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Spark 图计算ONEID 进阶版

0、环境信息

    本文采用阿里云maxcompute的spark环境为基础进行的，搭建本地spark环境参考搭建Windows开发环境_云原生大数据计算服务 MaxCompute-阿里云帮助中心

    版本spark 2.4.5，maven版本大于3.8.4

①配置pom依赖 详见2-1

②添加运行jar包

③添加配置信息

odps.project.name=
odps.access.id=
odps.access.key=
odps.end.point=

1、数据准备

create TABLE dwd_sl_user_ids(

user_name STRING COMMENT '用户'

,user_id STRING COMMENT '用户id'

,device_id STRING COMMENT '设备号'

,id_card STRING COMMENT '身份证号'

,phone STRING COMMENT '电话号'

,pay_id STRING COMMENT '支付账号'

,ssoid STRING COMMENT 'APPID'

) PARTITIONED BY (

ds BIGINT

)

;

INSERT OVERWRITE TABLE dwd_sl_user_ids PARTITION(ds=20230818)

VALUES

('大法_官网','1','device_a','130826','185133','zhi1111','U130311')

,('大神_官网','2','device_b','220317','165133','zhi2222','')

,('耀总_官网','3','','310322','133890','zhi3333','U120311')

,('大法_app','1','device_x','130826','','zhi1111','')

,('大神_app','2','device_b','220317','165133','','')

,('耀总_app','','','','133890','zhi333','U120311')

,('大法_小程序','','device_x','130826','','','U130311')

,('大神_小程序','2','device_b','220317','165133','','U140888')

,('耀总_小程序','','','310322','133890','','U120311')

;

结果表

create TABLE itsl_dev.dwd_patient_oneid_info_df(

oneid STRING COMMENT '生成的ONEID'

,id STRING COMMENT '用户的各类id'

,id_hashcode STRING COMMENT '用户各类ID的id_hashcode'

,guid STRING COMMENT '聚合的guid'

,guid_hashcode STRING COMMENT '聚合的guid_hashcode'

)PARTITIONED BY (

ds BIGINT

);

2、代码准备

①pom.xml

<?xml version="1.0" encoding="UTF-8"?>

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.gwm</groupId>
  <artifactId>graph</artifactId>
  <version>1.0-SNAPSHOT</version>

  <name>graph</name>
  <!-- FIXME change it to the project's website -->
  <url>http://www.example.com</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <maven.compiler.source>1.8</maven.compiler.source>
    <maven.compiler.target>1.8</maven.compiler.target>
    <spark.version>2.3.0</spark.version>
    <java.version>1.8</java.version>
    <cupid.sdk.version>3.3.8-public</cupid.sdk.version>
    <scala.version>2.11.8</scala.version>
    <scala.binary.version>2.11</scala.binary.version>
  </properties>

  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.11</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.11</artifactId>
      <version>${spark.version}</version>
<!--            <scope>provided</scope>-->
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>${spark.version}</version>
<!--            <scope>provided</scope>-->
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-graphx_2.11</artifactId>
      <version>${spark.version}</version>
<!--            <scope>provided</scope>-->
    </dependency>

    <dependency>
      <groupId>com.thoughtworks.paranamer</groupId>
      <artifactId>paranamer</artifactId>
      <version>2.8</version>
<!--            <scope>provided</scope>-->
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>2.6.5</version>
<!--      <scope>provided</scope>-->
    </dependency>

    <dependency>
      <groupId>com.aliyun.odps</groupId>
      <artifactId>cupid-sdk</artifactId>
      <version>${cupid.sdk.version}</version>
            <scope>provided</scope>
    </dependency>
    <!--    <dependency>-->
    <!--      <groupId>com.aliyun.odps</groupId>-->
    <!--      <artifactId>hadoop-fs-oss</artifactId>-->
    <!--      <version>${cupid.sdk.version}</version>-->
    <!--    </dependency>-->
    <dependency>
      <groupId>com.aliyun.odps</groupId>
      <artifactId>odps-spark-datasource_${scala.binary.version}</artifactId>
      <version>${cupid.sdk.version}</version>
            <scope>provided</scope>
    </dependency>
    <!-- https://mvnrepository.com/artifact/com.alibaba/fastjson -->
    <dependency>
      <groupId>com.alibaba</groupId>
      <artifactId>fastjson</artifactId>
      <version>1.2.73</version>
    </dependency>

    <dependency>
      <groupId>commons-codec</groupId>
      <artifactId>commons-codec</artifactId>
      <version>1.13</version>
    </dependency>

    <dependency>
      <groupId>commons-lang</groupId>
      <artifactId>commons-lang</artifactId>
      <version>2.6</version>
    </dependency>

