Explain Statements in the MPP Mode

TiDB supports using the MPP mode to execute queries. In the MPP mode, the TiDB optimizer generates execution plans for MPP. Note that the MPP mode is only available for tables that have replicas on TiFlash.

The examples in this document are based on the following sample data:

CREATE TABLE t1 (id int, value int);
INSERT INTO t1 values(1,2),(2,3),(1,3);
ALTER TABLE t1 set tiflash replica 1;
ANALYZE TABLE t1;
SET tidb_allow_mpp = 1;

MPP query fragments and MPP tasks

In the MPP mode, a query is logically sliced into multiple query fragments. Take the following statement as an example:

EXPLAIN SELECT COUNT(*) FROM t1 GROUP BY id;

This query is divided into two fragments in the MPP mode. One for the first-stage aggregation and the other for the second-stage aggregation, also the final aggregation. When this query is executed, each query fragment is instantiated into one or more MPP tasks.

Exchange operators

ExchangeReceiver and ExchangeSenderare two exchange operators specific for MPP execution plans. The ExchangeReceiver operator reads data from downstream query fragments and the ExchangeSender operator sends data from downstream query fragments to upstream query fragments. In the MPP mode, the root operator of each MPP query fragment is ExchangeSender, meaning that query fragments are delimited by the ExchangeSender operator.

The following is a simple MPP execution plan:

EXPLAIN SELECT COUNT(*) FROM t1 GROUP BY id;

+------------------------------------+---------+-------------------+---------------+----------------------------------------------------+
| id                                 | estRows | task              | access object | operator info                                      |
+------------------------------------+---------+-------------------+---------------+----------------------------------------------------+
| TableReader_31                     | 2.00    | root              |               | data:ExchangeSender_30                             |
| └─ExchangeSender_30                | 2.00    | batchCop[tiflash] |               | ExchangeType: PassThrough                          |
|   └─Projection_26                  | 2.00    | batchCop[tiflash] |               | Column#4                                           |
|     └─HashAgg_27                   | 2.00    | batchCop[tiflash] |               | group by:test.t1.id, funcs:sum(Column#7)->Column#4 |
|       └─ExchangeReceiver_29        | 2.00    | batchCop[tiflash] |               |                                                    |
|         └─ExchangeSender_28        | 2.00    | batchCop[tiflash] |               | ExchangeType: HashPartition, Hash Cols: test.t1.id |
|           └─HashAgg_9              | 2.00    | batchCop[tiflash] |               | group by:test.t1.id, funcs:count(1)->Column#7      |
|             └─TableFullScan_25     | 3.00    | batchCop[tiflash] | table:t1      | keep order:false                                   |
+------------------------------------+---------+-------------------+---------------+----------------------------------------------------+

The above execution plan contains two query fragments:

• The first is [TableFullScan_25, HashAgg_9, ExchangeSender_28], which is mainly responsible for the first-stage aggregation.
• The second is [ExchangeReceiver_29, HashAgg_27, Projection_26, ExchangeSender_30], which is mainly responsible for the second-stage aggregation.

The operator info column of the ExchangeSender operator shows the exchange type information. Currently, there are three exchange types. See the following:

• HashPartition: The ExchangeSender operator firstly partitions data according to the Hash values and then distributes data to the ExchangeReceiver operator of upstream MPP tasks. This exchange type is often used for Hash Aggregation and Shuffle Hash Join algorithms.
• Broadcast: The ExchangeSender operator distributes data to upstream MPP tasks through broadcast. This exchange type is often used for Broadcast Join.
• PassThrough: The ExchangeSender operator sends data to the only upstream MPP task, which is different from the Broadcast type. This exchange type is often used when returning data to TiDB.

In the example execution plan, the exchange type of the operator ExchangeSender_28 is HashPartition, meaning that it performs the Hash Aggregation algorithm. The exchange type of the operator ExchangeSender_30 is PassThrough, meaning that it is used to return data to TiDB.

