Query Shaping

How query shaping works

Query shaping analyzes and modifies queries before execution, applying transformations that can include:

• Rewriting predicates for better index utilization
• Restructuring joins for optimal execution paths
• Converting complex subqueries into simpler forms
• Pushing down filters closer to data sources

Common shaping techniques

Predicate optimization

Query shaping often focuses on optimizing filter conditions. For example, transforming range queries into point lookups when possible:

SELECT * FROM trades
WHERE timestamp BETWEEN '2024-01-01' AND '2024-01-01T00:01'
-- Can be shaped into using exact timestamp matching when appropriate

Join restructuring

Complex joins can be reshaped for better performance:

# Before shaping
SELECT * FROM a JOIN b ON a.id = b.id JOIN c ON b.id = c.id

# After shaping (potentially reordered based on table statistics)
SELECT * FROM b JOIN a ON b.id = a.id JOIN c ON b.id = c.id

Benefits for time-series data

In time-series databases, query shaping is particularly valuable for:

• Optimizing time-range filters for efficient data access
• Improving windowed aggregation performance
• Enhancing temporal join efficiency

Performance impact

Query shaping can significantly improve:

• Query execution time
• Resource utilization
• Concurrent query handling
• Memory efficiency

Related techniques

Query shaping works alongside other optimization approaches:

• Predicate pushdown for filtering data early
• Query plan optimization
• Vector scan operations
• Partition pruning for time-series data

Best practices

1. Monitor query performance before and after shaping
2. Validate that transformed queries maintain data consistency
3. Consider the impact on query latency
4. Test shaped queries with representative data volumes