Predicate Pushdown

How predicate pushdown works

Predicate pushdown optimizes query execution by applying filter conditions as early as possible in the query processing pipeline. Instead of loading all data and then filtering it, the database pushes filtering conditions down to the storage layer, significantly reducing I/O and memory usage.

Benefits of predicate pushdown

1. Reduced I/O: By filtering data at the storage level, less data needs to be read from disk
2. Lower memory usage: Only relevant data is loaded into memory
3. Improved query performance: Less data processing leads to faster query execution
4. Network efficiency: In distributed systems, less data needs to be transferred between nodes

Implementation in time-series databases

Time-series databases often implement predicate pushdown specifically for temporal conditions. For example, when querying historical data with a time range filter:

SELECT * FROM trades
WHERE timestamp BETWEEN '2024-01-01' AND '2024-01-02'
    AND symbol = 'AAPL';

The database can use both the timestamp and symbol predicates to:

1. Identify relevant time partitions
2. Skip reading irrelevant data blocks
3. Apply filters at the storage layer

Common predicate types

Time-based predicates

• Timestamp ranges
• Time windows
• Calendar-based filters

Value-based predicates

• Equality conditions
• Range comparisons
• Pattern matching

These predicates can be combined to create complex filtering conditions while maintaining optimization benefits.

Relationship with other optimizations

Predicate pushdown often works in conjunction with other optimization techniques:

• Column Pruning for selecting specific columns
• Partition Pruning for skipping irrelevant partitions
• Query Plan optimization for overall execution strategy

Considerations and limitations

While predicate pushdown is powerful, several factors can affect its effectiveness:

1. Data organization: The physical storage layout must support efficient filtering
2. Predicate complexity: Complex expressions may limit pushdown opportunities
3. Storage engine capabilities: The underlying storage engine must support predicate evaluation

Performance impact example

Consider a time-series table with millions of records. With predicate pushdown:

Without predicate pushdown, all 1M rows would need to be loaded and filtered in memory, significantly impacting performance.

Best practices

1. Use specific predicates: More specific conditions allow better optimization
2. Leverage time-based filtering: Time predicates are particularly effective in time-series data
3. Monitor query plans: Verify that predicates are being pushed down as expected
4. Consider data organization: Structure data to maximize predicate pushdown benefits