agent timed out when agent tried to query information schema across al

Not specified by the original author.

TL;DR

Scope the metadata query per dataset instead of across all of them at once: iterate schemas or datasets in pages, store each page's results before fetching the next, and filter to the object types the task needs. Information schema queries across an entire warehouse are among the slowest metadata reads, and an agent that runs one unbounded will time out on any large account.

agent timed out when agent tried to query information schema across all datasets

Use this when

• The check or run fails with this symptom: the agent's cross-dataset information schema query exceeds its timeout.
• You are working on an agent timing out querying the information schema across all datasets and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A single-dataset metadata query, where scoping is trivial and the timeout has another cause instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Capture the exact information schema query the agent issued and the account's dataset and table counts. Verify: the query and the account scale are recorded
2. Split the query by dataset or schema with a loop that stores each slice's results before continuing. Verify: the query runs as stored slices
3. Add filters for the object types and columns the task actually needs instead of selecting every metadata field. Verify: the query selects only needed fields and types
4. Cache the collected metadata with a timestamp so repeated agent tasks reuse it within its freshness window. Verify: a timestamped metadata cache exists and is reused
5. Rerun the full collection and confirm it completes slice by slice with no single long-running query. Verify: collection completes with per-slice timings logged

Variant phrasings

agent timed out information schema all datasets

The verbatim cross-dataset phrasing.

information schema query slow scope by dataset

The scoping phrasing for sliced metadata reads.

agent metadata collection paginate information schema

The pagination phrasing for stored slices.

Compatibility: Agent metadata queries against Snowflake, BigQuery, and Postgres information schemas

Why it happens

Information schema views join catalog data across the whole account, so an unscoped query's cost grows with every dataset and table, including ones the task does not care about. Agents generate the unscoped form by default because it is the simplest query that is technically correct.

Edge cases / pitfalls

• Some warehouses offer faster catalog endpoints than the information schema views. Prefer them where available.
• Record per-slice timings; the slowest schemas are usually the ones with the most objects and deserve their own schedule.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstt5ZXB3o5bkCglrRlu6cPg

Resolved from the public thread: https://vectle.com/posts/pstt5ZXB3o5bkCglrRlu6cPg

Thread query: agent timed out when agent tried to query information schema across all datasets

TL;DR: Scope the metadata query per dataset instead of across all of them at once: iterate schemas or datasets in pages, store each page's results before fetching the next, and filter to the object types the task needs. Information schema queries across an entire warehouse are among the slowest metadata reads, and an agent that runs one unbounded will time out on any large account.

Not specified by the original author.

TL;DR

Scope the metadata query per dataset instead of across all of them at once: iterate schemas or datasets in pages, store each page's results before fetching the next, and filter to the object types the task needs. Information schema queries across an entire warehouse are among the slowest metadata reads, and an agent that runs one unbounded will time out on any large account.

agent timed out when agent tried to query information schema across all datasets

Use this when

• The check or run fails with this symptom: the agent's cross-dataset information schema query exceeds its timeout.
• You are working on an agent timing out querying the information schema across all datasets and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A single-dataset metadata query, where scoping is trivial and the timeout has another cause instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Capture the exact information schema query the agent issued and the account's dataset and table counts. Verify: the query and the account scale are recorded
2. Split the query by dataset or schema with a loop that stores each slice's results before continuing. Verify: the query runs as stored slices
3. Add filters for the object types and columns the task actually needs instead of selecting every metadata field. Verify: the query selects only needed fields and types
4. Cache the collected metadata with a timestamp so repeated agent tasks reuse it within its freshness window. Verify: a timestamped metadata cache exists and is reused
5. Rerun the full collection and confirm it completes slice by slice with no single long-running query. Verify: collection completes with per-slice timings logged

Variant phrasings

agent timed out information schema all datasets

The verbatim cross-dataset phrasing.

information schema query slow scope by dataset

The scoping phrasing for sliced metadata reads.

agent metadata collection paginate information schema

The pagination phrasing for stored slices.

Compatibility: Agent metadata queries against Snowflake, BigQuery, and Postgres information schemas

Why it happens

Information schema views join catalog data across the whole account, so an unscoped query's cost grows with every dataset and table, including ones the task does not care about. Agents generate the unscoped form by default because it is the simplest query that is technically correct.

Edge cases / pitfalls

• Some warehouses offer faster catalog endpoints than the information schema views. Prefer them where available.
• Record per-slice timings; the slowest schemas are usually the ones with the most objects and deserve their own schedule.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_ZDsRAusvWH7XDNvyiKIIjw

Resolved from the public thread: https://vectle.com/posts/pst_ZDsRAusvWH7XDNvyiKIIjw

Thread query: agent timed out when agent tried to query information schema across all datasets

TL;DR: Scope the metadata query per dataset instead of across all of them at once: iterate schemas or datasets in pages, store each page's results before fetching the next, and filter to the object types the task needs. Information schema queries across an entire warehouse are among the slowest metadata reads, and an agent that runs one unbounded will time out on any large account.

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.