From 0cb4aa5e064568d16f850ced3e8236a9a2477d32 Mon Sep 17 00:00:00 2001 From: xufei Date: Mon, 9 Sep 2024 14:41:36 +0800 Subject: [PATCH 01/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 97 +++++++++++++++++++++++++++++ 1 file changed, 97 insertions(+) create mode 100644 tiflash/tiflash-mintso-scheduler.md diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md new file mode 100644 index 000000000000..3a59817dcb06 --- /dev/null +++ b/tiflash/tiflash-mintso-scheduler.md @@ -0,0 +1,97 @@ +--- +title: TiFlash MinTSO Scheduler +summary: 介绍 TiFlash MinTSO 调度器。 +--- + +# TiFlash MinTSO Scheduler + +本文介绍 TiFlash MinTSO scheduler 的原理与实现。 + +## 背景 +在 TiDB 中，对于 MPP query，TiDB 会将 query 拆成一个或多个 MPPTask，并将 MPPTask 发送给对应的 TiFlash 节点。而在 TiFlash 节点内部，对每个 MPPTask 进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md) 之前，对于每个 MPPTask，TiFlash 都需要使用若干个线程（具体线程数取决于 MPPTask 的复杂度以及 TiFlash 并发参数的设置）来执行。在高并发场景中，TiFlash 节点会同时接受到多个 MPPTask，如果 TiFlash 不对 MPPTask 的执行有所控制，则 TiFlash 需要向系统申请的线程数会随着 MPPTask 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面操作系统本身支持的线程数也是有限的，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会碰到无法申请线程的错误。 + +为了提升在高并发场景下 TiFlash 的处理能力，我们需要在 TiFlash 中引入一个 task 的 scheduler。 + +## 设计实现 + +如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPPTask，调度器根据当前系统已经使用的线程数以及该 MPPTask 预期使用的线程数，决定该 MPPTask 是否能够被调度： +``` +if (used_threads + mpptask.estimate_threads >= threads_limit) + // task need to wait +else + // task can run +``` +尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: +``` +mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; ++--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ +| id | estRows | task | access object | operator info | ++--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ +| HashAgg_44 | 1.00 | root | | funcs:count(Column#8)->Column#7 | +| └─TableReader_46 | 1.00 | root | | MppVersion: 2, data:ExchangeSender_45 | +| └─ExchangeSender_45 | 1.00 | mpp[tiflash] | | ExchangeType: PassThrough | +| └─HashAgg_13 | 1.00 | mpp[tiflash] | | funcs:count(1)->Column#8 | +| └─Projection_43 | 12487.50 | mpp[tiflash] | | test.t0.id | +| └─HashJoin_42 | 12487.50 | mpp[tiflash] | | inner join, equal:[eq(test.t0.id, test.t0.id)] | +| ├─ExchangeReceiver_22(Build) | 9990.00 | mpp[tiflash] | | | +| │ └─ExchangeSender_21 | 9990.00 | mpp[tiflash] | | ExchangeType: Broadcast, Compression: FAST | +| │ └─Selection_20 | 9990.00 | mpp[tiflash] | | not(isnull(test.t0.id)) | +| │ └─TableFullScan_19 | 10000.00 | mpp[tiflash] | table:a | pushed down filter:empty, keep order:false, stats:pseudo | +| └─Selection_24(Probe) | 9990.00 | mpp[tiflash] | | not(isnull(test.t0.id)) | +| └─TableFullScan_23 | 10000.00 | mpp[tiflash] | table:b | pushed down filter:empty, keep order:false, stats:pseudo | ++--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ +``` +上面的 query 会在每个 TiFlash 节点中生成 2 个 MPPTask，其中 `ExchangeSender_21` 所在的 MPPTask 依赖于 `ExchangeSender_45` 所在的 MPPTask。假设这个 query 高并发的情况下，调度器调度了每个 query 中 `ExchangeSender_45` 所在的 MPPTask，则系统就会进入死锁的状态。 + +为了避免系统陷入死锁的状态，我们引入了两层 thread 的限制： +* threads_soft_limit +* threads_hard_limit +其中 soft limit 主要用来限制系统使用的线程数，但是对于特定的 MPPTask，为了避免死锁可以打破这个限制，而 hard limit 则是为了保护系统，一旦超过 hard limit，TiFlash 可以通过报错来避免系统陷入死锁状态。 + +有了 soft limit 和 hard limit 之后，我们需要约定允许 MPPTask 突破 soft limit 的条件。突破 soft limit 主要是为了 +.如果调度器不考虑 MPPTask 之间的依赖关系的话，可能会导致系统出现死锁的状态。 +TiFlash 原有执行模型 Stream Model 是线程调度执行模型，每一个查询会独立申请若干条线程协同执行。 + +线程调度模型存在两个缺陷： + +- 在高并发场景下，过多的线程会引起较多上下文切换，导致较高的线程调度代价。 + +- 线程调度模型无法精准计量查询的资源使用量以及做细粒度的资源管控。 + +在新的执行模型 Pipeline Model 中进行了以下优化： + +- 查询会被划分为多个 pipeline 并依次执行。在每个 pipeline 中，数据块会被尽可能保留在缓存中，从而实现更好的时间局部性，从而提高整个执行过程的效率。 + +- 为了摆脱操作系统原生的线程调度模型，实现更加精细的调度机制，每个 pipeline 会被实例化成若干个 task，使用 task 调度模型，同时使用固定线程池，减少了操作系统申请和调度线程的开销。 + +TiFlash Pipeline Model 的架构如下： + +![TiFlash Pipeline Model Design](/media/tiflash/tiflash-pipeline-model.png) + +如上图所示，Pipeline Model 中有两个主要组成部分：Pipeline Query Executor 和 Task Scheduler。 + +- Pipeline Query Executor + + 负责将从 TiDB 节点发过来的查询请求转换为 pipeline dag。 + + 它会找到查询中的 pipeline breaker 算子，以 pipeline breaker 为边界将查询切分成若干个 pipeline，根据 pipeline 之间的依赖关系，将 pipeline 组装成一个有向无环图。 + + pipeline breaker 用于指代存在停顿/阻塞逻辑的算子，这一类算子会持续接收上游算子传来的数据块，直到所有数据块都被接收后，才会将处理结果返回给下游算子。这类算子会破坏数据处理流水线，所以被称为 pipeline breaker。pipeline breaker 的代表有 Aggregation，它会将上游算子的数据都写入到哈希表后，才对哈希表中的数据做计算返回给下游算子。 + + 在查询被转换为 pipeline dag 后，Pipeline Query Executor 会按照依赖关系依次执行每个 pipeline。pipeline 会根据查询并发度被实例化成若干个 task 提交给 Task Scheduler 执行。 + +- Task Scheduler + + 负责执行由 Pipeline Query Executor 提交过来的 task。task 会根据执行的逻辑的不同，在 Task Scheduler 里的不同组件中动态切换执行。 + + - CPU Task Thread Pool + + 执行 task 中 CPU 密集型的计算逻辑，比如数据过滤、函数计算等。 + + - IO Task Thread Pool + + 执行 task 中 IO 密集型的计算逻辑，比如计算中间结果落盘等。 + + - Wait Reactor + + 执行 task 中的等待逻辑，比如等待网络层将数据包传输给计算层等。 From 89cf328c2444b949c172f43bb2684b9ca41358e0 Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 14:46:02 +0800 Subject: [PATCH 02/21] save work Signed-off-by: xufei --- media/tiflash/tiflash_mintso_v1.png | Bin 0 -> 120608 bytes media/tiflash/tiflash_mintso_v2.png | Bin 0 -> 194219 bytes tiflash/tiflash-mintso-scheduler.md | 18 +++++++++--------- 3 files changed, 9 insertions(+), 9 deletions(-) create mode 100644 media/tiflash/tiflash_mintso_v1.png create mode 100644 media/tiflash/tiflash_mintso_v2.png diff --git a/media/tiflash/tiflash_mintso_v1.png b/media/tiflash/tiflash_mintso_v1.png new file mode 100644 index 0000000000000000000000000000000000000000..964dcc6daacfe342636db3a12a2b01b1b82b6af4 GIT binary 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|}klGo_n_}!!