Vector Scan

How vector scans work

Vector scans process data in chunks that fit into CPU cache lines and registers, allowing for parallel operations on multiple data elements. This approach is particularly effective for columnar databases where data is stored in contiguous memory blocks.

Benefits for time-series data

Time-series data often requires sequential scanning of large datasets, making vector scans particularly valuable. Benefits include:

1. Reduced CPU cycles per operation
2. Better cache utilization
3. Lower memory bandwidth requirements
4. Improved throughput for analytical queries

Implementation considerations

Vector scans work best with:

• Aligned memory access patterns
• Predictable data layouts
• Sequential access patterns
• Columnar storage formats

Performance impact

Vector scans can significantly improve query performance, especially for:

• Aggregation operations
• Filter conditions
• Mathematical computations
• Time-series analytics

The effectiveness depends on:

• Hardware SIMD support
• Data layout and alignment
• Query access patterns
• Memory bandwidth

Integration with query optimization

Query planners can leverage vector scans by:

• Identifying vectorizable operations
• Aligning memory access patterns
• Batching compatible operations
• Minimizing data movement

For example, in QuestDB:

SELECT avg(price), sum(amount)
FROM trades
WHERE timestamp > '2024-01-01'
SAMPLE BY 1h;

This query benefits from vector scans when:

• Reading the price and amount columns
• Applying the timestamp filter
• Computing aggregations

Related optimizations

Vector scans often work in conjunction with other performance techniques:

• Zero-copy Reads
• Query Plan
• Vectorized Execution
• Column Pruning

These complementary approaches help maximize the benefits of vector scanning operations while minimizing system resource usage.