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：
Spark+Kudu的广告业务项目实战笔记(一)
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.StringUtilsimport org.apache.spark.internal.Loggingimport 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%d20200225
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 -e0 3 * * * /home/hadoop/lib/job.sh
搞定啦~
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