{^ZJ@#_t)XV3bwmb5^RU-2*BVxsI9Eq&JR(4k3URv z7YrR)Zn=Lg^E_n8K5B5m6eFQe!@gu#>@I40TWFprRv~(M1a;BDB_G6fF6Y=|G&g0m z;&lj~-)6cY=Y0rB;k&C%I|uT-WF9YGpQJ`{$N@(eex||6!(PRy^DpVd zlPBsAp>f+-UanV&gKDJzbS=+nrh7X$mmLHxs$BJWj&#P(TcpmpGltiu)+#`M|HvV) zcAk=RL~^}lIM%Pui$*Y_GLi;*!R&f6VeATW3u6Oj7wmbcp<-h5db>R{9{_cpoZ4wm zGhTi!8z~5=%lmc-7$%o!*~m@9bo+~8+D%c9$*IoD7+goC?wk5|((rZC{F=k!aENrX z0X)*7|`0EP$osqM-dgfyGDZc(4G82d~$k)yS z_v6U1;S}~vJ>Qi6@7odg+p)*UMJSY}z5R=dPMd}rDzmSYXj(og6*khx^|bQlaf1gf zVZ0-$UsBlZ%eQbT)F^#Vwa7VqnqP0I6q5HMU`4$*vJhV(SE-0B7QB&(8YA+3BG8y$T6mw@%0Ap>X;*mVJ z)Q?&A=E45e*0yFgtW4<;KL+Y$2G0}iTj0117>=S8G0N?of6zyBbtsIhivtNNvX-Sey9-shG0e|Q zCKTW?MOCqfh-)RJ1#glm1t(8h>76UgB>gQ#w@YlZQ%UH-K=?P4^M>xQEYp~AXIwa` zOj=OU!8V}o(^kfz<+-6x`J^Xh%bh}f0-ma86O+_v#H6AT&#ld>w0@5n`L4oiW-2qd zNH>NZ7J0;E*N$m8Ixq*D;XhQm4E_A)Yp!)UytW*S-Qn*;af1t(>Ea4`m4nsZCbEooY^B%vC*=zbQzKzV$tto95ZU62_3+niC1 zC**Onm_qT^Q%p1N=|+N({EFxMH#6ho1n1=mLRDhNY8#Bww)1I+Gp`Du&H5!tVCF;a 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引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPPTask，调度器根据当前系统已经使用的线程数以及该 MPPTask 预期使用的线程数，决定该 MPPTask 是否能够被调度： -``` -if (used_threads + mpptask.estimate_threads >= threads_limit) - // task need to wait -else - // task can run -``` + +![scheduler-v1](/media/tiflash/tiflash_mintso_v1.png) + 尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: ``` mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; @@ -48,9 +45,12 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; * threads_hard_limit 其中 soft limit 主要用来限制系统使用的线程数，但是对于特定的 MPPTask，为了避免死锁可以打破这个限制，而 hard limit 则是为了保护系统，一旦超过 hard limit，TiFlash 可以通过报错来避免系统陷入死锁状态。 -有了 soft limit 和 hard limit 之后，我们需要约定允许 MPPTask 突破 soft limit 的条件。突破 soft limit 主要是为了 -.如果调度器不考虑 MPPTask 之间的依赖关系的话，可能会导致系统出现死锁的状态。 -TiFlash 原有执行模型 Stream Model 是线程调度执行模型，每一个查询会独立申请若干条线程协同执行。 +利用 soft limit 来避免死锁的思想很简单，系统中存在一个特殊的 query，该 query 的所有 MPPTask 在调度时都可以突破 soft limit 的限制，这样只要系统的 thread 不超过 hard limit，系统中就必定存在一个 query 的所有 MPPTask 都可以正常执行，这样系统就不会出现死锁。 + +MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统中始终有一个特殊的 query，其所有的 MPPTask 都可以被调度到。 MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 仅根据自身信息对 MPPTask 进行调度，这样每个 TiFlash 的 MinTSO Scheduler 需要找到同一个“特殊”的 query。在 TiDB 中，每个 query 都会带有一个读的时间戳(TiDB 中称之为 start_ts)，MinTSO Scheduler 定义“特殊” query 的标准即为当前 TiFlash 节点上 start_ts 最小的 query，根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊” query 必然是同一个。我们称之为 MinTSO query。MinTSO scheduler 的调度流程如下： + +![scheduler-v1](/media/tiflash/tiflash_mintso_v1.png) + 线程调度模型存在两个缺陷： From 874c97e7cc82a69f81090a789b23feacf28a7d4a Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 14:52:15 +0800 Subject: [PATCH 03/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index 34d089395bc2..b5f9e6a453cb 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -16,7 +16,7 @@ summary: 介绍 TiFlash MinTSO 调度器。 如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPPTask，调度器根据当前系统已经使用的线程数以及该 MPPTask 预期使用的线程数，决定该 MPPTask 是否能够被调度： -![scheduler-v1](/media/tiflash/tiflash_mintso_v1.png) +![scheduler-v1](/media/tiflash/tiflash_mintso_v1.png =600*600) 尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: ``` @@ -49,7 +49,7 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统中始终有一个特殊的 query，其所有的 MPPTask 都可以被调度到。 MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 仅根据自身信息对 MPPTask 进行调度，这样每个 TiFlash 的 MinTSO Scheduler 需要找到同一个“特殊”的 query。在 TiDB 中，每个 query 都会带有一个读的时间戳(TiDB 中称之为 start_ts)，MinTSO Scheduler 定义“特殊” query 的标准即为当前 TiFlash 节点上 start_ts 最小的 query，根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊” query 必然是同一个。我们称之为 MinTSO query。MinTSO scheduler 的调度流程如下： -![scheduler-v1](/media/tiflash/tiflash_mintso_v1.png) +![scheduler-v2](/media/tiflash/tiflash_mintso_v2.png =600*600) 线程调度模型存在两个缺陷： From 314ab823f31eb3b98c707b300aa736064ae2b6a7 Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 14:53:45 +0800 Subject: [PATCH 04/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index b5f9e6a453cb..f42fe3a668f8 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -16,7 +16,7 @@ summary: 介绍 TiFlash MinTSO 调度器。 如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPPTask，调度器根据当前系统已经使用的线程数以及该 MPPTask 预期使用的线程数，决定该 MPPTask 是否能够被调度： -![scheduler-v1](/media/tiflash/tiflash_mintso_v1.png =600*600) +![scheduler-v1](/media/tiflash/tiflash_mintso_v1.png =600x600) 尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: ``` @@ -49,7 +49,7 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统中始终有一个特殊的 query，其所有的 MPPTask 都可以被调度到。 MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 仅根据自身信息对 MPPTask 进行调度，这样每个 TiFlash 的 MinTSO Scheduler 需要找到同一个“特殊”的 query。在 TiDB 中，每个 query 都会带有一个读的时间戳(TiDB 中称之为 start_ts)，MinTSO Scheduler 定义“特殊” query 的标准即为当前 TiFlash 节点上 start_ts 最小的 query，根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊” query 必然是同一个。我们称之为 MinTSO query。MinTSO scheduler 的调度流程如下： -![scheduler-v2](/media/tiflash/tiflash_mintso_v2.png =600*600) +![scheduler-v2](/media/tiflash/tiflash_mintso_v2.png =600x600) 线程调度模型存在两个缺陷： From 5f7cdce6faff2fe398145e8e21cd40f9df1c257e Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 14:58:46 +0800 Subject: [PATCH 05/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index f42fe3a668f8..1e2dda8e9146 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -16,7 +16,7 @@ summary: 介绍 TiFlash MinTSO 调度器。 