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The title pairs a documented PostgreSQL feature with several claims that the available sources do not verify. SKIP LOCKED is real and useful for queue-style workers: it lets a query step past rows that another transaction has already locked, instead of waiting for them. What the PostgreSQL documentation does not say is that this produces zero idle RAM, fair scheduling, a given throughput, or correct end-to-end job handling. Nor does it say anything about Hailuo H3, MiniMax Direct, or CIEDE2000. Those claims appear in one self-published article, which the sources we checked as of early October 2026 do not independently confirm, so treat them as the author’s description.
What each part of the title claims
The title bundles four ideas. They have very different levels of support, so it helps to separate them before judging the design.
| Title component | What it would have to mean | Status in the sources |
|---|---|---|
PostgreSQL SKIP LOCKED |
Queue workers claim rows without waiting on rows other workers hold | Documented in the PostgreSQL SELECT reference |
| Zero idle RAM | The scheduler consumes no meaningful memory while no jobs are waiting | Not addressed by the PostgreSQL documentation; stated only in the self-published article |
| Hailuo H3-augmented MiniMax Direct | A pipeline that sends queued jobs to specific model integrations | Not established; the sources do not show a verifiable integration matching this description |
| CIEDE2000 consistency enforcement | A check that keeps output consistent across jobs | Not explained as a database mechanism; CIEDE2000 is a colour-difference formula, so any use here would be an application-level check that PostgreSQL does not provide |
What SKIP LOCKED does
In the PostgreSQL SELECT reference, SKIP LOCKED is an option to the row-locking clauses FOR UPDATE, FOR NO KEY UPDATE, FOR SHARE, and FOR KEY SHARE. When a row the query would return is locked by another transaction, the query leaves that row out of its result rather than blocking. The alternative option, NOWAIT, raises an error instead of waiting. The option was added in PostgreSQL 9.5, so older servers do not support it.
The option changes one thing: which rows a locking query returns at that moment. It does not change how much memory the server uses, how jobs are ordered beyond what your ORDER BY specifies, or whether a job runs exactly once after a failure.
#1 Best Overall
What PostgreSQL warns about
The same reference page contains a direct caution about this option. It states:
“Skipping locked rows provides an inconsistent view of the data, so this is not suitable for general purpose work, but can be used to avoid lock contention with multiple consumers accessing a queue-like table.”
The sentence appears on the page without a named author, so cite it as PostgreSQL’s documentation rather than as a quotation from an individual. In practice, the inconsistency matters outside the claim query. A session that counts queued jobs or builds a report while workers hold locks will see a smaller set of rows than actually exists. Use SKIP LOCKED in the statement that claims work, and read status or reporting queries normally.
Rank #2
A claim-and-update pattern and its failure modes
A common way to use the option is to claim a row and change its status in one statement, then do the work outside the claim. The steps below describe that flow. They are a general illustration of the pattern, not code taken from the self-published article.
- Run a single statement that selects one queued row with
FOR UPDATE SKIP LOCKEDand sets its status to running, so the claim commits atomically. - Run the job outside that claim statement, using the returned job identifier.
- Mark the job succeeded or failed in a separate statement, and record the outcome.
WITH next_job AS (
SELECT id
FROM jobs
WHERE status = 'queued'
ORDER BY id
FOR UPDATE SKIP LOCKED
LIMIT 1
)
UPDATE jobs
SET status = 'running', claimed_at = now()
FROM next_job
WHERE jobs.id = next_job.id
RETURNING jobs.id;This flow has three failure modes that the lock clause does not solve:
- A crash after the claim commits. If the worker dies after the status change is committed, the row stays in
running. The lock is released, but nothing will pick the row up again unless the schema has a lease timeout or a reclaim query based onclaimed_at. - A transaction held open for the whole job. If you keep the row lock for the job’s duration, the transaction stays open, which keeps a connection busy and can delay cleanup of dead rows in the table. The claim-and-commit flow above avoids this at the cost of needing a reclaim path.
