Multiple PostgreSQL workers can claim different jobs concurrently by selecting eligible rows with FOR UPDATE SKIP LOCKED, changing those rows to a claimed state in the same short transaction, and committing. The row locks prevent competing claim transactions from taking the same rows while the transaction is open; the committed status change records the claim after the locks are released.
What row locking does in PostgreSQL
A SELECT ... FOR UPDATE locks the selected rows as though they were selected for update. Other transactions that try to update, delete, or take a conflicting row lock on those rows generally wait until the locking transaction ends. If a waiting locking query proceeds after a competing update, it locks and returns the updated row if it still exists; if that row was deleted, it may return no row. PostgreSQL normally holds row locks until transaction end, though rolling back to a relevant savepoint can release locks acquired after that savepoint. See the PostgreSQL 16 SELECT documentation.
Row locks coordinate writers and other lockers; ordinary reads are not blocked by them. PostgreSQL offers four row-locking clauses, with different strengths and conflict behavior:
| Clause | Practical role |
|---|---|
FOR UPDATE |
Strongest of these four row-lock modes; a straightforward choice when a queue claim will update a job’s status. |
FOR NO KEY UPDATE |
Weaker than FOR UPDATE; consider it when the operation does not need the stronger protection. |
FOR SHARE |
Shared row lock with narrower conflict behavior. |
FOR KEY SHARE |
Shared row lock with narrower conflict behavior, including protection relevant to key changes. |
These clauses are not interchangeable. Choose the least restrictive mode that still protects the operation’s invariants; for a simple claim that changes status, FOR UPDATE is easy to reason about. The locking documentation describes the exact conflicts for each mode.
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How workers avoid claiming the same job
By default, a locking query can wait when another transaction already holds a conflicting lock. NOWAIT changes that behavior to an immediate error; SKIP LOCKED instead omits rows that cannot be locked immediately. A second worker can therefore move on to other eligible jobs rather than wait on the first worker’s claim.
PostgreSQL explicitly identifies queue-like consumers as a useful case for SKIP LOCKED, while warning that it “provides an inconsistent view of the data” and is not suitable for general-purpose reads. The worker is deliberately seeing an available subset, not a complete, consistent snapshot of all pending work. These options affect row-lock behavior; PostgreSQL still takes the required table-level lock. See the PostgreSQL 16 SELECT documentation.
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Use one short transaction to select and record a claim
A row lock exists only while its transaction remains open. If the worker needs other transactions to see that a job has been claimed after commit, it must persist a state change as part of the claim transaction. A common shape is to select a bounded set of pending rows in the desired order, lock them with FOR UPDATE SKIP LOCKED, update their status, and return the claimed rows.
BEGIN;
WITH picked AS (
SELECT id
FROM jobs
WHERE status = 'pending'
ORDER BY priority DESC, created_at, id
LIMIT 10
FOR UPDATE SKIP LOCKED
)
UPDATE jobs AS j
SET status = 'running'
FROM picked
WHERE j.id = picked.id
RETURNING j.*;
COMMIT;This is an illustrative pattern, not a complete queue implementation. Adapt the table and column names, ordering policy, batch size, and SQL to your schema and PostgreSQL release. The official locking documentation explains the lock semantics, not every production queue concern.
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- Keep the transaction limited to selecting, locking, and recording the claim.
- Commit before calling external services or doing lengthy job work, so claim locks are not held throughout processing.
- Make the status transition and selection atomic: if the transaction fails before commit, its claim update does not become a committed claim.
- After commit, use an explicit recovery design—such as a lease or timeout—if jobs must be reclaimed after a worker crashes. Transaction-scoped row locks do not themselves define crash recovery.
Ordering, priority, and fairness
SQL does not promise a predictable row order without ORDER BY. Use a stable tie-breaker, typically a unique identifier, so equal priority or timestamp values have a defined order. For example, ORDER BY created_at, id expresses oldest-first intent, while ORDER BY priority DESC, created_at, id puts priority ahead of age.
SKIP LOCKED favors progress over strict queue order: a worker can bypass a locked row and claim a later one. A frequently locked high-priority job can consequently be bypassed repeatedly. Do not treat this pattern as a guarantee of strict FIFO ordering or starvation freedom; those properties depend on queue policy and workload.
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At the default READ COMMITTED isolation level, PostgreSQL warns that a locking query with ORDER BY can return rows apparently out of order if an ordering column changes while the query waits for a lock. If priority or age must remain stable during claims, prevent sort-key changes during that interval or use an application rule that otherwise serializes such changes. Test the chosen policy under the workload. Details are in the PostgreSQL 17 SELECT documentation.
Isolation levels and transaction failures
Under READ COMMITTED, a locking query can wait for a concurrent updater and then follow the updated-row behavior described above. Under REPEATABLE READ or SERIALIZABLE, a row changed since the transaction snapshot can cause the locking attempt to fail instead. Applications using either isolation level should handle transaction errors and retry when appropriate; a retry policy must account for the whole transaction, not just assume the lock will succeed on a second statement. See the PostgreSQL 16 SELECT documentation and PostgreSQL 17 transaction isolation documentation.
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Explicitly locking selected rows also does not automatically enforce every business rule spanning multiple rows. If correctness depends on a broader invariant, choose a transaction and consistency strategy that protects that invariant; row locks on the rows selected by one query are not a general guarantee of serializable behavior. PostgreSQL discusses application-level consistency approaches in its application-level consistency documentation.
Trade-offs to decide before deployment
- Wait behavior: ordinary locking can wait,
NOWAITerrors, andSKIP LOCKEDbypasses rows locked by another transaction. - Claim batch size: larger batches reduce claim round trips but keep more rows locked during the transaction; smaller batches reduce lock exposure but can increase coordination overhead.
- Queue order: a priority or FIFO intention requires explicit ordering and a decision about whether out-of-order claims are acceptable.
- Recovery: a committed
runningstate needs a separate lease, timeout, or other recovery policy if a worker can disappear mid-job. - Isolation: stronger isolation may produce transaction failures that the worker must handle rather than treating every claim attempt as guaranteed to succeed.
These are design trade-offs, not published performance measurements. Validate the SQL, lock behavior, retry handling, and recovery rules against the PostgreSQL version and schema you deploy.
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