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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesUse FOR UPDATE SKIP LOCKED to let concurrent PostgreSQL workers claim different ready jobs without waiting on rows another worker has locked. For a practical queue, select a bounded batch in a short transaction, update those rows to a claimed state atomically, and commit before running the work. This avoids duplicate claims during the transaction, but it does not guarantee strict FIFO, equal worker shares, or freedom from starvation.
How do I use FOR UPDATE SKIP LOCKED for a PostgreSQL job queue?
Store each job as a durable row with an explicit lifecycle, such as ready, running, done, and failed. Give jobs a stable enqueue time or sequence, plus a unique key to break ties. A worker can claim jobs with one statement that selects available rows, locks them without waiting on conflicting locks, changes their state, and returns the claimed records.
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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.*;This is an illustrative pattern, not a performance-tested query or a queue recipe guaranteed by PostgreSQL. Run the statement in a transaction and commit it before doing slow work. The selected rows are locked while the transaction is active; the update records the claim durably, and RETURNING provides the rows to process. PostgreSQL documents the locking clause in its SELECT reference and the data-modifying statement behavior in its UPDATE reference.
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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →The ordering above favors higher priority first, then older enqueue time, then lower ID. For oldest-first selection, remove priority. A bounded batch limits how many jobs a worker reserves at once and gives the application a straightforward way to apply backpressure. Keep the eligibility predicate and state change within the same transaction so that competing workers cannot both claim the same ready row.
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What does “fair” mean for a queue?
Fairness is a policy you define, not a property supplied by SKIP LOCKED. A deterministic ORDER BY defines preference among eligible rows a worker can see and lock. Put a unique key last so rows tied on earlier sort fields still have a predictable order; PostgreSQL says rows tied on every ordering expression may be returned in implementation-dependent order.
With several workers, an earlier row may be locked by one worker while another skips it and claims a later row. That is the intended trade-off: workers can make progress instead of waiting, but processing order can diverge from global FIFO. Nor does the feature promise equal work distribution between workers or starvation-free scheduling. A frequently locked, failing, or repeatedly retried job may wait while other jobs proceed.
If old jobs must not be neglected, make that requirement explicit in the scheduling policy. Possible application-level approaches include factoring age into priority, defining retry limits and backoff, using leases, or designing tenant-aware or weighted scheduling. Monitor the age of the oldest ready job; the PostgreSQL reference describes locking behavior, not a guarantee about long-term scheduling fairness.
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Does SKIP LOCKED guarantee FIFO?
No. It skips rows that cannot be locked immediately, so a worker can claim a later available row while an earlier row is locked. An ordering clause establishes a preference for eligible rows; it cannot force all concurrent workers to process or finish jobs in one global order.
There is also a separate ordering caveat in PostgreSQL’s documentation: at READ COMMITTED, a locking SELECT with ORDER BY can return rows out of order if it blocks while acquiring a lock and an ordering column changes during the wait. PostgreSQL describes a subquery-locking workaround when a strictly sorted result is required, but warns that it can lock all rows and materially affect performance. Under REPEATABLE READ or SERIALIZABLE, the described case produces a serialization failure instead. Queue workers using SKIP LOCKED generally avoid waiting on conflicting row locks, but applications should account for this caveat if ordering fields can change concurrently or the locking approach changes. See the PostgreSQL 16 SELECT documentation.
How do I prevent two workers from taking the same job?
Use the locking selection and state update as one atomic claim operation. A worker selects only rows in the eligible state, locks them, updates them to running, and commits. While the transaction holds the row locks, another worker using the same pattern skips those rows; after commit, the changed state keeps them out of the ready-job predicate.
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- Keep the claim transaction short and do not perform network calls or other slow job work before committing.
- Store who claimed the job and when, so operators and recovery logic can identify active claims.
- Use a unique, stable ordering key after policy fields to make selection predictable when values tie.
- Claim a bounded batch instead of reserving an unbounded queue.
PostgreSQL describes SKIP LOCKED as useful for avoiding contention among consumers of queue-like tables, while warning that it provides an inconsistent view of the data and is not suitable for general-purpose reads. Use it for coordination among queue consumers, not as a substitute for a consistent query elsewhere. The qualification is in the official locking-clause documentation.
How do I retry jobs after a worker crashes?
A row lock coordinates a claim only while its transaction is open. If a worker commits a claim and then crashes, the row remains marked running unless the application has a recovery policy. A lease or claim deadline lets recovery logic find claims that have expired and decide whether to retry or fail them.
- Record the claim. Store the worker identity, claim time, lease deadline, and attempt count when moving the job to
running. - Detect expired claims. A recovery process should identify running jobs whose lease has expired and determine whether they are eligible to return to
ready. - Apply retry policy. Define a maximum attempt count, retry delay or backoff, and a terminal outcome for jobs that cannot succeed.
- Make external effects safe to repeat. A worker may successfully perform an external action and crash before recording completion. Use idempotency or another application-level protocol to prevent a retry from duplicating that effect.
A database transaction cannot, by itself, atomically commit an unrelated network service’s side effect. Leases and retries improve recovery, but they do not create exactly-once external execution. Keeping a transaction open for the entire job is another design, but holds row locks during the work and ties recovery to transaction and connection cleanup; if used, runtime must be bounded carefully.
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How should I index and operate the queue?
Choose indexes to match the eligibility filter and ordering policy, then inspect execution plans and benchmark with representative concurrency. For a simple ready-state queue, a partial index over ordering columns restricted to ready rows may be a candidate. The right design depends on filters, priority distribution, scheduled times, and state transitions; there is no universal index or throughput figure established by PostgreSQL’s documentation.
Queue rows are updated repeatedly and may eventually be deleted or archived. Monitor operational signals that reveal both service delay and database maintenance needs:
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- Retry counts, terminal failures, and expired leases.
- Lock waits and transaction duration.
- Table and index growth, along with vacuum activity.
PostgreSQL’s routine vacuuming documentation explains vacuum’s maintenance role; it does not set queue-specific operating thresholds. Choose thresholds from the workload and service objectives.
Polling or notifications?
Polling the durable jobs table at a sensible interval is the simpler wake-up strategy. LISTEN/NOTIFY can optionally reduce idle polling latency, but it adds listener-connection lifecycle requirements. Treat notifications as a signal to check the table, not as the job record itself: the table remains the source of truth. PostgreSQL documents NOTIFY as a notification facility, not a durable queue.
When is a PostgreSQL-backed queue a good fit?
PostgreSQL can be a practical choice when jobs belong naturally alongside application data and transactional coupling matters. Whether it remains a good fit depends on actual workload and operational needs; there is no workload-independent point at which every queue should move elsewhere. Evaluate alternatives against the same concrete requirements:
- How delivery, retries, and failure recovery must work.
- Whether ordering, priority, or tenant fairness has specific guarantees.
- Throughput and latency at representative workload and concurrency.
- How much operational work recovery and queue visibility require.
- Whether scheduling or dead-letter support is needed.
Benchmark the workload and measure oldest-job age and claim latency rather than relying on a generic capacity claim. PostgreSQL’s concurrency-control and MVCC documentation explain database behavior, but do not establish a universal queue throughput threshold.
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