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本文介绍如何在ACK上运行Spark作业，并使用EMR Spark Core和JindoFS分布式缓存优化性能。

前提条件

  • ACK标准集群，节点规格选用ecs.d1ne.6xlarge大数据型，共20个Worker节点。
  • 阿里云OSS，并创建一个bucket，用来替换YAML文件中的OSS配置。
  • 利用TPC-DS生成1TB数据，存储在阿里云OSS上，详情参考生成数据。

环境准备

  • Worker节点挂载磁盘

    ecs.d1ne.6xlarge型实例默认自带12块5500G HDD数据盘，这些数据盘需要挂载后才能使用，挂载方式如下

    wget https://shilei-tpc-ds.oss-cn-beijing.aliyuncs.com/tools/mount.tgz
tar -xzvf mount.tgz
cd mount/
./mount
# SSH password: 此时输入SSH密码后，开始自动执行磁盘挂载

  • 安装ack-spark-operator

    通过安装ack-spark-operator组件，您可以使用ACK Spark Operator简化提交作业的操作。

    1). 登录容器服务管理控制台。

    2). 在控制台左侧导航栏中，选择市场 > 应用目录。

    3). 在应用目录页面，找到并单击ack-spark-operator。

    4). 在应用目录 - ack-spark-operator页面右侧，单击创建。

  • 安装ack-spark-history-server（可选）

    ACK Spark History Server通过记录Spark执行任务过程中的日志和事件信息，并提供UI界面，帮助排查问题。

    在创建ack-spark-history-server组件时，您需在参数页签配置OSS相关的信息，用于存储Spark历史数据。

    1). 登录容器服务管理控制台。

    2). 在控制台左侧导航栏中，选择市场 > 应用目录。

    3). 在应用目录页面，找到并单击ack-spark-history-server。

    4). 在应用目录 - ack-spark-history-server页面右侧，单击创建。

  • 安装JindoFS

    通过安装jindofs组件，您可以使用JindoFS的缓存加速服务，提升OSS读取速度，缓解带宽压力。

    1).登录容器服务管理控制台。

    2).在控制台左侧导航栏中，选择****市场** > *应用目录***。

    3).在应用目录页面，找到并单击jindofs

    4).在应用目录 - jindofs 页面右侧，选择对应的ACK集群并单击创建。

    注: 需要根据ACK集群的环境，修改对应jindofs的参数（如AccessKey、以及mount的磁盘路径），完整参数示例如下：

    # Default values for JindoFS.
# This is a YAML-formatted file.
# Declare variables to be passed into your templates.


image: registry-vpc.cn-beijing.aliyuncs.com/jindofs/smartdata
imageTag: "2.7.4"
imagePullPolicy: Always

fuseImage: registry-vpc.cn-beijing.aliyuncs.com/jindofs/jindo-fuse
fuseImageTag: "2.7.4"

user: 0
group: 0
fsGroup: 0

useHostNetwork: true
useHostPID: true

properties:
  logDir: /mnt/diskb/bigboot/log

master:
  replicaCount: 1
  resources:
    limits:
      cpu: "8"
      memory: "32G" # increase memory corresponding to filelet(blocklet) cache size
    requests:
      cpu: "1"
      memory: "1G"
  nodeSelector:
    beta.kubernetes.io/instance-type: ecs.d2s.10xlarge
  properties:
    namespace.rpc.port: 8101
    namespace.meta-dir: /mnt/diskb/bigboot/server
    namespace.filelet.cache.size: 100000
    namespace.blocklet.cache.size: 1000000
    namespace.backend.type: rocksdb
    jfs.namespaces: default
    jfs.namespaces.default.mode :  cache
    jfs.namespaces.default.oss.uri: YOUR-OSS-URI
    jfs.namespaces.default.oss.access.key: YOUR-ACCESS-KEY-ID
    jfs.namespaces.default.oss.access.secret: YOUR-ACCESS-KEY-SECRET

