描述了spark on k8s在京东的使用

范振发布于2018/09/10

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1.京东如何基于容 器 打 造高性能及效率的大数据平台 Zhen Fan fanzhen@jd.com Weiting Chen weiting.chen@intel.com

2.2 INTEL NOTICE & DISCLAIMER No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade. This document contains information on products, services and/or processes in development.  All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps. The products and services described may contain defects or errors known as errata which may cause deviations from published specifications. Current characterized errata are available on request. Copies of documents which have an order number and are referenced in this document may be obtained by calling 1-800-548-4725 or by visiting www.intel.com/design/literature.htm . Intel, the Intel logo, Intel® are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others Copyright © 2018 Intel Corporation.

3.3 AGENDA BACKGROUND SPARK ON KUBERNETES - Why use Spark-on-K8s - How it works - Current Status & Issues JD.COM CASE STUDY - JD.com’s MoonShot - Experience Sharing SUMMARY

4.BACKGROUND 4

5.5 ABOUT SPARK-ON-KUBERNETES https://github.com/apache-spark-on-k8s/spark Spark* on Kubernetes *(K8s) is a new project proposed by the companies including Bloomberg, Google, Intel, Palantir , Pepperdata , and Red Hat. The goal is to bring native support for Spark to use Kubernetes as a cluster manager like Spark Standalone, YARN*, or Mesos *. The feature is planning to be put into Spark 2.3.0 release( SPARK-18278 ).

6.6 WHY JD.COM CHOOSE SPARK-ON-K8S Customers are asking to use an unified cloud platform to manage their applications. Based on Kubernetes *, we can ease to set up a platform to support CPU, GPU, as well as FPGA resources for Big Data/AI workloads. H eterogeneous C omputing CPU + GPU + FPGA

7.7 HETEROGENEOUS CLOUD SOLUTION Kubelet Servers Servers Servers Servers Kubelet Kubelet Kubelet * Spark TensorFlow */ Caffe * Mllib * Spark* SQL CPU CPU CPU CPU CPU CPU FPGA FPGA CPU CPU GPU GPU Storage BigDL * Spark Streaming HARDWARE RESOURCE CONTAINER CLUSTER COMPUTING FRAMEWORK Docker Docker Docker Docker* Web Service Command Line USER INTERFACE Jupyter */Zeppelin* Storm* / Flink * Storage Hadoop*

8.SPARK ON KUBERNETES 8

9.9 SPARK ON DOCKER SOLUTIONS Solution1 - Spark* Standalone on Docker * - Run Spark standalone cluster in Docker . - Two-tiers resource allocation(K8s->Spark Cluster->Spark Applications). - Less efforts to migrate existing architecture into container environment. Solution2 - Spark on Kubernetes * - Use native way to run Spark on Kubernetes like Spark Standalone, YARN, or Mesos . - Single tier resource allocation(K8s->Spark Applications) for higher utilization. - Must re-write the entire logical program for resource allocation via K8s.

10.10 SOLUTION1 - SPARK STANDALONE ON DOCKER Kubelet * Kubelet Kubelet Kubelet Spark-Submit App1 Spark-Submit App2 Kubernetes* Master Spark Slave Pod Spark Slave Pod Spark Slave Pod Spark Slave Pod Spark Master App1 Executor App1 Executor App1 Executor App2 Executor App2 Executor App2 Executor Step2 Step3 Step1 Step4 Step2

11.11 SOLUTION2 - SPARK ON KUBERNETES Kubelet * Kubelet Kubelet Kubelet Spark-Submit App1 Spark-Submit App2 Kubernetes Master App1 Driver Pod App1 Executor Pod App1 Executor Pod App2 Executor Pod Step4 Step1 Step2 Step3 App2 Driver Pod App2 Executor Pod Step4

12.12 HOW TO USE SPARK ON K8S # bin/spark-submit \   --deploy-mode cluster \   --class org.apache.spark.examples.SparkPi \   --master k8s://http://127.0.0.1:8080 \   -- kubernetes -namespace default \   -- conf spark.executor.instances =5 \ -- conf spark.executor.cores =4 \ -- conf spark.executor.memory =4g \   -- conf spark.app.name=spark-pi \   -- conf spark.kubernetes.driver.docker.image =localhost:5000/spark-driver \   -- conf spark.kubernetes.executor.docker.image =localhost:5000/spark-executor \ -- conf spark.kubernetes.initcontainer.docker.image =localhost:5000/spark- init \ -- conf spark.kubernetes.resourceStagingServer.uri =http://$ip:31000 \   hdfs://examples/jars/spark-examples_2.11-2.1.0-k8s-0.1.0-SNAPSHOT.jar

