Saprk-日志实战

一、用户行为日志

1.概念

用户每次访问网站时所有的行为日志(访问、浏览、搜索、点击)

	用户行为轨迹，流量日志

2.原因

分析日志：
	网站页面访问量
	网站的粘性
	推荐

3.生产渠道

(1)Nginx

(2)Ajax

4.日志内容

spark ui错误日志 spark日志清理_spark

日志数据内容：
	1.访问的系统属性：操作系统、浏览器等
	2.访问特征：点击URL，跳转页面(referer)、页面停留时间
	3.访问信息：seesion_id、访问id信息(地市\运营商)

注意：Nginx配置，可以获取指定信息

5.意义

(1)网站的眼睛
	投放广告收益
(2)网站的神经
	网站布局(影响用户体验)
(3)网站的大脑

二、离线数据处理

1.处理流程

1)数据采集
Flume:
	产生的Web日志，写入到HDFS
	
2)数据清洗
	Spark\Hive\MapReduce--》HDFS(Hive/Spark SQL表)
	
3)数据处理
	按照业务逻辑进行统计分析
	Spark\Hive\MapReduce--》HDFS(Hive/Spark SQL表)
	
4)处理结果入库
	RDBMS(MySQL)\NoSQL(HBase、Redis)
	
5)数据可视化展示
	通过图形化展示：饼图、柱状图、地图、折线图
	Echarts、HUE、Zeppelin

spark ui错误日志 spark日志清理_大数据_02

三、项目需求

code/video
需求一：
	统计imooc主站最受欢迎的课程/手记Top N访问次数
	
需求二：
	按地市统计imooc主站最受欢迎的Top N课程
	a.根据IP地址获取出城市信息
	b.窗口函数在Spark SQL中的使用
	
需求三：
	按流量统计imooc主站最受欢迎的Top N课程

四、日志内容

需要字段：
	访问时间、访问URL、访问过程耗费流量、访问IP地址

日志处理：
	一般的日志处理方式,我们是需要进行分区的,
	按照日志中的访问时间进行相应的分区，比如: d, h,m5(每5分钟一个分区)
	
输入:访问时间、访问URL、耗费的流量、访问IP地址信息
输出:URL、cmsType(video/article)、cmsId(编号)、流量、ip、城市信息、访问时间、天

Maven打包

mvn install:install-file -Dfile=D:\ipdatabase-master\ipdatabase-master\target\ipdatabase-1.0-SNAPSHOT.jar -DgroupId=com.ggstar2 -DartifactId=ipdatabase -Dversion=1.0 -Dpackaging=jar

五、数据清洗

1.原始日志解析

package com.saddam.spark.MuKe.ImoocProject

import org.apache.spark.SparkContext
import org.apache.spark.sql.SparkSession

/**
  * 第一步清洗：抽取出所需要指定列数据
  *
  * 添加断点，可以查看各个字段
  */
object SparkStatFormatJob {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession
      .builder()
      .appName("SparkStatFormatJob")
      .master("local[2]")
      .getOrCreate()

    val logRDD = spark.sparkContext.textFile("D:\\Spark\\DataSets\\access.20161111.log")

//    logRDD.take(10).foreach(println)

    val result = logRDD.map(line => {
      
      val split = line.split(" ")
      val ip = split(0)
      /**
        * 原始日志的第三个和第四个字段拼接起来就是完整的时间字段:
        * [10/Nov/2016:00:01:02 +0800]==>yyyy-MM-dd HH
        */
      //TODO 使用时间解析工具类
      val time = split(3) + " " + split(4)

      //"http://www.imooc.com/code/1852" 引号需要放空
      val url = split(11).replaceAll("\"", "")

      val traffic = split(9)

      //使用元组
      // (ip,DateUtils.parse(time),url,traffic)
      
      DateUtils.parse(time) + "\t" + url + "\t" + traffic + "\t" + ip
    }).take(20).foreach(println)


//    result.saveAsTextFile("D:\\Spark\\OutPut\\log_local_2")
/*
(10.100.0.1,[10/Nov/2016:00:01:02 +0800])
(117.35.88.11,[10/Nov/2016:00:01:02 +0800]) 
(182.106.215.93,[10/Nov/2016:00:01:02 +0800])
(10.100.0.1,[10/Nov/2016:00:01:02 +0800])
 */

    spark.stop()
}
}

2.日期工具类

package com.saddam.spark.MuKe.ImoocProject

import java.util.{Date, Locale}
import org.apache.commons.lang3.time.FastDateFormat

/**
  * 日期时间解析工具类
  */
object DateUtils {

  // 输入文件日期时间格式
  //[10/Nov/2016:00:01:02 +0800]
  val YYYYMMDDHHMM_TIME_FOEMAT= FastDateFormat.getInstance("dd/MMM/yyyy:HH:mm:ss Z",Locale.ENGLISH)

   //目标日期格式
  //2016-11-10 00:01:02
  val TARGET_FORMAT=FastDateFormat.getInstance("yyyy-MM-dd HH:mm:ss")
  
  /**
    *解析时间
    * @param time
    * @return
    */
  def parse(time:String)={
    TARGET_FORMAT.format(new Date(getTime(time)))
  }

