Cloudera Enterprise 6.3.x | Other versions

MT_DOP Query Option

Sets the degree of parallelism used for certain operations that can benefit from multithreaded execution. You can specify values higher than zero to find the ideal balance of response time, memory usage, and CPU usage during statement processing.

  Note:

The Impala execution engine is being revamped incrementally to add additional parallelism within a single host for certain statements and kinds of operations. The setting MT_DOP=0 uses the "old" code path with limited intra-node parallelism.

Currently, the operations affected by the MT_DOP query option are:

  • COMPUTE [INCREMENTAL] STATS. Impala automatically sets MT_DOP=4 for COMPUTE STATS and COMPUTE INCREMENTAL STATS statements on Parquet tables.

  • Queries with execution plans containing only scan and aggregation operators. Other queries produce an error if MT_DOP is set to a non-zero value. Therefore, this query option is typically only set for the duration of specific long-running, CPU-intensive queries.

Type: integer

Default: 0

Because COMPUTE STATS and COMPUTE INCREMENTAL STATS statements for Parquet tables benefit substantially from extra intra-node parallelism, Impala automatically sets MT_DOP=4 when computing stats for Parquet tables.

Range: 0 to 64

Examples:

  Note:

Any timing figures in the following examples are on a small, lightly loaded development cluster. Your mileage may vary. Speedups depend on many factors, including the number of rows, columns, and partitions within each table.

The following example shows how to run a COMPUTE STATS statement against a Parquet table with or without an explicit MT_DOP setting:

-- Explicitly setting MT_DOP to 0 selects the old code path.
set mt_dop = 0;
MT_DOP set to 0

-- The analysis for the billion rows is distributed among hosts,
-- but uses only a single core on each host.
compute stats billion_rows_parquet;
+-----------------------------------------+
| summary                                 |
+-----------------------------------------+
| Updated 1 partition(s) and 2 column(s). |
+-----------------------------------------+

drop stats billion_rows_parquet;

-- Using 4 logical processors per host is faster.
set mt_dop = 4;
MT_DOP set to 4

compute stats billion_rows_parquet;
+-----------------------------------------+
| summary                                 |
+-----------------------------------------+
| Updated 1 partition(s) and 2 column(s). |
+-----------------------------------------+

drop stats billion_rows_parquet;

-- Unsetting the option reverts back to its default.
-- Which for COMPUTE STATS and a Parquet table is 4,
-- so again it uses the fast path.
unset MT_DOP;
Unsetting option MT_DOP

compute stats billion_rows_parquet;
+-----------------------------------------+
| summary                                 |
+-----------------------------------------+
| Updated 1 partition(s) and 2 column(s). |
+-----------------------------------------+

The following example shows the effects of setting MT_DOP for a query involving only scan and aggregation operations for a Parquet table:

set mt_dop = 0;
MT_DOP set to 0

-- COUNT(DISTINCT) for a unique column is CPU-intensive.
select count(distinct id) from billion_rows_parquet;
+--------------------+
| count(distinct id) |
+--------------------+
| 1000000000         |
+--------------------+
Fetched 1 row(s) in 67.20s

set mt_dop = 16;
MT_DOP set to 16

-- Introducing more intra-node parallelism for the aggregation
-- speeds things up, and potentially reduces memory overhead by
-- reducing the number of scanner threads.
select count(distinct id) from billion_rows_parquet;
+--------------------+
| count(distinct id) |
+--------------------+
| 1000000000         |
+--------------------+
Fetched 1 row(s) in 17.19s

The following example shows how queries that are not compatible with non-zero MT_DOP settings produce an error when MT_DOP is set:

set mt_dop=1;
MT_DOP set to 1

select * from a1 inner join a2
  on a1.id = a2.id limit 4;
ERROR: NotImplementedException: MT_DOP not supported for plans with
  base table joins or table sinks.

COMPUTE STATS Statement, Impala Aggregate Functions

Page generated August 29, 2019.