如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPPTask，调度器根据当前系统已经使用的线程数以及该 MPPTask 预期使用的线程数，决定该 MPPTask 是否能够被调度： -![scheduler-v1](/media/tiflash/tiflash_mintso_v1.png =600x600) + 尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: ``` @@ -49,7 +49,7 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统中始终有一个特殊的 query，其所有的 MPPTask 都可以被调度到。 MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 仅根据自身信息对 MPPTask 进行调度，这样每个 TiFlash 的 MinTSO Scheduler 需要找到同一个“特殊”的 query。在 TiDB 中，每个 query 都会带有一个读的时间戳(TiDB 中称之为 start_ts)，MinTSO Scheduler 定义“特殊” query 的标准即为当前 TiFlash 节点上 start_ts 最小的 query，根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊” query 必然是同一个。我们称之为 MinTSO query。MinTSO scheduler 的调度流程如下： -![scheduler-v2](/media/tiflash/tiflash_mintso_v2.png =600x600) + 线程调度模型存在两个缺陷： From 8c516aff634cb9413de31ae0f66a8eb9dd172df7 Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 14:59:48 +0800 Subject: [PATCH 06/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index 1e2dda8e9146..63e471db01e9 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -16,7 +16,7 @@ summary: 介绍 TiFlash MinTSO 调度器。 如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPPTask，调度器根据当前系统已经使用的线程数以及该 MPPTask 预期使用的线程数，决定该 MPPTask 是否能够被调度： - + 尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: ``` @@ -49,7 +49,7 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统中始终有一个特殊的 query，其所有的 MPPTask 都可以被调度到。 MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 仅根据自身信息对 MPPTask 进行调度，这样每个 TiFlash 的 MinTSO Scheduler 需要找到同一个“特殊”的 query。在 TiDB 中，每个 query 都会带有一个读的时间戳(TiDB 中称之为 start_ts)，MinTSO Scheduler 定义“特殊” query 的标准即为当前 TiFlash 节点上 start_ts 最小的 query，根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊” query 必然是同一个。我们称之为 MinTSO query。MinTSO scheduler 的调度流程如下： - + 线程调度模型存在两个缺陷： From 1f1d4ef2c5dcf431aa3ad564a2aa1b593c1af89f Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 15:04:19 +0800 Subject: [PATCH 07/21] aa Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index 63e471db01e9..c4bfd7b85af8 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -16,7 +16,7 @@ summary: 介绍 TiFlash MinTSO 调度器。 如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPPTask，调度器根据当前系统已经使用的线程数以及该 MPPTask 预期使用的线程数，决定该 MPPTask 是否能够被调度： - + 尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: ``` From 9f7d149e0f09e8026dd54c61b27bd65679e96bfa Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 15:05:20 +0800 Subject: [PATCH 08/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index c4bfd7b85af8..2f4af09dd0aa 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -16,7 +16,7 @@ summary: 介绍 TiFlash MinTSO 调度器。 如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPPTask，调度器根据当前系统已经使用的线程数以及该 MPPTask 预期使用的线程数，决定该 MPPTask 是否能够被调度： - + 尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: ``` @@ -49,7 +49,7 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统中始终有一个特殊的 query，其所有的 MPPTask 都可以被调度到。 MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 仅根据自身信息对 MPPTask 进行调度，这样每个 TiFlash 的 MinTSO Scheduler 需要找到同一个“特殊”的 query。在 TiDB 中，每个 query 都会带有一个读的时间戳(TiDB 中称之为 start_ts)，MinTSO Scheduler 定义“特殊” query 的标准即为当前 TiFlash 节点上 start_ts 最小的 query，根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊” query 必然是同一个。我们称之为 MinTSO query。MinTSO scheduler 的调度流程如下： - + 线程调度模型存在两个缺陷： From 53cfcdf0f157c72bca34915c78ba504662d4e416 Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 15:14:47 +0800 Subject: [PATCH 09/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 44 +---------------------------- 1 file changed, 1 insertion(+), 43 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index 2f4af09dd0aa..059215254307 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -52,46 +52,4 @@ MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统 -线程调度模型存在两个缺陷： - -- 在高并发场景下，过多的线程会引起较多上下文切换，导致较高的线程调度代价。 - -- 线程调度模型无法精准计量查询的资源使用量以及做细粒度的资源管控。 - -在新的执行模型 Pipeline Model 中进行了以下优化： - -- 查询会被划分为多个 pipeline 并依次执行。在每个 pipeline 中，数据块会被尽可能保留在缓存中，从而实现更好的时间局部性，从而提高整个执行过程的效率。 - -- 为了摆脱操作系统原生的线程调度模型，实现更加精细的调度机制，每个 pipeline 会被实例化成若干个 task，使用 task 调度模型，同时使用固定线程池，减少了操作系统申请和调度线程的开销。 - -TiFlash Pipeline Model 的架构如下： - -![TiFlash Pipeline Model Design](/media/tiflash/tiflash-pipeline-model.png) - -如上图所示，Pipeline Model 中有两个主要组成部分：Pipeline Query Executor 和 Task Scheduler。 - -- Pipeline Query Executor - - 负责将从 TiDB 节点发过来的查询请求转换为 pipeline dag。 - - 它会找到查询中的 pipeline breaker 算子，以 pipeline breaker 为边界将查询切分成若干个 pipeline，根据 pipeline 之间的依赖关系，将 pipeline 组装成一个有向无环图。 - - pipeline breaker 用于指代存在停顿/阻塞逻辑的算子，这一类算子会持续接收上游算子传来的数据块，直到所有数据块都被接收后，才会将处理结果返回给下游算子。这类算子会破坏数据处理流水线，所以被称为 pipeline breaker。pipeline breaker 的代表有 Aggregation，它会将上游算子的数据都写入到哈希表后，才对哈希表中的数据做计算返回给下游算子。 - - 在查询被转换为 pipeline dag 后，Pipeline Query Executor 会按照依赖关系依次执行每个 pipeline。pipeline 会根据查询并发度被实例化成若干个 task 提交给 Task Scheduler 执行。 - -- Task Scheduler - - 负责执行由 Pipeline Query Executor 提交过来的 task。task 会根据执行的逻辑的不同，在 Task Scheduler 里的不同组件中动态切换执行。 - - - CPU Task Thread Pool - - 执行 task 中 CPU 密集型的计算逻辑，比如数据过滤、函数计算等。 - - - IO Task Thread Pool - - 执行 task 中 IO 密集型的计算逻辑，比如计算中间结果落盘等。 - - - Wait Reactor - - 执行 task 中的等待逻辑，比如等待网络层将数据包传输给计算层等。 +通过引入 soft limit 与 hard limit，MinTSO 能保证系统线程数的同时有效地避免了系统死锁。不过对于高并发场景，可能会出现大部份 query 都只有部分 MPPTask 被调度的情况，对于只有部分 MPPTask 被调度的 query，它们实际上无法正常执行，从而导致系统执行效率低下。为了避免这种情况，我们给 MinTSO Scheduler 在 query 层面引入了一个限制，即 active_query_soft_limit，该限制的意思是系统最多只有 active_query_soft_limit 个 query 的 MPPTask 可以参与调度，对于其他的 query，其 MPPTask 不参与调度，只有等当前 query 结束之后，新的 query 才能参与调度。当然这个限制只是一个 soft limit，因为对于 MinTSO query 来说，其所有 MPPTask 在系统线程数不超过 hard limit 的时候都可以直接被调度。 