  /**
    * 获取输入日志时间：long类型
    *
    * time:[10/Nov/2016:00:01:02 +0800]
    * @param time
    * @return
    */
  def getTime(time:String)= {
    try {
      YYYYMMDDHHMM_TIME_FOEMAT.parse(time.substring(time.indexOf("[") + 1, time.lastIndexOf("]"))).getTime
    } catch {
      case e: Exception => {
        0l
      }
    }
  }
    def main(args: Array[String]): Unit = {
      println(parse("[10/Nov/2016:00:01:02 +0800]"))

  }
}

spark ui错误日志 spark日志清理_分布式_03

六、项目需求

需求一

统计imooc主站最受欢迎的课程/手记TopN访问次数
	
按照需求完成统计信息并将统计结果入库
	--使用DataFrame API完成统计分析
	--使用SQL API完成统计分析
package com.saddam.spark.MuKe

import java.sql.{Connection, DriverManager, PreparedStatement}


import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._

import scala.collection.mutable.ListBuffer

object PopularVideoVisits {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession
      .builder()
      .appName("TopNStatJob")
      .config("spark.sql.sources.partitionColumnTypeInference.enable", "false")
      .master("local[2]")
      .getOrCreate()

    val accessDF=spark.read.format("parquet").load("D:\\Spark\\DataSets\\clean_city")

    accessDF.printSchema()
    accessDF.show(false)
    /*
+----------------------------------+-------+-----+-------+---------------+----+-------------------+--------+
|url                               |cmsType|cmsId|traffic|ip             |city|time               |day     |
+----------------------------------+-------+-----+-------+---------------+----+-------------------+--------+
|http://www.imooc.com/video/4500   |video  |4500 |304    |218.75.35.226  |鏈煡 |2017-05-11 14:09:14|20170511|
|http://www.imooc.com/video/14623  |video  |14623|69     |202.96.134.133 |鏈煡 |2017-05-11 15:25:05|20170511|
|http://www.imooc.com/article/17894|article|17894|115    |202.96.134.133 |鏈煡 |2017-05-11 07:50:01|20170511|
     */

      //代码重构
    val day="20170511"
      
      
    videoAccessTopNStat(spark,accessDF,day)

    //MySQL工具类测试
    println(MySQLUtils.getConnection())
    
    /**
      * 按照流量进行统计
      */
    def videoAccessTopNStat(spark:SparkSession,accessDF:DataFrame,day:String)={
      //隐式转换
      import spark.implicits._

      //TODO 统计方式一:DataFrame方式统计video
      val  videoAccessTopNDF= accessDF
        .filter($"day"===day && $"cmsType"==="video")
        .groupBy("day","cmsId")
        .agg(count("cmsId").as("times"))
      videoAccessTopNDF.printSchema()
      videoAccessTopNDF.show(false)

      //TODO 统计方式二:SQL方式统计article
      accessDF.createOrReplaceTempView("temp")
      val videoAccessTopNSQL = spark.sql("select " +
        "day,cmsId,count(1) as times " +
        "from temp " +
        "where day='20170511' and cmsType='article' " +
        "group by day,cmsId " +
        "order by times desc")
      videoAccessTopNSQL.show(false)


      /**
        * TODO 将最受欢迎的TopN课程统计结果写入MySQL
        *
        */
      try{
        videoAccessTopNSQL.foreachPartition(partitionOfRecords=>{
          val list =new ListBuffer[DayVideoAccessStat]

          partitionOfRecords.foreach(info=>{
            val day=info.getAs[Integer]("day").toString
            val cmsId=info.getAs[Long]("cmsId")
            val times=info.getAs[Long]("times")

            list.append(DayVideoAccessStat(day,cmsId,times))
          })

          StatDAO.insertDayVideoAccessTopN(list)

        })}catch {
        case e:Exception=>e.printStackTrace()
      }

    }


    spark.stop()
  }

  /**
    * 课程访问次数实体类
    */
  case class  DayVideoAccessStat(day:String,cmsId:Long,times:Long)


  /**
   * TODO MySQL操作工具类
   */
  object MySQLUtils{
    def getConnection()={
      DriverManager.getConnection("jdbc:mysql://121.37.2x.xx:3306/imooc_project?user=root&password=xxxxxx&useSSL=false")
    }
    /**
      * 释放数据库连接等资源
      * @param connection
      * @param pstmt
      */
    def release(connection: Connection, pstmt: PreparedStatement): Unit = {
      try {
        if (pstmt != null) {
          pstmt.close()
        }
      } catch {
        case e: Exception => e.printStackTrace()
      } finally {
        if (connection != null) {
          connection.close()
        }
      }
    }
  }

  /**
    * TODO DAO数据库接口
    */
  object StatDAO{
    /**
      * 批量保存DayVideoAccessStat到数据库
      * insertDayVideoAccessTopN：每天访问视频的
      */
    def insertDayVideoAccessTopN(list: ListBuffer[DayVideoAccessStat]): Unit = {

      var connection:Connection = null
      var pstmt:PreparedStatement = null

      try {
        connection =MySQLUtils.getConnection()

        connection.setAutoCommit(false) //设置手动提交

        val sql = "insert into day_video_access_topn_stat2(day,cms_id,times) values (?,?,?)"
        pstmt = connection.prepareStatement(sql)

        for (ele <- list) {
          pstmt.setString(1, ele.day)
          pstmt.setLong(2, ele.cmsId)
          pstmt.setLong(3, ele.times)

          pstmt.addBatch()
        }

        pstmt.executeBatch() // 执行批量处理
        connection.commit() //手工提交
      } catch {
        case e: Exception => e.printStackTrace()
      } finally {
        MySQLUtils.release(connection, pstmt)
      }
    }
    
