating the same logical operation does not duplicate its effect.
- Use idempotency keys or deduplication when the external service supports them.
- For effects spanning systems, consider transactional outbox/inbox patterns appropriate to the application. These are engineering approaches to duplicate-risk management, not guarantees provided by PostgreSQL alone.
A lease-based design can let a crashed worker’s job become claimable again without holding a database transaction open while the handler runs. It must define expiry and recovery behavior, and protect against a stale worker finishing after a newer worker has reclaimed the job.
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How do ordering and fairness differ?
“Order” can mean when jobs become eligible, the sequence in which workers claim them, or the sequence in which their effects finish. An explicit ORDER BY controls selection preference; include a unique tie-breaker such as id when deterministic ties matter. PostgreSQL warns that without an order, results can arrive in whichever order is fastest to produce, and a LIMIT may select an unpredictable subset: SELECT documentation.
Even deterministic claim selection does not serialize work: multiple workers can execute concurrently and finish in a different order. PostgreSQL also documents a subtle READ COMMITTED case in which a locking query with ORDER BY may return rows out of order after waiting on a lock if ordering values change while it waits. SKIP LOCKED avoids waiting on locked rows, but neither approach establishes a general fairness contract.
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When strict per-entity order matters
If jobs for the same account, order, or resource must run sequentially, make that key part of the queue policy and serialize work per key. pg-boss documents key_strict_fifo as one library-specific example: successors for a key can be held behind active, retrying, or failed jobs for that key. This is pg-boss behavior, not a PostgreSQL feature: pg-boss queue API.
What fairness trade-offs should I expect?
A priority policy can indefinitely delay low-priority jobs if higher-priority work keeps arriving. Strict global FIFO can reduce concurrency when an early job is slow or blocked. Choose the ordering scope—global, per queue, or per entity—based on the invariant the application needs; do not assume a queue-head query or skipped locks guarantee starvation freedom.
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Should workers use LISTEN/NOTIFY or polling?
Keep the jobs table as the source of truth. A worker can listen for a notification as a wake-up hint, query the table when notified, and retain periodic polling or reconnect reconciliation so queued work remains discoverable after a listener disconnects.
PostgreSQL delivers a notification issued within a transaction only if that transaction commits. Notifications received while a listener is in its own transaction are not delivered to its client until that transaction ends, and identical channel/payload notifications in one transaction can be folded. Long listener transactions can delay cleanup; the notification queue is finite, and a transaction issuing NOTIFY can fail at commit if that queue is full. See PostgreSQL 17 NOTIFY documentation. Keep listener transactions short and never treat notifications as durable job records.
What should I monitor and maintain?
Build indexes around the actual eligibility predicate and ordering path, then inspect execution plans with realistic queue depth and contention. Keep claim batches bounded. Useful operational signals include claim latency, age of the oldest eligible job, retry and terminal-failure volume, lock waits, worker heartbeats, and database connection use; no single numeric threshold applies to every workload.
Frequent updates and deletes create obsolete row versions. Set retention for completed jobs and monitor vacuum behavior. PostgreSQL’s routine-vacuuming guidance explains how vacuum makes space from obsolete versions reusable: PostgreSQL 17 routine vacuuming. Choose maintenance settings from observed table statistics and workload rather than assuming a universal tuning value.
When is PostgreSQL the right queue?
Evaluate the design against the system’s actual requirements rather than assuming a database queue is always simpler or a separate broker is always faster. The evidence here does not establish a workload-matched throughput comparison between PostgreSQL and brokers.
Quick Recap
- Atomic enqueue: Must inserting application data and creating its background work happen in one transaction?
- Load and latency: What backlog, throughput, and response time must the system sustain?
- Delivery semantics: Can handlers tolerate redelivery, and how are duplicate effects controlled?
- Ordering: Is ordering required globally, per queue, or only per entity?
- Queue features: Which retry, scheduling, rate-limit, and dead-letter policies must be built or supplied by a library?
- Operations and failure domains: Can the database absorb queue retention and maintenance costs, and is it acceptable for background work to share PostgreSQL’s failure domain?
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