From 8f489ed668e8671545823cdb0a5c7421e4862d3f Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 15:23:22 +0800 Subject: [PATCH 10/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index 059215254307..e90fb4ecdd7e 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -8,6 +8,7 @@ summary: 介绍 TiFlash MinTSO 调度器。 本文介绍 TiFlash MinTSO scheduler 的原理与实现。 ## 背景 + 在 TiDB 中，对于 MPP query，TiDB 会将 query 拆成一个或多个 MPPTask，并将 MPPTask 发送给对应的 TiFlash 节点。而在 TiFlash 节点内部，对每个 MPPTask 进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md) 之前，对于每个 MPPTask，TiFlash 都需要使用若干个线程（具体线程数取决于 MPPTask 的复杂度以及 TiFlash 并发参数的设置）来执行。在高并发场景中，TiFlash 节点会同时接受到多个 MPPTask，如果 TiFlash 不对 MPPTask 的执行有所控制，则 TiFlash 需要向系统申请的线程数会随着 MPPTask 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面操作系统本身支持的线程数也是有限的，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会碰到无法申请线程的错误。 为了提升在高并发场景下 TiFlash 的处理能力，我们需要在 TiFlash 中引入一个 task 的 scheduler。 @@ -19,6 +20,7 @@ summary: 介绍 TiFlash MinTSO 调度器。 尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: + ``` mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; +--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ @@ -38,11 +40,13 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; | └─TableFullScan_23 | 10000.00 | mpp[tiflash] | table:b | pushed down filter:empty, keep order:false, stats:pseudo | +--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ ``` + 上面的 query 会在每个 TiFlash 节点中生成 2 个 MPPTask，其中 `ExchangeSender_21` 所在的 MPPTask 依赖于 `ExchangeSender_45` 所在的 MPPTask。假设这个 query 高并发的情况下，调度器调度了每个 query 中 `ExchangeSender_45` 所在的 MPPTask，则系统就会进入死锁的状态。 为了避免系统陷入死锁的状态，我们引入了两层 thread 的限制： * threads_soft_limit * threads_hard_limit + 其中 soft limit 主要用来限制系统使用的线程数，但是对于特定的 MPPTask，为了避免死锁可以打破这个限制，而 hard limit 则是为了保护系统，一旦超过 hard limit，TiFlash 可以通过报错来避免系统陷入死锁状态。 利用 soft limit 来避免死锁的思想很简单，系统中存在一个特殊的 query，该 query 的所有 MPPTask 在调度时都可以突破 soft limit 的限制，这样只要系统的 thread 不超过 hard limit，系统中就必定存在一个 query 的所有 MPPTask 都可以正常执行，这样系统就不会出现死锁。 @@ -51,5 +55,4 @@ MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统 - 通过引入 soft limit 与 hard limit，MinTSO 能保证系统线程数的同时有效地避免了系统死锁。不过对于高并发场景，可能会出现大部份 query 都只有部分 MPPTask 被调度的情况，对于只有部分 MPPTask 被调度的 query，它们实际上无法正常执行，从而导致系统执行效率低下。为了避免这种情况，我们给 MinTSO Scheduler 在 query 层面引入了一个限制，即 active_query_soft_limit，该限制的意思是系统最多只有 active_query_soft_limit 个 query 的 MPPTask 可以参与调度，对于其他的 query，其 MPPTask 不参与调度，只有等当前 query 结束之后，新的 query 才能参与调度。当然这个限制只是一个 soft limit，因为对于 MinTSO query 来说，其所有 MPPTask 在系统线程数不超过 hard limit 的时候都可以直接被调度。 From 64963cd31b4668af7c783b1051c3b9b68da4878c Mon Sep 17 00:00:00 2001 From: xufei Date: Thu, 12 Sep 2024 15:30:19 +0800 Subject: [PATCH 11/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 1 + 1 file changed, 1 insertion(+) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index e90fb4ecdd7e..c886d1196359 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -44,6 +44,7 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; 上面的 query 会在每个 TiFlash 节点中生成 2 个 MPPTask，其中 `ExchangeSender_21` 所在的 MPPTask 依赖于 `ExchangeSender_45` 所在的 MPPTask。假设这个 query 高并发的情况下，调度器调度了每个 query 中 `ExchangeSender_45` 所在的 MPPTask，则系统就会进入死锁的状态。 为了避免系统陷入死锁的状态，我们引入了两层 thread 的限制： + * threads_soft_limit * threads_hard_limit From 70f7033006f6cba5ce2a24d5cf84c15fd44b3371 Mon Sep 17 00:00:00 2001 From: xufei Date: Sat, 14 Sep 2024 16:31:24 +0800 Subject: [PATCH 12/21] Apply suggestions from code review Co-authored-by: joey-yez <104608045+joey-yez@users.noreply.github.com> --- tiflash/tiflash-mintso-scheduler.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index c886d1196359..c47b404ce2f9 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -50,9 +50,9 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; 其中 soft limit 主要用来限制系统使用的线程数，但是对于特定的 MPPTask，为了避免死锁可以打破这个限制，而 hard limit 则是为了保护系统，一旦超过 hard limit，TiFlash 可以通过报错来避免系统陷入死锁状态。 -利用 soft limit 来避免死锁的思想很简单，系统中存在一个特殊的 query，该 query 的所有 MPPTask 在调度时都可以突破 soft limit 的限制，这样只要系统的 thread 不超过 hard limit，系统中就必定存在一个 query 的所有 MPPTask 都可以正常执行，这样系统就不会出现死锁。 +使用 soft limit 和 hard limit 避免死锁的思想是：通过 soft limit 限制所有 query 使用的线程资源总量，充分利用资源的基础上，避免线程资源耗尽；通过 hard limit 确保在任何情况下，系统中至少存在一个 query 可以突破 soft limit 的限制，继续获取线程资源并继续运行，避免死锁。只要线程数不超过 hard limit，系统中就必定存在一个 query 的所有 MPPTask 都可以正常执行，这样系统就不会出现死锁。 -MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统中始终有一个特殊的 query，其所有的 MPPTask 都可以被调度到。 MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 仅根据自身信息对 MPPTask 进行调度，这样每个 TiFlash 的 MinTSO Scheduler 需要找到同一个“特殊”的 query。在 TiDB 中，每个 query 都会带有一个读的时间戳(TiDB 中称之为 start_ts)，MinTSO Scheduler 定义“特殊” query 的标准即为当前 TiFlash 节点上 start_ts 最小的 query，根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊” query 必然是同一个。我们称之为 MinTSO query。MinTSO scheduler 的调度流程如下： +MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统中始终有且只有一个特殊的 query，其所有的 MPPTask 都可以被调度到。 MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 仅根据自身信息对 MPPTask 进行调度，因此，所有 TiFlash 的 MinTSO Scheduler 需要找到同一个“特殊”的 query。在 TiDB 中，每个 query 都会带有一个读的时间戳(TiDB 中称之为 start_ts)，MinTSO Scheduler 定义“特殊” query 的标准即为当前 TiFlash 节点上 start_ts 最小的 query，根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊” query 必然是同一个。我们称之为 MinTSO query。