churn agent crashed on a Snowflake session expired error mid-model-run and left the staging table locked

TL;DR

Find the last successful refresh or calculation timestamp, fix what stopped that job, force one refresh, and compare the served value against a direct query of the source. Start in the Snowflake query history and the warehouse page, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

churn agent crashed on a Snowflake session expired error mid-model-run and left the staging table locked

Use this when

• You see this exact message or symptom in Snowflake: churn agent crashed on a Snowflake session expired error mid-model-run and left the staging table locked
• One run, record, build, or object is affected and you can name it
• A retry without changing anything reproduces the same text

Not for this skill when

• Snowflake fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once in the Snowflake query history and the warehouse page and search the log or history for the exact text in the block above, recording the time, the workspace or project, and the one object it names. Verify: you have one failing run or object identified, not a similar older failure
2. Inspect the session warehouse setting in the job connection, the staging table lock, and the account parameters for the run, and check the last successful refresh timestamp, the forced refresh result, and the served value versus the direct source query. Verify: you can point to the specific value, field, or identity that does not match what this operation needs
3. Apply the fix at that source: find the last successful refresh or calculation timestamp, fix what stopped that job, force one refresh, and compare the served value against a direct query of the source. Verify: the corrected value or setting is visible in Snowflake itself, not only in a local config file
4. Re-run the original operation on the single affected item first: re-run the scoring job with an explicit warehouse set at session start for one partition of accounts. Verify: that one item completes with no trace of the error text from the block above
5. Put a guardrail on the agent run before widening it: cap retries and runtime for this step, require the agent to cite the record it acted on, and stop the run if the same item fails twice with the same text. Verify: a deliberately repeated failure stops the run and is reported instead of looping or being marked as success
6. Check the downstream result a user or the next job would see: the query history showing completed queries and the scoring output table updated for the partition. Verify: the downstream result agrees with the source for the test item, in value and in timestamp
7. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the error and the changed setting, and no earlier item regressed

Variant phrasings

churn agent crashed on a Snowflake session expired error mid-model-run and left the staging table locked

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the object the log names.

Snowflake version of this failure

The tool-first phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does churn agent crashed on a Snowflake session expired error mid-model-run and left the staging table locked keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the object state, not in the attempt.

Compatibility: Snowflake current release, session parameters, virtual warehouses, Python connector jobs. The steps assume you can read the job or build log and change the named object in Snowflake.

Why it happens

In Snowflake, this failure usually means an upstream refresh stopped or a cached or default value is being served in place of a freshly calculated one, so the surface looks plausible while being old. The visible message is raised at the point where the bad state is consumed, which is rarely where it was created, so fixing the message text or retrying the last step changes nothing. Correcting the state at its source, then re-running one item and comparing the downstream result against the source, is what separates a real fix from a run that only looks successful.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_nEDbAnkCm6W784xrx6muBQ