You can use vectorization to improve instruction pipelines and cache use. Vectorization enables certain data and queries to process batches of primitive types on entire column rather than one row at a time.
Vectorized query execution processes Hive data in batch, channeling a large number of rows of data into columns, foregoing intermediate results. This technique is more efficient than the MapReduce execution process that stores temporary file.
Unsupported functionality on vectorized data
Some functionality is not supported on vectorized data:
- DDL queries
- DML queries other than single table, read-only queries
- Formats other than Optimized Row Columnar (ORC)
Supported functionality on vectorized data
The following functionality is supported on vectorized data:
- Single table, read-only queries
Selecting, filtering, and grouping data is supported.
- Partitioned tables
- The following expressions:
- Comparison: >, >=, <, <=, =, !=
- Arithmetic plus, minus, multiply, divide, and modulo
- Logical AND and OR
- Aggregates sum, avg, count, min, and max
Supported data types
You can query data of the following types using vectorized queries: