我使用亚马逊网络服务的EC2指南安装了Spark,我可以使用bin/pyspark
脚本很好地启动程序,进入spark提示符,还可以成功地执行Quick Start quide。
然而,我无论如何也想不出如何在每个命令之后停止所有详细的INFO
日志记录。
我已经在下面的代码中尝试了几乎所有可能的场景(注释掉,设置为OFF),在我启动应用程序的conf
文件夹中的log4j.properties
文件中,以及在每个节点上,都没有任何效果。在执行每条语句之后,我仍然可以打印日志记录INFO
语句。
我对这应该如何工作感到非常困惑。
#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=INFO
下面是我使用SPARK_PRINT_LAUNCH_COMMAND
时的完整类路径
Spark命令: /Library/Java/JavaVirtualMachines/jdk1.8.0_05.jdk/Contents/Home/bin/java -cp Sparkhadoop2/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
spark-env.sh
的内容
#!/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="$FWDIR/conf"
发布于 2014-09-30 22:36:30
只需在spark目录中执行以下命令:
cp conf/log4j.properties.template conf/log4j.properties
编辑log4j.properties:
# Set everything to be logged to the console
log4j.rootCategory=INFO, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
# Settings to quiet third party logs that are too verbose
log4j.logger.org.eclipse.jetty=WARN
log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
在第一行替换:
log4j.rootCategory=INFO, console
出自:
log4j.rootCategory=WARN, console
保存并重新启动shell。它适用于我在OS上的Spark 1.1.0和Spark 1.5.1。
发布于 2016-11-09 18:07:50
在Spark2.0中,您还可以使用setLogLevel为您的应用程序动态配置它
from pyspark.sql import SparkSession
spark = SparkSession.builder.\
master('local').\
appName('foo').\
getOrCreate()
spark.sparkContext.setLogLevel('WARN')
在pyspark控制台中,默认的spark
会话已经可用。
发布于 2015-08-25 23:46:47
受到pyspark/tests.py的启发,我做了
def quiet_logs(sc):
logger = sc._jvm.org.apache.log4j
logger.LogManager.getLogger("org"). setLevel( logger.Level.ERROR )
logger.LogManager.getLogger("akka").setLevel( logger.Level.ERROR )
在创建SparkContext之后调用它将我的测试记录的stderr行从2647减少到163。然而,创建SparkContext本身的日志163最高可达
15/08/25 10:14:16 INFO SparkDeploySchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0
我不清楚如何通过编程来调整它们。
https://stackoverflow.com/questions/25193488
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