import org.apache.log4j.{Level, Logger}
Logger.getLogger(classOf[RackResolver]).getLevel
Logger.getLogger("org").setLevel(Level.OFF)
Logger.getLogger("akka").setLevel(Level.OFF)Logging
Spark uses log4j for logging.
Logging Levels
The valid logging levels are log4j’s Levels (from most specific to least):
-
OFF(most specific, no logging) -
FATAL(most specific, little data) -
ERROR -
WARN -
INFO -
DEBUG -
TRACE(least specific, a lot of data) -
ALL(least specific, all data)
conf/log4j.properties
You can set up the default logging for Spark shell in conf/log4j.properties. Use conf/log4j.properties.template as a starting point.
Setting Log Levels in Spark Applications
In standalone Spark applications or while in Spark Shell session, use the following:
sbt
When running a Spark application from within sbt using run task, you can use the following build.sbt to configure logging levels:
fork in run := true
javaOptions in run ++= Seq(
"-Dlog4j.debug=true",
"-Dlog4j.configuration=log4j.properties")
outputStrategy := Some(StdoutOutput)With the above configuration log4j.properties file should be on CLASSPATH which can be in src/main/resources directory (that is included in CLASSPATH by default).
When run starts, you should see the following output in sbt:
[spark-activator]> run
[info] Running StreamingApp
log4j: Trying to find [log4j.properties] using context classloader sun.misc.Launcher$AppClassLoader@1b6d3586.
log4j: Using URL [file:/Users/jacek/dev/oss/spark-activator/target/scala-2.11/classes/log4j.properties] for automatic log4j configuration.
log4j: Reading configuration from URL file:/Users/jacek/dev/oss/spark-activator/target/scala-2.11/classes/log4j.properties