PolarDB-X HTAP架构更多信息，请参见混合负载HTAP。
HTAP集群
您购买的PolarDB-X主实例，主要面向在线通用业务场景。如果业务针对同一份数据有分析、专注离线拖数、跑批等场景，您可以在PolarDB-X主实例上购买多个只读实例。
业务如果有在线HTAP混合流量或者读写分离的需求，推荐使用集群地址。PolarDB-X内部会基于智能路由或者读写权重将部分流量转发给只读实例；业务上只有离线数据分析需求时，推荐使用只读地址，只读地址会直接访问只读实例，只读地址的流量会采用MPP加速。关于连接地址信息，请参见集群地址和只读地址。
路由
智能路由
PolarDB-X优化器会基于代价分析出查询物理扫描行数、CPU、内存、IO、网络等核心资源消耗量，将请求区分为TP与AP负载。当您在集群地址上开启了智能路由，会主动识别SQL的工作负载类型来做路由，比如将识别为AP负载的流量路由给只读实例。您可以通过explain cost指令查看SQL工作负载类型的识别情况。例如以下查询，该查询涉及到物理扫描行数rowcount很小，计算资源（CPU&Memory）也消耗比较少，所以这个查询被识别为TP负载。
mysql> explain cost 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; | +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | TopN(sort="cnt ASC", offset=?2, fetch=?3): rowcount = 1.0, cumulative cost = value = 2.8765038E7, cpu = 37.0, memory = 64.0, io = 3.0, net = 5.75, id = 163602178 | | Filter(condition="cnt > ?1"): rowcount = 1.0, cumulative cost = value = 2.8765026E7, cpu = 26.0, memory = 47.0, io = 3.0, net = 5.75, id = 163602177 | | HashAgg(group="k", cnt="COUNT()"): rowcount = 1.0, cumulative cost = value = 2.8765025E7, cpu = 25.0, memory = 47.0, io = 3.0, net = 5.75, id = 163602171 | | BKAJoin(condition="k = id", type="inner"): rowcount = 1.0, cumulative cost = value = 2.8765012E7, cpu = 12.0, memory = 18.0, io = 3.0, net = 5.75, id = 163602169 | | Gather(concurrent=true): rowcount = 1.0, cumulative cost = value = 2.3755003E7, cpu = 3.0, memory = 0.0, io = 1.0, net = 4.75, id = 163602164 | | LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `id`, `k` FROM `sbtest1` AS `sbtest1` WHERE (`id` > ?)"): rowcount = 1.0, cumulative cost = value = 2.3755002E7, cpu = 2.0, memory = 0.0, io = 1.0, net = 4.75, id = 163601451 | | Gather(concurrent=true): rowcount = 1.0, cumulative cost = value = 5003.0, cpu = 3.0, memory = 0.0, io = 1.0, net = 0.0, id = 163602167 | | LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `k` FROM `sbtest1` AS `sbtest1` WHERE ((`k` > ?) AND (`k` IN (...)))"): rowcount = 1.0, cumulative cost = value = 5002.0, cpu = 2.0, memory = 0.0, io = 1.0, net = 0.0, id = 163601377 | | | WorkloadType: TP | | +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
工作负载的识别，对于HTAP路由至关重要。这里也允许您通过HINT WORKLOAD_TYPE指定工作负载。同样以上述查询为例，可以将查询负载强制指定为AP。
mysql> explain cost /*+TDDL:WORKLOAD_TYPE=AP*/ 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; |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+| TopN(sort="cnt ASC", offset=?2, fetch=?3): rowcount = 1.0, cumulative cost = value = 2.8765038E7, cpu = 37.0, memory = 64.0, io = 3.0, net = 5.75, id = 163602178 |
| Filter(condition="cnt > ?1"): rowcount = 1.0, cumulative cost = value = 2.8765026E7, cpu = 26.0, memory = 47.0, io = 3.0, net = 5.75, id = 163602177 |
| HashAgg(group="k", cnt="COUNT()"): rowcount = 1.0, cumulative cost = value = 2.8765025E7, cpu = 25.0, memory = 47.0, io = 3.0, net = 5.75, id = 163602171 |
| BKAJoin(condition="k = id", type="inner"): rowcount = 1.0, cumulative cost = value = 2.8765012E7, cpu = 12.0, memory = 18.0, io = 3.0, net = 5.75, id = 163602169 |
| Gather(concurrent=true): rowcount = 1.0, cumulative cost = value = 2.3755003E7, cpu = 3.0, memory = 0.0, io = 1.0, net = 4.75, id = 163602164 |
| LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `id`, `k` FROM `sbtest1` AS `sbtest1` WHERE (`id` > ?)"): rowcount = 1.0, cumulative cost = value = 2.3755002E7, cpu = 2.0, memory = 0.0, io = 1.0, net = 4.75, id = 163601451 |
