Snowflake warehouse overloaded what to do

Not specified by the original author.

TL;DR

Check whether queries are queued (too much concurrency) or just slow (too little compute), then scale out with multi-cluster for queuing or scale up a size for slow single queries. An overloaded warehouse is a capacity problem with two different shapes, and the fix depends on which one you have.

Snowflake "warehouse overloaded" what to do

Use this when

• Queries sit queued instead of executing
• You get warehouse overload or queuing messages
• You need to choose between a bigger warehouse and more clusters

Not for this skill when

• Queries fail with SQL syntax or permission errors
• The question is purely about credit costs
• One specific query is slow but nothing is queuing

Steps

1. Confirm queuing in the query history. Queued means overloaded; slow-but-running means undersized:

SELECT query_text, warehouse_name, execution_status,
       queued_overload_time, total_elapsed_time
FROM snowflake.account_usage.query_history
WHERE start_time > DATEADD(hour, -2, CURRENT_TIMESTAMP())
  AND queued_overload_time > 0
ORDER BY queued_overload_time DESC
LIMIT 20;

Expected output: the queries that waited, and how long each waited. If this returns rows, you have a concurrency problem.

2. Check how loaded the warehouse actually was:

SELECT warehouse_name,
       AVG(avg_running) AS avg_running,
       AVG(avg_queued_load) AS avg_queued
FROM snowflake.account_usage.warehouse_load_history
WHERE start_time > DATEADD(hour, -2, CURRENT_TIMESTAMP())
GROUP BY warehouse_name;

Expected output: average running vs queued load per warehouse. Sustained queuing with running at the max means you need more capacity, not better queries.

3. For queuing under concurrency, enable multi-cluster (scale out):

ALTER WAREHOUSE bi_warehouse SET warehouse_size = 'MEDIUM'
  MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 3
  SCALING_POLICY = 'STANDARD';

Expected output: Snowflake spins up extra clusters automatically when queries queue. Scale-out adds parallel capacity, which is what concurrency needs.

4. For a single heavy query that is slow on its own, scale up one size and test:

ALTER WAREHOUSE etl_warehouse SET warehouse_size = 'LARGE';
-- rerun the heavy query, compare elapsed time, then consider scaling back

Expected output: the query runs faster on more compute per node. Scale-up helps one big query; it does almost nothing for fifty small queued ones.

5. Separate the workloads so they stop fighting. ETL and BI on one warehouse is the usual overload story:

CREATE WAREHOUSE etl_wh WITH warehouse_size = 'LARGE';
CREATE WAREHOUSE bi_wh WITH warehouse_size = 'MEDIUM'
  MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 2;
-- point ETL jobs at etl_wh, dashboards at bi_wh

Expected output: the nightly ETL spike no longer queues the morning dashboard rush. Isolation beats sizing when the contention is between workload types.

Variant phrasings

snowflake queries queued warehouse busy

Step 1 confirms it, step 3 fixes it. Queued overload time is the metric that proves overload vs slowness.

snowflake scale up vs scale out

Up (bigger size) for single-query speed, out (multi-cluster) for concurrent-query throughput. Steps 3-4 are the decision in action.

warehouse size XSMALL overloaded

An XSMALL has very little compute; even modest concurrency queues on it. Medium is the usual floor for shared BI workloads.

Why it happens

A Snowflake warehouse is a fixed pool of compute: one cluster runs a limited number of queries in parallel, and the rest queue. Overload arrives two ways, too many queries at once (concurrency) or queries too big for the cluster size (compute). The engine queues rather than fails, so overload shows up as latency first and errors only when queue timeouts hit.

Edge cases

• Auto-suspend plus a flood of queries causes a cold-start queue spike; a minimum cluster count of 1 avoids the worst of it for latency-sensitive dashboards.
• One runaway query can hog a whole cluster; set STATEMENTQUEUEDTIMEOUTINSECONDS so bad queries fail instead of blocking everyone.
• Multi-cluster in ECONOMY mode only scales for queued queries, STANDARD also scales for anticipated load; pick based on how bursty the traffic is.
• Credits: scaling up or out costs more while it runs. Auto-suspend aggressively on the new clusters so idle capacity doesnt burn credits.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstdt5wZP8PTUgC4eZhV5lZg

Resolved from the public thread: https://vectle.com/posts/pstdt5wZP8PTUgC4eZhV5lZg

Thread query: Snowflake "warehouse overloaded" what to do

TL;DR: Check whether queries are queued (too much concurrency) or just slow (too little compute), then scale out with multi-cluster for queuing or scale up a size for slow single queries. An overloaded warehouse is a capacity problem with two different shapes, and the fix depends on which one you have.

Not specified by the original author.