  }

}

需求二

按地市统计imooc主站最受欢迎的Top N课程
package com.saddam.spark.MuKe

import java.sql.{Connection, DriverManager, PreparedStatement}


import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._

import scala.collection.mutable.ListBuffer

object PopularCiytVideoVisits {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession
      .builder()
      .appName("TopNStatJob")
      .config("spark.sql.sources.partitionColumnTypeInference.enable", "false")
      .master("local[2]")
      .getOrCreate()

    val accessDF=spark.read.format("parquet").load("D:\\Spark\\DataSets\\clean_city")

    accessDF.printSchema()
    accessDF.show(false)
      
     //代码重构
    val day="20170511"
      
    //TODO 按照地市进行统计TopN课程
    cityAccessTopNStat(spark,accessDF,day)
      
    /**
    * 按照地市进行统计TopN课程
    * @param spark
    * @param accessDf
    */
  def cityAccessTopNStat(spark: SparkSession,accessDF:DataFrame,day:String)={
    import spark.implicits._
        val cityAccessTopNDF=accessDF.filter($"day"===day&&$"cmsType"==="video").groupBy("day","city","cmsId").agg(count("cmsId").as("times")).orderBy($"times".desc)
        cityAccessTopNDF.printSchema()
        cityAccessTopNDF.show(false)

    //Windows函数在Spark SQL的使用

    val top3DF=cityAccessTopNDF.select(
    cityAccessTopNDF("day"),
    cityAccessTopNDF("city"),
    cityAccessTopNDF("cmsId"),
    cityAccessTopNDF("times"),

    row_number().over(Window.partitionBy(cityAccessTopNDF("city"))
      .orderBy(cityAccessTopNDF("times").desc)
    ).as("times_rank")
    ).filter("times_rank <=3") //.show(false)  //Top3
    /**
      * 将地市进行统计TopN课程统计结果写入MySQL
      *
      */
    try{
      top3DF.foreachPartition(partitionOfRecords=>{
        val list =new ListBuffer[DayCityVideoAccessStat]

        partitionOfRecords.foreach(info=>{
          val day=info.getAs[Integer]("day").toString
          val cmsId=info.getAs[Long]("cmsId")
          val city=info.getAs[String]("city")
          val times=info.getAs[Long]("times")
          val timesRank=info.getAs[Int]("times_rank")
          list.append(DayCityVideoAccessStat(day,cmsId,city,times,timesRank))


        })

        StatDAO.insertDayCityVideoAccessTopN(list)

      })}catch {
      case e:Exception=>e.printStackTrace()
    }
  }
       spark.stop()
  }   
    
    /**
    * 实体类
    */
    case class DayCityVideoAccessStat(day:String, cmsId:Long, city:String,times:Long,timesRank:Int)
    
     /**
   * TODO MySQL操作工具类
   */
  object MySQLUtils{
    def getConnection()={
      DriverManager.getConnection("jdbc:mysql://121.37.2x.xx:3306/imooc_project?user=root&password=xxxxxx&useSSL=false")
    }
    /**
      * 释放数据库连接等资源
      * @param connection
      * @param pstmt
      */
    def release(connection: Connection, pstmt: PreparedStatement): Unit = {
      try {
        if (pstmt != null) {
          pstmt.close()
        }
      } catch {
        case e: Exception => e.printStackTrace()
      } finally {
        if (connection != null) {
          connection.close()
        }
      }
    }
  }
    
    
  /**
    * TODO DAO数据库接口
    */
  object StatDAO{
      /**
    * 批量保存DayCityVideoAccessStat到数据库
    */
  def insertDayCityVideoAccessTopN(list: ListBuffer[DayCityVideoAccessStat]): Unit = {

    var connection: Connection = null
    var pstmt: PreparedStatement = null

    try {
      connection = MySQLUtils.getConnection()

      connection.setAutoCommit(false) //设置手动提交

      val sql = "insert into day_video_city_access_topn_stat(day,cms_id,city,times,times_rank) values (?,?,?,?,?) "
      pstmt = connection.prepareStatement(sql)

      for (ele <- list) {
        pstmt.setString(1, ele.day)
        pstmt.setLong(2, ele.cmsId)
        pstmt.setString(3, ele.city)
        pstmt.setLong(4, ele.times)
        pstmt.setInt(5, ele.timesRank)
        pstmt.addBatch()
      }

      pstmt.executeBatch() // 执行批量处理
      connection.commit() //手工提交
    } catch {
      case e: Exception => e.printStackTrace()
    } finally {
      MySQLUtils.release(connection, pstmt)
    }
  }
  }
}

需求三

按流量统计imooc主站最受欢迎的Top N课程
package com.saddam.spark.MuKe

import java.sql.{Connection, DriverManager, PreparedStatement}


import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._

import scala.collection.mutable.ListBuffer

object VideoTrafficVisits {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession
      .builder()
      .appName("TopNStatJob")
      .config("spark.sql.sources.partitionColumnTypeInference.enable", "false")
      .master("local[2]")
      .getOrCreate()

    val accessDF=spark.read.format("parquet").load("D:\\Spark\\DataSets\\clean_city")

    accessDF.printSchema()
    accessDF.show(false)
    
