我正在使用带有gradle的IntelliJ来构建spark项目。我尝试了多种方法来禁用日志记录，但无济于事。
以下是我尝试过的一些事情。 1.使用以下配置
在src / main / resources下添加log4j.properties
# Set everything to be logged to the console
log4j.rootCategory=WARN, 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 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.
log4j.logger.org.apache.spark.repl.Main=WARN
# Change this to set Spark log level
log4j.logger.org.apache.spark=WARN
# Silence akka remoting
log4j.logger.Remoting=WARN
# Ignore messages below warning level from Jetty, because it's a bit verbose
log4j.logger.org.eclipse.jetty=WARN
# Settings to quiet third party logs that are too verbose
log4j.logger.org.spark_project.jetty=WARN
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
Logger.getLogger("org").setLevel(Level.OFF)
Logger.getLogger("akka").setLevel(Level.OFF)
Logger.getLogger("spark").setLevel(Level.OFF)
LogManager.getRootLogger.setLevel(Level.OFF)
val spark: SparkSession = SparkSession.builder()
.appName("test").master("local")
.enableHiveSupport()
.getOrCreate()
spark.sparkContext.setLogLevel("OFF")
我正在使用mac OS运行IntelliJ， scalaVersion = 2.11.8 sparkVersion = 2.1.1
请帮助我，我在互联网上上下打量，但仍无法找到有效的内容
万分感谢！ 可能。