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.
Using Vectorized Query Execution
Enabling vectorized execution
set hive.vectorized.execution.enabled = true;
Vectorized execution is off by default, so your queries only utilize it if this variable is turned on. To disable vectorized execution and go back to standard execution, do the following:
set hive.vectorized.execution.enabled = false;
Additional configuration variables for vectorized execution are documented in Configuration Properties – Vectorization.
Supported data types and operations
The following data types are currently supported for vectorized execution:
string. Using other data types will cause your query to execute using standard, row-at-a-time execution.
The following expressions can be vectorized when used on supported types:
- 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
column1 = 10 and myUDF(column2) = "x"
myUDF(column2) = "x" and column1 = 10
This will allow the optimized filter to run first, potentially eliminating many rows from consideration, before running the UDF via the bridge. The UDF will only be run for rows that pass the filter on the left hand side of the AND operation.
Using a built-in operator or function that is not supported for vectorization will cause your query to run in standard row-at-a-time mode. If a compile time or run time error occurs that appears related to vectorization, please file a Hive JIRA. To work around such an error, disable vectorization by setting
false for the specific query that is failing, to run it in standard mode.
Vectorized support continues to be added for additional functions and expressions. If you have a request for one, please comment on this page, or open a JIRA for it.
Seeing whether vectorization is used for a query
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:
the explain output contains this:
Vectorized execution: true shows that the operator containing that notation is vectorized. Absence of this notation means the operator is not vectorized, and uses the standard row-at-a-time execution path.
Vectorized execution is available in Hive 0.13.0 and later (HIVE-5283).