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="$FWDIR/conf"
poiuytrez.. 150
只需在spark目录中执行以下命令:
cp conf/log4j.properties.template conf/log4j.properties
编辑log4j.properties:
# Set everything to be logged to the console log4j.rootCategory=INFO, console log4j.appender.console=org.apache.log4j.ConsoleAppender log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose log4j.logger.org.eclipse.jetty=WARN log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
在第一行替换:
log4j.rootCategory=INFO, console
通过:
log4j.rootCategory=WARN, console
保存并重新启动shell.它适用于OS X上的Spark 1.1.0和Spark 1.5.1.
只需在spark目录中执行以下命令:
cp conf/log4j.properties.template conf/log4j.properties
编辑log4j.properties:
# Set everything to be logged to the console log4j.rootCategory=INFO, console log4j.appender.console=org.apache.log4j.ConsoleAppender log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose log4j.logger.org.eclipse.jetty=WARN log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$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.
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.
>>> log4j = sc._jvm.org.apache.log4j >>> log4j.LogManager.getRootLogger().setLevel(log4j.Level.ERROR)
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.
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
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.
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)
您可以使用setLogLevel
val spark = SparkSession .builder() .config("spark.master", "local[1]") .appName("TestLog") .getOrCreate() spark.sparkContext.setLogLevel("WARN")
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
.
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/