    //代码重构
    val day="20170511"
      
    //TODO 按照流量进行统计
    videoTrafficsTopNStat(spark,accessDF,day)
      
    /**
    * 按照流量进行统计
    */
  def videoTrafficsTopNStat(spark: SparkSession, accessDF:DataFrame,day:String): Unit = {
    import spark.implicits._

    val cityAccessTopNDF = accessDF.filter($"day" === day && $"cmsType" === "video")
      .groupBy("day", "cmsId").agg(sum("traffic").as("traffics"))
      .orderBy($"traffics".desc)
      //.show(false)

    /**
      * 将流量进行统计TopN课程统计结果写入MySQL
      *
      */
    try{
      cityAccessTopNDF.foreachPartition(partitionOfRecords=>{
        val list =new ListBuffer[DayVideoTrafficsStat]
        partitionOfRecords.foreach(info=>{
          val day=info.getAs[Integer]("day").toString
          val cmsId=info.getAs[Long]("cmsId")
          val traffics=info.getAs[Long]("traffics")
          list.append(DayVideoTrafficsStat(day,cmsId,traffics))
        })

        StatDAO.insertDayVideoTrafficsAccessTopN(list)

      })}catch {
      case e:Exception=>e.printStackTrace()
    }
  }
      
      spark.stop()
  }
    
  /**
    * 实体类
    */
  case class DayVideoTrafficsStat(day:String,cmsId:Long,traffics:Long)
    
    
   
     /**
   * TODO MySQL操作工具类
   */
  object MySQLUtils{
    def getConnection()={
      DriverManager.getConnection("jdbc:mysql://121.37.2x.2xx1:3306/imooc_project?user=root&password=xxxxxx&useSSL=false")
    }
    /**
      * 释放数据库连接等资源
      * @param connection
      * @param pstmt
      */
    def release(connection: Connection, pstmt: PreparedStatement): Unit = {
      try {
        if (pstmt != null) {
          pstmt.close()
        }
      } catch {
        case e: Exception => e.printStackTrace()
      } finally {
        if (connection != null) {
          connection.close()
        }
      }
    }
  }
    
     /**
    * TODO DAO数据库接口
    */
  object StatDAO{
      /**
    * 批量保存DayVideoTrafficsStat到数据库
    */
  def insertDayVideoTrafficsAccessTopN(list: ListBuffer[DayVideoTrafficsStat]): Unit = {

    var connection: Connection = null
    var pstmt: PreparedStatement = null

    try {
      connection = MySQLUtils.getConnection()

      connection.setAutoCommit(false) //设置手动提交

      val sql = "insert into day_video_traffics_topn_stat(day,cms_id,traffics) values (?,?,?) "
      pstmt = connection.prepareStatement(sql)

      for (ele <- list) {
        pstmt.setString(1, ele.day)
        pstmt.setLong(2, ele.cmsId)
        pstmt.setLong(3, ele.traffics)
        pstmt.addBatch()
      }

      pstmt.executeBatch() // 执行批量处理
      connection.commit() //手工提交
    } catch {
      case e: Exception => e.printStackTrace()
    } finally {
      MySQLUtils.release(connection, pstmt)
    }
  }
  }
}

删除已有数据

/**
    * 删除指定日期的数据
    */
  def deleteData(day: String): Unit = {

    val tables = Array("day_video_access_topn_stat",
      "day_video_city_access_topn_stat",
      "day_video_traffics_topn_stat")

    var connection:Connection = null
    var pstmt:PreparedStatement = null

    try{
      connection = MySQLUtils.getConnection()

      for(table <- tables) {
        // delete from table ....
        val deleteSQL = s"delete from $table where day = ?"
        pstmt = connection.prepareStatement(deleteSQL)
        pstmt.setString(1, day)
        pstmt.executeUpdate()
      }
    }catch {
      case e:Exception => e.printStackTrace()
    } finally {
      MySQLUtils.release(connection, pstmt)
    }
  }

七、Zeppelin

官网

https://zeppelin.apache.org/

1.解压缩

[root@hadoop src]# tar zxvf  zeppelin-0.7.1-bin-all

2.改名

[root@hadoop src]# mv  zeppelin-0.7.1-bin-all zeppelin

3.启动

[root@hadoop bin]# ./zeppelin-daemon.sh start

4.Web界面

http://121.37.2xx.xx:8080

5.修改JDBC驱动

spark ui错误日志 spark日志清理_spark_04

com.mysql.jdbc.Driver

xxxxxx

jdbc:mysql://121.37.2x.xx:3306/imooc_project?

root

#mysql驱动
/usr/local/src/mysql-connector-java-5.1.27-bin.jar

spark ui错误日志 spark日志清理_spark_05

6.创建note

spark ui错误日志 spark日志清理_大数据_06

7.查询表

%jdbc

show tables;

spark ui错误日志 spark日志清理_分布式_07

8.图形展示

%jdbc

select cms_id,times from day_video_access_topn_stat;

spark ui错误日志 spark日志清理_spark_08

八、Spark on Yarn

Spark运行模式

1）Local:开发时使用
2）Standalone:Spark自带的，若一个集群是standalone,则需要在多台机器上同时部署Spark
3）YARN:建议生产上使用该模式，统一使用yarn进行整个集群作业(MR、Spark)的资源调度
4）Mesos