13.13 KEY FEATURES Support Cluster Mode Client Mode Support is under reviewing. Support File Staging in local, HDFS, or running a File Stage Server container. Support Scala, Java, and PySpark . Support Static and Dynamic Allocation for Executors. Support running HDFS inside K8s or externally. Support for Kubernetes 1.6 - 1.7 Pre-built docker images

14.14 DATA PROCESSING MODEL Host Virtual Cluster Docker1 Docker2 Computing Task HDFS PATTERN 1: Internal HDFS* Host Virtual Cluster Docker1 Computing Task Object Store PATTERN 3: Object Store Host Use HDFS as file sharing server. HDFS runs in the same host to give elasticity to add/reduce compute nodes by request. Please refer to Spark & HDFS . Launch a File Staging Server to share data between nodes. Input and Output data can put in an object store Streaming data directly via object level storage like Amazon S3, Swift . Virtual Cluster Docker1 Computing Task HDFS PATTERN 2: External HDFS Use HDFS as file sharing server. HDFS runs outside in a long-running cluster to make sure data is persisted. Please refer to PR-350 Host Host The design rule is based on “whether the data must be persisted”. spark.local.dir : For Spark Data Shuffling. Use Ephemeral Volume. Now it uses docker -storage with diff. storage backend. EmptyDir is WIP. File Staging Server: For sharing data such as Jar or dependence file between computing nodes. Now it uses docker -storage. Local Storage support in Persist Volume(PV) is WIP. Storage Plan for Spark* on K8s*

15.15 STORAGE SUPPORT Spark* Shuffle: Uses Ephemeral Volumes Docker * Storage: Use devicemapper Shared Volumes: #439 Use EmptyDir for File Staging to share jar file. Local in Spark Executors( Docker Storage) Remote HDFS* Create a Staging Server Container Persistent Volumes(Ongoing): #306 Use PV Input/Output Data Remote HDFS Remote Object Storage such as GlusterFS * Spark Executor Docker Storage File Staging Server Spark Shuffle File Staging HDFS/ GlusterFS File Staging File Staging Input Data Output Data

16.16 STATIC RESOURCE ALLOCATION Kubelet Kubelet Kubelet Kubelet Spark-Submit App1 Kubernetes Master App1 Driver Pod App1 Executor Pod App1 Executor Pod docker storage The resources are allocated in the beginning and cannot change during the executors are running. Static resource allocation uses local storage( docker -storage) for data shuffle. docker storage Step4 Step1 Step2 Step3 App1 Executor Pod docker storage Use EmptyDir in K8s for this temporary data shuffle. Step4 Step4

17.17 DYNAMIC RESOURCE ALLOCATION Kubelet Kubelet Kubelet Kubelet Spark-Submit App1 Kubernetes Master App1 Driver Pod App1 Executor Pod App1 Executor Pod Step4 Step1 Step2 Step3 Shuffle Service Pod Shuffle Service Pod App1 Executor Pod Shuffle Service Pod The resources are allocated in the beginning, but applications can change the resource in run time. Dynamic resource allocation uses shuffle service container for data shuffle. There are two implementations: 1 st is to run shuffle service in a pod. 2 nd is to run shuffle service as a container with a executor. Step4 Step4

18.JD.COM CASE STUDY 18

19.19 JD.com MOONSHOT JD has used K8s* as cloud infrastructure management for several years. JD would like to use K8s to manage all the computing resources including CPU, GPU, FPGA, …etc. Target for all AI workloads; Using the same cluster for training/inference. Across multiple Machine Learning framework including Caffe , TensorFlow , XGBoost , MXNet , BigDL …etc. To optimize workloads for different resource allocation. Multi-tenancy support by different user accounts and resource pool. Reference: https://mp.weixin.qq.com/s?__biz=MzA5Nzc2NDAxMg%3D%3D&mid=2649864623&idx=1&sn=f476db89b3d0ec580e8a63ff78144a37

20.20 MOONSHOT ARCHITECTURE Infrastructure Container Cluster Computing Engine Applications Management Center Authority Mgmt. Task Mgmt. Procedure Mgmt. Monitor Center Logging Center Image Recognition NLP Security Solutions Finance Public Cloud TensorFlow * Caffe * MXNet * XGBoost * Spark BigDL MLlib Spark SQL Streaming DeepLearning4j Docker* + Kubernetes* CPU GPU FPGA Ethernet InfiniBand Omini -Path File System Network SSD HDD

21.21 JD.com CONSIDERATION Separate Compute and Storage cluster Use Kubernetes as resource manager for compute resource Use Stand-alone HDFS Cluster for data persistent Network Choices Host Network for batch job Calico Network for inference job Data locality depends on the workload types Feature Enabling

