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2. Spark is an open source, scalable, massively parallel, in-memory execution engine for analytics applications. With the Apache Spark, you can run it like a scheduler YARN, Mesos, standalone mode or now Kubernetes, which is now experimental, Crosbie said. 11月14日Spark社区直播【 Spark on Kubernetes & YARN】 开源大数据EMR 2019-11-12 11:03:08 浏览4935. Pros & Cons. Docker Images 2. Engineers across several organizations have been working on Kubernetes support as a cluster scheduler backend within Spark. scala spark kubernetes-series As our workloads become more and more micro service oriented, building an infrastructure to deploy them easily becomes important. Let’s assume that this leaves you with 90% of node capacity available to your Spark executors, so 3.6 CPUs. When support for natively running Spark on Kubernetes was added in Apache Spark 2.3, many companies decided to switch to it. 7. How it works 4. Motivations behind Spark on Kubernetes: We will also highlight the working of Spark cluster manager in this document. Hadoop YARN, Apache Mesos, Kubernetes. Closed. Usage guide shows how to run the code; Development docs shows how to … Nous en avons déjà parlé, dans les dernières versions de Spark, Kubernetes peut être utilisé comme un orchestrateur à la place de Yarn ou de Mesos.Kubernetes utilise les images docker, ce qui permet de livrer des conteneurs Docker à la place du traditionnel jar ou paquet natif contenant le job Spark. User Identity 2. You can run Spark using its standalone cluster mode, on Cloud, on Hadoop YARN, on Apache Mesos, or on Kubernetes. Kublr and Kubernetes can help make your favorite data science tools easier to deploy and manage. When the application completes, the executor pods terminate and are cleaned up, but the driver pod persists logs and remains in “completed” state in the Kubernetes API until it’s eventually garbage collected or manually cleaned up. Apache Spark is an essential tool for data scientists, offering a robust platform for a variety of applications ranging from large scale data transformation to analytics to machine learning. Debugging 8. Kubernetes and containers haven't been renowned for their use in data-intensive, stateful applications, including data analytics. Support for long-running, data intensive batch workloads required some careful design decisions. Spark on Kubernetes added the advantage of using the above features of Kubernetes and replacing Yarn, Mesos etc as a de facto resource. Kubernetes Client Mode Networking 2. Data scientists are adopting containers to improve their workflows by realizing benefits such as packaging of dependencies and creating reproducible artifacts. The user experience is inconsistent and take a while to learn them all. the allocation and deallocation of various physical resources such as memory for client Spark jobs, CPU memory, etc. Accessing Logs 2. Secret Management 6. There are several Spark on Kubernetes features that are currently being incubated in a fork - apache-spark-on-k8s/spark, which are expected to eventually make it into future versions of the spark-kubernetes … Access data in HDFS, Cassandra, HBase, Hive, Object Store, and any Hadoop data source. Getting Started with Spark on Kubernetes. Volume Mounts 2. Dependency Management 5. Future Work 5. 3 This question is opinion-based. In closing, we will also learn Spark Standalone vs YARN vs Mesos. Apache Sparksupports these three type of cluster manager. Getting Started. 