项目实施-14完结篇（配置自动调度）

本文详述了使用Azkaban进行大数据批处理任务的自动调度流程，包括Flink Table API离线数据计算、Sqoop数据导出至MySQL，以及Azkaban 2.0版本的流设计与任务调度配置。

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概述

本篇是用户综合分析系统专栏的最后一篇。到本篇为止，我们此次的项目实施宣告终结。

我们已经完成了大数据 流处理与批处理 的设计编码，已经实现了基本的数据处理任务，但是，因为批处理一般都是定期执行的，而且多数是在非服务器高峰期的半夜执行的，因此，我们需要用到 Azkaban的任务调度，帮我们完成批处理的自动执行。

设计计划

附：完整的计划zip包:

https://pan.baidu.com/s/1dR9NxB9TXE4vx879c6mMlg 获取码：jbyk

整体思路

批处理 的过程是：
①先执行Flink的Table api 完成离线数据计算
②再通过Sqoop将①中执行输出的数据写到MySQL

整体调度计划结构

说明： 此次的项目批处理设计中，批处理的读取文件地址和输出文件地址是作为参数传递的

libs下的代码实现

package com.baizhi.sql

import java.text.SimpleDateFormat

import org.apache.flink.api.scala.ExecutionEnvironment
import org.apache.flink.table.api.scala.BatchTableEnvironment
import org.apache.flink.table.api.scala._
import org.apache.flink.api.scala._
import java.math.{BigDecimal => JBigDecimal}

import org.apache.flink.api.common.typeinfo.TypeInformation
import org.apache.flink.api.java.io.jdbc.JDBCAppendTableSinkBuilder
import org.apache.flink.api.scala.typeutils.Types
object UserRiskErrorRateCount {
  def main(args: Array[String]): Unit = {

    val fbEnv = ExecutionEnvironment.getExecutionEnvironment
    val fbTableEnv = BatchTableEnvironment.create(fbEnv)

    //获取传递的参数
    var input=args(0)
    var output=args(1)

    val sdf= new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
    val dataSet:DataSet[(String,String,String,String,String,Boolean,Boolean,Boolean,Boolean,Boolean,Boolean,Boolean)] = fbEnv.readTextFile(input,
                            "utf-8")
                                          .map(_.split("\\s+"))
                                          .map(ts => (ts(0),ts(1),sdf.format(ts(3).toLong),ts(4),ts(5),ts(6).toBoolean,
                                           ts(7).toBoolean,ts(8).toBoolean,ts(9).toBoolean,ts(10).toBoolean,ts(11).toBoolean,ts(12).toBoolean))

    val table = fbTableEnv.fromDataSet(dataSet,'appName,'userIdentified,'evaluateTime,'cityName,'geoPoint,'area,'device,'inputfeature, 'similarity, 'speed,'timeslot,'total)
    //注册视图
    fbTableEnv.createTemporaryView("t_report",table)

    table.select("appName, userIdentified,evaluateTime,cityName,geoPoint,area,device,inputfeature,similarity, speed,timeslot,total")
      .toDataSet[(String,String,String,String,String,Boolean,Boolean,Boolean,Boolean,Boolean,Boolean,Boolean)]
      .print()


    var tableSink = new JDBCAppendTableSinkBuilder()
      .setDBUrl("jdbc:mysql://localhost:3306/test")
      .setDrivername("com.mysql.jdbc.Driver")
      .setUsername("root")
      .setPassword("root")
      .setBatchSize(1000)
      .setQuery("INSERT INTO error_rate(app_name,area_rate,device_rate,inputfeature_rate,similarity_rate,speed_rate,timeslot_rate,total_rate,total,start,end) values(?,?,?,?,?,?,?,?,?,?,?)")
      .setParameterTypes(Types.STRING, Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.INT,Types.STRING, Types.STRING)
      .build();

    fbTableEnv.registerTableSink("Result",
      Array("app_name","area_rate","device_rate","inputfeature_rate","similarity_rate","speed_rate","timeslot_rate","total_rate","total","start","end"),
      Array[TypeInformation[_]](Types.STRING, Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.JAVA_BIG_DEC,Types.INT, Types.STRING, Types.STRING),
      tableSink)

    //获取时间参数
    var start=args(2)
    var end=args(3)
    //计算因子触发率
    val sql=
      s"""
        select appName,
        round(sum(case area when true then 1.0 else 0 end)/count(area),2)  as  area_rate,
        round(sum(case device when true then 1.0 else 0 end)/count(device),2)  as  device_rate,
        round(sum(case inputfeature when true then 1.0 else 0 end)/count(inputfeature),2)  as  inputfeature_rate,
        round(sum(case similarity when true then 1.0 else 0 end)/count(similarity),2)  as similarity_rate,
        round(sum(case speed when true then 1.0 else 0 end)/count(speed),2)  as  speed_rate,
        round(sum(case timeslot when true then 1.0 else 0 end)/count(timeslot),2)  as  timeslot_rate,
        round(sum(case total when true then 1.0 else 0 end)/count(total),2)  as  total_rate,
        count(*) as total
        from (select * from t_report where evaluateTime between '${start}' and '${end}')
        group by appName
      """

    fbTableEnv.sqlQuery(sql)
      .addColumns(s"'${start}' as start")
      .addColumns(s"'${end}' as end")
      .toDataSet[(String,JBigDecimal,JBigDecimal,JBigDecimal,JBigDecimal,JBigDecimal,JBigDecimal,JBigDecimal,Long,String,String)]
      .map(t => t._1+","+t._2+","+t._3+","+t._4+","+t._5+","+t._6+","+t._7+","+t._8+","+t._9+","+t._10+","+t._11)
      .writeAsText(output,WriteMode.OVERWRITE)

          fbEnv.execute("UserRiskErrorRateCount")

  }
}

flow20.project

2.0版本的任务调度

 azkaban-flow-version: 2.0

demo10.flow

---
config:
  user.to.proxy: root
  failure.emails: zyl.m@qq.com
  success.emails: zyl.m@qq
  notify.emails: zyl.m@qq

nodes:
  - name: UserRiskErrorRateCount
    type: command
    config:
      command: sh ./demo10/bin/submit-job.sh ${input} ${output}

  - name: ExportToMysQL
    type: command
    dependsOn:
      - UserRiskErrorRateCount
    config:
      command: sh ./demo10/bin/export-to-mysql.sh ${out}

${}----->表示传参，第二个任务的参数out,由第一个任务的执行计划传递

第一个任务 Submit-job.sh

#!/usr/bin/env bash 
base_path=$(pwd)
jar_path=$base_path/demo10/libs/UserRiskEvaluate-1.0-SNAPSHOT.jar
start_time=$(date -d -1month '+%Y-%m-%d')
end_time=$(date '+%Y-%m-%d')

input=$1
output=$2

/usr/flink-1.10.0/bin/flink run --class com.baizhi.sql.UserRiskErrorRateCount --parallelism 4 --jobmanager centos:8081 $jar_path $input $output $start_time  $end_time 

echo '{"out":"'$output'"}' > $JOB_OUTPUT_PROP_FILE

说明： ${date -d -1month ‘+%Y-%m-%d’} 表示获取当前时间减去一个月的时间，作为传递到批处理计算的第三个参数，后面的end_time作为第四个参数

第二个任务 export-to-mysql.sh

#!/usr/bin/env bash 
sqoop export --connect jdbc:mysql://CentOS:3306/test --username root --password root --table error_rate --update-key app_name --update-mode allowinsert --export-dir $1 --input-fields-terminated-by ','