Optimizing Snowflake Workloads

When to use

• Snowflake queries are slow, queue, or spill to remote storage.
• Credit/cost is higher than expected.
• Choosing warehouse size, multi-cluster settings, or clustering keys.
• Do NOT use for writing the SQL logic itself (use optimizing-sql-queries).

Workflow

- [ ] Open the Query Profile: check partitions scanned, spilling, and pruning
- [ ] Right-size: scale UP for heavy single queries, OUT for concurrency
- [ ] Set auto-suspend low and auto-resume on
- [ ] Add clustering only to very large, selectively-filtered tables
- [ ] Verify with ACCOUNT_USAGE / WAREHOUSE_METERING history

1. Read the Query Profile. Key signals: "Partitions scanned vs total" (pruning), "Bytes spilled to local/remote storage" (warehouse too small), and the most expensive operator.
2. Right-size the warehouse. Scale up (bigger size) for one heavy query that spills; scale out (multi-cluster) for many concurrent queries that queue. Bigger warehouses cost more per second but can be cheaper if they finish proportionally faster.
3. Auto-suspend + auto-resume — you pay per running second. Set auto-suspend to ~60s so idle warehouses stop.
4. Clustering keys only on large tables filtered by a high-cardinality column; check SYSTEM$CLUSTERING_INFORMATION and beware reclustering cost.

Patterns

Right-sizing decision: spilling to remote storage → scale up; queries QUEUED under concurrency → add clusters (multi-cluster min>1) rather than a bigger size.

Separate warehouses per workload (ELT vs BI vs ad-hoc) so a heavy job doesn't starve dashboards and each can be sized/monitored independently.

Exploit caching (free):

• Result cache: identical query text + unchanged data returns instantly, no compute.
• Warehouse (local) cache: keep related queries on the same warehouse to reuse cached micro-partitions.

Find cost drivers:

select warehouse_name, sum(credits_used) as credits
from snowflake.account_usage.warehouse_metering_history
where start_time >= dateadd('day', -7, current_timestamp())
group by 1 order by 2 desc;

Common pitfalls

• Always-on warehouses with no/high auto-suspend — the top source of wasted credits.
• Scaling up for a concurrency problem — a bigger warehouse doesn't fix queuing; add clusters instead.
• Clustering small or low-selectivity tables — reclustering costs more than it saves; only cluster large, selectively-filtered tables.
• SELECT * in dashboards — reads all columns' micro-partitions; select what you need.
• One shared warehouse for everything — noisy neighbors; split by workload.
• Ignoring spilling — remote spill silently multiplies runtime and cost.

References

• Snowflake cost monitoring queries