spark学习进度16——spark运行出现问题

一、maven项目运行spark时遇到hadoop相关问题

 

解决方法：

在http://hadoop.apache.org/releases.html下载对应版本hadoop并解压

下载对应版本的hadoop-common，我下载的是hadoop-common-3.0.0-bin-master.zip，将其中的winutils.exe复制在hadoop的bin目录下

添加系统环境变量

将系统变量添加到Path变量中（%HADOOP_HOME%和%HADOOP_HOME%\bin）：

 

 在命令行输入hadoop -version检查是否成功

 

 

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二、运行spark出现大量日志信息

解决方式：

若不想要打印日志信息，可以将如下代码粘贴到resources下的log4j.properties中

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 loglevel,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 verboselog4j.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 upnonexistent 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