Why virtual warehouses control compute independently in Snowflake - Performance Analysis
We want to understand how the work done by virtual warehouses changes as more queries run.
How does controlling compute separately affect the number of operations?
Analyze the time complexity of managing compute resources independently.
-- Create two virtual warehouses
CREATE WAREHOUSE warehouse_a WITH WAREHOUSE_SIZE = 'SMALL';
CREATE WAREHOUSE warehouse_b WITH WAREHOUSE_SIZE = 'SMALL';
-- Run queries on each warehouse independently
USE WAREHOUSE warehouse_a;
SELECT * FROM table1 WHERE condition_a;
USE WAREHOUSE warehouse_b;
SELECT * FROM table2 WHERE condition_b;
This sequence shows two warehouses running queries separately, each controlling its own compute.
Look at what happens repeatedly when queries run on separate warehouses.
- Primary operation: Query execution on each warehouse's compute cluster.
- How many times: Once per query per warehouse, independently.
As the number of queries increases, each warehouse handles its own queries separately.
| Input Size (n) | Approx. Api Calls/Operations |
|---|---|
| 10 | 10 operations split across warehouses |
| 100 | 100 operations split across warehouses |
| 1000 | 1000 operations split across warehouses |
Pattern observation: Operations grow linearly with queries, but each warehouse manages its own share independently.
Time Complexity: O(n)
This means the total work grows directly with the number of queries, handled separately by each warehouse.
[X] Wrong: "All queries share the same compute, so adding warehouses doesn't change total work."
[OK] Correct: Each warehouse controls its own compute, so work scales with queries per warehouse, not combined.
Understanding how separate compute clusters handle work helps you explain cloud scaling clearly and confidently.
"What if we combined all queries into one warehouse instead of multiple? How would the time complexity change?"