MinTSO scheduler 的调度流程如下： From fb8d34b6bd6b34964977887e3fceb7aba39b96ca Mon Sep 17 00:00:00 2001 From: lilin90 Date: Fri, 25 Oct 2024 14:46:56 +0800 Subject: [PATCH 13/21] Add new doc to TOC.md --- TOC.md | 1 + 1 file changed, 1 insertion(+) diff --git a/TOC.md b/TOC.md index eaf011f7223e..e9b5c457bf20 100644 --- a/TOC.md +++ b/TOC.md @@ -676,6 +676,7 @@ - [TiFlash 延迟物化](/tiflash/tiflash-late-materialization.md) - [TiFlash 数据落盘](/tiflash/tiflash-spill-disk.md) - [TiFlash 数据校验](/tiflash/tiflash-data-validation.md) + - [TiFlash MinTSO 调度器](/tiflash/tiflash-mintso-scheduler.md) - [TiFlash 兼容性说明](/tiflash/tiflash-compatibility.md) - [TiFlash Pipeline Model 执行模型](/tiflash/tiflash-pipeline-model.md) - TiDB 分布式执行框架 From d4d0dacf85e18fb27bc7dca56d8de2e38ebf9aff Mon Sep 17 00:00:00 2001 From: xufei Date: Fri, 25 Oct 2024 20:00:51 +0800 Subject: [PATCH 14/21] Apply suggestions from code review Co-authored-by: Lilian Lee --- tiflash/tiflash-mintso-scheduler.md | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index c47b404ce2f9..b9207d250cc7 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -9,20 +9,23 @@ summary: 介绍 TiFlash MinTSO 调度器。 ## 背景 -在 TiDB 中，对于 MPP query，TiDB 会将 query 拆成一个或多个 MPPTask，并将 MPPTask 发送给对应的 TiFlash 节点。而在 TiFlash 节点内部，对每个 MPPTask 进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md) 之前，对于每个 MPPTask，TiFlash 都需要使用若干个线程（具体线程数取决于 MPPTask 的复杂度以及 TiFlash 并发参数的设置）来执行。在高并发场景中，TiFlash 节点会同时接受到多个 MPPTask，如果 TiFlash 不对 MPPTask 的执行有所控制，则 TiFlash 需要向系统申请的线程数会随着 MPPTask 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面操作系统本身支持的线程数也是有限的，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会碰到无法申请线程的错误。 +对于 MPP 查询，TiDB 会将查询拆成一个或多个 MPP Task，并将 MPP Task 发送给对应的 TiFlash 节点。在 TiFlash 节点内部，对每个 MPP Task 进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md) 之前，对于每个 MPP Task，TiFlash 都需要使用若干个线程来执行，具体线程数取决于 MPP Task 的复杂度以及 TiFlash 并发参数的设置。在高并发场景中，TiFlash 节点会同时接收到多个 MPP Task，如果 TiFlash 不对 MPP Task 的执行有所控制，则 TiFlash 需要向系统申请的线程数会随着 MPP Task 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面操作系统本身支持的线程数有限，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会遇到无法申请线程的错误。 -为了提升在高并发场景下 TiFlash 的处理能力，我们需要在 TiFlash 中引入一个 task 的 scheduler。 +为了提升 TiFlash 在高并发场景下的处理能力，需要在 TiFlash 中引入一个 task 调度器。 ## 设计实现 -如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPPTask，调度器根据当前系统已经使用的线程数以及该 MPPTask 预期使用的线程数，决定该 MPPTask 是否能够被调度： +如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPP Task，调度器根据当前系统已经使用的线程数以及该 MPP Task 预期使用的线程数，决定该 MPP Task 是否能够被调度： -尽管上述调度策略能有效控制系统的线程数，但是 MPPTask 并不是一个最小的独立执行单元，不同 MPPTask 之间会有依赖关系: +尽管上述调度策略能有效控制系统的线程数，但是 MPP Task 并不是一个最小的独立执行单元，不同 MPP Task 之间会有依赖关系： -``` +```sql mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; +``` + +``` +--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ | id | estRows | task | access object | operator info | +--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ @@ -41,19 +44,17 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; +--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ ``` -上面的 query 会在每个 TiFlash 节点中生成 2 个 MPPTask，其中 `ExchangeSender_21` 所在的 MPPTask 依赖于 `ExchangeSender_45` 所在的 MPPTask。假设这个 query 高并发的情况下，调度器调度了每个 query 中 `ExchangeSender_45` 所在的 MPPTask，则系统就会进入死锁的状态。 - -为了避免系统陷入死锁的状态，我们引入了两层 thread 的限制： +以上查询会在每个 TiFlash 节点中生成 2 个 MPP Task，其中 `ExchangeSender_21` 所在的 MPP Task 依赖于 `ExchangeSender_45` 所在的 MPP Task。在该查询高并发的情况下，如果调度器调度了每个查询中 `ExchangeSender_45` 所在的 MPP Task，则系统就会进入死锁状态。 -* threads_soft_limit -* threads_hard_limit +为了避免系统陷入死锁状态，TiFlash 引入了两层线程的限制： -其中 soft limit 主要用来限制系统使用的线程数，但是对于特定的 MPPTask，为了避免死锁可以打破这个限制，而 hard limit 则是为了保护系统，一旦超过 hard limit，TiFlash 可以通过报错来避免系统陷入死锁状态。 +* threads_soft_limit：主要用来限制系统使用的线程数。对于特定的 MPP Task，为了避免死锁，可以打破该限制。 +* threads_hard_limit：主要为了保护系统，一旦系统使用的线程数超过 hard limit，TiFlash 会通过报错来避免系统陷入死锁状态。 -使用 soft limit 和 hard limit 避免死锁的思想是：通过 soft limit 限制所有 query 使用的线程资源总量，充分利用资源的基础上，避免线程资源耗尽；通过 hard limit 确保在任何情况下，系统中至少存在一个 query 可以突破 soft limit 的限制，继续获取线程资源并继续运行，避免死锁。只要线程数不超过 hard limit，系统中就必定存在一个 query 的所有 MPPTask 都可以正常执行，这样系统就不会出现死锁。 +使用 soft limit 和 hard limit 避免死锁的原理是：通过 soft limit 限制所有查询使用的线程资源总量，在充分利用资源的基础上，避免线程资源耗尽。通过 hard limit 确保在任何情况下，系统中至少存在一个查询可以突破 soft limit 的限制，继续获取线程资源并继续运行，避免死锁。只要线程数不超过 hard limit，系统中就必定存在一个查询，该查询所有的 MPP Task 都可以正常执行，这样系统就不会出现死锁。 -MinTSO Scheduler 的目标就是在控制系统线程数的同时，确保系统中始终有且只有一个特殊的 query，其所有的 MPPTask 都可以被调度到。 MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 仅根据自身信息对 MPPTask 进行调度，因此，所有 TiFlash 的 MinTSO Scheduler 需要找到同一个“特殊”的 query。在 TiDB 中，每个 query 都会带有一个读的时间戳(TiDB 中称之为 start_ts)，MinTSO Scheduler 定义“特殊” query 的标准即为当前 TiFlash 节点上 start_ts 最小的 query，根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊” query 必然是同一个。我们称之为 MinTSO query。MinTSO scheduler 的调度流程如下： +MinTSO Scheduler 的目标是在控制系统线程数的同时，确保系统中始终有且只有一个特殊的查询，其所有的 MPP Task 都可以被调度到。MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 节点仅根据自身信息对 MPP Task 进行调度，因此，所有 TiFlash 节点的 MinTSO Scheduler 需要找到同一个“特殊”的查询。在 TiDB 中，每个 查询都会带有一个读的时间戳 (`start_ts`)，MinTSO Scheduler 定义“特殊”查询的标准为当前 TiFlash 节点上 `start_ts` 最小的查询。根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊”查询必然是同一个，将其称之为 MinTSO 查询。MinTSO Scheduler 的调度流程如下： -通过引入 soft limit 与 hard limit，MinTSO 能保证系统线程数的同时有效地避免了系统死锁。不过对于高并发场景，可能会出现大部份 query 都只有部分 MPPTask 被调度的情况，对于只有部分 MPPTask 被调度的 query，它们实际上无法正常执行，从而导致系统执行效率低下。为了避免这种情况，我们给 MinTSO Scheduler 在 query 层面引入了一个限制，即 active_query_soft_limit，该限制的意思是系统最多只有 active_query_soft_limit 个 query 的 MPPTask 可以参与调度，对于其他的 query，其 MPPTask 不参与调度，只有等当前 query 结束之后，新的 query 才能参与调度。当然这个限制只是一个 soft limit，因为对于 MinTSO query 来说，其所有 MPPTask 在系统线程数不超过 hard limit 的时候都可以直接被调度。 +通过引入 soft limit 与 hard limit，MinTSO 调度器在控制系统线程数的同时，有效地避免了系统死锁。不过对于高并发场景，可能会出现大部份查询都只有部分 MPP Task 被调度的情况。只有部分 MPP Task 被调度的查询实际上无法正常执行，从而导致系统执行效率低下。为了避免这种情况，TiFlash 给 MinTSO Scheduler 在查询层面引入了一个限制，即 active_query_soft_limit，该限制的意思是系统最多只有 active_query_soft_limit 个查询的 MPP Task 可以参与调度；对于其它的查询，其 MPP Task 不参与调度，只有等当前查询结束之后，新的查询才能参与调度。