| Gather(concurrent=true): rowcount = 1.0, cumulative cost = value = 5003.0, cpu = 3.0, memory = 0.0, io = 1.0, net = 0.0, id = 163602167 |
| LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `k` FROM `sbtest1` AS `sbtest1` WHERE ((`k` > ?) AND (`k` IN (...)))"): rowcount = 1.0, cumulative cost = value = 5002.0, cpu = 2.0, memory = 0.0, io = 1.0, net = 0.0, id = 163601377 | |
| WorkloadType: AP | |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
规则路由
除了基于代价的智能路由以外，我们也支持基于读写规则的路由。允许您在控制台参数管理上设置读写分离权重参数MASTER_READ_WEIGHT，默认值为100，可配置值区间[0, 100]。如果配置为Weight=60，意味着占60%的流量会继续在主实例执行，40%的剩余流量会路由到只读实例执行，如果只读实例有多个会进行自动分配。
智能路由和规则路由这两者关系是解耦的，具体关系请查看下表格。
| 智能路由规则 | 规则路由 (MASTER_READ_WEIGHT) | 路由结果 |
| 开启 | 以代价的读写分离为主规则路由建议保持默认值为100 |
|
| 关闭 | 以规则的读写分离为主规则路由的可选范围：[0-100] |
|
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，往往会采样这种执行模式，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，您在没有购买只读实例的前提下，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）：您若购买了只读实例，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。
为了让您可以准确知道执行模式，我们在原有EXPLAIN指令上，扩展出了EXPLAIN PHYSICAL。例如以下查询，通过指令可以查看到当前查询采样的是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` > ?)") | +-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
工作负载和执行模式有一定的耦合关系，AP工作负载会采用多机并行计算。同样的也允许您通过HINT EXECUTOR_MODE指定执行模式。假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。当然也同样支持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.id > 1000 group by k having cnt > 1300 order by cnt limit 5, 10;
调度策略
假设您购买了多个只读实例并加入到集群地址中，您通过集群地址的查询SQL路由到只读实例的流量，会被均匀调度到只读实例多个节点上执行，调度会考虑各个节点的资源负载，确保各个节点的负载压力差不多。比如PolarDB-X会将只读实例延迟作为调度参考指标，避免将流量调度到延迟较大的只读实例上执行。
业务如果有在线HTAP混合流量或者读写分离的需求，推荐使用集群地址。PolarDB-X内部会基于智能路由或者读写权重将部分流量转发给只读实例；业务上只有离线数据分析需求时，推荐使用只读地址，只读地址会直接访问只读实例，只读地址的流量会采用MPP加速。关于连接地址信息，请参见集群地址和只读地址。
路由
智能路由
PolarDB-X优化器会基于代价分析出查询物理扫描行数、CPU、内存、IO、网络等核心资源消耗量，将请求区分为TP与AP负载。当您在集群地址上开启了智能路由，会主动识别SQL的工作负载类型来做路由，比如将识别为AP负载的流量路由给只读实例。您可以通过explain cost指令查看SQL工作负载类型的识别情况。例如以下查询，该查询涉及到物理扫描行数rowcount很小，计算资源（CPU&Memory）也消耗比较少，所以这个查询被识别为TP负载。
mysql> explain cost 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; | +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | TopN(sort="cnt ASC", offset=?2, fetch=?3): rowcount = 1.0, cumulative cost = value = 2.8765038E7, cpu = 37.0, memory = 64.0, io = 3.0, net = 5.75, id = 163602178 | | Filter(condition="cnt > ?1"): rowcount = 1.0, cumulative cost = value = 2.8765026E7, cpu = 26.0, memory = 47.0, io = 3.0, net = 5.75, id = 163602177 | | HashAgg(group="k", cnt="COUNT()"): rowcount = 1.0, cumulative cost = value = 2.8765025E7, cpu = 25.0, memory = 47.0, io = 3.0, net = 5.75, id = 163602171 | | BKAJoin(condition="k = id", type="inner"): rowcount = 1.0, cumulative cost = value = 2.8765012E7, cpu = 12.0, memory = 18.0, io = 3.0, net = 5.75, id = 163602169 | | Gather(concurrent=true): rowcount = 1.0, cumulative cost = value = 2.3755003E7, cpu = 3.0, memory = 0.0, io = 1.0, net = 4.75, id = 163602164 | | LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `id`, `k` FROM `sbtest1` AS `sbtest1` WHERE (`id` > ?)"): rowcount = 1.0, cumulative cost = value = 2.3755002E7, cpu = 2.0, memory = 0.0, io = 1.0, net = 4.75, id = 163601451 | | Gather(concurrent=true): rowcount = 1.0, cumulative cost = value = 5003.0, cpu = 3.0, memory = 0.0, io = 1.0, net = 0.0, id = 163602167 | | LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `k` FROM `sbtest1` AS `sbtest1` WHERE ((`k` > ?) AND (`k` IN (...)))"): rowcount = 1.0, cumulative cost = value = 5002.0, cpu = 2.0, memory = 0.0, io = 1.0, net = 0.0, id = 163601377 | | | WorkloadType: TP | | +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
