如何关闭Spark中的INFO日志记录？ [英] How to turn off INFO logging in Spark?

问题描述

我使用AWS EC2指南安装了Spark，并且我可以使用 bin / pyspark 脚本启动程序以获得spark提示，并且还可以执行快速启动但是，我不能为我的生活找出如何停止所有详细的 INFO 在每条命令后面进行日志记录。

在我的 log4j.properties中，我尝试了几乎所有可能的场景（注释掉，设置为OFF）在 conf 文件夹中的文件中，我从哪里启动应用程序以及在每个节点上执行任何操作。在执行每条语句后，我仍然记录日志 INFO 语句。

我很困惑这是怎么回事上班。

 ＃设置所有要记录到控制台的信息log4j.rootCategory =信息，控制台
 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 
 
＃安静的第三方日志过于冗长
 log4j.logger.org.eclipse.jetty = WARN 
 log4j.logger.org.apache.spark.repl.SparkIMain $ exprTyper = INFO 
 log4j.logger.org.apache.spark。 repl.SparkILoop $ SparkILoopInterpreter = INFO 
  

当我使用时， SPARK_PRINT_LAUNCH_COMMAND ：

lockquote
Spark命令
/Library/Java/JavaVirtualMachines/jdk1.8.0_05。 jdk / Contents / Home / bin / java
-cp：/root/spark-1.0.1-bin-hadoop2/conf：/ roo T /火花1.0.1彬hadoop2 / conf目录：/root/spark-1.0.1-bin-hadoop2/lib/spark-assembly-1.0.1-hadoop2.2.0.jar：/root/spark-1.0。 1彬hadoop2 / LIB / DataNucleus将-API-JDO-3.2.1.jar：/root/spark-1.0.1-bin-hadoop2/lib/datanucleus-core-3.2.2.jar：/根/火花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的内容-env.sh ：

 ＃！/ usr / bin / env bash 
 
＃当运行各种Spark程序时，该文件来源于。 
＃将其复制为spark-env.sh并编辑，为您的站点配置Spark。 
 
＃使用
＃./bin/run-example或./bin/spark-submit 
＃本地启动程序时读取的选项 -  HADOOP_CONF_DIR，指向Hadoop配置的Spark文件
＃ -  SPARK_LOCAL_IP，设置Spark绑定到此节点上的IP地址
＃ -  SPARK_PUBLIC_DNS，设置驱动程序的公共dns名称
＃ -  SPARK_CLASSPATH = / root / spark -1.0.1-bin-hadoop2 / conf / 
 
＃集群中运行的执行程序和驱动程序读取的选项
＃ -  SPARK_LOCAL_IP，设置Spark绑定到此节点上的IP地址
＃ -  SPARK_PUBLIC_DNS，设置驱动程序的公共DNS名称
＃ -  SPARK_CLASSPATH，缺省类路径条目以追加
＃ -  SPARK_LOCAL_DIRS，在此节点上使用的存储目录用于混洗和RDD数据
＃ -  MESOS_NATIVE_LIBRARY，指向你的libmesos.so，如果你使用Mesos 
 
＃在YARN客户端模式下读取的选项
＃ -  HADOOP_CONF_DIR，指向Hadoop配置文件
＃ -  SPARK_EXEC UTOR_INSTANCES，要开始的工作人员数量（默认值：2）
＃ -  SPARK_EXECUTOR_CORES，工作人员的核心数量（默认值：1）。 
＃ -  SPARK_EXECUTOR_MEMORY，每个工作者的内存（例如1000M，2G）（默认：1G）
＃ -  SPARK_DRIVER_MEMORY，主内存（例如1000M，2G）（默认值：512 Mb）
＃ -  SPARK_YARN_APP_NAME，您的应用程序的名称（默认：Spark）
＃ -  SPARK_YARN_QUEUE，用于分配请求的hadoop队列（默认值：'default'）
＃ -  SPARK_YARN_DIST_FILES，逗号分隔的文件列表与工作一起分发。 
＃ -  SPARK_YARN_DIST_ARCHIVES，与作业一起分发的逗号分隔列表。 
 
＃在独立部署模式下使用的守护程序选项：
＃ -  SPARK_MASTER_IP，将主机绑定到不同的IP地址或主机名
＃ -  SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT，为master 
＃ -  SPARK_MASTER_OPTS使用非默认端口，仅为master设置配置属性（例如-Dx = y）
＃ -  SPARK_WORKER_CORES，以设置要使用的核心数这台机器
＃ -  SPARK_WORKER_MEMORY，用来设置工作人员必须给予执行程序（例如1000m，2g）
＃ -  SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT多少内存，以便为worker使用非默认端口
 ＃ -  SPARK_WORKER_INSTANCES，设置每个节点的工作进程数量
＃ -  SPARK_WORKER_DIR，设置工作进程的工作目录
＃ -  SPARK_WORKER_OPTS，仅为工作人员设置配置属性（例如-Dx = y）
＃ -  SPARK_HISTORY_OPTS，仅为历史记录服务器设置配置属性（例如-Dx = y）
＃ -  SPARK_DAEMON_JAVA_OPTS，设置c所有守护进程的属性（例如-Dx = y）
＃ -  SPARK_PUBLIC_DNS，设置master或worker的公共dns名称
 
 export SPARK_SUBMIT_CLASSPATH =$ FWDIR / conf
  
解决方案
只需在spark目录中执行此命令：
  cp conf / log4j.properties.template conf / log4j.properties 
  
编辑log4j.properties：
 ＃将所有要记录的内容设置到控制台
 log4j .rootCategory = INFO，控制台
 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 
 
＃设置用于清除过于冗长的第三方日志
 log4j.logger.org.eclipse.jetty = WARN 
 log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle =错误
 log4j.logger.org.apache.spark.repl.S parkIMain $ exprTyper = INFO 
 log4j.logger.org.apache.spark.repl.SparkILoop $ SparkILoopInterpreter = INFO 
  
在第一行替换： 
 
 
  log4j.rootCategory =信息，控制台
  
 
 
 
 $ p 
 $ b $ $ $ $ p $ log4j.rootCategory = WARN，console 
  
保存并重新启动您的shell。它适用于OS X上的Spark 1.1.0和Spark 1.5.1。
 
I installed Spark using the AWS EC2 guide and I can launch the program fine using the bin/pyspark script to get to the spark prompt and can also do the Quick Start quide successfully.

However, I cannot for the life of me figure out how to stop all of the verbose INFO logging after each command.

I have tried nearly every possible scenario in the below code (commenting out, setting to OFF) within my log4j.properties file in the conf folder in where I launch the application from as well as on each node and nothing is doing anything. I still get the logging INFO statements printing after executing each statement.

I am very confused with how this is supposed to work. 
#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
Here is my full classpath when I use SPARK_PRINT_LAUNCH_COMMAND:

  
Spark Command:
  /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
contents of 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"

解决方案
Just execute this command in the spark directory:
cp conf/log4j.properties.template conf/log4j.properties
Edit 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
Replace at the first line:
log4j.rootCategory=INFO, console
by:
log4j.rootCategory=WARN, console
Save and restart your shell. It works for me for Spark 1.1.0 and Spark 1.5.1 on OS X.

                        
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