当然该限制只是一个 soft limit，因为对于 MinTSO 查询来说，其所有 MPP Task 在系统线程数不超过 hard limit 时都可以直接被调度。 From 036487da6b1c0cfcaeea51ba2eb6d4208ee3320a Mon Sep 17 00:00:00 2001 From: xufei Date: Fri, 25 Oct 2024 20:01:07 +0800 Subject: [PATCH 15/21] Update tiflash/tiflash-mintso-scheduler.md --- tiflash/tiflash-mintso-scheduler.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index b9207d250cc7..e9909fb1f0e8 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -5,7 +5,7 @@ summary: 介绍 TiFlash MinTSO 调度器。 # TiFlash MinTSO Scheduler -本文介绍 TiFlash MinTSO scheduler 的原理与实现。 +TiFlash MinTSO 调度器是 TiFlash 中一个分布式的基于 MPP Task 的调度器，本文介绍 TiFlash MinTSO scheduler 的原理与实现。 ## 背景 From 7961692f0dbd23b6e8a624df81cdf526501ecafb Mon Sep 17 00:00:00 2001 From: lilin90 Date: Mon, 28 Oct 2024 11:16:57 +0800 Subject: [PATCH 16/21] tiflash: update wording and format --- tiflash/tiflash-mintso-scheduler.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index e9909fb1f0e8..40efa6a15fdc 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -1,23 +1,23 @@ --- -title: TiFlash MinTSO Scheduler -summary: 介绍 TiFlash MinTSO 调度器。 +title: TiFlash MinTSO 调度器 +summary: 了解 TiFlash MinTSO 调度器的原理与实现。 --- -# TiFlash MinTSO Scheduler +# TiFlash MinTSO 调度器 -TiFlash MinTSO 调度器是 TiFlash 中一个分布式的基于 MPP Task 的调度器，本文介绍 TiFlash MinTSO scheduler 的原理与实现。 +TiFlash MinTSO 调度器是 TiFlash 中一个分布式的基于 MPP Task 的调度器。本文介绍 TiFlash MinTSO 调度器的原理与实现。 ## 背景 -对于 MPP 查询，TiDB 会将查询拆成一个或多个 MPP Task，并将 MPP Task 发送给对应的 TiFlash 节点。在 TiFlash 节点内部，对每个 MPP Task 进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md) 之前，对于每个 MPP Task，TiFlash 都需要使用若干个线程来执行，具体线程数取决于 MPP Task 的复杂度以及 TiFlash 并发参数的设置。在高并发场景中，TiFlash 节点会同时接收到多个 MPP Task，如果 TiFlash 不对 MPP Task 的执行有所控制，则 TiFlash 需要向系统申请的线程数会随着 MPP Task 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面操作系统本身支持的线程数有限，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会遇到无法申请线程的错误。 +对于 MPP 查询，TiDB 会将查询拆成一个或多个 MPP Task，并将 MPP Task 发送给对应的 TiFlash 节点。在 TiFlash 节点内部，对每个 MPP Task 进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md)之前，对于每个 MPP Task，TiFlash 都需要使用若干个线程来执行，具体线程数取决于 MPP Task 的复杂度以及 TiFlash 并发参数的设置。在高并发场景中，TiFlash 节点会同时接收到多个 MPP Task，如果 TiFlash 不对 MPP Task 的执行有所控制，则 TiFlash 需要向系统申请的线程数会随着 MPP Task 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面操作系统本身支持的线程数有限，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会遇到无法申请线程的错误。 -为了提升 TiFlash 在高并发场景下的处理能力，需要在 TiFlash 中引入一个 task 调度器。 +为了提升 TiFlash 在高并发场景下的处理能力，需要在 TiFlash 中引入一个 Task 调度器。 ## 设计实现 -如背景所述，TiFlash task scheudler 引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPP Task，调度器根据当前系统已经使用的线程数以及该 MPP Task 预期使用的线程数，决定该 MPP Task 是否能够被调度： +如背景所述，TiFlash Task 调度器引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPP Task，调度器根据当前系统已经使用的线程数以及该 MPP Task 预期使用的线程数，决定该 MPP Task 是否能够被调度： - +![TiFlash MinTSO Scheduler v1](/media/tiflash/tiflash_mintso_v1.png) 尽管上述调度策略能有效控制系统的线程数，但是 MPP Task 并不是一个最小的独立执行单元，不同 MPP Task 之间会有依赖关系： @@ -44,17 +44,17 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; +--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ ``` -以上查询会在每个 TiFlash 节点中生成 2 个 MPP Task，其中 `ExchangeSender_21` 所在的 MPP Task 依赖于 `ExchangeSender_45` 所在的 MPP Task。在该查询高并发的情况下，如果调度器调度了每个查询中 `ExchangeSender_45` 所在的 MPP Task，则系统就会进入死锁状态。 +以上查询会在每个 TiFlash 节点中生成 2 个 MPP Task，其中 `ExchangeSender_21` 所在的 MPP Task 依赖于 `ExchangeSender_45` 所在的 MPP Task。在该查询高并发的情况下，如果调度器调度了每个查询中 `ExchangeSender_45` 所在的 MPP Task，系统就会进入死锁状态。 -为了避免系统陷入死锁状态，TiFlash 引入了两层线程的限制： +为了避免系统陷入死锁状态，TiFlash 引入了以下两层线程的限制： * threads_soft_limit：主要用来限制系统使用的线程数。对于特定的 MPP Task，为了避免死锁，可以打破该限制。 * threads_hard_limit：主要为了保护系统，一旦系统使用的线程数超过 hard limit，TiFlash 会通过报错来避免系统陷入死锁状态。 使用 soft limit 和 hard limit 避免死锁的原理是：通过 soft limit 限制所有查询使用的线程资源总量，在充分利用资源的基础上，避免线程资源耗尽。通过 hard limit 确保在任何情况下，系统中至少存在一个查询可以突破 soft limit 的限制，继续获取线程资源并继续运行，避免死锁。只要线程数不超过 hard limit，系统中就必定存在一个查询，该查询所有的 MPP Task 都可以正常执行，这样系统就不会出现死锁。 -MinTSO Scheduler 的目标是在控制系统线程数的同时，确保系统中始终有且只有一个特殊的查询，其所有的 MPP Task 都可以被调度到。MinTSO Scheduler 是一个完全分布式的调度器，每个 TiFlash 节点仅根据自身信息对 MPP Task 进行调度，因此，所有 TiFlash 节点的 MinTSO Scheduler 需要找到同一个“特殊”的查询。在 TiDB 中，每个 查询都会带有一个读的时间戳 (`start_ts`)，MinTSO Scheduler 定义“特殊”查询的标准为当前 TiFlash 节点上 `start_ts` 最小的查询。根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊”查询必然是同一个，将其称之为 MinTSO 查询。MinTSO Scheduler 的调度流程如下： +MinTSO 调度器的目标是在控制系统线程数的同时，确保系统中始终有且只有一个特殊的查询，其所有的 MPP Task 都可以被调度到。MinTSO 调度器是一个完全分布式的调度器，每个 TiFlash 节点仅根据自身信息对 MPP Task 进行调度，因此，所有 TiFlash 节点的 MinTSO 调度器需要找到同一个“特殊”的查询。在 TiDB 中，每个查询都会带有一个读的时间戳 (`start_ts`)，MinTSO 调度器定义“特殊”查询的标准为当前 TiFlash 节点上 `start_ts` 最小的查询。根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊”查询必然是同一个，将其称之为 MinTSO 查询。MinTSO 调度器的调度流程如下： - +![TiFlash MinTSO Scheduler v2](/media/tiflash/tiflash_mintso_v2.png) 通过引入 soft limit 与 hard limit，MinTSO 调度器在控制系统线程数的同时，有效地避免了系统死锁。不过对于高并发场景，可能会出现大部份查询都只有部分 MPP Task 被调度的情况。只有部分 MPP Task 被调度的查询实际上无法正常执行，从而导致系统执行效率低下。为了避免这种情况，TiFlash 给 MinTSO Scheduler 在查询层面引入了一个限制，即 active_query_soft_limit，该限制的意思是系统最多只有 active_query_soft_limit 个查询的 MPP Task 可以参与调度；对于其它的查询，其 MPP Task 不参与调度，只有等当前查询结束之后，新的查询才能参与调度。当然该限制只是一个 soft limit，因为对于 MinTSO 查询来说，其所有 MPP Task 在系统线程数不超过 hard limit 时都可以直接被调度。 From 882e821fd3feeecce0f0db04fa2c76eada045132 Mon Sep 17 00:00:00 2001 From: lilin90 Date: Mon, 28 Oct 2024 11:18:42 +0800 Subject: [PATCH 17/21] tiflash: update wording for consistency --- tiflash/tiflash-mintso-scheduler.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index 40efa6a15fdc..ce198fc6ddcf 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -1,11 +1,11 @@ --- title: TiFlash MinTSO 调度器 -summary: 了解 TiFlash MinTSO 调度器的原理与实现。 +summary: 了解 TiFlash MinTSO 调度器的实现原理。 --- # TiFlash MinTSO 调度器 -TiFlash MinTSO 调度器是 TiFlash 中一个分布式的基于 MPP Task 的调度器。本文介绍 TiFlash MinTSO 调度器的原理与实现。 +TiFlash MinTSO 调度器是 TiFlash 中一个分布式的基于 MPP Task 的调度器。本文介绍 TiFlash MinTSO 调度器的实现原理。 ## 背景 @@ -13,7 +13,7 @@ TiFlash MinTSO 调度器是 TiFlash 中一个分布式的基于 MPP Task 的调 为了提升 TiFlash 在高并发场景下的处理能力，需要在 TiFlash 中引入一个 Task 调度器。 -## 设计实现 +## 实现原理 如背景所述，TiFlash Task 调度器引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPP Task，调度器根据当前系统已经使用的线程数以及该 MPP Task 预期使用的线程数，决定该 MPP Task 是否能够被调度： From d103e7d89bdd73ae5d015b33cd0b2cda39a7cbe8 Mon Sep 17 00:00:00 2001 From: lilin90 Date: Mon, 28 Oct 2024 11:22:13 +0800 Subject: [PATCH 18/21] tiflash: update wording --- tiflash/tiflash-mintso-scheduler.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index ce198fc6ddcf..25acd9d03256 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -9,7 +9,9 @@ TiFlash MinTSO 调度器是 TiFlash 中一个分布式的基于 MPP Task 的调 ## 背景 -对于 MPP 查询，TiDB 会将查询拆成一个或多个 MPP Task，并将 MPP Task 发送给对应的 TiFlash 节点。在 TiFlash 节点内部，对每个 MPP Task 进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md)之前，对于每个 MPP Task，TiFlash 都需要使用若干个线程来执行，具体线程数取决于 MPP Task 的复杂度以及 TiFlash 并发参数的设置。在高并发场景中，TiFlash 节点会同时接收到多个 MPP Task，如果 TiFlash 不对 MPP Task 的执行有所控制，则 TiFlash 需要向系统申请的线程数会随着 MPP Task 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面操作系统本身支持的线程数有限，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会遇到无法申请线程的错误。 +对于 MPP 查询，TiDB 会将查询拆成一个或多个 MPP Task，并将 MPP Task 发送给对应的 TiFlash 节点。在 TiFlash 节点内部，对每个 MPP Task 进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md)之前，对于每个 MPP Task，TiFlash 都需要使用若干个线程来执行，具体线程数取决于 MPP Task 的复杂度以及 TiFlash 并发参数的设置。 + +在高并发场景中，TiFlash 节点会同时接收到多个 MPP Task，如果 TiFlash 不对 MPP Task 的执行有所控制，则 TiFlash 需要向系统申请的线程数会随着 MPP Task 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面操作系统本身支持的线程数有限，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会遇到无法申请线程的错误。 为了提升 TiFlash 在高并发场景下的处理能力，需要在 TiFlash 中引入一个 Task 调度器。 @@ -57,4 +59,4 @@ MinTSO 调度器的目标是在控制系统线程数的同时，确保系统中 ![TiFlash MinTSO Scheduler v2](/media/tiflash/tiflash_mintso_v2.png) -通过引入 soft limit 与 hard limit，MinTSO 调度器在控制系统线程数的同时，有效地避免了系统死锁。不过对于高并发场景，可能会出现大部份查询都只有部分 MPP Task 被调度的情况。只有部分 MPP Task 被调度的查询实际上无法正常执行，从而导致系统执行效率低下。为了避免这种情况，TiFlash 给 MinTSO Scheduler 在查询层面引入了一个限制，即 active_query_soft_limit，该限制的意思是系统最多只有 active_query_soft_limit 个查询的 MPP Task 可以参与调度；对于其它的查询，其 MPP Task 不参与调度，只有等当前查询结束之后，新的查询才能参与调度。当然该限制只是一个 soft limit，因为对于 MinTSO 查询来说，其所有 MPP Task 在系统线程数不超过 hard limit 时都可以直接被调度。 +通过引入 soft limit 与 hard limit，MinTSO 调度器在控制系统线程数的同时，有效地避免了系统死锁。不过对于高并发场景，可能会出现大部份查询都只有部分 MPP Task 被调度的情况。只有部分 MPP Task 被调度的查询实际上无法正常执行，从而导致系统执行效率低下。为了避免这种情况，TiFlash 给 MinTSO 调度器在查询层面引入了一个限制，即 active_query_soft_limit，该限制的意思是系统最多只有 active_query_soft_limit 个查询的 MPP Task 可以参与调度；对于其它的查询，其 MPP Task 不参与调度，只有等当前查询结束之后，新的查询才能参与调度。当然该限制只是一个 soft limit，因为对于 MinTSO 查询来说，其所有 MPP Task 在系统线程数不超过 hard limit 时都可以直接被调度。 From b3aa9bc51280aba1c61b0b8be640940f5cd447b8 Mon Sep 17 00:00:00 2001 From: xufei Date: Mon, 28 Oct 2024 14:33:37 +0800 Subject: [PATCH 19/21] Apply suggestions from code review Co-authored-by: Grace Cai --- tiflash/tiflash-mintso-scheduler.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index 25acd9d03256..25db1acb226e 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -5,26 +5,26 @@ summary: 了解 TiFlash MinTSO 调度器的实现原理。 # TiFlash MinTSO 调度器 -TiFlash MinTSO 调度器是 TiFlash 中一个分布式的基于 MPP Task 的调度器。本文介绍 TiFlash MinTSO 调度器的实现原理。 +TiFlash MinTSO 调度器是 TiFlash 中针对 [MPP](/glossary.md#mpp) Task 的分布式调度器。本文介绍 TiFlash MinTSO 调度器的实现原理。 ## 背景 -对于 MPP 查询，TiDB 会将查询拆成一个或多个 MPP Task，并将 MPP Task 发送给对应的 TiFlash 节点。在 TiFlash 节点内部，对每个 MPP Task 进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md)之前，对于每个 MPP Task，TiFlash 都需要使用若干个线程来执行，具体线程数取决于 MPP Task 的复杂度以及 TiFlash 并发参数的设置。 +在处理 MPP 查询时，TiDB 会将查询拆分成一个或多个 MPP Task，并将这些 MPP Task 发送给对应的 TiFlash 节点进行编译与执行。在 TiFlash 使用 [pipeline 执行模型](/tiflash/tiflash-pipeline-model.md)之前，对于每个 MPP Task，TiFlash 都需要使用若干个线程来执行，具体线程数取决于 MPP Task 的复杂度以及 TiFlash 并发参数的设置。 -在高并发场景中，TiFlash 节点会同时接收到多个 MPP Task，如果 TiFlash 不对 MPP Task 的执行有所控制，则 TiFlash 需要向系统申请的线程数会随着 MPP Task 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面操作系统本身支持的线程数有限，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会遇到无法申请线程的错误。 +在高并发场景中，TiFlash 节点会同时接收到多个 MPP Task，如果不对 MPP Task 的执行进行控制，TiFlash 需要向系统申请的线程数会随着 MPP Task 数量的增加而线性增加。过多的线程一方面会影响 TiFlash 的执行效率，另一方面由于操作系统本身支持的线程数有限，当 TiFlash 申请的线程数超过操作系统的限制时，TiFlash 就会遇到无法申请线程的错误。 -为了提升 TiFlash 在高并发场景下的处理能力，需要在 TiFlash 中引入一个 Task 调度器。 +为了提升 TiFlash 在高并发场景下的处理能力，需要在 TiFlash 中引入一个 MPP Task 调度器。 ## 实现原理 -如背景所述，TiFlash Task 调度器引入的初衷是控制运行时使用的线程数。一个简单的想法是指定 TiFlash 可以申请的最大线程数，对于每个 MPP Task，调度器根据当前系统已经使用的线程数以及该 MPP Task 预期使用的线程数，决定该 MPP Task 是否能够被调度： +如背景所述，TiFlash Task 调度器引入的初衷是控制 MPP 查询运行时使用的线程数。一种简单的调度策略是指定 TiFlash 可以申请的最大线程数。对于每个 MPP Task，调度器根据当前系统已经使用的线程数以及该 MPP Task 预期使用的线程数，决定该 MPP Task 是否能够被调度： ![TiFlash MinTSO Scheduler v1](/media/tiflash/tiflash_mintso_v1.png) 尽管上述调度策略能有效控制系统的线程数，但是 MPP Task 并不是一个最小的独立执行单元，不同 MPP Task 之间会有依赖关系： ```sql -mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; +EXPLAIN SELECT count(*) FROM t0 a JOIN t0 b ON a.id = b.id; ``` ``` @@ -46,17 +46,17 @@ mysql> explain select count(*) from t0 a join t0 b on a.id = b.id; +--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ ``` -以上查询会在每个 TiFlash 节点中生成 2 个 MPP Task，其中 `ExchangeSender_21` 所在的 MPP Task 依赖于 `ExchangeSender_45` 所在的 MPP Task。