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Spark实战-基于Spark日志清洗与数据统计以及Zeppelin使用

news 2025/7/5 3:10:41

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

在这里插入图片描述

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HDFS2)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

在这里插入图片描述

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.ImoocProjectimport 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.ImoocProjectimport 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 00:01:02val 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]"))}
}

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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.MuKeimport java.sql.{Connection, DriverManager, PreparedStatement}import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._import scala.collection.mutable.ListBufferobject 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videoval  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articleaccessDF.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").toStringval 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 = nullvar pstmt:PreparedStatement = nulltry {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.MuKeimport java.sql.{Connection, DriverManager, PreparedStatement}import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._import scala.collection.mutable.ListBufferobject 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").toStringval 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 = nullvar pstmt: PreparedStatement = nulltry {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.MuKeimport java.sql.{Connection, DriverManager, PreparedStatement}import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._import scala.collection.mutable.ListBufferobject 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").toStringval 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 = nullvar pstmt: PreparedStatement = nulltry {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 = nullvar pstmt:PreparedStatement = nulltry{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

在这里插入图片描述

com.mysql.jdbc.Driverxxxxxxjdbc:mysql://121.37.2x.xx:3306/imooc_project?root#mysql驱动
/usr/local/src/mysql-connector-java-5.1.27-bin.jar

在这里插入图片描述

6.创建note

在这里插入图片描述

7.查询表

%jdbcshow tables;

在这里插入图片描述

8.图形展示

%jdbcselect cms_id,times from day_video_access_topn_stat;

在这里插入图片描述

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
在这里插入图片描述

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
在这里插入图片描述

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 \4Pi 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/Adiagnostics: N/AApplicationMaster host: N/AApplicationMaster RPC port: -1queue: defaultstart time: 1646045675928final status: UNDEFINEDtracking 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/Adiagnostics: N/AApplicationMaster host: 192.168.184.135ApplicationMaster RPC port: 0queue: defaultstart time: 1646045675928final status: UNDEFINEDtracking 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/Adiagnostics: N/AApplicationMaster host: 192.168.184.135ApplicationMaster RPC port: 0queue: defaultstart time: 1646045675928final status: SUCCEEDEDtracking 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_000322/02/28 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

在这里插入图片描述

九、Spark项目运行到YARN

maven打包依赖

在这里插入图片描述

1.IDEA项目代码-词频统计

package com.bigdataimport org.apache.spark.sql.SparkSessionobject 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)=argsval 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.LogCleanimport 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)=argsval accessRDD = spark.sparkContext.textFile(inputPath)//TODO RDD->DFval 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：https://www.infoq.cn/article/bigdata-store-choose/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：https://www.infoq.cn/article/bigdata-store-choose/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("","")

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