Query Optimization

Key Characteristics

• ▶Reduce query execution time and resource consumption
• ▶Maintain identical results while improving efficiency
• ▶Use cost-based analysis to evaluate alternative execution strategies
• ▶Leverage partitioning, indexing, and materialization
• ▶Require accurate table statistics for effective optimization
• ▶Employ both automatic optimizer techniques and manual rewrites

Why It Matters

• ▶Directly reduces cloud infrastructure costs and resource consumption
• ▶Improves analyst productivity through faster query execution
• ▶Enables interactive analysis that would be infeasible with slow queries
• ▶Maximizes capacity of fixed infrastructure investments
• ▶Supports larger datasets by enabling efficient processing
• ▶Prevents query runaway that degrades system performance for other users

Example

`
-- Unoptimized query (full table scans)
SELECT customer_id, SUM(order_value) as total
FROM orders
WHERE EXTRACT(YEAR FROM order_date) = 2024
GROUP BY customer_id;

-- Optimized version (partitioning enables predicate pushdown)
SELECT customer_id, SUM(order_value) as total
FROM orders
WHERE order_date >= '2024-01-01' AND order_date < '2025-01-01'
GROUP BY customer_id;

-- Even better: use materialized aggregate
SELECT customer_id, total_2024
FROM customer_annual_totals
WHERE year = 2024;

-- Index optimization example
-- Add index on frequently filtered columns
CREATE INDEX idx_orders_date_customer 
ON orders(order_date, customer_id);

-- Optimizer now considers index-based execution paths:
-- Option 1: Index range scan + aggregation
-- Option 2: Full table scan with filtering
-- Optimizer chooses based on cardinality estimates
`

Coginiti Perspective

Coginiti contributes to query optimization at multiple levels. CoginitiScript's execution mechanics choose between CTEs, temporary tables, and ephemeral tables based on query complexity and platform capabilities. The semantic layer uses Apache DataFusion to analyze and optimize Semantic SQL before translating it to platform-specific queries. Query tags let teams track query cost by department and project, providing the data needed to identify optimization opportunities across the organization.

Related Concepts