不管使用那种模式，Spark应用程序代码是一模一样的，只需要在提交的时候指定--master指定

1.概述

Spark支持可插拔的集群管理模式

对于yarn而言，Spark Application仅仅只是一个客户端而已

2.client模式

Driver运行在Client端(提交Spark作业的机器)

Client会和请求到的Container进行通信来完成作业的调度和执行，Client是不能退出的

日志信息在控制台输出，便于我们测试

spark ui错误日志 spark日志清理_数据分析_09

3.cluster模式

Driver运行在ApplicationMaster中

Client提交完作业就可以关掉，因为作业已在Yarn上运行了

日志在终端输出，看控制台不到的，因为日志在Driver端，只能通过yarn logs -applicationId

spark ui错误日志 spark日志清理_spark_10

4.两种模式对比

Driver运行位置

ApplicationMaster的职责

运行输出日志的位置

5.案例

设置HADOOP_CONF_DIR=?

Client模式
./bin/spark-submit \
  --class org.apache.spark.examples.SparkPi \
  --master yarn \
  --executor-memory 1G \
  --num-executors 1 \
  /usr/local/src/spark/examples/jars/spark-examples_2.11-2.1.1.jar \
  4
Pi is roughly 3.1411378528446323
22/02/28 18:52:26 INFO server.ServerConnector: Stopped Spark@1b0a7baf{HTTP/1.1}{0.0.0.0:4040}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@8a589a2{/stages/stage/kill,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@192f2f27{/jobs/job/kill,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@1bdf8190{/api,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@4f8969b0{/,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@6fefce9e{/static,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@74cec793{/executors/threadDump/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@f9b7332{/executors/threadDump,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@18e7143f{/executors/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@209775a9{/executors,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@5db4c359{/environment/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@2c177f9e{/environment,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@33617539{/storage/rdd/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@47874b25{/storage/rdd,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@290b1b2e{/storage/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@1fc0053e{/storage,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@77307458{/stages/pool/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@389adf1d{/stages/pool,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@7bf9b098{/stages/stage/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@72e34f77{/stages/stage,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@6e9319f{/stages/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@6fa590ba{/stages,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@2416a51{/jobs/job/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@293bb8a5{/jobs/job,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@37ebc9d8{/jobs/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@5217f3d0{/jobs,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO ui.SparkUI: Stopped Spark web UI at http://192.168.184.135:4040
22/02/28 18:52:26 INFO cluster.YarnClientSchedulerBackend: Interrupting monitor thread
22/02/28 18:52:26 INFO cluster.YarnClientSchedulerBackend: Shutting down all executors
22/02/28 18:52:26 INFO cluster.YarnSchedulerBackend$YarnDriverEndpoint: Asking each executor to shut down
22/02/28 18:52:26 INFO cluster.SchedulerExtensionServices: Stopping SchedulerExtensionServices
(serviceOption=None,
 services=List(),
 started=false)
22/02/28 18:52:26 INFO cluster.YarnClientSchedulerBackend: Stopped
22/02/28 18:52:26 INFO spark.MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
22/02/28 18:52:26 INFO memory.MemoryStore: MemoryStore cleared
22/02/28 18:52:26 INFO storage.BlockManager: BlockManager stopped
22/02/28 18:52:26 INFO storage.BlockManagerMaster: BlockManagerMaster stopped
22/02/28 18:52:26 INFO scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
22/02/28 18:52:26 INFO spark.SparkContext: Successfully stopped SparkContext
22/02/28 18:52:26 INFO util.ShutdownHookManager: Shutdown hook called
22/02/28 18:52:26 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-a95834c0-d38b-457b-89b2-fed00d5bef56
Cluster模式
./bin/spark-submit \
  --class org.apache.spark.examples.SparkPi \
  --master yarn-cluster \
  --executor-memory 1G \
  --num-executors 1 \
  /usr/local/src/spark/examples/jars/spark-examples_2.11-2.1.1.jar \
  4


