Introduction

Vectorized query execution is a Hive feature that greatly reduces the CPU usage for typical query operations like scans, filters, aggregates, and joins. A standard query execution system processes one row at a time. This involves long code paths and significant metadata interpretation in the inner loop of execution. Vectorized query execution streamlines operations by processing a block of 1024 rows at a time. Within the block, each column is stored as a vector (an array of a primitive data type). Simple operations like arithmetic and comparisons are done by quickly iterating through the vectors in a tight loop, with no or very few function calls or conditional branches inside the loop. These loops compile in a streamlined way that uses relatively few instructions and finishes each instruction in fewer clock cycles, on average, by effectively using the processor pipeline and cache memory. A detailed design document is attached to the vectorized query execution JIRA, at https://issues.apache.org/jira/browse/HIVE-4160.

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• arithmetic: +, -, *, /, %
• AND, OR, NOT
• comparisons <, >, <=, >=, =, !=, BETWEEN, IN ( list-of-constants ) as filters
• Boolean-valued expressions (non-filters) using AND, OR, NOT, <, >, <=, >=, =, !=
• IS [NOT] NULL
• all math functions (SIN, LOG, etc.)
• string functions SUBSTR, CONCAT, TRIM, LTRIM, RTRIM, LOWER, UPPER, LENGTH
• type casts
• Hive user-defined functions, including standard and generic UDFs
• date functions (YEAR, MONTH, DAY, HOUR, MINUTE, SECOND, UNIX_TIMESTAMP)
• the IF conditional expression

User-defined functions are supported using a backward compatibility bridge, so although they do run vectorized, they don't run as fast as optimized vector implementations of built-in operators and functions. Vectorized filter operations are evaluated left-to-right, so for best performance, put UDFs on the right in an ANDed list of expressions in the WHERE clause. E.g., use

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You can verify which parts of your query are being vectorized using the explain feature. For example, with vectorization enabled and the table alltypesorc stored in ORC format, for this query:

select csmallint
from alltypesorc
where csmallint > 0;

the explain output contains this:

STAGE PLANS:
  Stage: Stage-1
    Map Reduce
      Alias -> Map Operator Tree:
        alltypesorc
          TableScan
            alias: alltypesorc
            Filter Operator
              predicate:
                  expr: (csmallint > 0)
                  type: boolean
              Vectorized execution: true
              Select Operator
                expressions:
                      expr: csmallint
                      type: smallint
                outputColumnNames: _col0
                Vectorized execution: true
                File Output Operator
                  compressed: false
                  GlobalTableId: 0
                  table:
                      input format: org.apache.hadoop.mapred.TextInputFormat
                      output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
                      serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
                  Vectorized execution: true

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