/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