Java kubernetes上的Spark作业失败，没有特定错误

我正在尝试在Kubernetes上部署Spark作业，使用

kubectl apply-f

（在基于Dockerfile构建Docker映像之后）。pod在K8s上成功创建，然后以失败状态快速停止。日志中的任何内容都不能帮助理解错误的来源。已使用相同的Dockerfile和config文件在K8s群集上成功部署了其他作业

spark作业应该从kafka主题中读取数据，解析并在控制台中输出

知道是什么导致了工作失败吗

Dockerfile，使用docker构建--rm-f“Dockerfile”xxxxxxxx:80/apache/myapp测试构建docker push xxxxxxxx:80/apache/myapp测试：

FROM xxxxxxxx:80/apache/spark:v2.4.4-gcs-prometheus

#USER root

ADD myapp.jar /jars

RUN adduser --no-create-home --system spark

RUN chown -R spark /prometheus /opt/spark

USER spark

config_file.yml：

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  name: myapp
  namespace: spark
  labels:
    app: myapp-test
    release: spark-2.4.4
spec:
  type: Java
  mode: cluster
  image: "xxxxxxxx:80/apache/myapp-test"
  imagePullPolicy: Always
  mainClass: spark.jobs.app.streaming.Main
  mainApplicationFile: "local:///jars/myapp.jar"
  sparkVersion: "2.4.4"
  restartPolicy:
    type: OnFailure
    onFailureRetries: 5
    onFailureRetryInterval: 30
    onSubmissionFailureRetries: 0
    onSubmissionFailureRetryInterval: 0
  driver:
    cores: 1
    memory: "1G"
    labels:
      version: 2.4.4
monitoring:
    exposeDriverMetrics: true
    exposeExecutorMetrics: true
    prometheus:
      jmxExporterJar: "/prometheus/jmx_prometheus_javaagent-0.11.0.jar"
      port: 8090
  imagePullSecrets:
  - xxx

日志：

++ id -u
+ myuid=100
++ id -g
+ mygid=65533
+ set +e
++ getent passwd 100
+ uidentry='spark:x:100:65533:Linux User,,,:/home/spark:/sbin/nologin'
+ set -e
+ '[' -z 'spark:x:100:65533:Linux User,,,:/home/spark:/sbin/nologin' ']'
+ SPARK_K8S_CMD=driver
+ case "$SPARK_K8S_CMD" in
+ shift 1
+ SPARK_CLASSPATH=':/opt/spark/jars/*'
+ env
+ grep SPARK_JAVA_OPT_
+ + sed sort -t_ 's/[^=]*=\(.*\)/\1/g'-k4 
-n
+ readarray -t SPARK_EXECUTOR_JAVA_OPTS
+ '[' -n '' ']'
+ '[' -n '' ']'
+ PYSPARK_ARGS=
+ '[' -n '' ']'
+ R_ARGS=
+ '[' -n '' ']'
+ '[' '' == 2 ']'
+ '[' '' == 3 ']'
+ case "$SPARK_K8S_CMD" in
+ CMD=("$SPARK_HOME/bin/spark-submit" --conf "spark.driver.bindAddress=$SPARK_DRIVER_BIND_ADDRESS" --deploy-mode client "$@")
+ exec /sbin/tini -s -- /opt/spark/bin/spark-submit --conf spark.driver.bindAddress=192.168.225.14 --deploy-mode client --properties-file /opt/spark/conf/spark.properties --class spark.jobs.app.streaming.Main spark-internal
20/04/20 09:27:20 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
log4j:WARN No appenders could be found for logger (org.apache.spark.deploy.SparkSubmit$$anon$2).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.

如kubectl所示的Pod事件描述Pod：

Events:
  Type     Reason            Age                From                             Message
  ----     ------            ----               ----                             -------
  Normal   Scheduled         15m                default-scheduler                Successfully assigned spark/myapp-driver to xxxxxxxx.preprod.local
  Warning  FailedMount       15m                kubelet, xxxxxxxx.preprod.local  MountVolume.SetUp failed for volume "spark-conf-volume" : configmap "myapp-1587388343593-driver-conf-map" not found
  Warning  DNSConfigForming  15m (x4 over 15m)  kubelet, xxxxxxxx.preprod.local  Search Line limits were exceeded, some search paths have been omitted, the applied search line is: spark.svc.cluster.local svc.cluster.local cluster.local preprod.local
  Normal   Pulling           15m                kubelet, xxxxxxxx.preprod.local  Pulling image "xxxxxxxx:80/apache/myapp-test"
  Normal   Pulled            15m                kubelet, xxxxxxxx.preprod.local  Successfully pulled image "xxxxxxxx:80/apache/myapp-test"
  Normal   Created           15m                kubelet, xxxxxxxx.preprod.local  Created container spark-kubernetes-driver
  Normal   Started           15m                kubelet, xxxxxxxx.preprod.local  Started container spark-kubernetes-driver 

---

您必须查看

conf/spark env.（sh|cmd）

从配置日志开始

Spark使用log4j进行日志记录。您可以通过添加 conf目录中的log4j.properties文件。一个开始的方法是 复制位于此处的现有log4j.properties.template

---

谢谢，成功了！问题完全是另外一回事，但是通过正确配置log4j，我们能够识别它并继续前进。在阅读其他stackoverflow问题和答案时，我的印象是这些log4j警告可以忽略，但我想不会

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# Set everything to be logged to the console
log4j.rootCategory=INFO, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Set the default spark-shell log level to WARN. When running the spark-shell, the
# log level for this class is used to overwrite the root logger's log level, so that
# the user can have different defaults for the shell and regular Spark apps.
log4j.logger.org.apache.spark.repl.Main=WARN

# 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.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
log4j.logger.org.apache.parquet=ERROR
log4j.logger.parquet=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