工作负载的识别，对于HTAP路由至关重要。这里也允许您通过HINT WORKLOAD_TYPE指定工作负载。同样以上述查询为例，可以将查询负载强制指定为AP。
mysql> explain cost /*+TDDL:WORKLOAD_TYPE=AP*/ 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; |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+| TopN(sort="cnt ASC", offset=?2, fetch=?3): rowcount = 1.0, cumulative cost = value = 2.8765038E7, cpu = 37.0, memory = 64.0, io = 3.0, net = 5.75, id = 163602178 |
| Filter(condition="cnt > ?1"): rowcount = 1.0, cumulative cost = value = 2.8765026E7, cpu = 26.0, memory = 47.0, io = 3.0, net = 5.75, id = 163602177 |
| HashAgg(group="k", cnt="COUNT()"): rowcount = 1.0, cumulative cost = value = 2.8765025E7, cpu = 25.0, memory = 47.0, io = 3.0, net = 5.75, id = 163602171 |
| BKAJoin(condition="k = id", type="inner"): rowcount = 1.0, cumulative cost = value = 2.8765012E7, cpu = 12.0, memory = 18.0, io = 3.0, net = 5.75, id = 163602169 |
| Gather(concurrent=true): rowcount = 1.0, cumulative cost = value = 2.3755003E7, cpu = 3.0, memory = 0.0, io = 1.0, net = 4.75, id = 163602164 |
| LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `id`, `k` FROM `sbtest1` AS `sbtest1` WHERE (`id` > ?)"): rowcount = 1.0, cumulative cost = value = 2.3755002E7, cpu = 2.0, memory = 0.0, io = 1.0, net = 4.75, id = 163601451 |
| Gather(concurrent=true): rowcount = 1.0, cumulative cost = value = 5003.0, cpu = 3.0, memory = 0.0, io = 1.0, net = 0.0, id = 163602167 |
| LogicalView(tables="[000000-000003].sbtest1_[00-15]", shardCount=16, sql="SELECT `k` FROM `sbtest1` AS `sbtest1` WHERE ((`k` > ?) AND (`k` IN (...)))"): rowcount = 1.0, cumulative cost = value = 5002.0, cpu = 2.0, memory = 0.0, io = 1.0, net = 0.0, id = 163601377 | |
| WorkloadType: AP | |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
规则路由
除了基于代价的智能路由以外，我们也支持基于读写规则的路由。允许您在控制台参数管理上设置读写分离权重参数MASTER_READ_WEIGHT，默认值为100，可配置值区间[0, 100]。如果配置为Weight=60，意味着占60%的流量会继续在主实例执行，40%的剩余流量会路由到只读实例执行，如果只读实例有多个会进行自动分配。
智能路由和规则路由这两者关系是解耦的，具体关系请查看下表格。
| 智能路由规则 | 规则路由 (MASTER_READ_WEIGHT) | 路由结果 |
| 开启 | 以代价的读写分离为主规则路由建议保持默认值为100 |
|
| 关闭 | 以规则的读写分离为主规则路由的可选范围：[0-100] |
|
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，往往会采样这种执行模式，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，您在没有购买只读实例的前提下，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）：您若购买了只读实例，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。
为了让您可以准确知道执行模式，我们在原有EXPLAIN指令上，扩展出了EXPLAIN PHYSICAL。例如以下查询，通过指令可以查看到当前查询采样的是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` > ?)") | +-------------------------------------------------------------------------------------------------------------------------------------------------------------------+
工作负载和执行模式有一定的耦合关系，AP工作负载会采用多机并行计算。同样的也允许您通过HINT EXECUTOR_MODE指定执行模式。假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。当然也同样支持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.id > 1000 group by k having cnt > 1300 order by cnt limit 5, 10;
调度策略
假设您购买了多个只读实例并加入到集群地址中，您通过集群地址的查询SQL路由到只读实例的流量，会被均匀调度到只读实例多个节点上执行，调度会考虑各个节点的资源负载，确保各个节点的负载压力差不多。比如PolarDB-X会将只读实例延迟作为调度参考指标，避免将流量调度到延迟较大的只读实例上执行。
