大数据入门：Spark+Kudu的广告业务项目实战笔记(六)

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Spark+Kudu的广告业务项目实战系列：

本章目标：将代码打包并运行在服务器上。

1.将数据放在HDFS上

先把Hadoop启动起来：

[hadoop@hadoop000 ~]$ cd app/[hadoop@hadoop000 app]$ lsapache-maven-3.6.3      hive-1.1.0-cdh5.15.1  spark-2.4.5-bin-hadoop2.6hadoop-2.6.0-cdh5.15.1  jdk1.8.0_91           tmp[hadoop@hadoop000 app]$ cd hadoop-2.6.0-cdh5.15.1/[hadoop@hadoop000 hadoop-2.6.0-cdh5.15.1]$ lsbin             etc                  include  LICENSE.txt  README.txt  srcbin-mapreduce1  examples             lib      logs         sbincloudera        examples-mapreduce1  libexec  NOTICE.txt   share[hadoop@hadoop000 hadoop-2.6.0-cdh5.15.1]$ cd sbin/[hadoop@hadoop000 sbin]$ lsdistribute-exclude.sh    slaves.sh            stop-all.shhadoop-daemon.sh         start-all.cmd        stop-balancer.shhadoop-daemons.sh        start-all.sh         stop-dfs.cmdhdfs-config.cmd          start-balancer.sh    stop-dfs.shhdfs-config.sh           start-dfs.cmd        stop-secure-dns.shhttpfs.sh                start-dfs.sh         stop-yarn.cmdkms.sh                   start-secure-dns.sh  stop-yarn.shLinux                    start-yarn.cmd       yarn-daemon.shmr-jobhistory-daemon.sh  start-yarn.sh        yarn-daemons.shrefresh-namenodes.sh     stop-all.cmd[hadoop@hadoop000 sbin]$ ./start-all.sh This script is Deprecated. Instead use start-dfs.sh and start-yarn.shStarting namenodes on [hadoop000]hadoop000: starting namenode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.15.1/logs/hadoop-hadoop-namenode-hadoop000.outhadoop000: starting datanode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.15.1/logs/hadoop-hadoop-datanode-hadoop000.outStarting secondary namenodes [0.0.0.0]0.0.0.0: starting secondarynamenode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.15.1/logs/hadoop-hadoop-secondarynamenode-hadoop000.outstarting yarn daemonsstarting resourcemanager, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.15.1/logs/yarn-hadoop-resourcemanager-hadoop000.outhadoop000: starting nodemanager, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.15.1/logs/yarn-hadoop-nodemanager-hadoop000.out

在HDFS上新建个目录，数据在HDFS上的规划是每天一个目录，YYYYMMDD格式：

[hadoop@hadoop000 sbin]$ hadoop fs -mkdir -p /tai/access/20181007

把data-test.json放到HDFS上：

[hadoop@hadoop000 sbin]$ hadoop fs -put ~/data/data-test.json /tai/access/20181007/

去50070端口瞅瞅：

成功

把ip.txt像之前一样上传到HDFS上：

hadoop fs -put ~/data/ip.txt /tai/access/

2.定时操作重构

每天跑一次，每天凌晨3点跑，需要传递一个需要处理的时间，在SparkApp.scala中：

package com.imooc.bigdata.cp08

import com.imooc.bigdata.cp08.business.{AppStatProcessor, AreaStatProcessor, LogETLProcessor, ProvinceCityStatProcessor}
import org.apache.commons.lang3.StringUtils
import org.apache.spark.internal.Logging
import org.apache.spark.sql.SparkSession


//整个项目的入口
object SparkApp extends Logging{

  def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
      .master("local[2]")
      .appName("SparkApp")
      .getOrCreate()
//spark-submit ... --conf time=20181007
//spark框架只认spark开头的代码
val time = spark.sparkContext.getConf.get("spark.time")
if(StringUtils.isBlank(time)){
//若为空则不执行
      logError("处理批次不能为空")
      System.exit(0)
    }


    LogETLProcessor.process(spark)
    ProvinceCityStatProcessor.process(spark)
    AreaStatProcessor.process(spark)
    AppStatProcessor.process(spark)
    spark.stop()
  }

