optimizing-query-by-id

name

optimizing-query-by-id

description

Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized SQL with expected improvements.

Optimize Query from Query ID

Fetch query → Get profile → Apply best practices → Verify improvement → Return optimized query

Workflow

1. Fetch Query Details from Query ID

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,
    rows_produced
FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY())
WHERE query_id = '<query_id>';

Note the key metrics:

• seconds: Total execution time
• gb_scanned: Data read (lower is better)
• gb_spilled: Spillage indicates memory pressure
• partitions_scanned/total: Partition pruning effectiveness

2. Get Query Profile Details

-- Get operator-level statistics
SELECT *
FROM TABLE(GET_QUERY_OPERATOR_STATS('<query_id>'));

Look for:

• Operators with high output_rows vs input_rows (explosions)
• TableScan operators with high bytes
• Sort/Aggregate operators with spillage

3. Identify Optimization Opportunities

Based on profile, look for:

Metric	Issue	Fix
partitions_scanned = partitions_total	No pruning	Add filter on cluster key
gb_spilled > 0	Memory pressure	Simplify query, increase warehouse
High bytes_scanned	Full scan	Add selective filters, reduce columns
Join explosion	Cartesian or bad key	Fix join condition, filter before join

4. Apply Optimizations

Rewrite the query:

• Select only needed columns
• Filter early (before joins)
• Use CTEs to avoid repeated scans
• Ensure filters align with clustering keys
• Add LIMIT if full result not needed

5. Get Explain Plan for Optimized Query

EXPLAIN USING JSON
<optimized_query>;

6. Compare Plans

Compare original vs optimized:

• Fewer partitions scanned?
• Fewer intermediate rows?
• Better join order?

7. Return Results

Provide:

1. Original query metrics (time, data scanned, spillage)
2. Identified issues
3. The optimized query
4. Summary of changes made
5. Expected improvement

Example Output

Original Query Metrics:

• Execution time: 45 seconds
• Data scanned: 12.3 GB
• Partitions: 500/500 (no pruning)
• Spillage: 2.1 GB

Issues Found:

1. No partition pruning - filtering on non-cluster column
2. SELECT * scanning unnecessary columns
3. Large table joined without pre-filtering

Optimized Query:

WITH filtered_events AS (
    SELECT event_id, user_id, event_type, created_at
    FROM events
    WHERE created_at >= '2024-01-01'
      AND created_at < '2024-02-01'
      AND event_type = 'purchase'
)
SELECT fe.event_id, fe.created_at, u.name
FROM filtered_events fe
JOIN users u ON fe.user_id = u.id;

Changes:

• Added date range filter matching cluster key
• Replaced SELECT * with specific columns
• Pre-filtered in CTE before join

Expected Improvement:

• Partitions: 500 → ~15 (97% reduction)
• Data scanned: 12.3 GB → ~0.4 GB
• Estimated time: 45s → ~3s