如何在Spark中关闭INFO日志记录？

Question

如何在Spark中关闭INFO日志记录？

pythonscalaapache-sparkhadooppyspark

185

我按照AWS EC2指南安装了Spark，使用bin/pyspark脚本可以成功启动程序并进入spark提示符，也能顺利完成快速入门指南。

但是，我却无法弄清楚如何在每个命令之后停止冗长的INFO日志输出。

我尝试了在启动应用程序的位置以及每个节点上的conf文件夹中的log4j.properties文件中几乎所有可能情况的代码（包括注释掉、设置为OFF），但没有任何效果。每次执行语句后，我仍然会看到INFO日志记录输出。

我对这个东西到底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

使用SPARK_PRINT_LAUNCH_COMMAND时，我的完整类路径如下：

Spark命令： /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

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"

- horatio1701d

3

在 Spark 程序中，创建会话后，您可以按照以下方式设置日志级别，对于 Java SparkSession spark= SparkSession.builder().master("local").getOrCreate(); spark.sparkContext().setLogLevel("INFO"); - iKing

16个回答

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- loneStar · Answer 1

编程方式

spark.sparkContext.setLogLevel("WARN")

可用选项

ERROR
WARN 
INFO

- oleksii · Answer 2

我曾在 Amazon EC2 上使用此方案，其中包括1台主机和2台从机，使用的是 Spark 1.2.1。

# Step 1. Change config file on the master node
nano /root/ephemeral-hdfs/conf/log4j.properties

# Before
hadoop.root.logger=INFO,console
# After
hadoop.root.logger=WARN,console

# Step 2. Replicate this change to slaves
~/spark-ec2/copy-dir /root/ephemeral-hdfs/conf/

- user3827333 · Answer 3

我做的方法是：

在我运行spark-submit脚本的位置执行

$ cp /etc/spark/conf/log4j.properties .
$ nano log4j.properties

将 INFO 更改为您想要的日志级别，然后运行您的 spark-submit

- santifinland · Answer 4

如果您想继续使用日志记录（Python的日志记录工具），您可以尝试将应用程序和Spark的配置拆分为两部分：

LoggerManager()
logger = logging.getLogger(__name__)
loggerSpark = logging.getLogger('py4j')
loggerSpark.setLevel('WARNING')

- Ram Ghadiyaram · Answer 5

这是一段适用于Scala用户的代码片段： 选项1： 您可以将下面的片段添加到文件级别：

import org.apache.log4j.{Level, Logger}
Logger.getLogger("org").setLevel(Level.WARN)

选项2：

注意：这适用于使用Spark会话的所有应用程序。

最初的回答已经涵盖了所有使用Spark会话的应用程序。

import org.apache.spark.sql.SparkSession

  private[this] implicit val spark = SparkSession.builder().master("local[*]").getOrCreate()

spark.sparkContext.setLogLevel("WARN")

选项3：

注意：此配置应添加到您的log4j.properties文件中（例如/etc/spark/conf/log4j.properties（其中spark安装在那里）或项目文件夹级别的log4j.properties），因为您正在模块级别上进行更改。这将适用于所有应用程序。

"最初的回答"

log4j.rootCategory=ERROR, console

我认为，选项1是明智的选择，因为它可以在文件级别上关闭。"最初的回答"

- rahul sharma · Answer 6

您也可以在程序开始时以编程方式设置它。

Logger.getLogger("org").setLevel(Level.WARN)