Use FOR UPDATE SKIP LOCKED to let multiple PostgreSQL workers claim different ready jobs without waiting on one another. It prevents two workers from claiming the same row at the same time, but it does not guarantee strict FIFO, equal shares among workers or tenants, or freedom from starvation. A fair queue needs an explicit ordering and scheduling policy, plus leases and retry rules for recovery.
How do I use FOR UPDATE SKIP LOCKED for a PostgreSQL job queue?
Store each job as a durable row with an explicit state, such as ready, running, done or failed. Give it a stable enqueue time or sequence and a unique identifier. In a short transaction, select a bounded batch of eligible rows in policy order, lock them while skipping rows already locked by another worker, update them to running, and return the claimed rows.
For example, the following statement favors higher priority, then older enqueue time, then lower ID. The five-minute lease is illustrative; choose a duration and renewal strategy appropriate to the work.
WITH picked AS (
SELECT id
FROM jobs
WHERE state = 'ready'
AND run_at <= now()
ORDER BY priority DESC, enqueued_at ASC, id ASC
LIMIT 20
FOR UPDATE SKIP LOCKED
)
UPDATE jobs AS j
SET state = 'running',
claimed_by = $1,
claimed_at = now(),
lease_until = now() + interval '5 minutes',
attempts = attempts + 1
FROM picked
WHERE j.id = picked.id
RETURNING j.*;Run the statement inside a transaction and commit the claim before doing slow work or calling an external service. The selection and state update must be part of the same transaction. The ordered selection expresses your preference; the final unique ID makes ties deterministic. The batch limit bounds how many jobs one worker takes at once and helps apply backpressure. PostgreSQL documents that locking stops once enough rows have been returned to satisfy LIMIT, and that SKIP LOCKED skips rows that cannot be locked immediately. See the [PostgreSQL 16 SELECT documentation](https://www.postgresql.org/docs/16/sql-select.html) and [UPDATE documentation](https://www.postgresql.org/docs/16/sql-update.html).
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This is an application pattern built from PostgreSQL primitives, not a queue workflow guaranteed by the database. If you want oldest-first service, remove priority from the ordering. If you need tenant fairness or weighted scheduling, encode that policy explicitly rather than assuming a row order provides it.
Does SKIP LOCKED guarantee FIFO or fairness?
No. A deterministic ORDER BY defines preference among rows visible and available to a particular worker. But if an earlier row is locked, another worker can skip it and claim a later one. As a result, concurrent claim order—and especially completion order—can differ from global FIFO. PostgreSQL describes the view produced by SKIP LOCKED as inconsistent, and identifies avoiding lock contention among queue-like consumers as its intended use, not general-purpose consistent reads. [PostgreSQL 16 SELECT documentation](https://www.postgresql.org/docs/16/sql-select.html)
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“Fair” therefore needs a concrete definition for your queue. It might mean older eligible work generally goes first, tenants receive weighted shares, or no eligible job waits beyond an acceptable age. These goals can conflict: strict priority can indefinitely postpone low-priority work, while strict oldest-first service can let one tenant dominate. Choose the policy, implement it in selection or scheduling logic, and measure whether it is being met.
- For deterministic preference: order by the policy fields and finish with a unique tie-breaker. PostgreSQL says rows tied on every
ORDER BYexpression have implementation-dependent order. - For starvation monitoring: track the age of the oldest ready job, not only queue depth or average claim latency. Add aging, retry limits, or an explicit tenant/priority scheduler if the metric shows work being bypassed too long.
- For strict ordering requirements: account for PostgreSQL’s documented caveat that a locking
SELECTwithORDER BYatREAD COMMITTEDcan return rows out of order if it waits for a lock while an ordering column changes. PostgreSQL describes a subquery-locking workaround, but warns it can lock all rows and materially affect performance. UnderREPEATABLE READorSERIALIZABLE, the described case produces a serialization failure. A queue usingSKIP LOCKEDnormally avoids waiting for conflicting row locks, but ordering columns should still be treated carefully if they can change concurrently or locking behavior differs.
How do I prevent two workers from taking the same job?
The row lock coordinates the claim while the transaction is open; the atomic state change makes the claim durable after commit. A competing worker skips a row locked by the first transaction, and once the first transaction commits, the row is no longer ready, so it no longer matches the claim predicate. Keep the claim transaction short and do not perform the job’s slow or external work while holding the row locks.
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That protection applies to database claims, not arbitrary side effects. If a worker commits a claim, performs an external action, and crashes before recording completion, the action may have happened even though the job appears unfinished. Make side effects idempotent where possible, and define how the system reconciles ambiguous outcomes; a PostgreSQL transaction alone cannot atomically commit an unrelated service’s network-side effect.
How do I retry jobs after a worker crashes?
Use a claim deadline or lease on running rows. A recovery process can find jobs whose lease has expired and return them to ready or move them to a retry state. Specify the retry count, backoff, and terminal-failure path as part of the application state machine. A lease replaces long-held database locks with explicit recovery logic; lease duration and renewal must account for normal job runtime and worker pauses.
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- On successful work: mark the job
doneafter the effect is confirmed. - On a retryable error: schedule a later
run_atusing backoff, increment or record the attempt, and make the row claimable again. - On exhausted retries or a permanent error: move the job to a terminal failure state and preserve enough error detail for diagnosis or manual recovery.
- On an expired lease: reclaim only according to a defined policy, and guard completion updates with the current claim owner or a claim token so an old worker cannot overwrite a newer claim.
The last safeguard is especially useful when an expired worker resumes after another worker has reclaimed its job. Exactly-once external effects are not provided by row locking; use idempotency keys or a broader protocol where duplicate effects would be harmful.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I index and monitor?
Choose indexes to support both the eligibility filter and the ordering policy, then inspect execution plans and benchmark with representative data and concurrency. For a simple queue, a partial index over ordering columns for ready rows may be a candidate, but scheduled-time filters, priority distribution, state changes, and tenant rules can alter the best design. There is no universal throughput figure established here; measure the workload you actually run.
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- Track claim latency, oldest ready-job age, retry counts, failures, and lock waits.
- Watch table and index growth, vacuum activity, and the effects of repeated state updates and eventual deletion or archival. PostgreSQL’s [routine vacuuming documentation](https://www.postgresql.org/docs/16/routine-vacuuming.html) explains vacuum’s maintenance role but does not set queue-specific thresholds.
- Use
LISTEN/NOTIFYonly as an optional wake-up aid. Workers must still check the durable jobs table: [PostgreSQL NOTIFY documentation](https://www.postgresql.org/docs/16/sql-notify.html) describes notifications, not a durable job queue. Polling is simpler; notifications can reduce idle polling latency but require managing listener connections and lifecycle.
When is a PostgreSQL queue the right fit?
A table-backed queue can be attractive when jobs need transactional coupling with application data and the workload fits the database’s operational model. Compare it with a broker or queue library against the actual requirements rather than a generic scale threshold:
- Transactional coupling to application data
- Delivery, retry, and dead-letter semantics
- Ordering and tenant or priority fairness
- Measured throughput and latency at expected concurrency
- Operational burden and crash-recovery behavior
- Visibility, scheduling, and administrative tooling
Confirm SQL behavior against the PostgreSQL major version you deploy; the links here document PostgreSQL 16. They establish database semantics and maintenance considerations, not a workload-independent point at which every application should switch to a dedicated broker.
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