log4j配置及意义不在控制台输出的问题

最新推荐文章于 2025-02-10 16:23:30 发布

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最新推荐文章于 2025-02-10 16:23:30 发布

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本文介绍了log4j配置的关键点，特别是如何确保日志信息能够输出到控制台。核心在于`log4j.rootLogger = DEBUG,A,D,E,W`配置项，必须在开始时设置为`DEBUG`级别。" 133085487,19694795,Python编程：随机数与随机字符串生成实例,"['Python', '开发语言', '数据模拟', '安全', '随机生成']

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# Set everything to be logged to the console
log4j.rootLogger = DEBUG,A,D,E,W


log4j.appender.A=org.apache.log4j.ConsoleAppender
log4j.appender.A.Threshold = DEBUG
log4j.appender.A.layout=org.apache.log4j.PatternLayout
log4j.appender.A.layout.ConversionPattern=[%-d{yyyy-MM-dd HH:mm:ss}][%t]-[ %p][%c][%l] %m%n

# Set the default spark-shell log level to WARN. 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.
### 输出DEBUG 级别以上的日志到=/data/log/flexstream ###
log4j.appender.D = org.apache.log4j.DailyRollingFileAppender
log4j.appender.D.File = /data/log/flexstream/debug.log
log4j.appender.D.Append = true
log4j.appender.D.Threshold = DEBUG
log4j.appender.D.layout = org.apache.log4j.PatternLayout
log4j.appender.D.layout.ConversionPattern = [%-d{yyyy-MM-dd HH:mm:ss}][%t]-[ %p][%c][%l] %m%n

### 输出DEBUG 级别以上的日志到=/data/log/flexstream ###
log4j.appender.W = org.apache.log4j.DailyRollingFileAppender
log4j.appender.W.File = /data/log/flexstream/warn.log
log4j.appender.W.Append = true
log4j.appender.W.Threshold = WARN
log4j.appender.W.layout = org.apache.log4j.PatternLayout
log4j.appender.W.layout.ConversionPattern = [%-d{yyyy-MM-dd HH:mm:ss}][%t]-[ %p][%c][%l] %m%n

### 输出ERROR 级别以上的日志到=/data/log/flexstream/error.log ###
log4j.appender.E = org.apache.log4j.DailyRollingFileAppender
log4j.appender.E.File = /data/log/flexstream/error.log
log4j.appender.E.Append = true
log4j.appender.E.Threshold = ERROR
log4j.appender.E.layout = org.apache.log4j.PatternLayout
log4j.appender.E.layout.ConversionPattern = [%-d{yyyy-MM-dd HH:mm:ss}][%t]-[ %p][%c][%l] %m%n

# 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 log4j.rootLogger = DEBUG,A,D,E,W中，必须在一开始的地方写DEBUG,这样才可以输出到控制台