在S3中捕获Kubernetes Spark驱动程序和执行程序日志并在历史服务器中查看

Question

我正在 Kubernetes 上使用 Spark-submit cli 运行 Spark 3.0.0 和 Hadoop 2.7，如下所示，

./spark-submit \
--master=k8s://https://api.k8s.my-domain.com \
--deploy-mode cluster \
--name sparkle \
--num-executors 2 \
--executor-cores 2 \
--executor-memory 2g \
--driver-memory 2g \
--class com.myorg.sparkle.Sparkle \
--conf spark.driver.extraJavaOptions=-Dlog4j.configuration=file:/opt/spark/conf/log4j.properties \
--conf spark.executor.extraJavaOptions=-Dlog4j.configuration=file:/opt/spark/conf/log4j.properties \
--conf spark.kubernetes.submission.waitAppCompletion=false \
--conf spark.kubernetes.allocation.batch.delay=10s \
--conf spark.kubernetes.appKillPodDeletionGracePeriod=20s \
--conf spark.kubernetes.node.selector.workloadType=spark \
--conf spark.kubernetes.driver.pod.name=sparkle-driver \
--conf spark.kubernetes.container.image=custom-registry/spark:latest \
--conf spark.kubernetes.namespace=spark \
--conf spark.eventLog.dir='s3a://my-bucket/spark-logs' \
--conf spark.history.fs.logDirectory='s3a://my-bucket/spark-logs' \
--conf spark.eventLog.enabled='true' \
--conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
--conf spark.kubernetes.authenticate.executor.serviceAccountName=spark \
--conf spark.hadoop.fs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem \
--conf spark.hadoop.fs.s3a.aws.credentials.provider=com.amazonaws.auth.WebIdentityTokenCredentialsProvider \
--conf spark.kubernetes.driver.annotation.iam.amazonaws.com/role=K8sRoleSpark \
--conf spark.kubernetes.executor.annotation.iam.amazonaws.com/role=K8sRoleSpark \
--conf spark.kubernetes.driver.secretKeyRef.AWS_ACCESS_KEY_ID=aws-secrets:key \
--conf spark.kubernetes.driver.secretKeyRef.AWS_SECRET_ACCESS_KEY=aws-secrets:secret \
--conf spark.kubernetes.executor.secretKeyRef.AWS_ACCESS_KEY_ID=aws-secrets:key \
--conf spark.kubernetes.executor.secretKeyRef.AWS_SECRET_ACCESS_KEY=aws-secrets:secret \
--conf spark.hadoop.fs.s3a.endpoint=s3.ap-south-1.amazonaws.com \
--conf spark.hadoop.com.amazonaws.services.s3.enableV4=true \
--conf spark.yarn.maxAppAttempts=4 \
--conf spark.yarn.am.attemptFailuresValidityInterval=1h \
s3a://dp-spark-jobs/sparkle/jars/sparkle.jar \
--commonConfigPath https://my-bucket.s3.ap-south-1.amazonaws.com/sparkle/configs/prod_main_configs.yaml \
--jobConfigPath https://my-bucket.s3.ap-south-1.amazonaws.com/sparkle/configs/cc_configs.yaml \
--filePathDate 2021-03-29 20000

我托管了一个不同的 Pod，运行具有相同映像的历史服务器。历史服务器能够读取所有事件日志并显示详细信息。作业执行成功。

我在历史服务器中没有看到驱动程序或执行程序日志。此外，没有可用的阶段日志。我正在传递包含以下内容的 log4j.properties，

# Define the root logger with appender file
log4j.rootLogger = INFO, console

# Redirect log messages to console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.Target=System.out
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n

# Settings to quiet third party logs that are too verbose
log4j.logger.org.spark-project.jetty=WARN
log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.parquet=ERROR
log4j.logger.parquet=ERROR

# Silence akka remoting
log4j.logger.Remoting=ERROR
log4j.logger.akka.event.slf4j=ERROR
log4j.logger.org.spark-project.jetty.server=ERROR
# log4j.logger.org.apache.spark=ERROR
log4j.logger.org.apache.spark.deploy=INFO
log4j.logger.com.anjuke.dm=${dm.logging.level}
log4j.logger.org.eclipse.jetty=WARN
log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
log4j.logger.org.apache.hadoop=ERROR
log4j.logger.org.apache.hive=ERROR
log4j.logger.org.apache.spark.sql.hive=ERROR
log4j.logger.org.apache.hadoop.hive=ERROR
log4j.logger.org.datanucleus=ERROR

# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR

# Silence Spark Streaming
log4j.logger.org.apache.spark.sql.execution.streaming.state=ERROR
log4j.logger.org.apache.spark.SparkContext=ERROR
log4j.logger.org.apache.spark.executor=ERROR
log4j.logger.org.apache.spark.storage=ERROR
log4j.logger.org.apache.spark.scheduler=ERROR
log4j.logger.org.apache.spark.ContextCleaner=ERROR
log4j.logger.org.apache.spark.sql=ERROR
log4j.logger.org.apache.parquet.hadoop=ERROR
log4j.logger.org.apache.spark.sql.kafka010.KafkaSource=ERROR

log4j.additivity.org.apache.spark=false
log4j.additivity.org.apache.hadoop=false
log4j.logger.org.spark_project.jetty=WARN
log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR

# Silence Kafka
log4j.logger.org.apache.kafka=ERROR, stdout
log4j.additivity.org.apache.kafka=false
log4j.logger.org.apache.kafka.clients.consumer=ERROR
log4j.logger.org.apache.kafka.clients.consumer.internals.Fetcher=ERROR

历史服务器在“执行程序”选项卡中不显示日志列。对于阶段部分，日志列为空。似乎只记录事件，没有从任何 Pod 捕获标准输出、标准错误

如何确保驱动程序和执行程序 stdout、stderr 记录到 S3 并在历史服务器中可用？