From 0748df1edd8ce14342d659d596b9188381aee353 Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Wed, 7 Aug 2024 14:43:48 +0800 Subject: [PATCH 01/12] Add temp.md --- temp.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 temp.md diff --git a/temp.md b/temp.md new file mode 100644 index 000000000000..af27ff4986a7 --- /dev/null +++ b/temp.md @@ -0,0 +1 @@ +This is a test file. \ No newline at end of file From 4b79544e13360a920e3fa0bce1ac10d0d8dcfb67 Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Wed, 7 Aug 2024 14:43:52 +0800 Subject: [PATCH 02/12] Delete temp.md --- temp.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 temp.md diff --git a/temp.md b/temp.md deleted file mode 100644 index af27ff4986a7..000000000000 --- a/temp.md +++ /dev/null @@ -1 +0,0 @@ -This is a test file. \ No newline at end of file From 9d386777c32fa0b0f0ebc140dae691dd83969718 Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Wed, 7 Aug 2024 17:51:33 +0800 Subject: [PATCH 03/12] add translation --- TOC.md | 1 + best-practices-on-public-cloud.md | 214 ++++++++++++++++++++++++++++++ 2 files changed, 215 insertions(+) create mode 100644 best-practices-on-public-cloud.md diff --git a/TOC.md b/TOC.md index 89ce0a4ceea2..52b93b1c59a1 100644 --- a/TOC.md +++ b/TOC.md @@ -241,6 +241,7 @@ - [TiFlash 性能分析方法](/tiflash-performance-tuning-methods.md) - [TiCDC 性能分析方法](/ticdc-performance-tuning-methods.md) - [延迟的拆解分析](/latency-breakdown.md) + - [在公有云上部署 TiDB 的最佳实践](/best-practices-on-public-cloud.md) - 配置调优 - [操作系统性能参数调优](/tune-operating-system.md) - [TiDB 内存调优](/configure-memory-usage.md) diff --git a/best-practices-on-public-cloud.md b/best-practices-on-public-cloud.md new file mode 100644 index 000000000000..e06469aab24c --- /dev/null +++ b/best-practices-on-public-cloud.md @@ -0,0 +1,214 @@ +--- +title: 在公有云上部署 TiDB 的最佳实践 +summary: 了解在公有云上部署 TiDB 的最佳实践。 +--- + +# 在公有云上部署 TiDB 的最佳实践 + +随着公有云基础设施的普及,越来越多的用户选择在公有云上部署和管理 TiDB。然而,要想在公有云上充分发挥 TiDB 的性能,需要关注多个方面,包括性能优化、成本控制、系统可靠性和可扩展性。 + +本文将介绍在公有云上部署 TiDB 的一系列最佳实践,例如减少 KV RocksDB 中的压缩 I/O 流量、为 Raft Engine 配置专用磁盘、优化跨可用区的流量成本、缓解 Google Cloud 上的实时迁移维护事件带来的性能影响,以及在大规模集群中调整 PD 服务器的设置。遵循这些最佳实践,可以显著提升 TiDB 在公有云上的性能、成本效率、可靠性和可扩展性。 + +## 减少 KV RocksDB 中的压缩 I/O 流量 + +[RocksDB](https://rocksdb.org/) 是 TiKV 的存储引擎,负责存储用户数据。出于成本考虑,云上提供的 EBS IO 吞吐量通常受限,因此 RocksDB 可能会表现出较高的写放大,导致磁盘吞吐量成为负载的瓶颈。随着时间的推移,待压缩的字节总量会不断增加从而触发流量控制,这表明 TiKV 缺乏足够的磁盘带宽来处理前台写入流量。 + +要缓解由磁盘吞吐量受限引起的瓶颈,你可以通过[启用 Titan](#enable-titan) 来改善性能。如果数据的平均行大小低于 512 字节,Titan 不适用,此时你可以通过[提高所有压缩级别](#increase-all-the-compression-levels) 来改善性能。 + +### 启用 Titan + +[Titan](/storage-engine/titan-overview.md) 是基于 [RocksDB](https://github.com/facebook/rocksdb) 的高性能单机 key-value 存储引擎插件。当 value 较大时,Titan 可以减少 RocksDB 中的写放大。 + +如果数据的平均行大小超过 512 字节,可以通过启用 Titan,设置 `min-blob-size` 为 `"512B"` 或 `"1KB"`,以及设置 `blob-file-compression` 为 `"zstd"` 来减少压缩 I/O 流量: + +```toml +[rocksdb.titan] +enabled = true +[rocksdb.defaultcf.titan] +min-blob-size = "1KB" +blob-file-compression = "zstd" +``` + +> **注意:** +> +> 启用 Titan 后,主键范围扫描的性能可能会略有下降。更多信息,请参考 [`min-blob-size` 对性能的影响](/storage-engine/titan-overview.md#min-blob-size-对性能的影响)。 + +### 提高所有压缩级别 + +如果数据的平均行大小低于 512 字节,你可以将默认 Column Family 的所有压缩级别设置为 `"zstd"` 来提高性能,如下所示: + +```toml +[rocksdb.defaultcf] +compression-per-level = ["zstd", "zstd", "zstd", "zstd", "zstd", "zstd", "zstd"] +``` + +## 为 Raft Engine 分配专用磁盘 + +[Raft Engine](/glossary.md#raft-engine) 在 TiKV 中扮演着类似传统数据库中预写日志(WAL)的关键角色。为了实现最佳性能和稳定性,在公有云上部署 TiDB 时,为 Raft Engine 分配专用磁盘至关重要。以下 `iostat` 显示了写密集型工作负载下 TiKV 节点的 I/O 特点。 + +``` +Device r/s rkB/s w/s wkB/s f/s aqu-sz %util +sdb 1649.00 209030.67 1293.33 304644.00 13.33 5.09 48.37 +sdd 1033.00 4132.00 1141.33 31685.33 571.00 0.94 100.00 +``` + +设备 `sdb` 用于 KV RocksDB,而 `sdd` 用于存储 Raft Engine 日志。注意,`sdd` 的 `f/s` 值明显更高,这表示该设备每秒完成的刷新请求数量更高。在 Raft Engine 中,当 batch 写入被标记为同步时,batch leader 将在写入后调用 `fdatasync()`,以确保缓存的数据被实际写入存储。通过为 Raft Engine 分配专用磁盘,TiKV 可以减少请求的平均队列长度,从而确保写入延迟的最优化和稳定性。 + +不同的云服务提供商提供了多种性能各异的磁盘类型(例如,IOPS 和 MBPS)。根据你的工作负载,选择合适的云服务提供商、磁盘类型和磁盘大小非常重要。 + +### 在公有云上为 Raft Engine 选择合适的磁盘 + +本节介绍了在不同公有云上为 Raft Engine 选择合适磁盘的最佳实践,并根据性能需求,推荐了两种磁盘类型。 + +#### 中端磁盘 + +在不同公有云上,推荐的中端磁盘如下: + +- AWS:推荐使用 [gp3](https://aws.amazon.com/ebs/general-purpose/)。gp3 volume 提供 3000 IOPS 和 125 MB/s 的免费吞吐量配额,无论 volume 大小如何,这通常足以满足 Raft Engine 的需求。 + +- Google Cloud:推荐使用 [pd-ssd](https://cloud.google.com/compute/docs/disks#disk-types/)。IOPS 和 MBPS 随分配的磁盘大小而不同。为了满足性能需求,建议为 Raft Engine 分配 200 GB 的空间。尽管 Raft Engine 并不需要这么大的空间,但这样可以确保最佳性能。 + +- Azure:推荐使用 [Premium SSD v2](https://learn.microsoft.com/en-us/azure/virtual-machines/disks-types#premium-ssd-v2)。类似于 AWS gp3,Premium SSD v2 提供 3000 IOPS 和 125 MB/s 的免费吞吐量配额,无论 volume 大小如何,这通常足以满足 Raft Engine 的需求。 + +#### 高端磁盘 + +如果你希望 Raft Engine 有更低的延迟,可以考虑使用高端磁盘。在不同公有云上,推荐的高端磁盘如下: + +- AWS:推荐使用 [io2](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-volume-types.html)。磁盘大小和 IOPS 可以根据你的具体需求进行配置。 + +- Google Cloud:推荐使用 [pd-extreme](https://cloud.google.com/compute/docs/disks#disk-types/)。磁盘大小、IOPS 和 MBPS 可以进行配置,但仅在 CPU 核数超过 64 的实例上可用。 + +- Azure:推荐使用 [ultra disk](https://learn.microsoft.com/en-us/azure/virtual-machines/disks-types#ultra-disks)。磁盘大小、IOPS 和 MBPS 可以根据你的具体需求进行配置。 + +### 示例 1:在 AWS 上运行社交网络工作负载 + +在 AWS 上,一个 20 GB 的 [gp3](https://aws.amazon.com/ebs/general-purpose/) 卷提供 3000 IOPS 和 125 MBPS/s 的吞吐量。 + +为写密集型的社交网络应用分配一个专用的 20 GB [gp3](https://aws.amazon.com/ebs/general-purpose/) Raft Engine 磁盘后,性能提升如下,而成本仅增加 0.4%: + +- QPS(每秒查询数)提高 17.5% +- 插入语句的平均延迟降低 18.7% +- 插入语句的 p99 延迟降低 45.6% + +| 指标 | 共享 Raft Engine 磁盘 | 专用 Raft Engine 磁盘 | 差异 (%) | +| ------------- | ------------- | ------------- | ------------- | +| QPS (K/s) | 8.0 | 9.4 | 17.5 | +| 平均插入延迟 (ms) | 11.3 | 9.2 | -18.7 | +| p99 插入延迟 (ms) | 29.4 | 16.0 | -45.6 | + +### 示例 2:在 Azure 上运行 TPC-C/Sysbench 工作负载 + +在 Azure 上,为 Raft Engine 分配一个专用的 32 GB [ultra disk](https://learn.microsoft.com/en-us/azure/virtual-machines/disks-types#ultra-disks) 后,性能提升如下: + +- Sysbench `oltp_read_write` 工作负载:QPS 提高 17.8%,平均延迟降低 15.6%。 +- TPC-C 工作负载:QPS 提高 27.6%,平均延迟降低 23.1%。 + +| 指标 | 工作负载 | 共享 Raft Engine 磁盘 | 专用 Raft Engine 磁盘 | 差异 (%) | +| ------------- | ------------- | ------------- | ------------- | ------------- | +| QPS (K/s) | Sysbench `oltp_read_write` | 60.7 | 71.5 | 17.8 | +| QPS (K/s) | TPC-C | 23.9 | 30.5 | 27.6 | +| 平均延迟 (ms) | Sysbench `oltp_read_write` | 4.5 | 3.8 | -15.6 | +| 平均延迟 (ms) | TPC-C | 3.9 | 3.0 | -23.1 | + +### 示例 3:在 Google Cloud 上为 TiKV 配置专用的 pd-ssd 磁盘 + +以下 TiKV 配置示例展示了如何为由 [TiDB Operator](https://docs.pingcap.com/tidb-in-kubernetes/stable) 部署到 Google Cloud 上的集群额外配置一个 512 GB 的 [pd-ssd](https://cloud.google.com/compute/docs/disks#disk-types/) 磁盘,其中 `raft-engine.dir` 的配置为将 Raft Engine 日志存储在该磁盘上。 + +``` +tikv: + config: | + [raft-engine] + dir = "/var/lib/raft-pv-ssd/raft-engine" + enable = true + enable-log-recycle = true + requests: + storage: 4Ti + storageClassName: pd-ssd + storageVolumes: + - mountPath: /var/lib/raft-pv-ssd + name: raft-pv-ssd + storageSize: 512Gi +``` + +## 降低跨可用区网络流量的成本 + +在跨多个可用区部署 TiDB 时,跨可用区的数据传输可能会增加部署成本。为了降低这些成本,减少跨可用区的网络流量非常重要。 + +要减少跨可用区的读取流量,你可以启用 [Follower Read 功能](/follower-read.md),该功能允许 TiDB 优先选择在同一可用区内的副本进行读取。要启用该功能,请将 [`tidb_replica_read`](/system-variables.md#tidb_replica_read-new-in-v40) 变量设置为 `closest-replicas` 或 `closest-adaptive`。 + +要减少 TiKV 实例中跨可用区的写入流量,你可以启用 gRPC 压缩功能,该功能在网络传输数据之前会对其进行压缩。以下配置示例展示了如何为 TiKV 启用 gzip gRPC 压缩: + +``` +server_configs: + tikv: + server.grpc-compression-type: gzip +``` + +要减少 TiFlash MPP 任务中 data shuffle 所带来的网络流量,建议在同一可用区内部署多个 TiFlash 实例。从 v6.6.0 开始,[Exchange 数据压缩](/explain-mpp.md#mpp-version-和-exchange-数据压缩)功能默认启用,以减少了 MPP data shuffle 导致的网络流量。 + +## 缓解 Google Cloud 上的实时迁移维护事件带来的性能影响 + +Google Cloud 的 [实时迁移功能](https://cloud.google.com/compute/docs/instances/live-migration-process)允许虚拟机在主机之间无缝迁移而不会导致停机。然而,这些迁移事件虽不频繁,但可能会显著影响虚拟机的性能,其中包括那些在 TiDB 集群中运行的虚拟机。在这些事件发生期间,受影响的虚拟机可能会出现性能下降,导致 TiDB 集群中的查询处理时间增加。 + +为了检测 Google Cloud 启动的实时迁移事件并减轻这些事件对性能的影响,TiDB 提供了一个[监控脚本](https://github.com/PingCAP-QE/tidb-google-maintenance)(该脚本基于 Google 元数据[示例](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/compute/metadata/main.py))。你可以将此脚本部署在 TiDB、TiKV 和 PD 节点上,以检测维护事件。当检测到维护事件时,该脚本可以自动采取以下适当措施,以尽量减少中断并优化集群行为: + +- TiDB:通过下线 TiDB 节点并删除 TiDB pod 来处理维护事件(假设 TiDB 实例的节点池设置为自动扩展且专用于 TiDB)。节点上运行的其他 pod 可能会遇到中断,而被隔离的节点将会被自动扩展程序回收。 +- TiKV:在维护期间驱逐受影响的 TiKV 存储上的 Leader。 +- PD:如果当前 PD 实例是 PD Leader,则会重新分配 Leader。 + +需要注意的是,此监控脚本是专门为 [TiDB Operator](https://docs.pingcap.com/tidb-in-kubernetes/dev/tidb-operator-overview) 部署的 TiDB 集群设计的,TiDB Operator 为 Kubernetes 环境中的 TiDB 提供了增强的管理功能。 + +通过使用该监控脚本,并在维护事件期间采取必要的措施,TiDB 集群可以更好地应对 Google Cloud 上的实时迁移事件,确保查询处理和响应时间的平稳运行。 + +## 为具有高 QPS 的大规模 TiDB 集群优化 PD 性能 + +在 TiDB 集群中,一个活跃的 Placement Driver (PD) 服务器承担着许多关键任务,例如处理提供 TSO (Timestamp Oracle) 和处理请求。然而,依赖单个活跃 PD 服务器可能会限制 TiDB 集群的扩展性。 + +### PD 限制的特征 + +以下图表展示了一个由三个 PD 服务器组成的大规模 TiDB 集群的特征,其中每个 PD 服务器均配置了 56 核的 CPU。可以看出,当每秒查询数(QPS)超过 100 万次且每秒 TSO 请求数超过 162,000 次时,CPU 利用率达到约 4,600%。这一高 CPU 利用率表明 PD Leader 的负载已经相当高且耗尽可用的 CPU 资源。 + +![pd-server-cpu](/media/performance/public-cloud-best-practice/baseline_cpu.png) +![pd-server-metrics](/media/performance/public-cloud-best-practice/baseline_metrics.png) + +### 优化 PD 性能 + +为了解决 PD 服务器的高 CPU 利用率问题,可以进行以下调整: + +#### 调整 PD 配置 + +[`tso-update-physical-interval`](/pd-configuration-file.md#tso-update-physical-interval):此参数控制 PD 服务器更新物理 TSO batch 的间隔。通过缩短此间隔,PD 服务器可以更频繁地分配 TSO batch,从而减少下一次分配的等待时间。 + +``` +tso-update-physical-interval = "10ms" # 默认值: 50ms +``` + +#### 调整 TiDB 全局变量 + +除了 PD 配置外,启用 TSO 客户端攒批操作的等待功能可以进一步优化 TSO 客户端的行为。要启用此功能,可以将全局变量 [`tidb_tso_client_batch_max_wait_time`](/system-variables.md#tidb_tso_client_batch_max_wait_time-new-in-v530) 设置为非零值: + +``` +set global tidb_tso_client_batch_max_wait_time = 2; # 默认值: 0 +``` + +#### 调整 TiKV 配置 + +为了减少 Region 数量并降低系统的心跳开销,建议将 TiKV 配置中的 Region 大小从 `96MB` 增加到 `256MB`。 + +``` +[coprocessor] + region-split-size = "256MB" +``` + +## 调整后的效果 + +调整后的效果如下: + +- 每秒 TSO 请求数减少到 64,800 次。 +- CPU 利用率显著降低,从约 4,600% 降低到 1,400%。 +- `PD server TSO handle time` 的 P999 值从 2ms 降低到 0.5ms。 + +以上性能提升表明,这些调整措施成功地降低了 PD 服务器的 CPU 利用率,同时保持了稳定的 TSO 处理性能。 + +![pd-server-cpu](/media/performance/public-cloud-best-practice/after_tuning_cpu.png) +![pd-server-metrics](/media/performance/public-cloud-best-practice/after_tuning_metrics.png) \ No newline at end of file From c7e2bb6ae0125b9431a33da6796f1a869b49fa11 Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Thu, 8 Aug 2024 18:25:00 +0800 Subject: [PATCH 04/12] Update best-practices-on-public-cloud.md --- best-practices-on-public-cloud.md | 62 +++++++++++++++---------------- 1 file changed, 31 insertions(+), 31 deletions(-) diff --git a/best-practices-on-public-cloud.md b/best-practices-on-public-cloud.md index e06469aab24c..d7ec890930b9 100644 --- a/best-practices-on-public-cloud.md +++ b/best-practices-on-public-cloud.md @@ -7,13 +7,13 @@ summary: 了解在公有云上部署 TiDB 的最佳实践。 随着公有云基础设施的普及,越来越多的用户选择在公有云上部署和管理 TiDB。然而,要想在公有云上充分发挥 TiDB 的性能,需要关注多个方面,包括性能优化、成本控制、系统可靠性和可扩展性。 -本文将介绍在公有云上部署 TiDB 的一系列最佳实践,例如减少 KV RocksDB 中的压缩 I/O 流量、为 Raft Engine 配置专用磁盘、优化跨可用区的流量成本、缓解 Google Cloud 上的实时迁移维护事件带来的性能影响,以及在大规模集群中调整 PD 服务器的设置。遵循这些最佳实践,可以显著提升 TiDB 在公有云上的性能、成本效率、可靠性和可扩展性。 +本文介绍在公有云上部署 TiDB 的一系列最佳实践,例如减少 KV RocksDB 中的压缩 I/O 流量、为 Raft Engine 配置专用磁盘、优化跨可用区的流量成本、缓解 Google Cloud 上实时迁移维护事件带来的性能影响,以及对大规模集群中的 PD Server 进行性能调优。遵循这些最佳实践,可以显著提升 TiDB 在公有云上的性能、成本效率、可靠性和可扩展性。 ## 减少 KV RocksDB 中的压缩 I/O 流量 -[RocksDB](https://rocksdb.org/) 是 TiKV 的存储引擎,负责存储用户数据。出于成本考虑,云上提供的 EBS IO 吞吐量通常受限,因此 RocksDB 可能会表现出较高的写放大,导致磁盘吞吐量成为负载的瓶颈。随着时间的推移,待压缩的字节总量会不断增加从而触发流量控制,这表明 TiKV 缺乏足够的磁盘带宽来处理前台写入流量。 +[RocksDB](https://rocksdb.org/) 是 TiKV 的存储引擎,负责存储用户数据。出于成本考虑,云上提供的 EBS IO 吞吐量通常比较有限,因此 RocksDB 可能会表现出较高的写放大,导致磁盘吞吐量成为负载的瓶颈。随着时间的推移,待压缩的字节总量会不断增加从而触发流量控制,这意味着此时 TiKV 缺乏足够的磁盘带宽来处理前台写入流量。 -要缓解由磁盘吞吐量受限引起的瓶颈,你可以通过[启用 Titan](#enable-titan) 来改善性能。如果数据的平均行大小低于 512 字节,Titan 不适用,此时你可以通过[提高所有压缩级别](#increase-all-the-compression-levels) 来改善性能。 +要缓解由磁盘吞吐量受限引起的瓶颈,你可以通过[启用 Titan](#enable-titan) 来改善性能。如果数据的平均行大小低于 512 字节,Titan 并不适用,此时你可以通过[提高所有压缩级别](#increase-all-the-compression-levels) 来改善性能。 ### 启用 Titan @@ -44,7 +44,7 @@ compression-per-level = ["zstd", "zstd", "zstd", "zstd", "zstd", "zstd", "zstd"] ## 为 Raft Engine 分配专用磁盘 -[Raft Engine](/glossary.md#raft-engine) 在 TiKV 中扮演着类似传统数据库中预写日志(WAL)的关键角色。为了实现最佳性能和稳定性,在公有云上部署 TiDB 时,为 Raft Engine 分配专用磁盘至关重要。以下 `iostat` 显示了写密集型工作负载下 TiKV 节点的 I/O 特点。 +在 TiKV 中,[Raft Engine](/glossary.md#raft-engine) 扮演着类似传统数据库中预写日志(WAL)的关键角色。