worker:
  resources:
    limits:
      cpu: "8"
      memory: "32G" # increase memory corresponding to the number of concurrent reading/writing files
    requests:
      cpu: "1"
      memory: "1G"
  nodeSelector:
    beta.kubernetes.io/instance-type: ecs.d2s.10xlarge
  properties:
    storage.rpc.port: 6101
    storage.data-dirs: /mnt/diskc/bigboot,/mnt/diskd/bigboot,/mnt/diske/bigboot,/mnt/diskf/bigboot,/mnt/diskg/bigboot,/mnt/diskh/bigboot,/mnt/diski/bigboot,/mnt/diskj/bigboot,/mnt/diskk/bigboot,/mnt/diskl/bigboot,/mnt/diskm/bigboot
    storage.temp-data-dirs: /mnt/diskb/bigboot/tmp
    storage.watermark.high.ratio: 0.4
    storage.watermark.low.ratio: 0.2
    storage.data-dirs.capacities: 2000g,2000g,2000g,2000g,2000g,2000g,2000g,2000g,2000g,2000g,2000g
    storage.meta-dir: /mnt/diskb/bigboot/bignode

fuse:
  args:
  hostPath: /mnt/jfs
  properties:
    client.storage.rpc.port: 6101
    client.oss.retry: 5
    client.oss.upload.threads: 4
    client.oss.upload.queue.size: 5
    client.oss.upload.max.parallelism: 16
    client.oss.timeout.millisecond: 30000
    client.oss.connection.timeout.millisecond: 3000

mounts:
  master:
    - /mnt/diskb
  workersAndClients:
    - /mnt/diskb
    - /mnt/diskc
    - /mnt/diskd
    - /mnt/diske
    - /mnt/diskf
    - /mnt/diskg
    - /mnt/diskh
    - /mnt/diski
    - /mnt/diskj
    - /mnt/diskk
    - /mnt/diskl
    - /mnt/diskm