TL;DR

Fix this in Snowflake usage agent workflow by acting on the exact message first: split the Snowflake usage history query into account batches of about 250, pre-aggregate daily usage in the warehouse before the agent reads it, checkpoint each finished batch to disk, and resume the refresh from the last completed batch after any timeout. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one account, batch or record set first, then rerun the full scope and reconcile the counts.

agent timed out querying Snowflake usage history for 3000 accounts expansion refresh stalled

Use this when

• The operation in Snowflake usage agent workflow fails with this exact message or symptom: agent timed out querying Snowflake usage history for 3000 accounts expansion refresh stalled.
• The same failure repeats on retry with unchanged inputs.
• Expansion scoring, alerting or outreach 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 error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Snowflake usage agent workflow, and copy the full message plus the account, signal or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: split the Snowflake usage history query into account batches of about 250, pre-aggregate daily usage in the warehouse before the agent reads it, checkpoint each finished batch to disk, and resume the refresh from the last completed batch after any timeout. Verify: the smallest failing scope now completes, or the named account, signal or job behaves as the fix predicts.
3. Check the neighbouring accounts, signals or jobs that share the same config, schema or source, 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 Snowflake usage agent workflow 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, scores or status against the source in Snowflake usage agent workflow 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

One query covering thousands of accounts returns more rows than a single agent step can finish inside its time limit, so the refresh stalls with nothing saved.

Variant phrasings

agent timed out querying Snowflake usage history for 3000 accounts expansion refresh stalled

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

How to fix - agent timed out querying Snowflake usage history for 3000 accounts expansion refresh stalled

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

Snowflake usage agent workflow 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 sample scope but the full rerun still fails, a second account or record with the same fault is present; widen the scope in stages until the next failing item is found.
• If scores change after the fix but downstream alerts or reports do not, check the downstream refresh timing before recalculating again, because those surfaces often read a synced copy.
• If the source information itself is missing for the affected accounts, record that as a source gap rather than scoring the accounts as zero.

Compatibility: Snowflake usage agent workflow at current versions, plus the platform API or console 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_Qwtyj2okXCeCp6XuSGpQtw

Resolved from the public thread: https://vectle.com/posts/pst_Qwtyj2okXCeCp6XuSGpQtw

Thread query: agent timed out querying Snowflake usage history for 3000 accounts expansion refresh stalled

TL;DR: Fix this in Snowflake usage agent workflow by acting on the exact message first: split the Snowflake usage history query into account batches of about 250, pre-aggregate daily usage in the warehouse before the agent reads it, checkpoint each finished batch to disk, and resume the refresh from the last completed batch after any timeout. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one account, batch or record set first, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in Snowflake usage scraper by acting on the exact message first: run the Snowflake usage query directly and save its result to a file, check the warehouse and schema names after the schema change, fix the query to the new names, and backfill from the saved result instead of the empty scraper output. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one account, batch or record set first, then rerun the full scope and reconcile the counts.

scraper failed on Snowflake usage query result returning empty results for backfilling usage history error scraping failure

Use this when

• The operation in Snowflake usage scraper fails with this exact message or symptom: scraper failed on Snowflake usage query result returning empty results for backfilling usage history error scraping failure.
• The same failure repeats on retry with unchanged inputs.
• Health scoring, alerting or routing 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 error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Snowflake usage scraper, and copy the full message plus the account, measure or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: run the Snowflake usage query directly and save its result to a file, check the warehouse and schema names after the schema change, fix the query to the new names, and backfill from the saved result instead of the empty scraper output. Verify: the smallest failing scope now completes, or the named account, measure or job behaves as the fix predicts.
3. Check the neighbouring accounts, measures or jobs that share the same config, schema or source, 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 Snowflake usage scraper 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, scores or status against the source in Snowflake usage scraper 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

A scraper wrapping an old query returns empty results after a schema change, while the underlying usage is intact under the new schema names.

Variant phrasings

scraper failed on Snowflake usage query result returning empty results for backfilling usage history error scraping failure

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

How to fix - scraper failed on Snowflake usage query result returning empty results for backfilling usage history error scraping failure

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

Snowflake usage scraper 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 sample scope but the full rerun still fails, a second account or record with the same fault is present; widen the scope in stages until the next failing item is found.
• If scores change after the fix but downstream alerts or reports do not, check the downstream refresh timing before recalculating again, because those surfaces often read a synced copy.
• If the source information itself is missing for the affected accounts, record that as a source gap rather than scoring the accounts as zero.

Compatibility: Snowflake usage scraper at current versions, plus the platform API or console 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/pst9wOi95vIZiFbkq_ChuinQ

Resolved from the public thread: https://vectle.com/posts/pst9wOi95vIZiFbkq_ChuinQ

Thread query: scraper failed on Snowflake usage query result returning empty results for backfilling usage history error scraping failure

TL;DR: Fix this in Snowflake usage scraper by acting on the exact message first: run the Snowflake usage query directly and save its result to a file, check the warehouse and schema names after the schema change, fix the query to the new names, and backfill from the saved result instead of the empty scraper output. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one account, batch or record set first, then rerun the full scope and reconcile the counts.