Pi is roughly 3.1411378528446323
22/02/28 18:52:26 INFO server.ServerConnector: Stopped Spark@1b0a7baf{HTTP/1.1}{0.0.0.0:4040}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@8a589a2{/stages/stage/kill,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@192f2f27{/jobs/job/kill,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@1bdf8190{/api,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@4f8969b0{/,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@6fefce9e{/static,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@74cec793{/executors/threadDump/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@f9b7332{/executors/threadDump,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@18e7143f{/executors/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@209775a9{/executors,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@5db4c359{/environment/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@2c177f9e{/environment,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@33617539{/storage/rdd/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@47874b25{/storage/rdd,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@290b1b2e{/storage/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@1fc0053e{/storage,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@77307458{/stages/pool/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@389adf1d{/stages/pool,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@7bf9b098{/stages/stage/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@72e34f77{/stages/stage,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@6e9319f{/stages/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@6fa590ba{/stages,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@2416a51{/jobs/job/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@293bb8a5{/jobs/job,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@37ebc9d8{/jobs/json,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO handler.ContextHandler: Stopped o.s.j.s.ServletContextHandler@5217f3d0{/jobs,null,UNAVAILABLE,@Spark}
22/02/28 18:52:26 INFO ui.SparkUI: Stopped Spark web UI at http://192.168.184.135:4040
22/02/28 18:52:26 INFO cluster.YarnClientSchedulerBackend: Interrupting monitor thread
22/02/28 18:52:26 INFO cluster.YarnClientSchedulerBackend: Shutting down all executors
22/02/28 18:52:26 INFO cluster.YarnSchedulerBackend$YarnDriverEndpoint: Asking each executor to shut down
22/02/28 18:52:26 INFO cluster.SchedulerExtensionServices: Stopping SchedulerExtensionServices
(serviceOption=None,
 services=List(),
 started=false)
22/02/28 18:52:26 INFO cluster.YarnClientSchedulerBackend: Stopped
22/02/28 18:52:26 INFO spark.MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
22/02/28 18:52:26 INFO memory.MemoryStore: MemoryStore cleared
22/02/28 18:52:26 INFO storage.BlockManager: BlockManager stopped
22/02/28 18:52:26 INFO storage.BlockManagerMaster: BlockManagerMaster stopped
22/02/28 18:52:26 INFO scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
22/02/28 18:52:26 INFO spark.SparkContext: Successfully stopped SparkContext
22/02/28 18:52:26 INFO util.ShutdownHookManager: Shutdown hook called
22/02/28 18:52:26 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-a95834c0-d38b-457b-89b2-fed00d5bef56
[root@hadoop01 spark]# ./bin/spark-submit   --class org.apache.spark.examples.SparkPi   --master yarn-cluster   --executor-memory 1G   --num-executors 1   /usr/local/src/spark/examples/jars/spark-examples_2.11-2.1.1.jar 4
Warning: Master yarn-cluster is deprecated since 2.0. Please use master "yarn" with specified deploy mode instead.
22/02/28 18:54:32 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
22/02/28 18:54:32 WARN util.Utils: Your hostname, hadoop01.localdomain resolves to a loopback address: 127.0.0.1; using 192.168.184.135 instead (on interface ens33)
22/02/28 18:54:32 WARN util.Utils: Set SPARK_LOCAL_IP if you need to bind to another address
22/02/28 18:54:32 INFO client.RMProxy: Connecting to ResourceManager at hadoop01/192.168.184.135:8032
22/02/28 18:54:32 INFO yarn.Client: Requesting a new application from cluster with 1 NodeManagers
22/02/28 18:54:33 INFO yarn.Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)
22/02/28 18:54:33 INFO yarn.Client: Will allocate AM container, with 1408 MB memory including 384 MB overhead
22/02/28 18:54:33 INFO yarn.Client: Setting up container launch context for our AM
22/02/28 18:54:33 INFO yarn.Client: Setting up the launch environment for our AM container
22/02/28 18:54:33 INFO yarn.Client: Preparing resources for our AM container
22/02/28 18:54:33 WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
22/02/28 18:54:35 INFO yarn.Client: Uploading resource file:/tmp/spark-c7e3fb91-c7d0-4f59-86ba-705a9f256144/__spark_libs__3085975169933820625.zip -> hdfs://hadoop01:9000/user/root/.sparkStaging/application_1646041633964_0004/__spark_libs__3085975169933820625.zip
22/02/28 18:54:35 INFO yarn.Client: Uploading resource file:/usr/local/src/spark/examples/jars/spark-examples_2.11-2.1.1.jar -> hdfs://hadoop01:9000/user/root/.sparkStaging/application_1646041633964_0004/spark-examples_2.11-2.1.1.jar
22/02/28 18:54:35 INFO yarn.Client: Uploading resource file:/tmp/spark-c7e3fb91-c7d0-4f59-86ba-705a9f256144/__spark_conf__2818552262823480245.zip -> hdfs://hadoop01:9000/user/root/.sparkStaging/application_1646041633964_0004/__spark_conf__.zip
22/02/28 18:54:35 INFO spark.SecurityManager: Changing view acls to: root
22/02/28 18:54:35 INFO spark.SecurityManager: Changing modify acls to: root
22/02/28 18:54:35 INFO spark.SecurityManager: Changing view acls groups to:
22/02/28 18:54:35 INFO spark.SecurityManager: Changing modify acls groups to:
22/02/28 18:54:35 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(root); groups with view permissions: Set(); users  with modify permissions: Set(root); groups with modify permissions: Set()
22/02/28 18:54:35 INFO yarn.Client: Submitting application application_1646041633964_0004 to ResourceManager
22/02/28 18:54:35 INFO impl.YarnClientImpl: Submitted application application_1646041633964_0004
22/02/28 18:54:36 INFO yarn.Client: Application report for application_1646041633964_0004 (state: ACCEPTED)
22/02/28 18:54:36 INFO yarn.Client:
         client token: N/A
         diagnostics: N/A
         ApplicationMaster host: N/A
         ApplicationMaster RPC port: -1
         queue: default
         start time: 1646045675928
         final status: UNDEFINED
         tracking URL: http://hadoop01:8088/proxy/application_1646041633964_0004/
         user: root
22/02/28 18:54:37 INFO yarn.Client: Application report for application_1646041633964_0004 (state: ACCEPTED)
22/02/28 18:54:38 INFO yarn.Client: Application report for application_1646041633964_0004 (state: ACCEPTED)
22/02/28 18:54:39 INFO yarn.Client: Application report for application_1646041633964_0004 (state: RUNNING)
22/02/28 18:54:39 INFO yarn.Client:
         client token: N/A
         diagnostics: N/A
         ApplicationMaster host: 192.168.184.135
         ApplicationMaster RPC port: 0
         queue: default
         start time: 1646045675928
         final status: UNDEFINED
         tracking URL: http://hadoop01:8088/proxy/application_1646041633964_0004/
         user: root
22/02/28 18:54:40 INFO yarn.Client: Application report for application_1646041633964_0004 (state: RUNNING)
22/02/28 18:54:41 INFO yarn.Client: Application report for application_1646041633964_0004 (state: RUNNING)
22/02/28 18:54:42 INFO yarn.Client: Application report for application_1646041633964_0004 (state: RUNNING)
22/02/28 18:54:43 INFO yarn.Client: Application report for application_1646041633964_0004 (state: FINISHED)
22/02/28 18:54:43 INFO yarn.Client:
         client token: N/A
         diagnostics: N/A
         ApplicationMaster host: 192.168.184.135
         ApplicationMaster RPC port: 0
         queue: default
         start time: 1646045675928
         final status: SUCCEEDED
         tracking URL: http://hadoop01:8088/proxy/application_1646041633964_0004/
         user: root
22/02/28 18:54:43 INFO util.ShutdownHookManager: Shutdown hook called
22/02/28 18:54:43 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-c7e3fb91-c7d0-4f59-86ba-705a9f256144

