Diagnosing spill and queueing before adding more warehouse
SnowflakedbtAirflowTerraformThe nightly pipeline came back inside its window and stopped colliding with business hours. Remote spilling was eliminated on the worst queries, and credit consumption fell relative to the larger-ware…
Overview
An insurance group's nightly transformation pipeline started overrunning its window and colliding with the business day. The instinctive fix was a larger warehouse, which had already been tried once without much effect. AceMQ was engaged to find what was actually consuming the time.
Challenge
Two distinct problems were producing the same symptom. A small number of transformation queries were spilling to remote storage, which is orders of magnitude slower than local, and those long-running queries were occupying the warehouse while everything else queued behind them. Increasing warehouse size had helped the spilling queries slightly while making the queueing worse per credit spent.
Environment
Snowflake on Azure running dbt transformations orchestrated by Airflow across policy and claims data.
Approach
AceMQ used query history to separate execution time, queueing time, and spill volume per query so the two problems could be addressed independently. Query-level fixes came first because a query that spills will spill on any warehouse size that does not fit its working set.
Solution
- 1Separated execution time, queue time, and remote spill volume per query from query history
- 2Rewrote the joins and window functions in the worst-spilling queries to reduce intermediate result size
- 3Split the pipeline across separate warehouses so long transformations no longer blocked short jobs
- 4Configured multi-cluster scaling on the warehouse serving concurrent short queries
- 5Corrected clustering and filtering on the largest source tables to cut scanned micro-partitions
- 6Added spill volume and queue depth to pipeline monitoring as leading indicators of window overrun
Outcome
The nightly pipeline came back inside its window and stopped colliding with business hours. Remote spilling was eliminated on the worst queries, and credit consumption fell relative to the larger-warehouse approach that had been tried previously.
Technologies
SnowflakedbtAirflowTerraformRelated Use Cases
Snowflake Ingestion Pipeline Support
Ongoing support for Snowpipe, stream, and task failures including stale streams past their retention window and silent partial-load conditions.
Snowflake Warehouse Right-Sizing and Credit Consumption Consulting
Restructuring warehouse sizing, auto-suspend policy, and workload isolation to bring credit consumption in line with the work actually being done.
Facing a Snowflake Production Issue?
AceMQ's senior Snowflake engineers have handled this exact type of engagement before. Whether you need architectural guidance, hands-on remediation, or an ongoing managed partnership, we're ready to help.