Subquery

How subqueries work

Subqueries operate by executing their inner query first, producing a result set that the outer query then uses. This nested structure allows for sophisticated data analysis by:

1. Filtering data based on aggregated results
2. Comparing values across different time periods
3. Creating derived tables for complex joins

Types of subqueries

Scalar subqueries

Return a single value used for comparison or calculation. Particularly useful in time-series analysis for comparing current values against historical aggregates.

SELECT symbol, price 
FROM trades 
WHERE price > (
    SELECT avg(price) 
    FROM trades_OHLC_15m 
    WHERE symbol = 'AAPL'
);

Correlated subqueries

Reference columns from the outer query, enabling row-by-row processing. These are powerful for time-based comparisons but can impact performance.

Performance considerations

Execution order

Subqueries can significantly impact query performance depending on their type and usage:

1. Non-correlated subqueries execute once
2. Correlated subqueries execute for each outer query row
3. Query plan optimization may rewrite subqueries as joins

Optimization techniques

• Use predicate pushdown when possible
• Consider replacing subqueries with joins for better performance
• Leverage materialized views for frequently used subqueries

Common applications in time-series data

Moving averages and comparisons

Subqueries excel at calculating moving averages and comparing current values against historical periods.

SELECT timestamp, price,
    (SELECT avg(price) 
     FROM trades 
     WHERE symbol = 'AAPL' 
     SAMPLE BY 1h) as hourly_avg
FROM trades
WHERE symbol = 'AAPL';

Complex aggregations

Enable sophisticated analysis by combining multiple levels of aggregation:

• Comparing intraday patterns across different time periods
• Calculating relative performance metrics
• Identifying anomalies against historical baselines

Market analysis

In financial markets, subqueries are essential for:

• Calculating VWAP across different time windows
• Comparing trading volumes against historical averages
• Identifying price movements outside normal ranges

Best practices

1. Performance optimization

   • Use indexes effectively
   • Consider alternatives to correlated subqueries
   • Test query performance with representative data volumes

2. Maintainability

   • Break complex queries into manageable components
   • Document subquery purpose and dependencies
   • Consider creating views for frequently used subqueries

3. Error handling

   • Validate subquery results
   • Handle null values appropriately
   • Consider edge cases in time-series data

By following these guidelines and understanding the performance implications, developers can effectively use subqueries to build sophisticated time-series analysis solutions while maintaining system performance.