用 EXPLAIN 查看 MPP 模式查询的执行计划
TiDB 支持使用 MPP 模式来执行查询。在 MPP 执行模式下，SQL 优化器会生成 MPP 的执行计划。注意 MPP 模式仅对有 TiFlash 副本的表生效。
本文档使用的示例数据如下：
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 查询片段和 MPP 任务
在 MPP 模式下，一个查询在逻辑上会被切分为多个 MPP 查询片段 (query fragment)。示例如下：
EXPLAIN SELECT COUNT(*) FROM t1 GROUP BY id;
这个查询在 MPP 模式下会包含两个查询片段，一个为一阶段聚合，一个为二阶段聚合（最终聚合）。在查询执行的时候每个查询片段都会被实例化为一个或者多个 MPP 任务。
Exchange 算子
MPP 查询的执行计划中有两个 MPP 特有的 Exchange 算子，分别为 ExchangeReceiver 和 ExchangeSender。ExchangeReceiver 表示从下游查询片段读取数据，ExchangeSender 表示下游查询片段向上游查询片段发送数据。在 MPP 执行模式下，每个 MPP 查询片段的根算子均为 ExchangeSender 算子，即每个查询片段以 ExchangeSender 为界进行划分。一个简单的 MPP 计划如下：
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 |
+------------------------------------+---------+-------------------+---------------+----------------------------------------------------+
以上执行计划中有两个查询片段：
[TableFullScan_25, HashAgg_9, ExchangeSender_28]为第一个查询片段，其主要完成一阶段聚合的计算。[ExchangeReceiver_29, HashAgg_27, Projection_26, ExchangeSender_30]为第二个查询片段，其主要完成二阶段聚合的计算。
ExchangeSender 算子的 operator info 列输出了 ExchangeType 信息。目前有以下三种 ExchangeType：
- HashPartition：ExchangeSender 把数据按 Hash 值进行分区之后分发给上游的 MPP 任务的 ExchangeReceiver 算子，通常在 Hash Aggregation 以及 Shuffle Hash Join 算法中使用。
- Broadcast：ExchangeSender 通过广播的方式把数据分发给上游的 MPP 任务，通常在 Broadcast Join 中使用。
- PassThrough：ExchangeSender 把数据分发给上游的 MPP Task，与 Broadcast 的区别是此时上游有且仅有一个 MPP 任务，通常用于向 TiDB 返回数据。
上述例子中 ExchangeSender 的 ExchangeType 为 HashPartition 以及 PassThrough，分别对应于 Hash Aggregation 运算以及向 TiDB 返回数据。
另外一个典型的 MPP 应用为 join 运算。TiDB MPP 支持两种类型的 join，分别为：
- Shuffle Hash Join：join 的 input 通过 HashPartition 的方式 shuffle 数据，上游的 MPP 任务进行分区内的 join。
- Broadcast Join：join 中的小表以 Broadcast 的方式把数据广播到各个节点，各个节点各自进行 join。
典型的 Shuffle Hash Join 执行计划如下：
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)
以上执行计划中，
[TableFullScan_20, Selection_21, ExchangeSender_22]完成表 b 的数据读取并通过 HashPartition 的方式把数据 shuffle 给上游 MPP 任务。[TableFullScan_16, Selection_17, ExchangeSender_18]完成表 a 的数据读取并通过 HashPartition 的方式把数据 shuffle 给上游 MPP 任务。[ExchangeReceiver_19, ExchangeReceiver_23, HashJoin_44, ExchangeSender_47]完成 join 并把数据返回给 TiDB。
典型的 Broadcast Join 执行计划如下：
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 |
+----------------------------------------+---------+--------------+---------------+------------------------------------------------+
以上执行计划中，
[TableFullScan_17, Selection_18, ExchangeSender_19]从小表（表 a）读数据并广播给大表（表 b）数据所在的各个节点。[TableFullScan_21, Selection_22, ExchangeReceiver_20, HashJoin_43, ExchangeSender_46]完成 join 并将数据返回给 TiDB。
对 MPP 模式的查询使用 EXPLAIN ANALYZE
EXPLAIN ANALYZE 语句与 EXPLAIN 类似，但还会输出一些运行时的信息。一个简单的 EXPLAIN ANALYZE 输出信息如下：
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}, tiflash_scan:{dtfile:{total_scanned_packs:1,...}} | keep order:false | N/A | N/A |
+------------------------------------+---------+---------+-------------------+---------------+---------------------------------------------------------------------------------------------------+----------------------------------------------------------------+--------+------+
与 EXPLAIN 相比，ExchangeSender 的 operator info 中多了 task id 的输出，其记录了该查询片段实例化成的 MPP 任务的任务 ID。此外 MPP 算子中都会有 threads 这一列，这列记录了 MPP 在执行该算子时使用的并发数（如果集群由多个节点组成，该并发数是所有节点并发数相加的结果）。
MPP Version 和 Exchange 数据压缩
从 v6.6.0 开始，MPP 执行计划新增字段 MppVersion 和 Compression。
MppVersion：MPP 执行计划的版本号，可通过系统变量mpp_version设置。Compression：Exchange算子的数据压缩模式，可通过系统变量mpp_exchange_compression_mode设置。如果未启用数据压缩，则执行计划中不显示该字段。
mysql > EXPLAIN SELECT COUNT(*) AS count_order FROM lineitem GROUP BY l_returnflag, l_linestatus ORDER BY l_returnflag, l_linestatus;
+----------------------------------------+--------------+--------------+----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+----------------------------------------+--------------+--------------+----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Projection_6 | 3.00 | root | | Column#18 |
| └─Sort_8 | 3.00 | root | | tpch100.lineitem.l_returnflag, tpch100.lineitem.l_linestatus |
| └─TableReader_36 | 3.00 | root | | MppVersion: 1, data:ExchangeSender_35 |
| └─ExchangeSender_35 | 3.00 | mpp[tiflash] | | ExchangeType: PassThrough |
| └─Projection_31 | 3.00 | mpp[tiflash] | | Column#18, tpch100.lineitem.l_returnflag, tpch100.lineitem.l_linestatus |
| └─HashAgg_32 | 3.00 | mpp[tiflash] | | group by:tpch100.lineitem.l_linestatus, tpch100.lineitem.l_returnflag, funcs:sum(Column#23)->Column#18, funcs:firstrow(tpch100.lineitem.l_returnflag)->tpch100.lineitem.l_returnflag, funcs:firstrow(tpch100.lineitem.l_linestatus)->tpch100.lineitem.l_linestatus, stream_count: 20 |
| └─ExchangeReceiver_34 | 3.00 | mpp[tiflash] | | stream_count: 20 |
| └─ExchangeSender_33 | 3.00 | mpp[tiflash] | | ExchangeType: HashPartition, Compression: FAST, Hash Cols: [name: tpch100.lineitem.l_returnflag, collate: utf8mb4_bin], [name: tpch100.lineitem.l_linestatus, collate: utf8mb4_bin], stream_count: 20 |
| └─HashAgg_14 | 3.00 | mpp[tiflash] | | group by:tpch100.lineitem.l_linestatus, tpch100.lineitem.l_returnflag, funcs:count(1)->Column#23 |
| └─TableFullScan_30 | 600037902.00 | mpp[tiflash] | table:lineitem | keep order:false |
+----------------------------------------+--------------+--------------+----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
例如上面的执行计划结果中，TiDB 使用了版本为 1 的 MPP 执行计划来构建 TableReader。其中类型为 HashPartition 的 ExchangeSender 算子使用 FAST 数据压缩模式，类型为 PassThrough 的 ExchangeSender 算子未启用数据压缩。