- Retries that duplicate side effects. Skipping locked rows prevents two workers from claiming the same row at once, but it does not make a job’s external effects idempotent. A job that calls a paid API and then fails before marking success can run its side effects twice.
Idle memory and the wake-up model
The phrase “zero idle RAM” depends on how workers learn that new jobs exist. The sources do not state a memory measurement for any version of this design, so the question has to be answered by the wake-up model rather than by the locking clause.
Polling
A polling worker runs the claim query on a timer. When the queue is empty, each poll returns no rows and costs a short round trip plus a backend connection. Idle RAM is then set by the number of open connections and worker processes, not by SKIP LOCKED. Latency is bounded by the poll interval.
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LISTEN and NOTIFY
A worker can instead issue LISTEN on a channel and wait for a notification. The producer issues NOTIFY after inserting a job. The PostgreSQL documentation describes notifications as delivered only to sessions that are listening when the notification is sent. A worker that disconnects during that window will miss the signal. So a sound design uses notifications as a wake-up hint and still polls at a slower interval to catch anything missed.
| Question | Polling with a claim query | LISTEN/NOTIFY wake-up with a claim query |
|---|---|---|
| What starts a claim attempt | A timer | A notification, usually with a timer as backup |
| Work on an empty queue | One short query per poll interval | Little or none between notifications, if the listener stays connected |
| Risk of a missed wake-up | None beyond the poll interval | A signal is lost if the listener is disconnected when it is sent |
| Source of memory figures | Not stated in the sources | Not stated in the sources |
The iTechGuides assessment noted below flags that the self-published article describes polling and LISTEN/NOTIFY in ways that do not fully agree. An implementation’s idle cost depends on which one it actually uses, so check the code rather than the description.
The Hailuo H3, MiniMax Direct and CIEDE2000 parts
The sources we checked do not establish what a Hailuo H3-augmented integration or a MiniMax Direct pipeline consists of, how it would connect to a PostgreSQL queue, or whether it runs as described. A reader who wants to evaluate such a system should look for a public API reference, a repository with the integration code, and logs from a run. The title alone supplies none of these.
CIEDE2000 is a colour-difference formula that compares two colours by perceived difference. It is not a locking, ordering, or transaction mechanism. If a pipeline uses it to check that generated images are consistent with each other, that check runs in application code after a job completes. PostgreSQL does not enforce it, and SKIP LOCKED has no bearing on it.
What the self-published article and the iTechGuides assessment say
The exact-title article is a self-published DEV Community post by Biffer Rowley. As of early October 2026 it is the only source we found that uses this title and combines all four elements. Its performance figures and deployment descriptions are the author’s own reports. They have not been reproduced by an independent party, and they do not state the hardware, PostgreSQL version, row counts, or method used to measure idle memory in a form that would allow someone else to repeat them.
A separate iTechGuides assessment of the pipeline’s verification limits reaches the same conclusion on verification and points to conflicting descriptions of polling versus LISTEN/NOTIFY. Use that assessment as a guide to which questions remain open, not as proof that the system does or does not work.
How to check a claim like this yourself
If you are deciding whether to adopt a similar design, run these checks against your own PostgreSQL instance. They are steps to perform, not results from any test reported here.
- Confirm the server supports the option. Run
SHOW server_version;. Anything at or above 9.5 includesSKIP LOCKED. - Measure idle load on an empty queue. Run
SELECT state, count(*) FROM pg_stat_activity GROUP BY state;and record the operating-system memory of the worker processes over a fixed window, noting that shared memory is counted differently across tools. - Check for duplicate claims. Start several workers against a table of queued jobs and confirm that each job identifier is claimed exactly once.
- Kill a worker mid-job. Confirm that the job is reclaimed by your lease or timeout logic and that its side effects are not repeated.
- Check the reported figures against this list. A claim should state the PostgreSQL version, hardware, number of workers, row volume, job duration, and how idle memory was measured.
A design that passes these checks is still only shown to work at your scale and under your workload. The option itself is a well-documented way to avoid waiting on locked rows. Everything else in the title needs its own evidence.
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