PostgreSQL deadlocks occur when transactions hold locks that prevent each other from progressing in a cycle. They are different from an ordinary wait that may resolve when another transaction finishes. PostgreSQL can detect a deadlock and abort a participating transaction, but the application still needs to handle that outcome correctly.
This guide explains diagnosis and recovery for systems you maintain. It focuses on transaction design and safe retries, not on blindly terminating sessions or increasing timeouts until symptoms become less visible. Use controlled test data when reproducing concurrency problems.
Define the PostgreSQL deadlocks problem
Capture the relevant error category, transaction context, and operation identifiers. A slow request or generic timeout is not sufficient evidence of a deadlock. Distinguish lock waits, resource pressure, and detected cycles through supported database evidence.
Identify which application paths participate. The conflict can involve background jobs, triggers, or maintenance work rather than two copies of the same visible request.
Keep the incident’s business impact clear. A transaction rejected to resolve a cycle may leave the application needing recovery, but it does not automatically mean the entire database is corrupt.
Review lock acquisition order
Transactions touching the same resources in different orders can form a cycle. Use a consistent supported order where the application can do so, and acquire the appropriate required lock mode deliberately.
Review indirect work too. A function or trigger may touch another table after the visible statement, changing the effective order. Document the complete operation rather than only the first SQL line.
The PostgreSQL locking reference explains deadlock behavior and avoidance considerations. Use the deployed version’s semantics when choosing changes.
Keep transactions focused
Avoid holding locks while waiting for user input, slow external calls, or unrelated computation where the consistency model permits another design. Longer transactions increase the interval during which conflicts can occur.
Do not split a logically atomic operation merely to eliminate one symptom. Review which updates must remain together and which work can safely move outside the transaction.
Measure duration and contention across real traffic. A local test with one client cannot establish the concurrent behavior of a busy deployment.
Collect safe diagnostic evidence
Use supported database views and logs with appropriate access to inspect the cycle and involved operations. Preserve enough context to understand relationships without exposing full private payloads or credentials.
Correlate database evidence with application operation identifiers. A raw statement can be difficult to map back to the business action, especially when frameworks or triggers generate part of the work.
Avoid drawing conclusions from an isolated log fragment. The useful question is which resources and transaction paths form the cycle under the observed conditions.
Retry the appropriate unit of work
When the database aborts a transaction due to a deadlock, follow the driver’s supported rollback and retry process. Retrying only the failed statement inside an already aborted transaction is not a general recovery design.
Use bounded attempts and backoff appropriate to the workload. Repeated immediate retries can increase contention. Report a clear failure when the policy is exhausted rather than looping indefinitely.
Ensure the retried operation has the same intended business meaning. Re-read or recompute state as required by the application’s consistency rules instead of replaying stale assumptions blindly.
Protect external side effects
A database rollback does not undo a message already sent or another service’s completed action. Keep such effects coordinated through the architecture’s supported transaction, outbox, or idempotency mechanisms where appropriate.
Do not report the entire operation complete merely because the final retry committed. Verify the surrounding business outcome and any work performed before the abort.
Authorization also remains independent. Our API authorization guide explains a boundary that should hold on every retry, not only the first attempt.
Test the actual concurrency path
Use an approved environment with synthetic rows and coordinated concurrent clients. Exercise the observed transaction paths and verify both the database result and the application’s response.
Test exhausted retries and uncertain outcomes as well as eventual success. A recovery path can appear correct while still duplicating a job or returning a misleading status under failure.
Keep the test repeatable with documented ordering and timing controls. Accidental reproduction once is weaker evidence than a controlled scenario that validates the correction.
A practical deadlock scenario
Consider two test operations that update the same pair of synthetic records in opposite orders. Observe the supported deadlock evidence and the aborted transaction’s application response. Then apply the intended consistent-order design and repeat the concurrency test.
Exercise the configured retry path without a real external payment or message. Confirm that rollback is handled before another attempt and that retries remain bounded. If the operation queues work, inspect the final queue records and outcomes too.
Record the database version, driver, transaction boundaries, resource order, error category, and recovery result. This separates cycle diagnosis from generic slow-query troubleshooting and provides evidence for future changes. A successful single-client run after the patch does not establish that the concurrency problem is resolved.
Review retry and transaction ownership
A concurrency correction can span application code, database procedures, and background processing. Keep ownership of the complete unit explicit so one component does not retry work that another component has already treated as completed.
- Identify the operation’s durable business identity and all relevant transaction boundaries. A local database rollback cannot establish whether every external action was also reversed or never executed.
- Record the resource order used by participating paths, including work performed indirectly by supported triggers or helper functions. The first visible statement may not describe the whole sequence.
- Review retry eligibility for each error category. A detected deadlock, a validation failure, and an unknown remote outcome need different handling rather than the same generic loop.
- Preserve current authorization and correct input interpretation on another attempt. Recovery should not reuse stale assumptions when permissions or relevant data have changed under the application’s contract.
- Monitor exhausted retries and repeated contention with safe identifiers. A successful eventual response can still conceal unnecessary load and a transaction design that deserves repair.
Keep tests focused on the observed business paths and supported database behavior. The durable result is an explained concurrency policy with bounded recovery, not simply a higher timeout that makes failures take longer to appear.
Frequently asked questions
Is every long lock wait a deadlock?
No. A wait can resolve without a cycle. Use the database’s supported error and diagnostic evidence to identify the actual condition.
Should I retry only the last SQL statement?
Not as a general rule after a transaction abort. Follow the driver’s supported transaction recovery and retry the appropriate unit safely.
What is a durable improvement?
Consistent resource ordering where possible, short focused transactions, bounded recovery, and controlled side effects. Validate them under representative concurrency.
