Spark&Hive：如何使用scala开发spark访问hive作业，如何使用yarn resourcemanager。

• 背景：

接到任务，需要在一个一天数据量在460亿条记录的hive表中，筛选出某些host为特定的值时才解析该条记录的http_content中的经纬度：

解析规则譬如：

需要解析host: api.map.baidu.com
需要解析的规则："result":{"location":{"lng":120.25088311933617,"lat":30.310684375444877},
"confidence":25
需要解析http_conent:renderReverse&&renderReverse({"status":0,"result":{"location":{"lng":120.25088311933617,"lat":30.310684375444877},"formatted_address":"???????????????????????????????????????","business":"","addressComponent":{"country":"??????","country_code":0,"province":"?????????","city":"?????????","district":"?????????","adcode":"330104","street":"????????????","street_number":"","direction":"","distance":""},"pois":[{"addr":"????????????5277???","cp":" ","direction":"???","distance":"68","name":"????????????????????????????????????","poiType":"????????????","point":{"x":120.25084961536486,"y":30.3112150

• Scala代码实现“访问hive，并保存结果到hive表”的spark任务：

开发工具为IDEA16,开发语言为scala,开发包有了spark对应集群版本下的很多个jar包，和对应集群版本下的很多个jar包，引入jar包：

scala代码：

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

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.hive.HiveContext
import java.util
import java.util.{UUID, Calendar, Properties}
import org.apache.spark.rdd.JdbcRDD
import org.apache.spark.sql.{Row, SaveMode, SQLContext}
import org.apache.spark.storage.StorageLevel
import org.apache.spark.{sql, SparkContext, SparkConf}
import org.apache.spark.sql.DataFrameHolder

/**
  * temp http_content
  **/
case class Temp_Http_Content_ParserResult(success: String, lnglatType: String, longitude: String, Latitude: String, radius: String)

/**
  * Created by Administrator on 2016/11/15.
  */
object ParserMain {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf() 
    //.setAppName("XXX_ParserHttp").setMaster("local[1]").setMaster("spark://172.21.7.10:7077").setJars(List("xxx.jar"))
        //.set("spark.executor.memory", "10g")
    val sc = new SparkContext(conf)
    val hiveContext = new HiveContext(sc)

    // use abc_hive_db;
    hiveContext.sql("use abc_hive_db")
    // error date format:2016-11-15，date format must be 20161115
    val rdd = hiveContext.sql("select host,http_content from default.http where hour>='20161115' and hour<'20161116'")

    // toDF() method need this line...
    import hiveContext.implicits._

    // (success, lnglatType, longitude, latitude, radius)
    val rdd2 = rdd.map(s => parse_http_context(s.getAs[String]("host"), s.getAs[String]("http_content"))).filter(s => s._1).map(s => Temp_Http_Content_ParserResult(s._1.toString(), s._2, s._3, s._4, s._5)).toDF()
    rdd2.registerTempTable("Temp_Http_Content_ParserResult_20161115")
    hiveContext.sql("create table Temp_Http_Content_ParserResult20161115 as select * from Temp_Http_Content_ParserResult_20161115")

    sc.stop()
  }

  /**
    * @ summary: 解析http_context字段信息
    * @ param http_context 参数信息
    * @ result 1:是否匹配成功；
    * @ result 2:匹配出的是什么经纬度的格式：
    * @ result 3:经度；
    * @ result 4:纬度,
    * @ result 5:radius
    **/
  def parse_http_context(host: String, http_context: String): (Boolean, String, String, String, String) = {
    if (host == null || http_context == null) {
      return (false, "", "", "", "")
    }

