Ich habe Spark mithilfe des AWS EC2-Handbuchs installiert und kann das Programm mithilfe des bin/pysparkSkripts problemlos starten, um zur Spark-Eingabeaufforderung zu gelangen. Außerdem kann ich den Schnellstart erfolgreich ausführen.
Ich kann jedoch für mein ganzes Leben nicht herausfinden, wie die gesamte ausführliche INFOProtokollierung nach jedem Befehl gestoppt werden kann .
Ich habe fast jedes mögliche Szenario im folgenden Code (Auskommentieren, Ausstellen auf AUS) in meiner log4j.propertiesDatei in dem confOrdner ausprobiert, in dem ich die Anwendung starte, sowie auf jedem Knoten, und nichts tut etwas. INFONach Ausführung jeder Anweisung werden die Protokollierungsanweisungen immer noch gedruckt.
Ich bin sehr verwirrt darüber, wie das funktionieren soll.
#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
# Settings to quiet third party logs that are too verbose
log4j.logger.org.eclipse.jetty=WARN
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFOHier ist mein vollständiger Klassenpfad, wenn ich benutze SPARK_PRINT_LAUNCH_COMMAND:
Spark-Befehl: /Library/Java/JavaVirtualMachines/jdk1.8.0_05.jdk/Contents/Home/bin/java -cp: /root/spark-1.0.1-bin-hadoop2/conf: /root/spark-1.0.1 -bin-hadoop2 / conf: /root/spark-1.0.1-bin-hadoop2/lib/spark-assembly-1.0.1-hadoop2.2.0.jar: /root/spark-1.0.1-bin-hadoop2/lib /datanucleus-api-jdo-3.2.1.jar:/root/spark-1.0.1-bin-hadoop2/lib/datanucleus-core-3.2.2.jar:/root/spark-1.0.1-bin-hadoop2 /lib/datanucleus-rdbms-3.2.1.jar -XX: MaxPermSize = 128m -Djava.library.path = -Xms512m -Xmx512m org.apache.spark.deploy.SparkSubmit spark-shell --class org.apache.spark. repl.Main
Inhalt von spark-env.sh:
#!/usr/bin/env bash
# This file is sourced when running various Spark programs.
# Copy it as spark-env.sh and edit that to configure Spark for your site.
# Options read when launching programs locally with
# ./bin/run-example or ./bin/spark-submit
# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node
# - SPARK_PUBLIC_DNS, to set the public dns name of the driver program
# - SPARK_CLASSPATH=/root/spark-1.0.1-bin-hadoop2/conf/
# Options read by executors and drivers running inside the cluster
# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node
# - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program
# - SPARK_CLASSPATH, default classpath entries to append
# - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data
# - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos
# Options read in YARN client mode
# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
# - SPARK_EXECUTOR_INSTANCES, Number of workers to start (Default: 2)
# - SPARK_EXECUTOR_CORES, Number of cores for the workers (Default: 1).
# - SPARK_EXECUTOR_MEMORY, Memory per Worker (e.g. 1000M, 2G) (Default: 1G)
# - SPARK_DRIVER_MEMORY, Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)
# - SPARK_YARN_APP_NAME, The name of your application (Default: Spark)
# - SPARK_YARN_QUEUE, The hadoop queue to use for allocation requests (Default: ‘default’)
# - SPARK_YARN_DIST_FILES, Comma separated list of files to be distributed with the job.
# - SPARK_YARN_DIST_ARCHIVES, Comma separated list of archives to be distributed with the job.
# Options for the daemons used in the standalone deploy mode:
# - SPARK_MASTER_IP, to bind the master to a different IP address or hostname
# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master
# - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y")
# - SPARK_WORKER_CORES, to set the number of cores to use on this machine
# - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g)
# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker
# - SPARK_WORKER_INSTANCES, to set the number of worker processes per node
# - SPARK_WORKER_DIR, to set the working directory of worker processes
# - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")
# - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")
# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
# - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers
export SPARK_SUBMIT_CLASSPATH="$FWDIR/conf"