Spark中日志文件log4j设置

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 \
# log 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 

 注意:可以在项目的resources目录中创建log4j.properties文件，并添加日志配置信息。