6.检测ID

[root@hadoop01 spark]# yarn logs -applicationId application_1646041633964_0003

22/02/28 18:59:05 INFO client.RMProxy: Connecting to ResourceManager at hadoop01/192.168.184.135:8032
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/local/src/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/local/src/hive/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
/tmp/logs/root/logs/application_1646041633964_0003 does not exist.
Log aggregation has not completed or is not enabled.

#未指定参数，看不到，未作聚合日志配置，需要通过webUI页面

7.WebUI查看结果

http://hadoop01:8042/node/containerlogs/container_1646041633964_0004_01_000001/root

spark ui错误日志 spark日志清理_大数据_11

九、Spark项目运行到YARN

maven打包依赖

spark ui错误日志 spark日志清理_spark ui错误日志_12

1.IDEA项目代码-词频统计

package com.bigdata

import org.apache.spark.sql.SparkSession

object WordCountYARN {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession
      .builder()
      .getOrCreate()

    if(args.length!=2){
      println("Usage:WordCountYARN <inputPath><outputPath>")
    }
    val Array(inputPath,outputPath)=args

    val rdd = spark.sparkContext.textFile(inputPath)

    val df = rdd.flatMap(x=>x.split("\t")).map(word=>(word,1)).reduceByKey((a,b)=>(a+b))

    df.saveAsTextFile(outputPath)

    spark.stop()
  }

}

2.spark-submit

spark-submit \
  --class com.bigdata.WordCountYARN \
  --name WordCount \
  --master yarn \
  --executor-memory 1G \
  --num-executors 1 \
  /usr/local/src/spark/spark_jar/BYGJ.jar \
  hdfs://hadoop01:9000/wordcount.txt hdfs://hadoop01:9000/wc_output

3.查询结果

[root@hadoop01 spark_jar]# hadoop fs -cat /wc_output/part-*

SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/local/src/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/local/src/hive/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
(hive,5)
(spark,5)
(hadoop,2)
(hbase,3)

-----------------------------------------

1.IDEA项目代码-日志清洗

package com.saddam.spark.MuKe.ImoocProject.LogClean

import org.apache.spark.sql.{SaveMode, SparkSession}

object SparkStatCleanJobYarn {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession
      .builder()
      .getOrCreate()

    if(args.length!=2){
      println("Usage:WordCountYARN <inputPath><outputPath>")
    }
    val Array(inputPath,outputPath)=args

    val accessRDD = spark.sparkContext.textFile(inputPath)

    //TODO RDD->DF
    val accessDF=spark.createDataFrame(accessRDD.map(x=>AccessConvertUtil.parseLog(x)),AccessConvertUtil.struct)

    accessDF
        .coalesce(1)
      .write
      .format("parquet")
      .mode(SaveMode.Overwrite)
      .partitionBy("day")
      .save(outputPath)
    
    spark.stop()
  }
}

2.spark-submit

spark-submit \
  --class com.saddam.spark.MuKe.ImoocProject.LogClean.SparkStatCleanJobYarn \
  --name SparkStatCleanJobYarn \
  --master yarn \
  --executor-memory 1G \
  --num-executors 1 \
  --files /usr/local/src/spark/spark_jar/ipDatabase.csv,/usr/local/src/spark/spark_jar/ipRegion.xlsx \
  /usr/local/src/spark/spark_jar/Spark.jar \
  hdfs://hadoop01:9000/access.log hdfs://hadoop01:9000/log_output

3.查询结果

进入spark-shell

[root@hadoop01 datas]# spark-shell --master local[2] --jars /usr/local/src/mysql-connector-java-5.1.27-bin.jar

获取hdfs输出文件

/log_output/day=20170511/part-00000-36e30abb-3e42-4237-ad9f-a9f93258d4b2.snappy.parquet