}

由于要将名字改为表名+时间命名，需要增加一个DateUtils：

package com.imooc.bigdata.cp08.utils

import org.apache.spark.sql.SparkSession

object DateUtils {

  def getTableName(tableName:String,spark:SparkSession)={
val time = spark.sparkContext.getConf.get("spark.time")
    tableName + "_" + time
  }
}

之后将所有的表名改为工具类的调用，举例来说：

val sourceTableName = DateUtils.getTableName("ods",spark)
val sinkTableName = DateUtils.getTableName("province_city_stat",spark)

之后需要制定两个源文件的路径：

val rawPath = spark.sparkContext.getConf.get("spark.raw.path")
    var jsonDF = spark.read.json(rawPath)
val ipRulePath = spark.sparkContext.getConf.get("spark.ip.path")
val ipRowRDD = spark.sparkContext.textFile(ipRulePath)

入参有：spark.time/spark.raw.path/spark.ip.path

另外在生产时spark应这样调用，不要硬编码，统一使用spark-submit提交时指定：

val spark = SparkSession.builder().getOrCreate()

3.代码打包

用Maven打包代码：

将包传到服务器的~/lib目录下。

找到本地的kudu-client-1.7.0.jar和kudu-spark2_2.11-1.7.0.jar，也上传到服务器的~/lib下。

打开spark：

cd ~/app/spark-2.4.5-bin-hadoop2.6/sbin/
sh start-all.sh

开始写spark提交脚本,先在本地测试：

vi job.sh

time=20181007
${SPARK_HOME}/bin/spark-submit \
--class com.imooc.bigdata.cp08.SparkApp \
--master local \
--jars /home/hadoop/lib/kudu-client-1.7.0.jar,/home/hadoop/lib/kudu-spark2_2.11-1.7.0.jar \
--conf spark.time=$time \
--conf spark.raw.path="hdfs://hadoop000:8020/tai/access/$time" \
--conf spark.ip.path="hdfs://hadoop000:8020/tai/access/ip.txt" \
/home/hadoop/lib/sparksql-train-1.0.jar

 启动脚本：

sh job.sh

查看结果：

数据已经成功上传。

4.Spark on Yarn

vi jobyarn.sh

只要把--master 改成yarn即可： 

time=20181007
${SPARK_HOME}/bin/spark-submit \
--class com.imooc.bigdata.cp08.SparkApp \
--master yarn \
--jars /home/hadoop/lib/kudu-client-1.7.0.jar,/home/hadoop/lib/kudu-spark2_2.11-1.7.0.jar \
--conf spark.time=$time \
--conf spark.raw.path="hdfs://hadoop000:8020/tai/access/$time" \
--conf spark.ip.path="hdfs://hadoop000:8020/tai/access/ip.txt" \
/home/hadoop/lib/sparksql-train-1.0.jar

sh jobyarn.sh

去8088看一下yarn：

已经在跑啦~

5.定时调度

调度框架：crontab/Azkaban/Ooize...

本项目使用crontab，crontab表达式的工具在这里。设定一小时运行一次的表达式：

0 */1 * * *

crontab -e 编辑crontab -l 查看crontab -r 删除

若需要凌晨三点开始执行作业，执行的是昨天的数据，其中昨天的表示命令为：

[hadoop@hadoop000 lib]$ date --date='1 day ago' +%Y%m%d
20200225

修改脚本：

time=`date --date='1 day ago' +%Y%m%d`
${SPARK_HOME}/bin/spark-submit \
--class com.imooc.bigdata.cp08.SparkApp \
--master local \
--jars /home/hadoop/lib/kudu-client-1.7.0.jar,/home/hadoop/lib/kudu-spark2_2.11-1.7.0.jar \
--conf spark.time=$time \
--conf spark.raw.path="hdfs://hadoop000:8020/tai/access/$time" \
--conf spark.ip.path="hdfs://hadoop000:8020/tai/access/ip.txt" \
/home/hadoop/lib/sparksql-train-1.0.jar

设定crontab：

crontab -e
0 3 * * * /home/hadoop/lib/job.sh

搞定啦~

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本文为《大数据技术与架构》整理，原创或原作者独家授权。未经原作者允许违规转载追究侵权责任。

本文编辑：冷眼丶

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