name: finding-expensive-queries description: | Finds and ranks expensive Snowflake queries by cost, time, or data scanned. Use when: (1) User asks to find slow, expensive, or problematic queries (2) Task mentions "query history", "top queries", "most expensive", or "slowest queries" (3) Analyzing warehouse costs or identifying optimization candidates (4) Finding queries that scan the most data or have the most spillage Returns ranked list of queries with metrics and optimization recommendations.

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