读取文件

scala>     spark.read.format("parquet").parquet("/log_output/day=20170511/part-00000-36e30abb-3e42-4237-ad9f-a9f93258d4b2.snappy.parquet").show(false)

SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
+----------------------------------+-------+-----+-------+---------------+----+-------------------+
|url                               |cmsType|cmsId|traffic|ip             |city|time               |
+----------------------------------+-------+-----+-------+---------------+----+-------------------+
|http://www.imooc.com/video/4500   |video  |4500 |304    |218.75.35.226  |    |2017-05-11 14:09:14|
|http://www.imooc.com/video/14623  |video  |14623|69     |202.96.134.133 |    |2017-05-11 15:25:05|
|http://www.imooc.com/article/17894|article|17894|115    |202.96.134.133 |    |2017-05-11 07:50:01|
|http://www.imooc.com/article/17896|article|17896|804    |218.75.35.226  |    |2017-05-11 02:46:43|
|http://www.imooc.com/article/17893|article|17893|893    |222.129.235.182|    |2017-05-11 09:30:25|
|http://www.imooc.com/article/17891|article|17891|407    |218.75.35.226  |    |2017-05-11 08:07:35|
|http://www.imooc.com/article/17897|article|17897|78     |202.96.134.133 |    |2017-05-11 19:08:13|
|http://www.imooc.com/article/17894|article|17894|658    |222.129.235.182|    |2017-05-11 04:18:47|
|http://www.imooc.com/article/17893|article|17893|161    |58.32.19.255   |    |2017-05-11 01:25:21|
|http://www.imooc.com/article/17895|article|17895|701    |218.22.9.56    |    |2017-05-11 13:37:22|
|http://www.imooc.com/article/17892|article|17892|986    |218.75.35.226  |    |2017-05-11 05:53:47|
|http://www.imooc.com/video/14540  |video  |14540|987    |58.32.19.255   |    |2017-05-11 18:44:56|
|http://www.imooc.com/article/17892|article|17892|610    |218.75.35.226  |    |2017-05-11 17:48:51|
|http://www.imooc.com/article/17893|article|17893|0      |218.22.9.56    |    |2017-05-11 16:20:03|
|http://www.imooc.com/article/17891|article|17891|262    |58.32.19.255   |    |2017-05-11 00:38:01|
|http://www.imooc.com/video/4600   |video  |4600 |465    |218.75.35.226  |    |2017-05-11 17:38:16|
|http://www.imooc.com/video/4600   |video  |4600 |833    |222.129.235.182|    |2017-05-11 07:11:36|
|http://www.imooc.com/article/17895|article|17895|320    |222.129.235.182|    |2017-05-11 19:25:04|
|http://www.imooc.com/article/17898|article|17898|460    |202.96.134.133 |    |2017-05-11 15:14:28|
|http://www.imooc.com/article/17899|article|17899|389    |222.129.235.182|    |2017-05-11 02:43:15|
+----------------------------------+-------+-----+-------+---------------+----+-------------------+
only showing top 20 rows

十、项目性能调优

1.集群优化

存储格式的选择：

压缩格式的选择：
	默认：snapy
.config("spark.sql.parquet.compression.codec","gzip")修改

2.代码优化

选择高性能算子

复用已有的数据

3.参数优化

并行度：
	spark.sql.shuffle.partitions	
	200	
	配置在为联接或聚合进行数据洗牌时使用的分区数。
spark-submit:
	--conf spark.sql.shuffle.partitions=500
IDEA:
	.config("","")
	
	
分区字段类型推测：
	 spark.sql.sources.partitionColumnTypeInference.enabled
spark-submit:
	--conf spark.sql.sources.partitionColumnTypeInference.enabled=false
IDEA:
	.config("","")

262 |58.32.19.255 | |2017-05-11 00:38:01|
|http://www.imooc.com/video/4600 |video |4600 |465 |218.75.35.226 | |2017-05-11 17:38:16|
|http://www.imooc.com/video/4600 |video |4600 |833 |222.129.235.182| |2017-05-11 07:11:36|
|http://www.imooc.com/article/17895|article|17895|320 |222.129.235.182| |2017-05-11 19:25:04|
|http://www.imooc.com/article/17898|article|17898|460 |202.96.134.133 | |2017-05-11 15:14:28|
|http://www.imooc.com/article/17899|article|17899|389 |222.129.235.182| |2017-05-11 02:43:15|
±---------------------------------±------±----±------±--------------±—±------------------+
only showing top 20 rows

## 十、项目性能调优

### 1.集群优化

~~~markdown
存储格式的选择：

压缩格式的选择：
	默认：snapy
.config("spark.sql.parquet.compression.codec","gzip")修改

2.代码优化

选择高性能算子

复用已有的数据

3.参数优化

并行度：
	spark.sql.shuffle.partitions	
	200	
	配置在为联接或聚合进行数据洗牌时使用的分区数。
spark-submit:
	--conf spark.sql.shuffle.partitions=500
IDEA:
	.config("","")
	
	
分区字段类型推测：
	 spark.sql.sources.partitionColumnTypeInference.enabled
spark-submit:
	--conf spark.sql.sources.partitionColumnTypeInference.enabled=false
IDEA:
	.config("","")