提交Spark作业

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  name: tpcds-benchmark-emrspark-ess-1t
  namespace: default
spec:
  type: Scala
  mode: cluster
  image: registry.cn-beijing.aliyuncs.com/zf-spark/spark-2.4.5:for-tpc-ds-2
  imagePullPolicy: Always
  mainClass: com.databricks.spark.sql.perf.tpcds.TPCDS_Standalone
  mainApplicationFile: "oss://<YOUR-BUCKET>/jars/spark-sql-perf-assembly-0.5.0-SNAPSHOT.jar"
  arguments:
    - "--dataset_location"
    - "oss://<YOUR-BUCKET>/datasets/"
    - "--output_location"
    - "oss://<YOUR-BUCKET>/outputs/ack-pr-10t-emr-with-ess"
    - "--iterations"
    - "1"
    - "--shuffle_partitions"
    - "1000"
    - "--scale_factor"
    - "10000"
    - "--regenerate_dataset"
    - "false"
    - "--regenerate_metadata"
    - "false"
    - "--only_generate_data_and_meta"
    - "false"
    - "--format"
    - "parquet"
    - "--query_exclude_list"
    - "q14a,q14b,q67"
  sparkVersion: 2.4.5
  restartPolicy:
    type: Never
  hadoopConf:
    hive.metastore.uris: thrift://service-hive-metastore.default:9083
    hive.metastore.client.socket.timeout: 600s
  sparkConf:
    spark.eventLog.enabled: "true"
    spark.eventLog.dir: "oss://<YOUR-BUCKET>/spark/eventlogs"
    spark.driver.extraJavaOptions: "-XX:-PrintGC -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintGCDateStamps"
    spark.driver.maxResultSize: 40g
    spark.executor.extraJavaOptions: "-XX:MaxDirectMemorySize=6g -XX:-PrintGC -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintGCDateStamps"
    spark.locality.wait.node: "0"
    spark.locality.wait.process: "0"
    spark.locality.wait.rack: "0"
    spark.locality.wait: "0"
    spark.memory.fraction: "0.8"
    spark.memory.offHeap.enabled: "false"
    spark.memory.offHeap.size: "17179869184"
    spark.sql.adaptive.bloomFilterJoin.enabled: "false"
    spark.sql.adaptive.enabled: "false"
    spark.sql.analyze.column.async.delay: "200"
    spark.sql.auto.reused.cte.enabled: "true"
    spark.sql.broadcastTimeout: "3600"
    spark.sql.columnVector.offheap.enabled: "false"
    spark.sql.crossJoin.enabled: "true"
    spark.sql.delete.optimizeInSubquery: "true"
    spark.sql.dynamic.runtime.filter.bbf.enabled: "false"
    spark.sql.dynamic.runtime.filter.enabled: "true"
    spark.sql.dynamic.runtime.filter.exact.enabled: "true"
    spark.sql.dynamic.runtime.filter.table.size.lower.limit: "1069547520"
    spark.sql.dynamic.runtime.filter.table.size.upper.limit: "5368709120"
    spark.sql.files.openCostInBytes: "34108864"
    spark.sql.inMemoryColumnarStorage.compressed: "true"
    spark.sql.join.preferNativeJoin: "false"
    spark.sql.native.codecache: "true"
    spark.sql.native.codegen.wholeStage: "false"
    spark.sql.native.nativewrite: "false"
    spark.sql.pkfk.optimize.enable: "true"
    spark.sql.pkfk.riJoinElimination: "true"
    spark.sql.shuffle.partitions: "1000"
    spark.sql.simplifyDecimal.enabled: "true"
    spark.sql.sources.parallelPartitionDiscovery.parallelism: "432"
    spark.sql.sources.parallelPartitionDiscovery.threshold: "32"
    spark.shuffle.reduceLocality.enabled: "false"
    spark.shuffle.service.enabled: "true"
    spark.dynamicAllocation.enabled: "false"
    spark.local.dir: /mnt/diskb/spark-data,/mnt/diskc/spark-data,/mnt/diskd/spark-data,/mnt/diske/spark-data,/mnt/diskf/spark-data,/mnt/diskg/spark-data,/mnt/diskh/spark-data,/mnt/diski/spark-data,/mnt/diskj/spark-data,/mnt/diskk/spark-data,/mnt/diskl/spark-data,/mnt/diskm/spark-data
    spark.shuffle.manager: org.apache.spark.shuffle.sort.SortShuffleManager
  volumes:
    - name: diskb
      hostPath:
        path: /mnt/diskb
        type: Directory
    - name: diskc
      hostPath:
        path: /mnt/diskc
        type: Directory
    - name: diskd
      hostPath:
        path: /mnt/diskd
        type: Directory
    - name: diske
      hostPath:
        path: /mnt/diske
        type: Directory
    - name: diskf
      hostPath:
        path: /mnt/diskf
        type: Directory
    - name: diskg
      hostPath:
        path: /mnt/diskg
        type: Directory
    - name: diskh
      hostPath:
        path: /mnt/diskh
        type: Directory
    - name: diski
      hostPath:
        path: /mnt/diski
        type: Directory
    - name: diskj
      hostPath:
        path: /mnt/diskj
        type: Directory
    - name: diskk
      hostPath:
        path: /mnt/diskk
        type: Directory
    - name: diskl
      hostPath:
        path: /mnt/diskl
        type: Directory
    - name: diskm
      hostPath:
        path: /mnt/diskm
        type: Directory
  driver:
    cores: 15
    coreLimit: 15000m
    memory: 50g
    labels:
      version: 2.4.5
    serviceAccount: spark
    env:
      - name: TZ
        value: "Asia/Shanghai"
  executor:
    cores: 4
    coreLimit: 6000m
    instances: 20
    memory: 24g
    memoryOverhead: 10g
    deleteOnTermination: false
    labels:
      version: 2.4.5
    env:
      - name: TZ
        value: "Asia/Shanghai"
    volumeMounts:
      - mountPath: /mnt/diskb
        name: diskb
      - mountPath: /mnt/diskc
        name: diskc
      - mountPath: /mnt/diskd
        name: diskd
      - mountPath: /mnt/diske
        name: diske
      - mountPath: /mnt/diskf
        name: diskf
      - mountPath: /mnt/diskg
        name: diskg
      - mountPath: /mnt/diskh
        name: diskh
      - mountPath: /mnt/diski
        name: diski
      - mountPath: /mnt/diskj
        name: diskj
      - mountPath: /mnt/diskk
        name: diskk
      - mountPath: /mnt/diskl
        name: diskl
      - mountPath: /mnt/diskm
        name: diskm

完整YAML文件可参考tpcds-benchmark-with-emrspark-jindofs，其中spec.mainApplicationFile中的jar包 可通过这里下载，放在自己的OSS中。