为了达到最佳性能和稳定性,在公有云上部署 TiDB 时,为 Raft Engine 分配专用磁盘至关重要。以下 `iostat` 显示了写密集型工作负载下 TiKV 节点的 I/O 特征。 ``` Device r/s rkB/s w/s wkB/s f/s aqu-sz %util @@ -52,39 +52,39 @@ sdb 1649.00 209030.67 1293.33 304644.00 13.33 5.09 48.37 sdd 1033.00 4132.00 1141.33 31685.33 571.00 0.94 100.00 ``` -设备 `sdb` 用于 KV RocksDB,而 `sdd` 用于存储 Raft Engine 日志。注意,`sdd` 的 `f/s` 值明显更高,这表示该设备每秒完成的刷新请求数量更高。在 Raft Engine 中,当 batch 写入被标记为同步时,batch leader 将在写入后调用 `fdatasync()`,以确保缓存的数据被实际写入存储。通过为 Raft Engine 分配专用磁盘,TiKV 可以减少请求的平均队列长度,从而确保写入延迟的最优化和稳定性。 +设备 `sdb` 用于 KV RocksDB,而 `sdd` 用于存储 Raft Engine 日志。注意,`sdd` 的 `f/s` 值明显更高,这表示该设备每秒完成的刷新请求数量更高。在 Raft Engine 中,当 batch 中的写入操作被标记为同步时,batch leader 将在写入后调用 `fdatasync()`,以确保缓存的数据被写入存储。通过为 Raft Engine 分配专用磁盘,TiKV 可以减少请求的平均队列长度,从而确保写入延迟的最优化和稳定性。 -不同的云服务提供商提供了多种性能各异的磁盘类型(例如,IOPS 和 MBPS)。根据你的工作负载,选择合适的云服务提供商、磁盘类型和磁盘大小非常重要。 +不同的云服务提供商提供了多种性能各异的磁盘类型(例如,IOPS 和 MBPS)。根据你的负载情况,选择合适的云服务提供商、磁盘类型和磁盘大小非常重要。 ### 在公有云上为 Raft Engine 选择合适的磁盘 -本节介绍了在不同公有云上为 Raft Engine 选择合适磁盘的最佳实践,并根据性能需求,推荐了两种磁盘类型。 +本节介绍在不同公有云上为 Raft Engine 选择合适磁盘的最佳实践。根据性能需求,推荐使用中端或高端这两种磁盘类型。 #### 中端磁盘 -在不同公有云上,推荐的中端磁盘如下: +对于不同的公有云,推荐的中端磁盘如下: -- AWS:推荐使用 [gp3](https://aws.amazon.com/ebs/general-purpose/)。gp3 volume 提供 3000 IOPS 和 125 MB/s 的免费吞吐量配额,无论 volume 大小如何,这通常足以满足 Raft Engine 的需求。 +- AWS:推荐使用 [gp3](https://aws.amazon.com/cn/ebs/general-purpose/) 卷。gp3 卷提供 3000 IOPS 和 125 MB/s 的免费吞吐量,无论为 gp3 卷的大小如何选择,通常都足以满足 Raft Engine 的需求。 -- Google Cloud:推荐使用 [pd-ssd](https://cloud.google.com/compute/docs/disks#disk-types/)。IOPS 和 MBPS 随分配的磁盘大小而不同。为了满足性能需求,建议为 Raft Engine 分配 200 GB 的空间。尽管 Raft Engine 并不需要这么大的空间,但这样可以确保最佳性能。 +- Google Cloud:推荐使用 [pd-ssd](https://cloud.google.com/compute/docs/disks?hl=zh-cn#disk-types/)。IOPS 和 MBPS 随分配的磁盘大小而不同。为了满足性能需求,建议为 Raft Engine 分配 200 GB 的空间。尽管 Raft Engine 并不需要这么大的空间,但这样可以确保最佳的性能。 -- Azure:推荐使用 [Premium SSD v2](https://learn.microsoft.com/en-us/azure/virtual-machines/disks-types#premium-ssd-v2)。类似于 AWS gp3,Premium SSD v2 提供 3000 IOPS 和 125 MB/s 的免费吞吐量配额,无论 volume 大小如何,这通常足以满足 Raft Engine 的需求。 +- Azure:推荐使用 [Premium SSD v2](https://learn.microsoft.com/zh-cn/azure/virtual-machines/disks-types#premium-ssd-v2)。类似于 AWS gp3,Premium SSD v2 提供 3000 IOPS 和 125 MB/s 的免费吞吐量,无论 Premium SSD v2 的大小如何选择,通常都足以满足 Raft Engine 的需求。 #### 高端磁盘 -如果你希望 Raft Engine 有更低的延迟,可以考虑使用高端磁盘。在不同公有云上,推荐的高端磁盘如下: +如果你希望 Raft Engine 有更低的延迟,可以考虑使用高端磁盘。对于不同的公有云,推荐的高端磁盘如下: -- AWS:推荐使用 [io2](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-volume-types.html)。磁盘大小和 IOPS 可以根据你的具体需求进行配置。 +- AWS:推荐使用 [io2](https://docs.aws.amazon.com/zh_cn/ebs/latest/userguide/ebs-volume-types.html)。磁盘大小和 IOPS 可以根据你的具体需求进行配置。 -- Google Cloud:推荐使用 [pd-extreme](https://cloud.google.com/compute/docs/disks#disk-types/)。磁盘大小、IOPS 和 MBPS 可以进行配置,但仅在 CPU 核数超过 64 的实例上可用。 +- Google Cloud:推荐使用 [pd-extreme](https://cloud.google.com/compute/docs/disks?hl=zh-cn#disk-types/)。磁盘大小、IOPS 和 MBPS 可以进行配置,但仅在 CPU 核数超过 64 的实例上可用。 -- Azure:推荐使用 [ultra disk](https://learn.microsoft.com/en-us/azure/virtual-machines/disks-types#ultra-disks)。磁盘大小、IOPS 和 MBPS 可以根据你的具体需求进行配置。 +- Azure:推荐使用 [ultra disk](https://learn.microsoft.com/zh-cn/azure/virtual-machines/disks-types#ultra-disks)。磁盘大小、IOPS 和 MBPS 可以根据你的具体需求进行配置。 ### 示例 1:在 AWS 上运行社交网络工作负载 -在 AWS 上,一个 20 GB 的 [gp3](https://aws.amazon.com/ebs/general-purpose/) 卷提供 3000 IOPS 和 125 MBPS/s 的吞吐量。 +在 AWS 上,一个 20 GB 的 [gp3](https://aws.amazon.com/cn/ebs/general-purpose/) 卷提供 3000 IOPS 和 125 MBPS/s 的吞吐量。 -为写密集型的社交网络应用分配一个专用的 20 GB [gp3](https://aws.amazon.com/ebs/general-purpose/) Raft Engine 磁盘后,性能提升如下,而成本仅增加 0.4%: +为一个写密集型的社交网络应用分配一个专用的 20 GB [gp3](https://aws.amazon.com/cn/ebs/general-purpose/) Raft Engine 磁盘后,性能提升如下,而成本仅增加 0.4%: - QPS(每秒查询数)提高 17.5% - 插入语句的平均延迟降低 18.7% @@ -98,7 +98,7 @@ sdd 1033.00 4132.00 1141.33 31685.33 571.00 0.94 100.00 ### 示例 2:在 Azure 上运行 TPC-C/Sysbench 工作负载 -在 Azure 上,为 Raft Engine 分配一个专用的 32 GB [ultra disk](https://learn.microsoft.com/en-us/azure/virtual-machines/disks-types#ultra-disks) 后,性能提升如下: +在 Azure 上,为 Raft Engine 分配一个专用的 32 GB [ultra disk](https://learn.microsoft.com/zh-cn/azure/virtual-machines/disks-types#ultra-disks) 后,性能提升如下: - Sysbench `oltp_read_write` 工作负载:QPS 提高 17.8%,平均延迟降低 15.6%。 - TPC-C 工作负载:QPS 提高 27.6%,平均延迟降低 23.1%。 @@ -112,7 +112,7 @@ sdd 1033.00 4132.00 1141.33 31685.33 571.00 0.94 100.00 ### 示例 3:在 Google Cloud 上为 TiKV 配置专用的 pd-ssd 磁盘 -以下 TiKV 配置示例展示了如何为由 [TiDB Operator](https://docs.pingcap.com/tidb-in-kubernetes/stable) 部署到 Google Cloud 上的集群额外配置一个 512 GB 的 [pd-ssd](https://cloud.google.com/compute/docs/disks#disk-types/) 磁盘,其中 `raft-engine.dir` 的配置为将 Raft Engine 日志存储在该磁盘上。 +以下 TiKV 配置示例展示了如何为由 [TiDB Operator](https://docs.pingcap.com/tidb-in-kubernetes/stable) 部署到 Google Cloud 上的集群额外配置一个 512 GB 的 [pd-ssd](https://cloud.google.com/compute/docs/disks?hl=zh-cn#disk-types/) 磁盘,其中 `raft-engine.dir` 的配置用于将 Raft Engine 日志存储在该磁盘上。 ``` tikv: @@ -134,7 +134,7 @@ tikv: 在跨多个可用区部署 TiDB 时,跨可用区的数据传输可能会增加部署成本。为了降低这些成本,减少跨可用区的网络流量非常重要。 -要减少跨可用区的读取流量,你可以启用 [Follower Read 功能](/follower-read.md),该功能允许 TiDB 优先选择在同一可用区内的副本进行读取。要启用该功能,请将 [`tidb_replica_read`](/system-variables.md#tidb_replica_read-new-in-v40) 变量设置为 `closest-replicas` 或 `closest-adaptive`。 +要减少跨可用区的读取流量,你可以启用 [Follower Read 功能](/follower-read.md),该功能允许 TiDB 优先选择在同一可用区内的副本进行读取。要启用该功能,请将 [`tidb_replica_read`](/system-variables.md#tidb_replica_read-从-v40-版本开始引入) 变量设置为 `closest-replicas` 或 `closest-adaptive`。 要减少 TiKV 实例中跨可用区的写入流量,你可以启用 gRPC 压缩功能,该功能在网络传输数据之前会对其进行压缩。以下配置示例展示了如何为 TiKV 启用 gzip gRPC 压缩: @@ -144,13 +144,13 @@ server_configs: server.grpc-compression-type: gzip ``` -要减少 TiFlash MPP 任务中 data shuffle 所带来的网络流量,建议在同一可用区内部署多个 TiFlash 实例。从 v6.6.0 开始,[Exchange 数据压缩](/explain-mpp.md#mpp-version-和-exchange-数据压缩)功能默认启用,以减少了 MPP data shuffle 导致的网络流量。 +要减少 TiFlash MPP 任务中数据交换(data shuffle 过程)所带来的网络流量,建议在同一可用区内部署多个 TiFlash 实例。从 v6.6.0 开始,[Exchange 数据压缩](/explain-mpp.md#mpp-version-和-exchange-数据压缩)功能默认启用,以减少了 MPP 数据交换导致的网络流量。 ## 缓解 Google Cloud 上的实时迁移维护事件带来的性能影响 -Google Cloud 的 [实时迁移功能](https://cloud.google.com/compute/docs/instances/live-migration-process)允许虚拟机在主机之间无缝迁移而不会导致停机。然而,这些迁移事件虽不频繁,但可能会显著影响虚拟机的性能,其中包括那些在 TiDB 集群中运行的虚拟机。在这些事件发生期间,受影响的虚拟机可能会出现性能下降,导致 TiDB 集群中的查询处理时间增加。 +Google Cloud 的 [实时迁移功能](https://cloud.google.com/compute/docs/instances/live-migration-process?hl=zh-cn)允许虚拟机在主机之间无缝迁移而不会导致停机。然而,这些迁移事件虽不频繁,但可能会显著影响虚拟机的性能,其中包括那些在 TiDB 集群中运行的虚拟机。在这些事件发生期间,受影响的虚拟机可能会出现性能下降,导致 TiDB 集群中的查询处理时间增加。 -为了检测 Google Cloud 启动的实时迁移事件并减轻这些事件对性能的影响,TiDB 提供了一个[监控脚本](https://github.com/PingCAP-QE/tidb-google-maintenance)(该脚本基于 Google 元数据[示例](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/compute/metadata/main.py))。你可以将此脚本部署在 TiDB、TiKV 和 PD 节点上,以检测维护事件。当检测到维护事件时,该脚本可以自动采取以下适当措施,以尽量减少中断并优化集群行为: +为了检测 Google Cloud 发起的实时迁移事件并减轻这些事件对性能的影响,TiDB 提供了一个[监控脚本](https://github.com/PingCAP-QE/tidb-google-maintenance)(该脚本基于 Google 元数据[示例](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/compute/metadata/main.py))。你可以将此脚本部署在 TiDB、TiKV 和 PD 节点上,以检测维护事件。当检测到维护事件时,该脚本将自动采取以下适当措施,以尽量减少中断并优化集群行为: - TiDB:通过下线 TiDB 节点并删除 TiDB pod 来处理维护事件(假设 TiDB 实例的节点池设置为自动扩展且专用于 TiDB)。节点上运行的其他 pod 可能会遇到中断,而被隔离的节点将会被自动扩展程序回收。 - TiKV:在维护期间驱逐受影响的 TiKV 存储上的 Leader。 @@ -158,26 +158,26 @@ Google Cloud 的 [实时迁移功能](https://cloud.google.com/compute/docs/inst 需要注意的是,此监控脚本是专门为 [TiDB Operator](https://docs.pingcap.com/tidb-in-kubernetes/dev/tidb-operator-overview) 部署的 TiDB 集群设计的,TiDB Operator 为 Kubernetes 环境中的 TiDB 提供了增强的管理功能。 -通过使用该监控脚本,并在维护事件期间采取必要的措施,TiDB 集群可以更好地应对 Google Cloud 上的实时迁移事件,确保查询处理和响应时间的平稳运行。 +通过使用该监控脚本,并在维护事件期间采取必要的措施,TiDB 集群可以更好地应对 Google Cloud 上的实时迁移事件,确保对查询处理和响应时间的影响最小以及系统的平稳运行。 ## 为具有高 QPS 的大规模 TiDB 集群优化 PD 性能 -在 TiDB 集群中,一个活跃的 Placement Driver (PD) 服务器承担着许多关键任务,例如处理提供 TSO (Timestamp Oracle) 和处理请求。然而,依赖单个活跃 PD 服务器可能会限制 TiDB 集群的扩展性。 +在 TiDB 集群中,一个活跃的 Placement Driver (PD) Server 承担着许多关键任务,例如处理提供 TSO (Timestamp Oracle) 和处理请求。然而,依赖单个活跃 PD Server 可能会限制 TiDB 集群的扩展性。 ### PD 限制的特征 -以下图表展示了一个由三个 PD 服务器组成的大规模 TiDB 集群的特征,其中每个 PD 服务器均配置了 56 核的 CPU。可以看出,当每秒查询数(QPS)超过 100 万次且每秒 TSO 请求数超过 162,000 次时,CPU 利用率达到约 4,600%。这一高 CPU 利用率表明 PD Leader 的负载已经相当高且耗尽可用的 CPU 资源。 +以下图表展示了一个由三个 PD Server 组成的大规模 TiDB 集群的特征,其中每个 PD Server 均配置了 56 核的 CPU。可以看出,当每秒查询数(QPS)超过 100 万次且每秒 TSO 请求数超过 162,000 次时,CPU 利用率达到约 4,600%。这一高 CPU 利用率表明 PD Leader 的负载已经相当高且可用的 CPU 资源即将耗尽。 ![pd-server-cpu](/media/performance/public-cloud-best-practice/baseline_cpu.png) ![pd-server-metrics](/media/performance/public-cloud-best-practice/baseline_metrics.png) ### 优化 PD 性能 -为了解决 PD 服务器的高 CPU 利用率问题,可以进行以下调整: +为了解决 PD Server 的高 CPU 利用率问题,可以进行以下调整: #### 调整 PD 配置 -[`tso-update-physical-interval`](/pd-configuration-file.md#tso-update-physical-interval):此参数控制 PD 服务器更新物理 TSO batch 的间隔。通过缩短此间隔,PD 服务器可以更频繁地分配 TSO batch,从而减少下一次分配的等待时间。 +[`tso-update-physical-interval`](/pd-configuration-file.md#tso-update-physical-interval):此参数控制 PD Server 更新物理 TSO batch 的间隔。通过缩短此间隔,PD Server 可以更频繁地分配 TSO batch,从而减少下一次分配的等待时间。 ``` tso-update-physical-interval = "10ms" # 默认值: 50ms @@ -185,10 +185,10 @@ tso-update-physical-interval = "10ms" # 默认值: 50ms #### 调整 TiDB 全局变量 -除了 PD 配置外,启用 TSO 客户端攒批操作的等待功能可以进一步优化 TSO 客户端的行为。要启用此功能,可以将全局变量 [`tidb_tso_client_batch_max_wait_time`](/system-variables.md#tidb_tso_client_batch_max_wait_time-new-in-v530) 设置为非零值: +除了 PD 配置外,启用 TSO 客户端攒批操作的等待功能可以进一步优化 TSO 客户端的行为。要启用此功能,可以将全局变量 [`tidb_tso_client_batch_max_wait_time`](/system-variables.md#tidb_tso_client_batch_max_wait_time-从-v530-版本开始引入) 设置为非零值: ``` -set global tidb_tso_client_batch_max_wait_time = 2; # 默认值: 0 +set global tidb_tso_client_batch_max_wait_time = 2; # 默认值为 0 ``` #### 调整 TiKV 配置 @@ -208,7 +208,7 @@ set global tidb_tso_client_batch_max_wait_time = 2; # 默认值: 0 - CPU 利用率显著降低,从约 4,600% 降低到 1,400%。 - `PD server TSO handle time` 的 P999 值从 2ms 降低到 0.5ms。 -以上性能提升表明,这些调整措施成功地降低了 PD 服务器的 CPU 利用率,同时保持了稳定的 TSO 处理性能。 +以上性能提升表明,这些调整措施成功地降低了 PD Server 的 CPU 利用率,同时保持了稳定的 TSO 处理性能。 ![pd-server-cpu](/media/performance/public-cloud-best-practice/after_tuning_cpu.png) ![pd-server-metrics](/media/performance/public-cloud-best-practice/after_tuning_metrics.png) \ No newline at end of file From b8d01b102396012f06cd74813eb88526afa633f2 Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Thu, 8 Aug 2024 18:27:51 +0800 Subject: [PATCH 05/12] Update best-practices-on-public-cloud.md --- best-practices-on-public-cloud.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/best-practices-on-public-cloud.md b/best-practices-on-public-cloud.md index d7ec890930b9..8fbb98505ddb 100644 --- a/best-practices-on-public-cloud.md +++ b/best-practices-on-public-cloud.md @@ -64,7 +64,7 @@ sdd 1033.00 4132.00 1141.33 31685.33 571.00 0.94 100.00 对于不同的公有云,推荐的中端磁盘如下: -- AWS:推荐使用 [gp3](https://aws.amazon.com/cn/ebs/general-purpose/) 卷。gp3 卷提供 3000 IOPS 和 125 MB/s 的免费吞吐量,无论为 gp3 卷的大小如何选择,通常都足以满足 Raft Engine 的需求。 +- AWS:推荐使用 [gp3](https://aws.amazon.com/cn/ebs/general-purpose/) 卷。gp3 卷提供 3000 IOPS 和 125 MB/s 的免费吞吐量,无论 gp3 卷的大小如何选择,通常都足以满足 Raft Engine 的需求。 - Google Cloud:推荐使用 [pd-ssd](https://cloud.google.com/compute/docs/disks?hl=zh-cn#disk-types/)。IOPS 和 MBPS 随分配的磁盘大小而不同。为了满足性能需求,建议为 Raft Engine 分配 200 GB 的空间。尽管 Raft Engine 并不需要这么大的空间,但这样可以确保最佳的性能。 From e3bbeafaddeabbbbe55eece18964589ca8918ae1 Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Thu, 8 Aug 2024 18:33:17 +0800 Subject: [PATCH 06/12] Update best-practices-on-public-cloud.md --- best-practices-on-public-cloud.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/best-practices-on-public-cloud.md b/best-practices-on-public-cloud.md index 8fbb98505ddb..b5b9b4c7563d 100644 --- a/best-practices-on-public-cloud.md +++ b/best-practices-on-public-cloud.md @@ -144,7 +144,7 @@ server_configs: server.grpc-compression-type: gzip ``` -要减少 TiFlash MPP 任务中数据交换(data shuffle 过程)所带来的网络流量,建议在同一可用区内部署多个 TiFlash 实例。从 v6.6.0 开始,[Exchange 数据压缩](/explain-mpp.md#mpp-version-和-exchange-数据压缩)功能默认启用,以减少了 MPP 数据交换导致的网络流量。 +要减少 TiFlash MPP 任务中数据交换(data shuffle 过程)所带来的网络流量,建议在同一可用区内部署多个 TiFlash 实例。从 v6.6.0 开始,[Exchange 数据压缩](/explain-mpp.md#mpp-version-和-exchange-数据压缩)功能默认启用,以减少 MPP 数据交换导致的网络流量。 ## 缓解 Google Cloud 上的实时迁移维护事件带来的性能影响 From 3bf852f7cef02c3be5e824866523a1e7fb77398b Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Mon, 12 Aug 2024 10:47:05 +0800 Subject: [PATCH 07/12] Apply suggestions from code review --- best-practices-on-public-cloud.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/best-practices-on-public-cloud.md b/best-practices-on-public-cloud.md index b5b9b4c7563d..90b2ab9047b9 100644 --- a/best-practices-on-public-cloud.md +++ b/best-practices-on-public-cloud.md @@ -7,11 +7,11 @@ summary: 了解在公有云上部署 TiDB 的最佳实践。 