    //    val result2 = parse_http_context(“api.map.baidu.com”,"renderReverse&&renderReverse({"status":0,"result":{"location":{"lng":120.25088311933617,"lat":30.310684375444877},"formatted_address":"???????????????????????????????????????","business":"","addressComponent":{"country":"??????","country_code":0,"province":"?????????","city":"?????????","district":"?????????","adcode":"330104","street":"????????????","street_number":"","direction":"","distance":""},"pois":[{"addr":"????????????5277???","cp":" ","direction":"???","distance":"68","name":"????????????????????????????????????","poiType":"????????????","point":{"x":120.25084961536486,"y":30.3112150")
    //    println(result2._1 + ":" + result2._2 + ":" + result2._3 + ":" + result2._4 + ":" + result2._5)
   
    var success = false
    var lnglatType = ""
    var longitude = ""
    var latitude = ""
    var radius = ""
    var lowerCaseHost = host.toLowerCase().trim();
    val lowerCaseHttp_Content = http_context.toLowerCase()
    //    api.map.baidu.com
    //    "result":{"location":{"lng":120.25088311933617,"lat":30.310684375444877},
    //    "confidence":25
    //     --renderReverse&&renderReverse({"status":0,"result":{"location":{"lng":120.25088311933617,"lat":30.310684375444877},"formatted_address":"???????????????????????????????????????","business":"","addressComponent":{"country":"??????","country_code":0,"province":"?????????","city":"?????????","district":"?????????","adcode":"330104","street":"????????????","street_number":"","direction":"","distance":""},"pois":[{"addr":"????????????5277???","cp":" ","direction":"???","distance":"68","name":"????????????????????????????????????","poiType":"????????????","point":{"x":120.25084961536486,"y":30.3112150
    if (lowerCaseHost.equals("api.map.baidu.com")) {
      val indexLng = lowerCaseHttp_Content.indexOf(""lng"")
      val indexLat = lowerCaseHttp_Content.indexOf(""lat"")
      if (lowerCaseHttp_Content.indexOf(""location"") != -1 && indexLng != -1 && indexLat != -1) {
        var splitstr: String = "\,|\{|\}"
        var uriItems: Array[String] = lowerCaseHttp_Content.split(splitstr)
        var tempItem: String = ""
        lnglatType = "BD"
        success = true
        for (uriItem <- uriItems) {
          tempItem = uriItem.trim()
          if (tempItem.startsWith(""lng":")) {
            longitude = tempItem.replace(""lng":", "").trim()
          } else if (tempItem.startsWith(""lat":")) {
            latitude = tempItem.replace(""lat":", "").trim()
          } else if (tempItem.startsWith(""confidence":")) {
            radius = tempItem.replace(""confidence":", "").trim()
          }
        }
      }
    }  
    else if (lowerCaseHost.equals("loc.map.baidu.com")) {
      。。。
    }

    longitude = longitude.replace(""", "")
    latitude = latitude.replace(""", "")
    radius = radius.replace(""", "")

    (success, lnglatType, longitude, latitude, radius)
  }
}

打包，注意应为我们使用的hadoop&hive&spark on yarn的集群，我们这里并不需要想spark&hadoop一样还需要在执行spark-submit时将spark-hadoop-xx.jar打包进来，也不需要在submit-spark脚本.sh中制定jars参数，yarn会自动诊断我们需要哪些集群系统包；但是，如果你应用的是第三方的包，比如ab.jar，那打包时可以打包进来，也可以在spark-submit 参数jars后边指定特定的包。

• 写spark-submit提交脚本.sh：

• 当执行spark-submit脚本出现错误时，怎么应对呢？

注意，我们这里不是spark而是spark on yarn，当我们使用yarn-cluster方式提交时，界面是看不到任何日志新的。我们需要借助yarn管理系统来查看日志：

1、根据返回的任务id查看历史日志：

yarn logs -applicationId  application_1475071482566_3329402

2、yarn页面查看日志

https://xx.xx.xx.xx:xxxxx/Yarn/ResourceManager/xxxx/cluster

用户名/密码：user/password

 

 

3、yarn关闭application:

从yarn resourcemanger界面中，可以查看到具体的applicationId，使用命令来杀掉该任务：

更多命令可以参考：http://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/YarnCommands.html

yarn application -kill application_1475071482566_3807023

或者从界面进入spark作业进度管理界面，进行查看作业具体执行进度，也可以kill application

参考资料：

http://blog.csdn.net/sparkexpert/article/details/50964732

Spark On YARN内存分配:http://blog.javachen.com/2015/06/09/memory-in-spark-on-yarn.html?utm_source=tuicool