在该查询高并发的情况下，如果调度器调度了每个查询中 `ExchangeSender_45` 所在的 MPP Task，系统就会进入死锁状态。 +例如，以上查询会在每个 TiFlash 节点中生成 2 个 MPP Task，其中 `ExchangeSender_45` 所在的 MPP Task 依赖于 `ExchangeSender_21` 所在的 MPP Task。在该查询高并发的情况下，如果调度器调度了每个查询中 `ExchangeSender_45` 所在的 MPP Task，系统就会进入死锁状态。 为了避免系统陷入死锁状态，TiFlash 引入了以下两层线程的限制： * threads_soft_limit：主要用来限制系统使用的线程数。对于特定的 MPP Task，为了避免死锁，可以打破该限制。 * threads_hard_limit：主要为了保护系统，一旦系统使用的线程数超过 hard limit，TiFlash 会通过报错来避免系统陷入死锁状态。 -使用 soft limit 和 hard limit 避免死锁的原理是：通过 soft limit 限制所有查询使用的线程资源总量，在充分利用资源的基础上，避免线程资源耗尽。通过 hard limit 确保在任何情况下，系统中至少存在一个查询可以突破 soft limit 的限制，继续获取线程资源并继续运行，避免死锁。只要线程数不超过 hard limit，系统中就必定存在一个查询，该查询所有的 MPP Task 都可以正常执行，这样系统就不会出现死锁。 +使用 soft limit 和 hard limit 避免死锁的原理是：通过 soft limit 限制所有查询使用的线程资源总量，在充分利用资源的基础上，避免线程资源耗尽。通过 hard limit 确保在任何情况下，系统中至少存在一个查询可以突破 soft limit 的限制，继续获取线程资源并运行，避免死锁。只要线程数不超过 hard limit，系统中就必定存在一个查询，该查询所有的 MPP Task 都可以正常执行，这样系统就不会出现死锁。 -MinTSO 调度器的目标是在控制系统线程数的同时，确保系统中始终有且只有一个特殊的查询，其所有的 MPP Task 都可以被调度到。MinTSO 调度器是一个完全分布式的调度器，每个 TiFlash 节点仅根据自身信息对 MPP Task 进行调度，因此，所有 TiFlash 节点的 MinTSO 调度器需要找到同一个“特殊”的查询。在 TiDB 中，每个查询都会带有一个读的时间戳 (`start_ts`)，MinTSO 调度器定义“特殊”查询的标准为当前 TiFlash 节点上 `start_ts` 最小的查询。根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊”查询必然是同一个，将其称之为 MinTSO 查询。MinTSO 调度器的调度流程如下： +MinTSO 调度器的目标是在控制系统线程数的同时，确保系统中始终有且只有一个特殊的查询，其所有的 MPP Task 都可以被调度。MinTSO 调度器是一个完全分布式的调度器，每个 TiFlash 节点仅根据自身信息对 MPP Task 进行调度，因此，所有 TiFlash 节点的 MinTSO 调度器需要识别出同一个“特殊”查询。在 TiDB 中，每个查询都会带有一个读的时间戳 (`start_ts`)，MinTSO 调度器定义“特殊”查询的标准为当前 TiFlash 节点上 `start_ts` 最小的查询。根据全局最小一定是局部最小的原理，所有的 TiFlash 选出的“特殊”查询必然是同一个，称为 MinTSO 查询。MinTSO 调度器的调度流程如下： ![TiFlash MinTSO Scheduler v2](/media/tiflash/tiflash_mintso_v2.png) -通过引入 soft limit 与 hard limit，MinTSO 调度器在控制系统线程数的同时，有效地避免了系统死锁。不过对于高并发场景，可能会出现大部份查询都只有部分 MPP Task 被调度的情况。只有部分 MPP Task 被调度的查询实际上无法正常执行，从而导致系统执行效率低下。为了避免这种情况，TiFlash 给 MinTSO 调度器在查询层面引入了一个限制，即 active_query_soft_limit，该限制的意思是系统最多只有 active_query_soft_limit 个查询的 MPP Task 可以参与调度；对于其它的查询，其 MPP Task 不参与调度，只有等当前查询结束之后，新的查询才能参与调度。当然该限制只是一个 soft limit，因为对于 MinTSO 查询来说，其所有 MPP Task 在系统线程数不超过 hard limit 时都可以直接被调度。 +通过引入 soft limit 与 hard limit，MinTSO 调度器在控制系统线程数的同时，有效地避免了系统死锁。不过对于高并发场景，可能会出现大多数查询都只有部分 MPP Task 被调度的情况。只有部分 MPP Task 被调度的查询无法正常执行，从而导致系统执行效率低下。为了避免这种情况，TiFlash 在查询层面为 MinTSO 调度器引入了一个限制，即 active_query_soft_limit，该限制要求系统最多只有 active_query_soft_limit 个查询的 MPP Task 可以参与调度；对于其它的查询，其 MPP Task 不参与调度，只有等当前查询结束之后，新的查询才能参与调度。该限制只是一个 soft limit，因为对于 MinTSO 查询来说，其所有 MPP Task 在系统线程数不超过 hard limit 时都可以直接被调度。 From ca5ec767e51baf7a3e2a438be8f7123ec53f6d64 Mon Sep 17 00:00:00 2001 From: xufei Date: Mon, 28 Oct 2024 17:13:36 +0800 Subject: [PATCH 20/21] save work Signed-off-by: xufei --- tiflash/tiflash-mintso-scheduler.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index 25db1acb226e..11a57b335dce 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -50,8 +50,8 @@ EXPLAIN SELECT count(*) FROM t0 a JOIN t0 b ON a.id = b.id; 为了避免系统陷入死锁状态，TiFlash 引入了以下两层线程的限制： -* threads_soft_limit：主要用来限制系统使用的线程数。对于特定的 MPP Task，为了避免死锁，可以打破该限制。 -* threads_hard_limit：主要为了保护系统，一旦系统使用的线程数超过 hard limit，TiFlash 会通过报错来避免系统陷入死锁状态。 +* thread_soft_limit：主要用来限制系统使用的线程数。对于特定的 MPP Task，为了避免死锁，可以打破该限制。 +* thread_hard_limit：主要为了保护系统，一旦系统使用的线程数超过 hard limit，TiFlash 会通过报错来避免系统陷入死锁状态。 使用 soft limit 和 hard limit 避免死锁的原理是：通过 soft limit 限制所有查询使用的线程资源总量，在充分利用资源的基础上，避免线程资源耗尽。通过 hard limit 确保在任何情况下，系统中至少存在一个查询可以突破 soft limit 的限制，继续获取线程资源并运行，避免死锁。只要线程数不超过 hard limit，系统中就必定存在一个查询，该查询所有的 MPP Task 都可以正常执行，这样系统就不会出现死锁。 @@ -59,4 +59,4 @@ MinTSO 调度器的目标是在控制系统线程数的同时，确保系统中 ![TiFlash MinTSO Scheduler v2](/media/tiflash/tiflash_mintso_v2.png) -通过引入 soft limit 与 hard limit，MinTSO 调度器在控制系统线程数的同时，有效地避免了系统死锁。不过对于高并发场景，可能会出现大多数查询都只有部分 MPP Task 被调度的情况。只有部分 MPP Task 被调度的查询无法正常执行，从而导致系统执行效率低下。为了避免这种情况，TiFlash 在查询层面为 MinTSO 调度器引入了一个限制，即 active_query_soft_limit，该限制要求系统最多只有 active_query_soft_limit 个查询的 MPP Task 可以参与调度；对于其它的查询，其 MPP Task 不参与调度，只有等当前查询结束之后，新的查询才能参与调度。该限制只是一个 soft limit，因为对于 MinTSO 查询来说，其所有 MPP Task 在系统线程数不超过 hard limit 时都可以直接被调度。 +通过引入 soft limit 与 hard limit，MinTSO 调度器在控制系统线程数的同时，有效地避免了系统死锁。不过对于高并发场景，可能会出现大多数查询都只有部分 MPP Task 被调度的情况。只有部分 MPP Task 被调度的查询无法正常执行，从而导致系统执行效率低下。为了避免这种情况，TiFlash 在查询层面为 MinTSO 调度器引入了一个限制，即 active_set_soft_limit，该限制要求系统最多只有 active_set_soft_limit 个查询的 MPP Task 可以参与调度；对于其它的查询，其 MPP Task 不参与调度，只有等当前查询结束之后，新的查询才能参与调度。该限制只是一个 soft limit，因为对于 MinTSO 查询来说，其所有 MPP Task 在系统线程数不超过 hard limit 时都可以直接被调度。 From 850385794f6f991e262817391fbc2ad0699e5c92 Mon Sep 17 00:00:00 2001 From: Lilian Lee Date: Mon, 4 Nov 2024 10:30:58 +0800 Subject: [PATCH 21/21] Update wording --- tiflash/tiflash-mintso-scheduler.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tiflash/tiflash-mintso-scheduler.md b/tiflash/tiflash-mintso-scheduler.md index 11a57b335dce..2b47dce0791c 100644 --- a/tiflash/tiflash-mintso-scheduler.md +++ b/tiflash/tiflash-mintso-scheduler.md @@ -46,7 +46,7 @@ EXPLAIN SELECT count(*) FROM t0 a JOIN t0 b ON a.id = b.id; +--------------------------------------------+----------+--------------+---------------+----------------------------------------------------------+ ``` -例如，以上查询会在每个 TiFlash 节点中生成 2 个 MPP Task，其中 `ExchangeSender_45` 所在的 MPP Task 依赖于 `ExchangeSender_21` 所在的 MPP Task。在该查询高并发的情况下，如果调度器调度了每个查询中 `ExchangeSender_45` 所在的 MPP Task，系统就会进入死锁状态。 +例如，以上查询会在每个 TiFlash 节点中生成 2 个 MPP Task，其中执行器 `ExchangeSender_45` 所在的 MPP Task 依赖于 `ExchangeSender_21` 所在的 MPP Task。在该查询高并发的情况下，如果调度器调度了每个查询中 `ExchangeSender_45` 所在的 MPP Task，系统就会进入死锁状态。 为了避免系统陷入死锁状态，TiFlash 引入了以下两层线程的限制：