MPP is also often applied to join operations. The MPP mode in TiDB supports the following two join algorithms:

• Shuffle Hash Join: Shuffle the data input from the join operation using the HashPartition exchange type. Then, upstream MPP tasks join data within the same partition.
• Broadcast Join: Broadcast data of the small table in the join operation to each node, after which each node joins the data separately.

The following is a typical execution plan for Shuffle Hash Join:

SET tidb_broadcast_join_threshold_count=0;
SET tidb_broadcast_join_threshold_size=0;
EXPLAIN SELECT COUNT(*) FROM t1 a JOIN t1 b ON a.id = b.id;

+----------------------------------------+---------+--------------+---------------+----------------------------------------------------+
| id                                     | estRows | task         | access object | operator info                                      |
+----------------------------------------+---------+--------------+---------------+----------------------------------------------------+
| StreamAgg_14                           | 1.00    | root         |               | funcs:count(1)->Column#7                           |
| └─TableReader_48                       | 9.00    | root         |               | data:ExchangeSender_47                             |
|   └─ExchangeSender_47                  | 9.00    | cop[tiflash] |               | ExchangeType: PassThrough                          |
|     └─HashJoin_44                      | 9.00    | cop[tiflash] |               | inner join, equal:[eq(test.t1.id, test.t1.id)]     |
|       ├─ExchangeReceiver_19(Build)     | 6.00    | cop[tiflash] |               |                                                    |
|       │ └─ExchangeSender_18            | 6.00    | cop[tiflash] |               | ExchangeType: HashPartition, Hash Cols: test.t1.id |
|       │   └─Selection_17               | 6.00    | cop[tiflash] |               | not(isnull(test.t1.id))                            |
|       │     └─TableFullScan_16         | 6.00    | cop[tiflash] | table:a       | keep order:false                                   |
|       └─ExchangeReceiver_23(Probe)     | 6.00    | cop[tiflash] |               |                                                    |
|         └─ExchangeSender_22            | 6.00    | cop[tiflash] |               | ExchangeType: HashPartition, Hash Cols: test.t1.id |
|           └─Selection_21               | 6.00    | cop[tiflash] |               | not(isnull(test.t1.id))                            |
|             └─TableFullScan_20         | 6.00    | cop[tiflash] | table:b       | keep order:false                                   |
+----------------------------------------+---------+--------------+---------------+----------------------------------------------------+
12 rows in set (0.00 sec)

In the above execution plan:

• The query fragment [TableFullScan_20, Selection_21, ExchangeSender_22] reads data from table b and shuffles data to upstream MPP tasks.
• The query fragment [TableFullScan_16, Selection_17, ExchangeSender_18] reads data from table a and shuffles data to upstream MPP tasks.
• The query fragment [ExchangeReceiver_19, ExchangeReceiver_23, HashJoin_44, ExchangeSender_47] joins all data and returns it to TiDB.

A typical execution plan for Broadcast Join is as follows:

EXPLAIN SELECT COUNT(*) FROM t1 a JOIN t1 b ON a.id = b.id;

+----------------------------------------+---------+--------------+---------------+------------------------------------------------+
| id                                     | estRows | task         | access object | operator info                                  |
+----------------------------------------+---------+--------------+---------------+------------------------------------------------+
| StreamAgg_15                           | 1.00    | root         |               | funcs:count(1)->Column#7                       |
| └─TableReader_47                       | 9.00    | root         |               | data:ExchangeSender_46                         |
|   └─ExchangeSender_46                  | 9.00    | cop[tiflash] |               | ExchangeType: PassThrough                      |
|     └─HashJoin_43                      | 9.00    | cop[tiflash] |               | inner join, equal:[eq(test.t1.id, test.t1.id)] |
|       ├─ExchangeReceiver_20(Build)     | 6.00    | cop[tiflash] |               |                                                |
|       │ └─ExchangeSender_19            | 6.00    | cop[tiflash] |               | ExchangeType: Broadcast                        |
|       │   └─Selection_18               | 6.00    | cop[tiflash] |               | not(isnull(test.t1.id))                        |
|       │     └─TableFullScan_17         | 6.00    | cop[tiflash] | table:a       | keep order:false                               |
|       └─Selection_22(Probe)            | 6.00    | cop[tiflash] |               | not(isnull(test.t1.id))                        |
|         └─TableFullScan_21             | 6.00    | cop[tiflash] | table:b       | keep order:false                               |
+----------------------------------------+---------+--------------+---------------+------------------------------------------------+

