Data Processing has a default Log4j configuration file that sets its logging properties.
The file is named log4j.properties and is located in the $BDD_HOME/dataprocessing/edp_cli/config directory.
############################################################ # Global properties ############################################################ log4j.rootLogger = INFO, console, edpMain ############################################################ # Handler specific properties. ############################################################ log4j.appender.console = org.apache.log4j.ConsoleAppender ############################################################ # EdpODPFormatterAppender is a custom log4j appender that gives two new optional # variables that can be added to the log4j.appender.*.Path property and are # filled in at runtime: # %timestamp - provides a timestamp in the format: yyyyMMddHHmmssSSS # %unique - provides a uniquified string ############################################################ log4j.appender.edpMain = com.oracle.endeca.pdi.logging.EdpODLFormatterAppender log4j.appender.edpMain.ComponentId = DataProcessing log4j.appender.edpMain.Path = /localdisk/Oracle/Middleware/1.2.0.31.801/logs/edp/edp_%timestamp_%unique.log log4j.appender.edpMain.Format = ODL-Text log4j.appender.edpMain.MaxSegmentSize = 100000000 log4j.appender.edpMain.MaxSize = 1000000000 log4j.appender.edpMain.Encoding = UTF-8 ############################################################ # Formatter specific properties. ############################################################ log4j.appender.console.layout = org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern = [%d{yyyy-MM-dd'T'HH:mm:ss.SSSXXX}] [DataProcessing] [%p] [] [%C] [tid:%t] [userID:${user.name}] %m%n ############################################################ # Facility specific properties. ############################################################ # These loggers from dependency libraries are explicitly set to different logging levels. # They are known to be very noisy and obscure other log statements. log4j.logger.org.eclipse.jetty = WARN log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper = INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter = INFO
Logging property | Description |
---|---|
log4j.rootLogger | The level of the root logger is defined as INFO and attaches the console and edpMain handlers to it. |
log4j.appender.console | Defines console as a Log4j ConsoleAppender. |
log4j.appender.edpMain | Defines edpMain as EdpODPFormatterAppender (a custom Log4j appender). |
log4j.appender.edpMain.ComponentId | Sets DataProcessing as the name of the component that generates the log messages. |
log4j.appender.edpMain.Path | Sets the path for the log files to
the
$BDD_HOME/logs/edp directory. Each log
file is named:
edp_%timestamp_%unique.logSee the comments in the log file for the definitions of the %timestamp and %unique variables. |
log4j.appender.edpMain.Format | Sets ODL-Text as the formatted string as specified by the conversion pattern. |
log4j.appender.edpMain.MaxSegmentSize | Sets the maximum size (in bytes) of a log file. When the file reaches this size, a rollover file is created. The default is 100000000 (about 100 MB). |
log4j.appender.edpMain.MaxSize | Sets the maximum amount of disk space to be used by the main log file and the logging rollover files. The default is 1000000000 (about 1GB). |
log4j.appender.edpMain.Encoding | Sets character encoding for the log file. The default UTF-8 value prints out UTF-8 characters in the file. |
log4j.appender.console.layout | Sets the PatternLayout class for the console layout. |
log4j.appender.console.layout.ConversionPattern | Defines the log entry conversion
pattern as:
For other conversion characters, see: https://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/PatternLayout.html |
log4j.logger.org.eclipse.jetty log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter |
Sets the default logging level for the Spark and Jetty loggers. |
These levels allow you to monitor events of interest at the appropriate granularity without being overwhelmed by messages that are not relevant. When you are initially setting up your application in a development environment, you might want to use the FINEST level to get all messages, and change to a less verbose level in production.