本文介绍如何在ACK上,使用EMR Spark和TPC-DS生成测试数据。
- 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。
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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。
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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。
apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
name: tpcds-data-generation-10t
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"
- "--iterations"
- "1"
- "--shuffle_partitions"
- "1000"
- "--scale_factor"
- "10000" #指定生成数据大小,默认单位为GB
- "--regenerate_dataset"
- "true"
- "--regenerate_metadata"
- "true"
- "--only_generate_data_and_meta"
- "true"
- "--format"
- "parquet"
sparkVersion: 2.4.5
restartPolicy:
type: Never
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=32g -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"
driver:
cores: 15
coreLimit: 15000m
memory: 30g
labels:
version: 2.4.5
serviceAccount: spark
env:
- name: TZ
value: "Asia/Shanghai"
executor:
cores: 8
coreLimit: 8000m
instances: 20
memory: 24g
labels:
version: 2.4.5
env:
- name: TZ
value: "Asia/Shanghai"
完整YAML文件可参考tpcds-data-generation,其中spec.mainApplicationFile中的jar包 可通过这里下载,放在自己的OSS中。