In the above execution plan:

• The query fragment [TableFullScan_17, Selection_18, ExchangeSender_19] reads data from the small table (table a) and broadcasts the data to each node that contains data from the large table (table b).
• The query fragment [TableFullScan_21, Selection_22, ExchangeReceiver_20, HashJoin_43, ExchangeSender_46] joins all data and returns it to TiDB.

EXPLAIN ANALYZE statements in the MPP mode

The EXPLAIN ANALYZE statement is similar to EXPLAIN, but it also outputs some runtime information.

The following is the output of a simple EXPLAIN ANALYZE example:

EXPLAIN ANALYZE SELECT COUNT(*) FROM t1 GROUP BY id;

+------------------------------------+---------+---------+-------------------+---------------+---------------------------------------------------------------------------------------------+----------------------------------------------------------------+--------+------+
| id                                 | estRows | actRows | task              | access object | execution info                                                                              | operator info                                                  | memory | disk |
+------------------------------------+---------+---------+-------------------+---------------+---------------------------------------------------------------------------------------------+----------------------------------------------------------------+--------+------+
| TableReader_31                     | 4.00    | 2       | root              |               | time:44.5ms, loops:2, cop_task: {num: 1, max: 0s, proc_keys: 0, copr_cache_hit_ratio: 0.00} | data:ExchangeSender_30                                         | N/A    | N/A  |
| └─ExchangeSender_30                | 4.00    | 2       | batchCop[tiflash] |               | tiflash_task:{time:16.5ms, loops:1, threads:1}                                              | ExchangeType: PassThrough, tasks: [2, 3, 4]                    | N/A    | N/A  |
|   └─Projection_26                  | 4.00    | 2       | batchCop[tiflash] |               | tiflash_task:{time:16.5ms, loops:1, threads:1}                                              | Column#4                                                       | N/A    | N/A  |
|     └─HashAgg_27                   | 4.00    | 2       | batchCop[tiflash] |               | tiflash_task:{time:16.5ms, loops:1, threads:1}                                              | group by:test.t1.id, funcs:sum(Column#7)->Column#4             | N/A    | N/A  |
|       └─ExchangeReceiver_29        | 4.00    | 2       | batchCop[tiflash] |               | tiflash_task:{time:14.5ms, loops:1, threads:20}                                             |                                                                | N/A    | N/A  |
|         └─ExchangeSender_28        | 4.00    | 0       | batchCop[tiflash] |               | tiflash_task:{time:9.49ms, loops:0, threads:0}                                              | ExchangeType: HashPartition, Hash Cols: test.t1.id, tasks: [1] | N/A    | N/A  |
|           └─HashAgg_9              | 4.00    | 0       | batchCop[tiflash] |               | tiflash_task:{time:9.49ms, loops:0, threads:0}                                              | group by:test.t1.id, funcs:count(1)->Column#7                  | N/A    | N/A  |
|             └─TableFullScan_25     | 6.00    | 0       | batchCop[tiflash] | table:t1      | tiflash_task:{time:9.49ms, loops:0, threads:0}                                              | keep order:false                                               | N/A    | N/A  |
+------------------------------------+---------+---------+-------------------+---------------+---------------------------------------------------------------------------------------------+----------------------------------------------------------------+--------+------+

Compared to the output of EXPLAIN, the operator info column of the operator ExchangeSender also shows tasks, which records the id of the MPP task that the query fragment instantiates into. In addition, each MPP operator has a threads field in the execution info column, which records the concurrency of operations when TiDB executes this operator. If the cluster consists of multiple nodes, this concurrency is the result of adding up the concurrency of all nodes.