22.22 NETWORK SOLUTIONS Flannel* A simple and easy to configure layer 3 network fabric designed for K8s. It runs flanneld on each host to allocate subnet and uses etcd to store network configuration. Flannel supports several backends including VXLAN, host- gw , UDP, …etc. Calico* An approach to virtual networking and network security for containers, VMs, and bare metal services, which provides a rich set of security enforcement capabilities running on top of a highly scalable and efficient virtual network fabric. Calico uses BGP to set up the network and it also supports IPIP methods to build up a tunnel network. Host Network OpenVSwitch * Others

23.23 Why CALICO? No overlay required Little overhead comparing to bare metal. Sometimes, overlay network(encapsulating packets inside an extra IP header) is an option, not MUST. Simple & Scalable The architecture is simple, the deployment is simple as well. We can easily deploy thousands of nodes in k8s by using yaml file. Policy-driven network security In many scenarios of JD.com, for example, multi-tenancy is necessary to make network isolation. Calico enables developers and operators to easily define network policy with fine granularity such as allowed or blocked connections. Widely deployed, and proven at scale W e leverage the experience from other big companies who share their issues in the community. These experience are very valuable for us at the very beginning of moonshot. Fortunately, Calico has passed the verified in our production environment.

24.24 NETWORK PERFORMANCE RESULT No. Scenario Concurrency # Total Time(s) Request per Second Waiting Time( ms ) 1 Client -> Nginx 50 50.044 19982 0.05 2 Weave: Client -> iptables -> Weave -> Pod 50 132.839 7527 0.133 3 Calico with IPIP: Client -> iptables -> Calico -> Pod 50 111.136 8998 0.111 4 Calico with BGP: Client -> iptables -> Calico -> Pod 50 59.218 16886 0.059 All scenarios use ab command to connect to nginx * server with different IP address. “ab -n 1000000 -c 100 - H"Host : nginx.jd.local " 172.20.141.72:80/index.html “ JD.com decides to pick up Calico since Calico provides better performance than Weave and Calico can still provide tunnel method(via IPIP) to set up network.

25.25 DATA LOCALITY ISSUE In cloud environment, compute and storage resource are separated. This could highlight data locality issue with performance drop. Some possible solutions can help to resolve data locality issues Choose right workloads, most workloads only need to read data and write data at beginning and end phase. HDFS* on Kubernetes Alluxio *

26.26 DATA LOCALITY IMPACT Workloads Types Locality Datasize Cluster Size Network Execution Time Notes Terasort IO Local 320GB 5 1Gb 2119.926sec 1x Terasort IO Remote 320GB 5 Spark + 3 Hadoop 1Gb 4212.029sec 1.98x Terasort IO Local 320GB 5 10Gb 500.198sec 1x Terasort IO Remote 320GB 5 Spark + 3 Hadoop 10Gb 548.549sec 1.10x Kmeans CPU Local 240GB 5 10Gb 1156.235sec 1x Kmeans CPU Remote 240GB 5 Spark + 3 Hadoop 10Gb 1219.138sec 1.05x Note1: This testing is using 5-nodes bare metal cluster. Note2: 4 SATA SSD per Spark and Hadoop node Note3: Performance may impact in different configuration including the number of disk, network bandwidth, as well as different platform.

27.27 SPARK PERFORMANCE COMPARISON Task# Remote in K8s (sec) Remote in Yarn (sec) Local in Yarn (sec) 1 - k8s/yarn local (X) 1 - k8s/yarn remote (X) Task 01 424.33 446.63 398.33 -6.53% 4.99% Task 02 996.00 1008.00 1155.67 13.82% 1.19% Task 03 420.67 431.33 391.33 -7.50% 2.47% Task 04 399.67 423.39 413.33 3.31% 5.60% Task 05 525.33 552.17 489.33 -7.36% 4.86% Task 06 659.67 701.31 647.67 -1.85% 5.96% Task 08 2224.33 2259.00 1717.67 -29.5% 1.53% Task 10 1013.33 1219.00 958.00 -5.78% 16.87% Task 11 1539.33 1740.33 1351.33 -13.91% 11.55% Run 3 times per task and list result in average. Separate compute and storage bring some performance loss compared to yarn local. Spark on K8s(remote) provide better performance comparing to yarn remote.

28.New Feature Enabling in JD.com Client Mode Support Spark SQL must use Client Mode via Spark Shell Zeppelin/ Jupyter Support Logging and Monitoring System Support Filebeat integration Multiple Disks for Shuffle Service Single Disk( EmpDir ) by default Distributed Image Repository Need to support Huge Traffic 28

29.SUMMARY 29