Vectorized Execution
A key technique in modern analytical engines that processes data in batches, applying a single operator to hundreds or thousands of values at once. This reduces function-call and branch-prediction overhead and makes full use of CPU pipelines and SIMD instructions.
Timeline (last 90 days)
The three technologies of vectorized execution, late materialization and spatially aware data layout are often used in combination, achieving significant synergistic acceleration effects on lakehouse columnar storage.
Traditional transactional systems (OLTP) use row-based storage, fine-grained locking, and ACID transaction guarantees; analytical systems (OLAP) use columnar storage, vectorized execution, and batch scans, and the two are almost diametrically opposed in storage layout, execution engine, and optimizer strategy.
Vectorized execution processes data in batches, and by reducing function call and branch prediction overhead and leveraging CPU pipelines and SIMD instructions, it can increase analytical query throughput severalfold.
OLAP systems usually adopt columnar storage, accelerating large-scale aggregation queries through vectorized execution and compressed encoding, optimizing read throughput.
Vectorized execution processes data in batches (usually hundreds to thousands of rows), amortizing the interpretation overhead per row.
All Facts (5)
The three technologies of vectorized execution, late materialization and spatially aware data layout are often used in combination, achieving significant synergistic acceleration effects on lakehouse columnar storage.
50%UnverifiedTraditional transactional systems (OLTP) use row-based storage, fine-grained locking, and ACID transaction guarantees; analytical systems (OLAP) use columnar storage, vectorized execution, and batch scans, and the two are almost diametrically opposed in storage layout, execution engine, and optimizer strategy.
50%UnverifiedVectorized execution processes data in batches, and by reducing function call and branch prediction overhead and leveraging CPU pipelines and SIMD instructions, it can increase analytical query throughput severalfold.
50%UnverifiedOLAP systems usually adopt columnar storage, accelerating large-scale aggregation queries through vectorized execution and compressed encoding, optimizing read throughput.
50%UnverifiedVectorized execution processes data in batches (usually hundreds to thousands of rows), amortizing the interpretation overhead per row.
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