随着公有云基础设施的普及,越来越多的用户选择在公有云上部署和管理 TiDB。然而,要想在公有云上充分发挥 TiDB 的性能,需要关注多个方面,包括性能优化、成本控制、系统可靠性和可扩展性。 -本文介绍在公有云上部署 TiDB 的一系列最佳实践,例如减少 KV RocksDB 中的压缩 I/O 流量、为 Raft Engine 配置专用磁盘、优化跨可用区的流量成本、缓解 Google Cloud 上实时迁移维护事件带来的性能影响,以及对大规模集群中的 PD Server 进行性能调优。遵循这些最佳实践,可以显著提升 TiDB 在公有云上的性能、成本效率、可靠性和可扩展性。 +本文介绍在公有云上部署 TiDB 的一系列最佳实践,例如减少 KV RocksDB 中的 compaction I/O 流量、为 Raft Engine 配置专用磁盘、优化跨可用区的流量成本、缓解 Google Cloud 上实时迁移维护事件带来的性能影响,以及对大规模集群中的 PD Server 进行性能调优。遵循这些最佳实践,可以显著提升 TiDB 在公有云上的性能、成本效率、可靠性和可扩展性。 -## 减少 KV RocksDB 中的压缩 I/O 流量 +## 减少 KV RocksDB 中的 compaction I/O 流量 -[RocksDB](https://rocksdb.org/) 是 TiKV 的存储引擎,负责存储用户数据。出于成本考虑,云上提供的 EBS IO 吞吐量通常比较有限,因此 RocksDB 可能会表现出较高的写放大,导致磁盘吞吐量成为负载的瓶颈。随着时间的推移,待压缩的字节总量会不断增加从而触发流量控制,这意味着此时 TiKV 缺乏足够的磁盘带宽来处理前台写入流量。 +[RocksDB](https://rocksdb.org/) 是 TiKV 的存储引擎,负责存储用户数据。出于成本考虑,云上提供的 EBS IO 吞吐量通常比较有限,因此 RocksDB 可能会表现出较高的写放大,导致磁盘吞吐量成为负载的瓶颈。随着时间的推移,待 compaction 的字节总量会不断增加从而触发流量控制,这意味着此时 TiKV 缺乏足够的磁盘带宽来处理前台写入流量。 要缓解由磁盘吞吐量受限引起的瓶颈,你可以通过[启用 Titan](#enable-titan) 来改善性能。如果数据的平均行大小低于 512 字节,Titan 并不适用,此时你可以通过[提高所有压缩级别](#increase-all-the-compression-levels) 来改善性能。 @@ -180,7 +180,7 @@ Google Cloud 的 [实时迁移功能](https://cloud.google.com/compute/docs/inst [`tso-update-physical-interval`](/pd-configuration-file.md#tso-update-physical-interval):此参数控制 PD Server 更新物理 TSO batch 的间隔。通过缩短此间隔,PD Server 可以更频繁地分配 TSO batch,从而减少下一次分配的等待时间。 ``` -tso-update-physical-interval = "10ms" # 默认值: 50ms +tso-update-physical-interval = "10ms" # 默认值为 50ms ``` #### 调整 TiDB 全局变量 From db6d5b9a16a17a70ae52d821f2d3c1a98d743a0b Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Thu, 15 Aug 2024 14:19:35 +0800 Subject: [PATCH 08/12] add diagrams --- .../after_tuning_cpu.png | Bin 0 -> 122628 bytes .../after_tuning_metrics.png | Bin 0 -> 170347 bytes .../baseline_cpu.png | Bin 0 -> 82655 bytes .../baseline_metrics.png | Bin 0 -> 239981 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 media/performance/public-cloud-best-practice/after_tuning_cpu.png create mode 100644 media/performance/public-cloud-best-practice/after_tuning_metrics.png create mode 100644 media/performance/public-cloud-best-practice/baseline_cpu.png create mode 100644 media/performance/public-cloud-best-practice/baseline_metrics.png diff --git a/media/performance/public-cloud-best-practice/after_tuning_cpu.png 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zcnN-79Kibe$-fzPYBoSr<<|&V`rE1eg(Ue`B;GRMM>=dn)K{jTdkXw7ifNxZkW-n! z-m>%i(e^*1?!OMZ^bv6J#GKsx`+t7?-;%Wak@2fUVdDNL&E>Bl{(a3~pC(d)i|xLr zJIel>f$(cEx?@Rd3oYylfb}?jCL3bm*vZ!C9EP&JAPVkP7Lhc`(2 z2=5Gr!~=ntS;k)kHNF6NK5npf&(orjkowmLK#mduvN?GQ`u~aIeuYilxF(kA86T7O4<<0CScc#JkaA& z*I5%={JAm|(B1B(pR9;vv-H^&ck7==Xgh^-9(uaAz45bn{+j3i_y>@k3@B>&ZWsLL z3WNVcQO4SU1vA#B%%l9L=lCCY`uXmu-j{%dE8fG`d;O<*{~x~^-=Si}GQK0xAS(9% zS5&_PCV=E}`T%H_`7bUG)dv#fL(lMb_x_K;{`s;KL6Y(%S!=t;%mSPO;~bg)X7vJ; co*PF# Date: Tue, 20 Aug 2024 17:37:15 +0800 Subject: [PATCH 09/12] Apply suggestions from code review Co-authored-by: xixirangrang --- best-practices-on-public-cloud.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/best-practices-on-public-cloud.md b/best-practices-on-public-cloud.md index 90b2ab9047b9..6b4ed12d2dde 100644 --- a/best-practices-on-public-cloud.md +++ b/best-practices-on-public-cloud.md @@ -78,7 +78,7 @@ sdd 1033.00 4132.00 1141.33 31685.33 571.00 0.94 100.00 - Google Cloud:推荐使用 [pd-extreme](https://cloud.google.com/compute/docs/disks?hl=zh-cn#disk-types/)。磁盘大小、IOPS 和 MBPS 可以进行配置,但仅在 CPU 核数超过 64 的实例上可用。 -- Azure:推荐使用 [ultra disk](https://learn.microsoft.com/zh-cn/azure/virtual-machines/disks-types#ultra-disks)。磁盘大小、IOPS 和 MBPS 可以根据你的具体需求进行配置。 +- Azure:推荐使用 [Ultra Disk](https://learn.microsoft.com/zh-cn/azure/virtual-machines/disks-types#ultra-disks)。磁盘大小、IOPS 和 MBPS 可以根据你的具体需求进行配置。 ### 示例 1:在 AWS 上运行社交网络工作负载 @@ -148,7 +148,7 @@ server_configs: ## 缓解 Google Cloud 上的实时迁移维护事件带来的性能影响 -Google Cloud 的 [实时迁移功能](https://cloud.google.com/compute/docs/instances/live-migration-process?hl=zh-cn)允许虚拟机在主机之间无缝迁移而不会导致停机。然而,这些迁移事件虽不频繁,但可能会显著影响虚拟机的性能,其中包括那些在 TiDB 集群中运行的虚拟机。在这些事件发生期间,受影响的虚拟机可能会出现性能下降,导致 TiDB 集群中的查询处理时间增加。 +Google Cloud 的[实时迁移功能](https://cloud.google.com/compute/docs/instances/live-migration-process?hl=zh-cn)允许虚拟机在主机之间无缝迁移而不会导致停机。然而,这些迁移事件虽不频繁,但可能会显著影响虚拟机的性能,其中包括那些在 TiDB 集群中运行的虚拟机。在这些事件发生期间,受影响的虚拟机可能会出现性能下降,导致 TiDB 集群中的查询处理时间增加。 为了检测 Google Cloud 发起的实时迁移事件并减轻这些事件对性能的影响,TiDB 提供了一个[监控脚本](https://github.com/PingCAP-QE/tidb-google-maintenance)(该脚本基于 Google 元数据[示例](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/compute/metadata/main.py))。你可以将此脚本部署在 TiDB、TiKV 和 PD 节点上,以检测维护事件。当检测到维护事件时,该脚本将自动采取以下适当措施,以尽量减少中断并优化集群行为: @@ -166,7 +166,7 @@ Google Cloud 的 [实时迁移功能](https://cloud.google.com/compute/docs/inst ### PD 限制的特征 -以下图表展示了一个由三个 PD Server 组成的大规模 TiDB 集群的特征,其中每个 PD Server 均配置了 56 核的 CPU。可以看出,当每秒查询数(QPS)超过 100 万次且每秒 TSO 请求数超过 162,000 次时,CPU 利用率达到约 4,600%。这一高 CPU 利用率表明 PD Leader 的负载已经相当高且可用的 CPU 资源即将耗尽。 +以下图表展示了一个由三个 PD Server 组成的大规模 TiDB 集群的特征,其中每个 PD Server 均配置了 56 核的 CPU。可以看出,当每秒查询数(QPS)超过 100 万次且每秒 TSO 请求数超过 162,000 次时,CPU 利用率达到约 4600%。这一高 CPU 利用率表明 PD Leader 的负载已经相当高且可用的 CPU 资源即将耗尽。 ![pd-server-cpu](/media/performance/public-cloud-best-practice/baseline_cpu.png) ![pd-server-metrics](/media/performance/public-cloud-best-practice/baseline_metrics.png) @@ -205,7 +205,7 @@ set global tidb_tso_client_batch_max_wait_time = 2; # 默认值为 0 调整后的效果如下: - 每秒 TSO 请求数减少到 64,800 次。 -- CPU 利用率显著降低,从约 4,600% 降低到 1,400%。 +- CPU 利用率显著降低,从约 4600% 降低到 1400%。 - `PD server TSO handle time` 的 P999 值从 2ms 降低到 0.5ms。 以上性能提升表明,这些调整措施成功地降低了 PD Server 的 CPU 利用率,同时保持了稳定的 TSO 处理性能。 From 67a49a04f743d2f796d6eb843a55784f2ac973f8 Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Tue, 20 Aug 2024 17:39:47 +0800 Subject: [PATCH 10/12] Update best-practices-on-public-cloud.md --- best-practices-on-public-cloud.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/best-practices-on-public-cloud.md b/best-practices-on-public-cloud.md index 6b4ed12d2dde..482de9005ad9 100644 --- a/best-practices-on-public-cloud.md +++ b/best-practices-on-public-cloud.md @@ -98,7 +98,7 @@ sdd 1033.00 4132.00 1141.33 31685.33 571.00 0.94 100.00 ### 示例 2:在 Azure 上运行 TPC-C/Sysbench 工作负载 -在 Azure 上,为 Raft Engine 分配一个专用的 32 GB [ultra disk](https://learn.microsoft.com/zh-cn/azure/virtual-machines/disks-types#ultra-disks) 后,性能提升如下: +在 Azure 上,为 Raft Engine 分配一个专用的 32 GB [Ultra Disk](https://learn.microsoft.com/zh-cn/azure/virtual-machines/disks-types#ultra-disks) 后,性能提升如下: - Sysbench `oltp_read_write` 工作负载:QPS 提高 17.8%,平均延迟降低 15.6%。 - TPC-C 工作负载:QPS 提高 27.6%,平均延迟降低 23.1%。 From 691f748aa1311579b0b70bcd00550dc0c368c833 Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Tue, 20 Aug 2024 17:40:18 +0800 Subject: [PATCH 11/12] Update best-practices-on-public-cloud.md --- best-practices-on-public-cloud.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/best-practices-on-public-cloud.md b/best-practices-on-public-cloud.md index 482de9005ad9..a5d999b7bb8d 100644 --- a/best-practices-on-public-cloud.md +++ b/best-practices-on-public-cloud.md @@ -206,7 +206,7 @@ set global tidb_tso_client_batch_max_wait_time = 2; # 默认值为 0 - 每秒 TSO 请求数减少到 64,800 次。 - CPU 利用率显著降低,从约 4600% 降低到 1400%。 -- `PD server TSO handle time` 的 P999 值从 2ms 降低到 0.5ms。 +- `PD server TSO handle time` 的 P999 值从 2 ms 降低到 0.5 ms。 以上性能提升表明,这些调整措施成功地降低了 PD Server 的 CPU 利用率,同时保持了稳定的 TSO 处理性能。 From 9e085131e385297887fb5c806312c068c434b707 Mon Sep 17 00:00:00 2001 From: Grace Cai Date: Tue, 20 Aug 2024 17:52:24 +0800 Subject: [PATCH 12/12] fix broken links --- best-practices-on-public-cloud.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/best-practices-on-public-cloud.md b/best-practices-on-public-cloud.md index a5d999b7bb8d..bbffb95b18db 100644 --- a/best-practices-on-public-cloud.md +++ b/best-practices-on-public-cloud.md @@ -13,7 +13,7 @@ summary: 了解在公有云上部署 TiDB 的最佳实践。 [RocksDB](https://rocksdb.org/) 是 TiKV 的存储引擎,负责存储用户数据。出于成本考虑,云上提供的 EBS IO 吞吐量通常比较有限,因此 RocksDB 可能会表现出较高的写放大,导致磁盘吞吐量成为负载的瓶颈。随着时间的推移,待 compaction 的字节总量会不断增加从而触发流量控制,这意味着此时 TiKV 缺乏足够的磁盘带宽来处理前台写入流量。 -要缓解由磁盘吞吐量受限引起的瓶颈,你可以通过[启用 Titan](#enable-titan) 来改善性能。如果数据的平均行大小低于 512 字节,Titan 并不适用,此时你可以通过[提高所有压缩级别](#increase-all-the-compression-levels) 来改善性能。 +要缓解由磁盘吞吐量受限引起的瓶颈,你可以通过[启用 Titan](#启用-titan) 来改善性能。如果数据的平均行大小低于 512 字节,Titan 并不适用,此时你可以通过[提高所有压缩级别](#提高所有压缩级别) 来改善性能。 ### 启用 Titan