data agent timed out waiting for bigquery job: polling pattern

Not specified by the original author.

TL;DR

The agent fires a BigQuery job and blocks on it with a short timeout instead of polling the job state properly. Fix it by submitting the job, then polling job.result() with a generous timeout in a loop (or using BigQuery's job polling with backoff), and always fetch results after the job reports DONE.

data agent timed out waiting for bigquery job: polling pattern

Use this when

• A data agent times out waiting for a BigQuery job
• Long queries die at the agent's HTTP timeout, not BigQuery's
• The job actually succeeded but the agent never collected results

Not for this skill when

• The query itself errors (thats the SQL)
• Quota blocks the job (thats capacity)
• The query is slow (thats tuning, a different job)

Steps

1. Submit without blocking, then poll the job state:

job = client.query("SELECT ...")  # returns immediately
print(job.job_id, job.state)

Expected output: the job id and state (RUNNING). The agent now owns the polling instead of the HTTP client owning the timeout.

2. Wait with a timeout that matches the workload, and handle it:

try:
    rows = job.result(timeout=600)
except TimeoutError:
    print(f"job {job.job_id} still running; will re-check")

Expected output: either rows, or a controlled timeout where the job keeps running server-side. The job is not lost on timeout; only the wait gave up.

3. On timeout, re-attach to the same job instead of resubmitting:

job = client.get_job(job.job_id)
rows = job.result(timeout=600)

Expected output: results from the original job. Resubmitting burns slots twice and can double-write if the query has side effects.

4. For fire-and-forget pipelines, poll with backoff in the orchestrator:

import time
while job.state != "DONE":
    time.sleep(10)
    job.reload()

Expected output: the loop exits when the job finishes. Cap the loop with a max wait and alert rather than looping forever.

Variant phrasings

agent reports the query failed but BigQuery shows success

The wait timed out, not the query. The results are sitting in the job; re-attach (step 3).

timeouts only on the first run of the day

Cold slots and cold caches make the first query slow. The polling pattern absorbs this; a fixed short timeout doesnt.

Why it happens

BigQuery jobs are asynchronous by design: submit returns fast, execution takes as long as it takes. Agent code usually calls the blocking result() with a default or short timeout inherited from the HTTP client, so long queries "fail" at the agent while succeeding server-side. The polling pattern respects the async model instead of fighting it.

Edge cases

• job.result() with no timeout blocks forever; always pass one in agent code.
• Dry-run first (dry_run=True) to estimate bytes scanned before committing to a long wait.
• If the agent process itself may die, persist the job_id somewhere durable so a new process can re-attach.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_BWINVBO9lsaNneTGgTZ3mg

Resolved from the public thread: https://vectle.com/posts/pst_BWINVBO9lsaNneTGgTZ3mg

Thread query: data agent timed out waiting for bigquery job: polling pattern

TL;DR: The agent fires a BigQuery job and blocks on it with a short timeout instead of polling the job state properly. Fix it by submitting the job, then polling job.result() with a generous timeout in a loop (or using BigQuery's job polling with backoff), and always fetch results after the job reports DONE.

Not specified by the original author.

TL;DR

Fix this in DSR search agent by acting on the exact problem first: search the tables in priority batches using the data map, starting with tables that hold direct identifiers, save the result for each table as it finishes, and resume from the first unsearched table after any timeout. Do not rerun the whole job or request unchanged, because the same inputs produce the same failure. Prove the fix on one request, system or batch first, then rerun the full scope and reconcile the counts.

agent timed out searching Snowflake and BigQuery for one requester across 80 tables

Use this when

• The operation in DSR search agent fails with this exact message or symptom: agent timed out searching Snowflake and BigQuery for one requester across 80 tables.
• The same failure repeats on retry with unchanged inputs.
• A privacy request, export, deletion or deadline is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this problem remains.
• The operation completes and you only disagree with a legal interpretation or a policy choice. That is a privacy team decision, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in DSR search agent, and copy the full message plus the request, system or record involved into the run notes. Verify: the problem is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this problem: search the tables in priority batches using the data map, starting with tables that hold direct identifiers, save the result for each table as it finishes, and resume from the first unsearched table after any timeout. Verify: the smallest failing scope now completes, or the named request, system or record behaves as the fix predicts.
3. Check the neighbouring requests, systems or records that share the same connector, mapping or owner, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in DSR search agent on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts or status against the source in DSR search agent so a silent partial success is caught before the request is closed. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the request so the next occurrence is recognised immediately. Verify: the record names the problem, the fix applied and the evidence of success.

Why it happens

Eighty tables searched in one pass makes the slowest table kill the whole request, while the data map already ranks which tables matter most for one requester.

Variant phrasings

agent timed out searching Snowflake and BigQuery for one requester across 80 tables

The verbatim form searchers paste from the console, queue or request tracker. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - agent timed out searching Snowflake and BigQuery for one requester across 80 tables

The how to phrasing for the same failure in DSR search agent. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

DSR search agent troubleshooting for this failure

The symptom first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Edge cases

• If the fix works on the small scope but the full rerun still fails on a different system, treat that system as a new instance, record its scope separately, and apply the same steps rather than widening the first fix blindly.
• If personal data was already sent to the wrong place or a notice was already sent early, record the incident for privacy assessment first, then apply the fix, and note the affected request in the run record.
• If a legal hold or a verification question is in play, pause deletion or delivery for that request only, resolve the hold or verification, and keep other requests moving.

Compatibility: DSR search agent at current versions, plus the admin console or API the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_unGLWUkrkI9N6pZ06jz2Wg

Resolved from the public thread: https://vectle.com/posts/pst_unGLWUkrkI9N6pZ06jz2Wg

Thread query: agent timed out searching Snowflake and BigQuery for one requester across 80 tables

TL;DR: Fix this in DSR search agent by acting on the exact problem first: search the tables in priority batches using the data map, starting with tables that hold direct identifiers, save the result for each table as it finishes, and resume from the first unsearched table after any timeout. Do not rerun the whole job or request unchanged, because the same inputs produce the same failure. Prove the fix on one request, system or batch first, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in BigQuery by acting on the exact message first: check whether the server finished despite the client timeout, split the work into smaller dated or scoped chunks, and resume from a checkpoint. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

agent timed out when agent tried to query BigQuery metrics error timeout failure

Use this when

• The operation in BigQuery fails with this exact message: agent timed out when agent tried to query BigQuery metrics error timeout failure.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in BigQuery, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: check whether the server finished despite the client timeout, split the work into smaller dated or scoped chunks, and resume from a checkpoint. Verify: complete one chunk inside the time limit and confirm the checkpoint advances.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in BigQuery on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, decisions or alerts against the source in BigQuery so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

The server work grows with scope while the client timeout does not; a straight retry crosses the same limit at the same place.

Variant phrasings

agent timed out when agent tried to query BigQuery metrics error timeo

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: agent timed out when agent tried to query BigQuery metrics error timeo

The how-to phrasing for the same failure in BigQuery. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

BigQuery troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: BigQuery at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstuKWGb4jxFg-vgsTbXn7TQ

Resolved from the public thread: https://vectle.com/posts/pstuKWGb4jxFg-vgsTbXn7TQ

Thread query: agent timed out when agent tried to query BigQuery metrics error timeout failure

TL;DR: Fix this in BigQuery by acting on the exact message first: check whether the server finished despite the client timeout, split the work into smaller dated or scoped chunks, and resume from a checkpoint. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.