如何在Spark中关闭INFO日志记录？

Question

我使用AWS EC2指南安装了Spark,我可以使用bin/pyspark脚本启动程序以获得spark提示,也可以成功执行Quick Start quide.

但是,我不能为我的生活弄清楚如何INFO在每个命令后停止所有详细的日志记录.

我已经在我的log4j.properties文件中的几乎所有可能的场景中尝试了我的conf文件,在我从中启动应用程序的文件夹以及每个节点上,没有做任何事情.INFO执行每个语句后,我仍然会打印日志语句.

我对这应该如何工作非常困惑.

#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

这是我使用时的完整类路径SPARK_PRINT_LAUNCH_COMMAND:

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

内容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"

Answer 1

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

通过:

log4j.rootCategory=WARN, console

保存并重新启动shell.它适用于OS X上的Spark 1.1.0和Spark 1.5.1.

Answer 2

灵感来自pyspark/tests.py我做到了

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之后调用它会减少为我的测试从2647到163记录的stderr行.但是创建SparkContext本身会记录163,最多

15/08/25 10:14:16 INFO SparkDeploySchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0

我不清楚如何以编程方式调整这些.

Answer 3

编辑您的conf/log4j.properties文件并更改以下行:

   log4j.rootCategory=INFO, console

至

    log4j.rootCategory=ERROR, console

另一种方法是:

Fireup spark-shell并输入以下内容:

import org.apache.log4j.Logger
import org.apache.log4j.Level

Logger.getLogger("org").setLevel(Level.OFF)
Logger.getLogger("akka").setLevel(Level.OFF)

之后你不会看到任何日志.

Answer 4

>>> log4j = sc._jvm.org.apache.log4j
>>> log4j.LogManager.getRootLogger().setLevel(log4j.Level.ERROR)

Answer 5

您还可以使用脚本设置日志级别sc.setLogLevel("FATAL").来自文档:

控制我们的logLevel.这将覆盖任何用户定义的日志设置.有效的日志级别包括:ALL,DEBUG,ERROR,FATAL,INFO,OFF,TRACE,WARN

Answer 6

在Spark 2.0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spark会话已经可用.

Answer 7

这可能是由于Spark如何计算其类路径.我的预感是Hadoop的log4j.properties文件出现在类路径上的Spark之前,阻止你的更改生效.

如果你跑

SPARK_PRINT_LAUNCH_COMMAND=1 bin/spark-shell

然后Spark将打印用于启动shell的完整类路径; 就我而言,我明白了

Spark Command: /usr/lib/jvm/java/bin/java -cp :::/root/ephemeral-hdfs/conf:/root/spark/conf:/root/spark/lib/spark-assembly-1.0.0-hadoop1.0.4.jar:/root/spark/lib/datanucleus-api-jdo-3.2.1.jar:/root/spark/lib/datanucleus-core-3.2.2.jar:/root/spark/lib/datanucleus-rdbms-3.2.1.jar -XX:MaxPermSize=128m -Djava.library.path=:/root/ephemeral-hdfs/lib/native/ -Xms512m -Xmx512m org.apache.spark.deploy.SparkSubmit spark-shell --class org.apache.spark.repl.Main

在/root/ephemeral-hdfs/conf类路径的顶部.

我在下一个版本中打开了一个问题[SPARK-2913]来解决这个问题(我应该尽快修补).

与此同时,这里有几个解决方法:

• 添加export SPARK_SUBMIT_CLASSPATH="$FWDIR/conf"到spark-env.sh.
• 删除(或重命名)/root/ephemeral-hdfs/conf/log4j.properties.

Answer 8

您可以使用setLogLevel

val spark = SparkSession
      .builder()
      .config("spark.master", "local[1]")
      .appName("TestLog")
      .getOrCreate()

spark.sparkContext.setLogLevel("WARN")

Answer 9

程序化方式

spark.sparkContext.setLogLevel("WARN")

可用选项

ERROR
WARN 
INFO 

Answer 10

Spark 1.6.2:

log4j = sc._jvm.org.apache.log4j
log4j.LogManager.getRootLogger().setLevel(log4j.Level.ERROR)

Spark 2.x:

spark.sparkContext.setLogLevel('WARN')

(Spark是SparkSession)

或者旧的方法,

重命名conf/log4j.properties.template到conf/log4j.properties在星火迪尔.

在log4j.properties,log4j.rootCategory=INFO, console改为log4j.rootCategory=WARN, console

可用的不同日志级别:

• 关闭(最具体,没有记录)
• 致命的(最具体的,小数据)
• 错误 - 仅在出现错误时记录
• 警告 - 仅在出现警告或错误时记录
• 信息(默认)
• 调试 - 记录详细步骤(以及上述所有日志)
• TRACE(最不具体,很多数据)
• ALL(最不具体,所有数据)

Answer 11

只需将以下参数添加到您的 spark-submit 命令

--conf "spark.driver.extraJavaOptions=-Dlog4jspark.root.logger=WARN,console"

这仅临时覆盖该作业的系统值。从 log4j.properties 文件中检查确切的属性名称（此处为 log4jspark.root.logger）。

希望这有帮助，干杯！

Answer 12

我在Amazon EC2上使用了1个master和2个slave以及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/