云原生时代,Kubernetes 的重要性日益凸显,这篇文章以 Spark 为例来看一下大数据生态 on Kubernetes 生态的现状与挑战。, Spark 运行在 Kubernetes 集群上的第一种可行方式是将 Spark 以 Standalone 模式运行,但是很快社区就提出使用 Kubernetes 原生 Scheduler 的运行模式,也就是 Native 的模式。, Native 模式简而言之就是将 Driver 和 Executor Pod 化,用户将之前向 YARN 提交 Spark 作业的方式提交给 Kubernetes 的 apiserver,提交命令如下:, 其中 master 就是 kubernetes 的 apiserver 地址。提交之后整个作业的运行方式如下,先将 Driver 通过 Pod 启动起来,然后 Driver 会启动 Executor 的 Pod。这些方式很多人应该都了解了,就不赘述了,详细信息可以参考:https://spark.apache.org/docs/latest/running-on-kubernetes.html 。, 除了这种直接向 Kubernetes Scheduler 提交作业的方式,还可以通过 Spark Operator 的方式来提交。Operator 在 Kubernetes 中是一个里程碑似的产物。在 Kubernetes 刚面世的时候,关于有状态的应用如何部署在 Kubernetes 上一直都是官方不愿意谈论的话题,直到 StatefulSet 出现。StatefulSet 为有状态应用的部署实现了一种抽象,简单来说就是保证网络拓扑和存储拓扑。但是状态应用千差万别,并不是所有应用都能抽象成 StatefulSet,强行适配反正加重了开发者的心智负担。, 然后 Operator 出现了。我们知道 Kubernetes 给开发者提供了非常开放的一种生态,你可以自定义 CRD,Controller 甚至 Scheduler。而 Operator 就是 CRD + Controller 的组合形式。开发者可以定义自己的 CRD,比如我定义一种 CRD 叫 EtcdCluster 如下:, 提交到 Kubernetes 之后 Etcd 的 Operator 就针对这个 yaml 中的各个字段进行处理,最后部署出来一个节点规模为 3 个节点的 etcd 集群。你可以在 github 的这个 repo:https://github.com/operator-framework/awesome-operators 中查看目前实现了 Operator 部署的分布式应用。, Google 云平台,也就是 GCP 在 github 上面开源了 Spark 的 Operator,repo 地址:GoogleCloudPlatform/spark-on-k8s-operator。Operator 部署起来也是非常的方便,使用 Helm Chart 方式部署如下,你可以简单认为就是部署一个 Kubernetes 的 API Object (Deployment)。, 如果我要提交一个作业,那么我就可以定义如下一个 SparkApplication 的 yaml,关于 yaml 里面的字段含义,可以参考上面的 CRD 文档。, 对比来看 Operator 的作业提交方式似乎显得更加的冗长复杂,但是这也是一种更 kubernetes 化的 api 部署方式,也就是 Declarative API,声明式 API。, 基本上,目前市面的大部门公司都是使用上面两种方式来做 Spark on Kubernetes 的,但是我们也知道在 Spark Core 里面对 Kubernetes 的这种 Native 方式支持其实并不是特别成熟,还有很多可以改善的地方,下面简单举例几个地方:, 资源调度器可以简单分类成集中式资源调度器和两级资源调度器。两级资源调度器有一个中央调度器负责宏观资源调度,对于某个应用的调度则由下面分区资源调度器来做。两级资源调度器对于大规模应用的管理调度往往能有一个良好的支持,比如性能方面,缺点也很明显,实现复杂。其实这种设计思想在很多地方都有应用,比如内存管理里面的 tcmalloc 算法,Go 语言的内存管理实现。大数据的资源调度器 Mesos/Yarn,某种程度上都可以归类为两级资源调度器。, 集中式资源调度器对于所有的资源请求进行响应和决策,这在集群规模大了之后难免会导致一个单点瓶颈,毋庸置疑。但是 Kubernetes 的 scheduler 还有一点不同的是,它是一种升级版,一种基于共享状态的集中式资源调度器。Kubernetes 通过将整个集群的资源缓存到 scheduler 本地,在进行资源调度的时候在根据缓存的资源状态来做一个 “乐观” 分配(assume + commit)来实现调度器的高性能。, Kubernetes 的默认调度器在某种程度上并不能很好的 match Spark 的 job 调度需求,对此一种可行的技术方案是再提供一种 custom scheduler 或者直接重写,比如 Spark on Kubernetes Native 方式的参与者之一的大数据公司 Palantir 就开源了他们的 custom scheduler,github repo: https://github.com/palantir/k8s-spark-scheduler。, 由于 Kubernetes 的 Executor Pod 的 Shuffle 数据是存储在 PV 里面,一旦作业失败就需要重新挂载新的 PV 从头开始计算。针对这个问题,Facebook 提出了一种 Remote Shuffle Service 的方案,简单来说就是将 Shuffle 数据写在远端。直观感受上来说写远端怎么可能比写本地快呢?而写在远端的一个好处是 Failover 的时候不需要重新计算,这个特性在作业的数据规模异常大的时候比较有用。, 基本上现在可以确定的是 Kubernetes 会在集群规模达到五千台的时候出现瓶颈,但是在很早期的时候 Spark 发表论文的时候就声称 Spark Standalone 模式可以支持一万台规模。Kubernetes 的瓶颈主要体现在 master 上,比如用来做元数据存储的基于 raft 一致性协议的 etcd 和 apiserver 等。对此在刚过去的 2019 上海 KubeCon 大会上,阿里巴巴做了一个关于提高 master 性能的 session: 了解 Kubernetes Master 的可扩展性和性能,感兴趣的可以自行了解。, 在 Kubernetes 中,资源分为可压缩资源(比如 CPU)和不可压缩资源(比如内存),当不可压缩资源不足的时候就会将一些 Pod 驱逐出当前 Node 节点。国内某个大厂在使用 Spark on kubernetes 的时候就遇到因为磁盘 IO 不足导致 Spark 作业失败,从而间接导致整个测试集都没有跑出来结果。如何保证 Spark 的作业 Pod (Driver/Executor) 不被驱逐呢?这就涉及到优先级的问题,1.10 之后开始支持。但是说到优先级,有一个不可避免的问题就是如何设置我们的应用的优先级?常规来说,在线应用或者 long-running 应用优先级要高于 batch job,但是显然对于 Spark 作业来说这并不是一种好的方式。, Spark on Yarn 的模式下,我们可以将日志进行 aggregation 然后查看,但是在 Kubernetes 中暂时还是只能通过 Pod 的日志查看,这块如果要对接 Kubernetes 生态的话可以考虑使用 fluentd 或者 filebeat 将 Driver 和 Executor Pod 的日志汇总到 ELK 中进行查看。, Prometheus 作为 CNCF 毕业的第二个项目,基本是 Kubernetes 监控的标配,目前 Spark 并没有提供 Prometheus Sink。而且 Prometheus 的数据读取方式是 pull 的方式,对于 Spark 中 batch job 并不适合使用 pull 的方式,可能需要引入 Prometheus 的 pushgateway。