基本概念
SQL执行器是PolarDB-X中执行逻辑层算子的组件。对于简单的点查SQL，往往可以整体下推存储层MySQL执行，因而感觉不到执行器的存在，MySQL的结果经过简单的解包封包又被回传给用户。但是对于较复杂的SQL，往往无法将SQL中的算子全部下推，这时候就需要PolarDB-X执行器执行无法下推的计算。
SELECT l_orderkey, sum(l_extendedprice *(1 - l_discount)) AS revenue FROM CUSTOMER, ORDERS, LINEITEM WHERE c_mktsegment = 'AUTOMOBILE' and c_custkey = o_custkey and l_orderkey = o_orderkey and o_orderdate < '1995-03-13' and l_shipdate > '1995-03-13' GROUP BY l_orderkey;
通过EXPLAIN命令看到PolarDB-X的执行计划如下：
HashAgg(group="l_orderkey", revenue="SUM(*)")
HashJoin(condition="o_custkey = c_custkey", type="inner")Gather(concurrent=true)
LogicalView(tables="ORDERS_[0-7],LINEITEM_[0-7]", shardCount=8, sql="SELECT `ORDERS`.`o_custkey`, `LINEITEM`.`l_orderkey`, (`LINEITEM`.`l_extendedprice` * (? - `LINEITEM`.`l_discount`)) AS `x` FROM `ORDERS` AS `ORDERS` INNER JOIN `LINEITEM` AS `LINEITEM` ON (((`ORDERS`.`o_orderkey` = `LINEITEM`.`l_orderkey`) AND (`ORDERS`.`o_orderdate` < ?)) AND (`LINEITEM`.`l_shipdate` > ?))")
Gather(concurrent=true)
LogicalView(tables="CUSTOMER_[0-7]", shardCount=8, sql="SELECT `c_custkey` FROM `CUSTOMER` AS `CUSTOMER` WHERE (`c_mktsegment` = ?)")
如下图所示，LogicalView的SQL在执行时被下发给MySQL，而不能下推的部分（除LogicalView以外的算子）由PolarDB-X执行器进行计算，得到最终用户SQL需要的结果。
执行模型
与传统数据库采用Volcano执行模型不一样，PolarDB-X采样的是Pull~Push混合执行模型。所有算子按照计算过程中是否需要缓存临时表，将执行过程切分成多个pipeline，pipeline内部采样next()接口，按批获取数据，完成在pipeline内部的计算，pipeline间采用push接口，上游pipeline在计算完成后，会将数据源源不断推送给下游pipeline做计算。下面的例子中，被切分成两个pipeline，在pipeline-A中扫描Table-A数据，完成构建哈希表。Pipeline-B扫描Table-B的数据，然后在HashJoin算子内部做关联得到JOIN结果，再返回客户端。
执行模式
目前 PolarDB-X 支持了三种执行模式:
- 单机单线程（TP_LOCAL）：查询过程中，是单线程计算，TP负载的查询涉及到的扫描行数比较少，往往会采用这种执行模式，比如基于主键的点查。
- 单机并行（AP_LOCAL）：查询过程中，会利用节点的多核资源做并行计算，如果您没有配置只读实例，针对AP负载的查询，往往会采样这种执行模式，一般也称之为Parallel Query模式。
- 多机并行（MPP）：您如果配置了只读实例，针对AP负载的查询，可以协调只读实例上多个节点的多核做分布式多机并行加速。
为了准确知道执行模式，在原有EXPLAIN和执行计划的基础上，扩展了EXPLAIN PHYSICAL例如以下查询，通过指令可以查看当前查询采样的是MPP模式，此外还可以获取到每个执行片段的并发数。
mysql> explain physical select a.k, count(*) cnt from sbtest1 a, sbtest1 b where a.id = b.k and a.id > 1000 group by k having cnt > 1300 or der by cnt limit 5, 10; +-------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | PLAN | +-------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | ExecutorType: MPP | | The Query's MaxConcurrentParallelism: 2 | | Fragment 1 | | Shuffle Output layout: [BIGINT, BIGINT] Output layout: [BIGINT, BIGINT] | | Output partitioning: SINGLE [] Parallelism: 1 | | TopN(sort="cnt ASC", offset=?2, fetch=?3) | | Filter(condition="cnt > ?1") | | HashAgg(group="k", cnt="COUNT()") | | BKAJoin(condition="k = id", type="inner") | | RemoteSource(sourceFragmentIds=[0], type=RecordType(INTEGER_UNSIGNED id, INTEGER_UNSIGNED k)) | | Gather(concurrent=true) | | LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `k` FROM `sbtest1` AS `sbtest1` WHERE ((`k` > ?) AND (`k` IN (...)))") | | Fragment 0 | | Shuffle Output layout: [BIGINT, BIGINT] Output layout: [BIGINT, BIGINT] | | Output partitioning: SINGLE [] Parallelism: 1 Splits: 16 | | LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `id`, `k` FROM `sbtest1` AS `sbtest1` WHERE (`id` > ?)") | +-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
同样的也允许您通过HINT EXECUTOR_MODE指定执行模式。比如主实例空闲资源很多，可以考虑强制设置为单机或者多机并行模式来加速。
mysql> explain physical /*+TDDL:EXECUTOR_MODE=AP_LOCAL*/select a.k, count(*) cnt from sbtest1 a, sbtest1 b where a.id = b.k and a.id > 1000 group by k having cnt > 1300 order by cnt limit 5, 10; |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------+| ExecutorMode: AP_LOCAL |
| Fragment 0 dependency: [] parallelism: 4 |
| BKAJoin(condition="k = id", type="inner") |
| Gather(concurrent=true) |
| LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `id`, `k` FROM `sbtest1` AS `sbtest1` WHERE (`id` > ?)") |
| Gather(concurrent=true) |
| LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `k` FROM `sbtest1` AS `sbtest1` WHERE ((`k` > ?) AND (`k` IN (...)))") |
| Fragment 1 dependency: [] parallelism: 8 |
| LocalBuffer |
| RemoteSource(sourceFragmentIds=[0], type=RecordType(INTEGER_UNSIGNED id, INTEGER_UNSIGNED k, INTEGER_UNSIGNED k0)) |
| Fragment 2 dependency: [0, 1] parallelism: 8 |
| Filter(condition="cnt > ?1") |
| HashAgg(group="k", cnt="COUNT()") |
| RemoteSource(sourceFragmentIds=[1], type=RecordType(INTEGER_UNSIGNED id, INTEGER_UNSIGNED k, INTEGER_UNSIGNED k0)) |
| Fragment 3 dependency: [0, 1] parallelism: 1 |
| LocalBuffer |
| RemoteSource(sourceFragmentIds=[2], type=RecordType(INTEGER_UNSIGNED k, BIGINT cnt)) |
| Fragment 4 dependency: [2, 3] parallelism: 1 |
| TopN(sort="cnt ASC", offset=?2, fetch=?3) |
| RemoteSource(sourceFragmentIds=[3], type=RecordType(INTEGER_UNSIGNED k, BIGINT cnt)) |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------+
在多机并行MPP执行模式的并发度是根据物理扫描行数、实例规格和计算所涉及到表的分表数来计算出来的，整体的并行度要考虑高并发场景，所以并行度的计算会偏保守，您可以通过上述EXPLAIN PHYSICAL指令查看并行度。也同样支持HINT MPP_PARALLELISM强制指定并行度。
/+TDDL:EXECUTOR_MODE=MPP MPP_PARALLELISM=8/select a.k, count(*) cnt from sbtest1 a, sbtest1 b where a.id = b.k and a
