From 40f10c159a2c08bd029632427e98e38408ff1eb3 Mon Sep 17 00:00:00 2001 From: Kick156 Date: Sun, 24 Sep 2023 23:00:45 +0800 Subject: [PATCH] add a new blog --- ...20\347\273\264\345\256\236\350\267\265.md" | 169 ++++++++++++++++++ .../LTYW/contribution_and_enhancement.png | Bin 0 -> 52061 bytes .../blog/LTYW/data_processing_processes.png | Bin 0 -> 68034 bytes static/blog/LTYW/development_efficiency.png | Bin 0 -> 76160 bytes static/blog/LTYW/devops_platform.png | Bin 0 -> 64491 bytes static/blog/LTYW/difficulties.png | Bin 0 -> 57748 bytes static/blog/LTYW/job_management.png | Bin 0 -> 59143 bytes static/blog/LTYW/multi_team_support.png | Bin 0 -> 40011 bytes .../multiple_environments_and_components.png | Bin 0 -> 82910 bytes static/blog/LTYW/multiple_execution_modes.png | Bin 0 -> 77690 bytes static/blog/LTYW/operational_background.png | Bin 0 -> 58989 bytes static/blog/LTYW/overall_architecture.png | Bin 0 -> 58965 bytes static/blog/LTYW/platform_background.png | Bin 0 -> 44176 bytes 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Flink 对数据源的实时解析、规则的实时计算以及实时产品;用户在可视化订阅平台上进行实时数据订阅,用户可以在地图上画一个电子围栏,并设置一些规则,如来自于哪里,在围栏里驻留多长时间等,还可以筛选一些特征,符合这些规则的用户信息会实时进行推送,然后是实时安全部分,当某个用户连接了高危基站或是有异常操作行为时,我们会认为可能存在诈骗行为,会对手机号码进行关停等等,还有用户的一些实时特征以及实时大屏。 + + + +![](/blog/LTYW/data_processing_processes.png) + +上图是数据处理的详细的流程。 + +第一部分是采集解析,我们的数据源来自于业务的数据库,包含 OGG 和 DTS 格式的消息、日志消息、用户行为和用户位置数据,总共 50 多种数据源,后续还会逐渐增加,所有数据源均使用 Flink 做实时解析;并增加了 Metrics 来监控数据源的延迟情况。 + +第二部分是实时计算, 这个环节处理的数据量很大,数据量在万亿级别,支撑了 10000+的数据实时订阅,有 200 多个 Flink 任务,我们将某一种同类型的业务封装成一种场景,同一个 Flink 作业可以支持相同场景的多个订阅,目前 Flink 作业数还在不停的增长,后续可能会增加到 500 多个;其中面临的一个很大挑战是每天万亿级的数据实时关联电子围栏、用户特征等信息,电子围栏有几万个,用户特征涉及数亿用户,最初我们将电子围栏信息和用户特征放到 HBase, 但这样会导致 HBase 压力很大,经常遇到性能问题造成数据延迟,而且一旦产生数据积压,需要很长的时间去消化,得益于 Flink State 的强大,我们将电子围栏信息和用户特征放到状态里,目前已经很好的支撑了大并发的场景,同时我们也增加了数据处理的性能监控;最后是实时产品和营销触达前端的一些应用。 + +![](/blog/LTYW/platform_evolution.png) + +2018 年采用了三方黑盒的计算引擎,不能支持灵活定制个性化功能,且依赖过多外部系统,导致外部系统负载高,运维复杂;2019 年使用了 Spark Streaming 的微批处理,2020 年开始使用 Flink 的流式计算,从 2021 年开始,几乎所有 Spark Streaming 的微批处理都被 Flink 替代了,同时上线了 Apache StreamPark 对我们的 Flink 作业进行管理。 + +![](/blog/LTYW/platform_background.png) + +总结一下平台背景,主要包含以下几部分: + +- 数据量大:日均万亿的数据处理。 +- 数据源多:集成了 50 多种实时数据源。 +- 订阅多:支撑了 10000+的数据服务订阅。 +- 用户多:支撑了 30 多个内部和外部用户使用。 + +![](/blog/LTYW/operational_background.png) + +运维背景也可以分为以下几部分: + +- 支撑需求多:50 多种数据源,10000+的数据服务订阅。 +- 实时作业多:现在有 200+Flink 生产作业,并且持续快速增长中, 未来可达 500+。 +- 上线频率高:每天都有新增的或增强的 Flink 作业上线操作。 +- 开发人员多:50+研发人员参与开发 Flink 实时计算任务。 +- 使用用户多:30+内部和外部组织的用户使用。 +- 监控延迟低:一旦发现问题我们要立马进行处理,避免引起用户的投诉。 + +## **Flink 实时作业运维挑战** + +![](/blog/LTYW/difficulties.png) + +基于平台和运维背景,尤其是 Flink 作业越来越多的情况下,遇到了很大的挑战,主要有两方面,分别是作业运维困境和业务支撑困境。 + + + +在作业运维困境上,首先作业部署流程长、效率低;在联通安全是第一红线下,在服务器上部署程序的时候,要连接 VPN、登录 4A、打包编译、部署、然后再启动,整个流程比较长,最初在开发 Flink 的时候,都是用脚本启动的,导致代码分支是不可控的,部署完之后也难以追溯,再就是脚本很难与 git 上的代码进行同步,因为对于脚本代码,开发人员更喜欢在服务器上直接改,很容易忘记上传 git。 + + + +由于作业运维困境上的种种因素,会产生业务支撑困境,如导致上线故障率高、影响数据质量、上线时间长、数据延迟高、告警漏发处理等,引起的投诉,此外,我们的业务影响不明确,一旦出现问题,处理问题会成为第一优先级。 + +## **基于 StreamPark 一体化管理** + +![](/blog/LTYW/job_management.png) + +对于以上的两种困境,我们基于 StreamPark 一体化管理解决了很多问题,首先来看一下 StreamPark 的双线演进,分别是 Flink 作业管理和 Flink 作业 DevOps 平台;在作业管理上,StreamPark 支持将 Flink 实时作业部署到不同的集群里去,比如 Flink 原生自带的 Standalone 模式,Flink on Yarn 的 Session、Application、PerJob 模式,在最新的版本中将支持 Kubernetes Native Session 模式;中间层是项目管理、作业管理、集群管理、团队管理、变量管理、告警管理。 + + + +- 项目管理:当部署 Flink 程序的时候,可以在项目管理里填写 git 地址,同时选择要部署的分支。 +- 作业管理:可以指定 Flink 作业的执行模式,比如你要提交到什么类型的集群里去,同时还可以配置一些资源,比如 TaskManager 的数量、TaskManager/JobManager 的内存大小、并行度等等,还可以设置一些容错,比如 Flink 作业失败后,StreamPark 可以支持它自动拉起,同时支持传入一些动态参数。 +- 集群管理:可以在界面上添加和管理大数据集群。 +- 团队管理:在企业的实际生产过程中会有多个团队,团队之间是隔离的。 +- 变量管理:可以把一些变量统一维护在一个地方,比如 Kafka 的 Broker 地址定义成一个变量,在配置 Flink 作业或者 SQL 的时候,就可以以变量的方式来替换 Broker 的 IP,且后续这个 Kafka 要下线的时候,也可以通过这个变量去查看到底哪些作业使用了这个集群,方便我们去做一些后续的流程。 +- 告警管理:支持多种告警模式,如微信、钉钉、短信和邮件。 + + + +StreamPark 支持 Flink SQL、Flink Jar 的提交,支持资源配置,支持状态跟踪,如状态是运行状态,失败状态等,同时支持指标大屏和各种日志查看。 + +![](/blog/LTYW/devops_platform.png) + +Flink 作业 DevOps 平台,主要包括以下几部分: +- 团队:StreamPark 支持多个团队,每个团队都有团队的管理员,他拥有所有权限,同时还有团队的开发者,他只有少量的一部分权限。 +- 编译、打包:在创建 Flink 项目时,可以把 git 地址、分支、打包的命令等配置在项目里,然后一键点击 build 按钮进行编译、打包。 +- 发布、部署:发布和部署的时候会创建 Flink 作业,在 Flink 作业里可以选择执行模式、部署集群、资源设置、容错设置、变量填充,最后通过一键启动停止,启动 Flink 作业。 +- 状态监测:Flink 作业启动完成之后,就是状态的实时跟踪,包括 Flink 的运行状态、运行时长、Checkpoint 信息等,并支持一键跳转到 Flink 的 Web UI。 +- 日志、告警:包含构建的一些日志和启动日志,同时支持钉钉、微信、邮件、短信等告警方式。 + +![](/blog/LTYW/multi_team_support.png) + +企业一般有多个团队同时开发实时作业,在我们公司包含实时采集团队、数据处理团队和实时的营销团队,StreamPark 支持多个团队的资源隔离。 + +![](/blog/LTYW/platformized_management.png) + +Flink 作业平台化管理面临如下挑战: +- 脚本数量多:平台有几百个脚本,分散在多个服务器上。 +- 脚本类型多:在启动 Flink 作业时,会有启动脚本、停止脚本和守护脚本,而且操作权限很难控制。 +- 脚本不一致:服务器上的脚本与 git 上的脚本不一致。 +- 脚本确权难:Flink 作业的责任人,用途不明确。 +- 分支不可控:启动作业的时候,需要在脚本里指定 git 分支,导致分支不可追溯的。 + + + +基于以上的挑战,StreamPark 通过项目管理来解决了责任人不明确,分支不可追溯的问题,因为在创建项目的时候需要手动指定一些分支,一旦打包成功,这些分支是有记录的;通过作业管理对配置进行了集中化,避免了脚本太过于分散,而且作业启动、停止的权限有严格的控制,避免了脚本化权限不可控的状态,StreamPark 以接口的方式与集群进行交互来获取作业信息,这样做会让作业控制更加精细。 + + + +可以看一下上图中下面的图,通过项目管理进行打包,通过作业管理进行配置,然后发布,可以进行一键启停,通过 API 提交作业。 + +![图片](/blog/LTYW/development_efficiency.png) + +早期我们需要通过 7 步进行部署,包括连接 VPN、登录 4A、执行编译脚本、执行启动脚本、打开 Yarn、搜索作业名、进入 Flink UI 等 7 个步骤,StreamPark 可以支持 4 个一键进行部署,包括一键打包、一键发布、一键启动、一键到 Flink UI。 + +![图片](/blog/LTYW/submission_process.png) + +上图是我们 StreamPark 的作业提交流程,首先 StreamPark 会将作业进行发布,发布的时候会上传一些资源,然后会进行作业的提交,提交的时候会带上配置的一些参数,以 Flink Submit 的方式调用接口发布到集群上;这里会有多个 Flink Submit 对应着不同的执行模式,比如 Yarn Session、Yarn Application、Kubernetes Session、Kubernetes Application 等都是在这里控制的,提交作业之后,如果是 Flink on Yarn 作业,会得到这个 Flink 作业的 Application ID 或者 Job ID,这个 ID 会保存在我们的数据库中,如果是基于 Kubernetes 执行的话,也会得到 Job ID,后面我们在跟踪作业状态的时候,主要就是通过保存的这些 ID 去跟踪作业的状态。 + +![图片](/blog/LTYW/status_acquisition_bottleneck.png) + +如上所述,如果是 Flink on Yarn 作业,在提交作业的时候会获取两个 ID,Application ID 或者 Job ID,基于这两个 ID 可以获取我们的状态,但当 Flink 作业非常多的时候会遇到一些问题,StreamPark 它是有一个状态获取器,它会通过我们保存的数据库里的 Application ID 或者 Job ID,去向 ResourceManager 做一个请求,会做每五秒钟周期性的轮询,如果作业特别多,每次轮询 ResourceManager 会负责再去调用 Job Manager 的地址访问它的状态,这就会导致 ResourceManager 的连接数压力较大和连接数过高。 + + + +上图中 ResourceManager 的连接数阶段性、周期性的持续走高,可以看到 ResourceManager 处于比较红的状态,从主机上去监控的时候,它的连接数确实比较高。 + +![图片](/blog/LTYW/state_optimization.png) + +针对上面的问题,我们做了一些优化,首先 StreamPark 保存了提交作业之后的 Application ID 或者 Job ID,同时也会获取 Job Manager 直接访问的地址,并保存在数据库中,每次轮询时不再通过 ResourceManager 获取作业的状态,它可以直接调用各个 Job Manager 的地址实时获取状态,极大的降低了 ResourceManager 的连接数;从上图最后的部分可以看到,基本不会产生太大的连接数,大大减轻了 ResourceManager 的压力,且后续当 Flink 作业越来越多时获取状态也不会遇到瓶颈的问题。 + +![图片](/blog/LTYW/state_recovery.png) + +StreamPark 解决的另一个问题是 Flink 从状态恢复的保障,以前我们用脚本做运维的时候,在启动 Flink 的时候,尤其是在业务升级的时候,要从上一个最新的 Checkpoint 来恢复,但经常有开发人员忘记从上一个检查点进行恢复,导致数据质量产生很大的问题,遭到投诉,StreamPark 的流程是在首次启动的时候,每五秒钟轮询一次获取 Checkpoint 的记录,同时保存在数据库之中,在 StreamPark 上手动停止 Flink 作业的时候,可以选择做不做 Savepoint,如果选择了做 Savepoint,会将 Savepoint 的路径保存在数据库中,同时每次的 Checkpoint 记录也保存在数据库中,当下次启动 Flink 作业的时候,默认会选择最新的 Checkpoint 或者 Savepoint 记录,有效避免了无法从上一个检查点去恢复的问题,也避免了导致问题后要进行 offset 回拨重跑作业造成的资源浪费,同时也保证了数据处理的一致性。 + +![图片](/blog/LTYW/multiple_environments_and_components.png) + +StreamPark 还解决了在多环境下多个组件的引用挑战,比如在企业中通常会有多套环境,如开发环境、测试环境、生产环境等,一般来说每套环境下都会有多个组件,比如 Kafka,HBase、Redis 等,而且在同一套环境里还可能会存在多个相同的组件,比如在联通的实时计算平台,从上游的 Kafka 消费数据的时候,将符合要求的数据再写到下游的 Kafka,这个时候同一套环境会涉及到两套 Kafka,单纯从 IP 很难判断是哪个环境哪个组件,所以我们将所有组件的 IP 地址都定义成一个变量,比如 Kafka 集群,开发环境、测试环境、生产环境都有 Kafka.cluster 这个变量,但它们指向的 Broker 的地址是不一样的,这样不管是在哪个环境下配置 Flink 作业,只要引用这个变量就可以了,大大降低了生产上的故障率。 + +![图片](/blog/LTYW/multiple_execution_modes.png) + +StreamPark 支持 Flink 多执行的模式,包括基于 on Yarn 的 Application/ Perjob / Session 三种部署模式,还支持 Kubernetes 的 Application 和 Session 两种部署模式,还有一些 Remote 的模式。 + +![图片](/blog/LTYW/versioning.png) + +StreamPark 也支持 Flink 的多版本,比如联通现在用的是 1.14.x,现在 1.16.x 出来后我们也想体验一下,但不可能把所有的作业都升级到 1.16.x,我们可以把新上线的升级到 1.16.x,这样可以很好的满足使用新版本的要求,同时也兼容老版本。 + +## **未来规划与演进** + +![图片](/blog/LTYW/contribution_and_enhancement.png) + +未来我们将加大力度参与 StreamPark 建设,以下我们计划要增强的方向。 +- 高可用:StreamPark 目前不支持高可用,这方面还需要做一些加强。 +- 状态的管理:在企业实践中 Flink 作业在上线时,每个算子会有 UID。如果 Flink UID 不做设置,做 Flink 作业的升级的时候,就有可能出现状态无法恢复的情况,目前通过平台还无法解决这个问题,所以我们想在平台上增加这个功能,在 Flink Jar 提交时,增加检测算子是否设置 UID 的功能,如果没有,会发出提醒,这样可以避免每次上线 Flink 作业时,作业无法恢复的问题;之前遇到这种情况的时候,我们需要使用状态处理的 API,从原来的状态里进行反序列化,然后再用状态处理 API 去制作新的状态,供升级后的 Flink 加载状态。 +- 更细致的监控:目前支持 Flink 作业失败之后,StreamPark 发出告警。我们希望 Task 失败之后也可以发出告警,我们需要知道失败的原因;还有作业反压监控告警、Checkpoint 超时、失败告警性能指标采集,也有待加强。 +- 流批一体:结合 Flink 流批一体引擎和数据湖流批一体存储探索流批一体平台。 + +![](/blog/LTYW/road_map.png) + +上图是 StreamPark 的 Roadmap。 +- 数据源:StreamPark 会支持更多数据源的快速接入,达到数据一键入户。 +- 运维中心:获取更多 Flink Metrics 进一步加强监控运维的能力。 +- K8S-operator:现有的 Flink on K8S 还是有点重,经历了打 Jar 包、打镜像、推镜像的过程,后续需要改进优化,积极拥抱上游对接的 K8S-operator。 +- 流式数仓:增强对 Flink SQL 作业能力的支持,简化 Flink SQL 作业的提交,计划对接 Flink SQL Gateway;SQL 数仓方面的能力加强,包括元数据存储、统一建表语法校验、运行测试、交互式查询,积极拥抱 Flink 上游,探索实时数仓和流式数仓。 + + diff --git a/static/blog/LTYW/contribution_and_enhancement.png b/static/blog/LTYW/contribution_and_enhancement.png new file mode 100644 index 0000000000000000000000000000000000000000..ac45a0d073eb44bc70a0642f736692ddaa795199 GIT binary 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