Spark 学习之 log4j.properties 文件配置

解决Spark程序在运行时候产生大量执行日志问题：

1.在 resource 目录下新建 log4j.properties 文件后将以下配置写入

log4j.rootCategory=ERROR, 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

	# Set the default spark-shell log level to ERROR. When running the spark-shell, the
	# og level for this class is used to overwrite the root logger’s log level, so that
	# the user can have different defaults for the shell and regular Spark apps.

log4j.logger.org.apache.spark.repl.Main=ERROR
	
	#Settings to quiet third party logs that are too verbose
	
log4j.logger.org.spark_project.jetty=ERROR
log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=ERROR
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=ERROR
log4j.logger.org.apache.parquet=ERROR
log4j.logger.parquet=ERROR

	# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support

log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR

2.执行程序如果还会显示执行日志的话则需要检查 target 文件下的 classes 文件中是否有 log4j.properties 文件，如果没有则将其拷贝进来