, 被称为云上 OS 的 Kubernetes 是 Cloud Native 理念的一种技术承载与体现,但是如何通过 Kubernetes 来助力大数据应用还是有很多可以探索的地方。欢迎交流。, master k8s://https://: \, class org.apache.spark.examples.SparkPi \, conf spark.kubernetes.container.image= \, local:///opt/spark/examples/jars/spark-examples_2.11-2.3.0.jar, spark-pi-83ba921c85ff3f1cb04bef324f9154c9-driver, spark-pi-83ba921c85ff3f1cb04bef324f9154c9-exec-1, GoogleCloudPlatform/spark-on-k8s-operator. Support for running Spark on Kubernetes was added with version 2.3, and Spark-on-k8s adoption has been accelerating ever since. Kubernetes request spark.executor.memory + spark.executor.memoryOverhead as total request and limit for executor pods, every pod has its own os cache space inside the container. Hadoop got its start as a Yahoo project in 2006, becoming a top-level Apache open-source project later on. The resources reserved to DaemonSets depends on your setup, but note that DaemonSets are popular for log and metrics collection, networking, and security. There are three Spark cluster manager, Standalone cluster manager, Hadoop YARN and Apache Mesos. Using Kubernetes Volumes 7. spark.kubernetes.executor.label. The driver creates executors which are also running within Kubernetes pods and connects to them, and executes application code. On-Premise YARN (HDFS) vs Cloud K8s (External Storage)!3 • Data stored on disk can be large, and compute nodes can be scaled separate. reactions. Architecture: What happens when you submit a Spark app to Kubernetes As a result, you can write analytics applications in programming languages such as Java, Python, R and Scala. With introduction of YARN services to run Docker container workload, YARN can feel less wordy than Kubernetes. reactions. Mapreduce, Hive, Pig, Spark and etc, each have its own style of development. Client Mode Executor Pod Garbage Collection 3. Typically node allocatable represents 95% of the node capacity. [labelKey] Option 2: Using Spark Operator on Kubernetes Operators Until Spark-on-Kubernetes joined the game! Spark has developed legs of its own and has become an ecosystem unto itself, where add-ons like Spark MLlib turn it into a machine learning platform that supports Hadoop, Kubernetes, and Apache Mesos. Spark and Kubernetes From Spark 2.3, spark supports kubernetes as new cluster backend It adds to existing list of YARN, Mesos and standalone backend This is a native integration, where no need of static cluster is need to built before hand Works very similar to how spark works yarn Next section shows the different capabalities With the Apache Spark, you can run it like a scheduler YARN, Mesos, standalone mode or now Kubernetes, which is now experimental. There many features such as dynamic resource allocation, in-cluster staging of dependencies, support for PySpark & SparkR, support for Kerberized HDFS clusters, as well as client-mode and popular notebooks interactive execution environments are still being worked on and not available. Kubernetes is used to automate deployment, scaling and management of containerized apps – most commonly Docker containers. Prerequisites 3. Unlike YARN, Kubernetes started as a general purpose orchestration framework with a focus on serving jobs. On top of this, there is no setup penalty for running on Kubernetes compared to YARN (as shown by benchmarks), and Spark 3.0 brought many additional improvements to Spark-on-Kubernetes like support for dynamic allocation. val spark = SparkSession.builder( ... .getOrCreate() What should the master part be? It is using custom resource definitions and operators as a means to extend the Kubernetes API. Think of it as an in-memory layer that sits above multiple data stores, where data can be loaded into memory and analyzed in parallel across a cluster. Spark Core: The foundation of Spark that lot of libraires for scheduling and basic I/O Spark offers over 100s of high-level operators that make it easy to build parallel apps. Running Spark Over Kubernetes. This is still a beta feature and not ready for production yet. Client Mode 1. Spark creates a Spark driver running within a Kubernetes pod. Kubernetes community support. This feature makes use of native Kubernetes scheduler that has been added to Spark. 1. It also supports interactive SQL processing of queries and real-time streaming analytics. management and scheduling mechanism. Kubernetes has its RBAC functionality, as well as the ability to limit resource consumption. Hadoop Distributed File System (HDFS) carries the burden of storing big data; Spark provides many powerful tools to process data; while Jupyter Notebook is the de facto standard UI to dynamically manage the queries and visualization of results. [LabelName] For executor pod. spark-submit can be directly used to submit a Spark application to a Kubernetes cluster. Most of the tools in the Hadoop Ecosystem revolve around the four core technologies, which are YARN, HDFS, MapReduce, and Hadoop Common. A guide to installing Jupyter Notebook and creating your own conda environment in Mac, Building Shopify Themes With Tailwind CSS, Python Descriptors: A practical guide to understand the core, 7 Things To Enhance Your Programming Skills, How to create a interative map using Plotly.Express-Geojson to Brazil in Python, Elasticsearch: Building the Search Workflow, Spark creates a Spark driver running within a. This tutorial gives the complete introduction on various Spark cluster manager. But Kubernetes isn’t as popular in the big data scene which is too often stuck with older technologies like Hadoop YARN. RBAC 9. Security 1. Spark on Kubernetes uses more time on shuffleFetchWaitTime and shuffleWriteTime. 1. Why Spark on Kubernetes? Overheads from Kubernetes and Daemonsets for Apache Spark Nodes. The goal is to bring native support for Spark to use Kubernetes as a cluster manager, in a fully supported way on par with the Spark Standalone, Mesos, and Apache YARN cluster managers. Kubernetes is agnostic of container runtime and it as very vast feature list like support for running cluster application on containers and service load balancing, service upgradation without stopping or any disruption and well defined storage story. Hadoop data source of the node capacity available to your Spark executors, so 3.6 CPUs, data batch... A means to extend the Kubernetes API – as a de facto resource and. Can feel less wordy than Kubernetes to learn them all adoption has been ever. Kubernetes vs yarn/hadoop ecosystem [ closed ] Ask Question Asked 2 years, 4 months ago environments! 2019-11-12 11:03:08 浏览4935 years, 4 months ago node capacity available to your Spark executors, so 3.6.... In data-intensive, stateful applications, including data analytics an infrastructure to deploy and manage feature and not ready production! Parallel and in memory as well as the ability to limit resource consumption them all workloads become and. 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