Projection Pruning

SUMMARY

Projection pruning is a database optimization technique that reduces I/O overhead by reading only the columns required for query execution, rather than loading entire rows from storage. This optimization is particularly valuable for time-series databases and wide tables where selective column access can significantly improve query performance.

How projection pruning works

Projection pruning analyzes SQL queries during planning to determine the minimal set of columns needed for:

SELECT clause columns
WHERE clause conditions
JOIN conditions
GROUP BY expressions
ORDER BY expressions

The database then creates an execution plan that reads only these required columns from storage, reducing:

I/O bandwidth usage
Memory consumption
CPU processing time

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Benefits for time-series data

In time-series databases, projection pruning is especially valuable because:

Tables often have many columns (sensor readings, metrics, etc.)
Queries frequently access only a subset of columns
Time-series data is often stored in a columnar database format

For example, when analyzing temperature sensors:

SELECT timestamp, temperature 
FROM weather 
WHERE temperature > 75

Projection pruning ensures only the timestamp and temperature columns are read, even though the weather table contains many other columns like windSpeed, humidity, etc.

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Impact on query performance

The performance impact of projection pruning depends on:

Table width (number of columns)
Column data types and sizes
Storage format (columnar vs row-based)
Query patterns

For wide tables with selective column access, projection pruning can reduce query execution time by:

Minimizing storage I/O
Reducing memory pressure
Decreasing CPU processing overhead

This optimization is particularly effective when combined with other techniques like partition pruning and predicate pushdown.

Best practices

To maximize the benefits of projection pruning:

Avoid SELECT * when only specific columns are needed
Design schemas to group frequently accessed columns
Monitor query patterns to identify optimization opportunities
Consider column ordering in table definitions
Use columnar storage for wide tables

These practices help ensure the query optimizer can effectively eliminate unnecessary column reads and improve overall performance.