Our Snowflake bill has spiked recently and I noticed a lot of credits are being burned on auto-clustering for large tables. Is there a better way to handle micro-partitions manually, or should I be looking at my warehouse sizing for these DML operations to avoid constant credit drain?
3 answers
To manage costs effectively, you should first audit your clustering depth. Auto-clustering is great but it can be expensive if your data churn is high. Instead of letting Snowflake manage it 24/7, try to sort your data by a cluster key during the initial INSERT or COPY command. This natural clustering often reduces the work the background service has to do. Also, ensure you aren't using a warehouse that is too large for small, frequent queries, as you pay for the full 60 seconds of the first minute every time a suspended warehouse resumes.
Have you checked if your tables actually need a cluster key, or are you seeing high partition scanning even on queries that use your primary filters?
Always set a RESOURCE_MONITOR at the account and warehouse level. It’s the only way to stop a runaway query from eating your entire monthly budget in one weekend.
I agree, Michael. We once had a developer run a cross-join without a limit and the resource monitor saved us thousands by killing the warehouse at 110% of the daily limit.
James, we are definitely seeing massive partition scans on our multi-terabyte 'Events' table. We use a timestamp for most filters, but the data arrives out of order. If we implement a cluster key on the 'event_date' column, will Snowflake's background process automatically handle the re-sorting, or do we need to rebuild the table to see the performance gains immediately?