Finding Expensive Queries

Finding Expensive Queries

Query history → Rank by metric → Identify patterns → Recommend optimizations

Workflow

1. Ask What to Optimize For

Before querying, clarify:

• Time period? (last day, week, month)
• Metric? (execution time, bytes scanned, cost, spillage)
• Warehouse? (specific or all)
• User? (specific or all)

2. Find Expensive Queries by Cost

Use QUERY_ATTRIBUTION_HISTORY for credit/​cost analysis:

SELECT
    query_id,
    warehouse_name,
    user_name,
    credits_attributed_compute,
    start_time,
    end_time,
    query_tag
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_ATTRIBUTION_HISTORY
WHERE start_time >= DATEADD('days', -7, CURRENT_TIMESTAMP())
ORDER BY credits_attributed_compute DESC
LIMIT 20;

3. Get Performance Stats for Specific Queries

Use QUERY_HISTORY for detailed performance metrics (run separately, not joined):

SELECT
    query_id,
    query_text,
    total_elapsed_time/​1000 as seconds,
    bytes_scanned/​1e9 as gb_scanned,
    bytes_spilled_to_local_storage/​1e9 as gb_spilled_local,
    bytes_spilled_to_remote_storage/​1e9 as gb_spilled_remote,
    partitions_scanned,
    partitions_total
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE query_id IN ('<query_id_1>', '<query_id_2>', ...)
  AND start_time >= DATEADD('days', -7, CURRENT_TIMESTAMP());

4. Identify Patterns

Look for:

• High credits_attributed_compute queries
• Same query_hash repeated (caching opportunity)
• partitions_scanned = partitions_total (no pruning)
• High gb_spilled (memory pressure)

5. Return Results

Provide:

1. Ranked list of expensive queries with key metrics
2. Common patterns identified
3. Top 3-5 optimization recommendations
4. Specific queries to investigate further

Common Filters

-- Time range (required)
WHERE start_time >= DATEADD('days', -7, CURRENT_TIMESTAMP())

-- By warehouse
AND warehouse_name = 'ANALYTICS_WH'

-- By user
AND user_name = 'ETL_USER'

-- Only queries over cost threshold
AND credits_attributed_compute > 0.01

-- Only queries over time threshold
AND total_elapsed_time > 60000  -- over 1 minute