  </dependencies>

  <!--  <build>-->
  <!--    <pluginManagement>&lt;!&ndash; lock down plugins versions to avoid using Maven defaults (may be moved to parent pom) &ndash;&gt;-->
  <!--      <plugins>-->
  <!--        &lt;!&ndash; clean lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#clean_Lifecycle &ndash;&gt;-->
  <!--        <plugin>-->
  <!--          <artifactId>maven-clean-plugin</artifactId>-->
  <!--          <version>3.1.0</version>-->
  <!--        </plugin>-->
  <!--        &lt;!&ndash; default lifecycle, jar packaging: see https://maven.apache.org/ref/current/maven-core/default-bindings.html#Plugin_bindings_for_jar_packaging &ndash;&gt;-->
  <!--        <plugin>-->
  <!--          <artifactId>maven-resources-plugin</artifactId>-->
  <!--          <version>3.0.2</version>-->
  <!--        </plugin>-->
  <!--        <plugin>-->
  <!--          <artifactId>maven-compiler-plugin</artifactId>-->
  <!--          <version>3.8.0</version>-->
  <!--        </plugin>-->
  <!--        <plugin>-->
  <!--          <artifactId>maven-surefire-plugin</artifactId>-->
  <!--          <version>2.22.1</version>-->
  <!--        </plugin>-->
  <!--        <plugin>-->
  <!--          <artifactId>maven-jar-plugin</artifactId>-->
  <!--          <version>3.0.2</version>-->
  <!--        </plugin>-->
  <!--        <plugin>-->
  <!--          <artifactId>maven-install-plugin</artifactId>-->
  <!--          <version>2.5.2</version>-->
  <!--        </plugin>-->
  <!--        <plugin>-->
  <!--          <artifactId>maven-deploy-plugin</artifactId>-->
  <!--          <version>2.8.2</version>-->
  <!--        </plugin>-->
  <!--        &lt;!&ndash; site lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#site_Lifecycle &ndash;&gt;-->
  <!--        <plugin>-->
  <!--          <artifactId>maven-site-plugin</artifactId>-->
  <!--          <version>3.7.1</version>-->
  <!--        </plugin>-->
  <!--        <plugin>-->
  <!--          <artifactId>maven-project-info-reports-plugin</artifactId>-->
  <!--          <version>3.0.0</version>-->
  <!--        </plugin>-->
  <!--        <plugin>-->
  <!--          <groupId>org.scala-tools</groupId>-->
  <!--          <artifactId>maven-scala-plugin</artifactId>-->
  <!--          <version>2.15.2</version>-->
  <!--          <executions>-->
  <!--            <execution>-->
  <!--              <goals>-->
  <!--                <goal>compile</goal>-->
  <!--                <goal>testCompile</goal>-->
  <!--              </goals>-->
  <!--            </execution>-->
  <!--          </executions>-->
  <!--        </plugin>-->
  <!--      </plugins>-->
  <!--    </pluginManagement>-->
  <!--  </build>-->
  <build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-assembly-plugin</artifactId>
        <version>3.1.1</version>
        <configuration>
          <archive>
            <manifest>
              <mainClass>com.gwm.OdpsGraphx</mainClass>
            </manifest>
          </archive>
          <descriptorRefs>
            <descriptorRef>jar-with-dependencies</descriptorRef>
          </descriptorRefs>
        </configuration>
        <executions>
          <execution>
            <id>make-assembly</id>
            <phase>package</phase>
            <goals>
              <goal>single</goal>
            </goals>
          </execution>
        </executions>
      </plugin>
      <plugin>
        <groupId>org.scala-tools</groupId>
        <artifactId>maven-scala-plugin</artifactId>
        <version>2.15.2</version>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>
</project>

②代码

package com.gwm

import java.math.BigInteger
import java.text.SimpleDateFormat
import java.util.Calendar

import org.apache.commons.codec.digest.DigestUtils
import org.apache.spark.SparkConf
import org.apache.spark.graphx.{Edge, Graph}
import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}
import org.spark_project.jetty.util.StringUtil

import scala.collection.mutable.ListBuffer

/**
 * @author yangyingchun
 * @date 2023/8/18 10:32
 * @version 1.0
 */
object OneID {
  val sparkConf = (new SparkConf).setAppName("OdpsGraph").setMaster("local[1]")
  sparkConf.set("spark.hadoop.odps.access.id", "your's  access.id ")
  sparkConf.set("spark.hadoop.odps.access.key", "your's  access.key")
  sparkConf.set("spark.hadoop.odps.end.point", "your's  end.point")
  sparkConf.set("spark.hadoop.odps.project.name", "your's  project.name")
  sparkConf.set("spark.sql.catalogImplementation", "hive") //in-memory  2.4.5以上hive

  val spark = SparkSession
              .builder
              .appName("Oneid")
              .master("local[1]")
              .config("spark.sql.broadcastTimeout", 1200L)
              .config("spark.sql.crossJoin.enabled", true)
              .config("odps.exec.dynamic.partition.mode", "nonstrict")
              .config(sparkConf)
              .getOrCreate

  val sc = spark.sparkContext
  def main(args: Array[String]): Unit = {
    val bizdate=args(0)
    val c = Calendar.getInstance
    val format = new SimpleDateFormat("yyyyMMdd")
    c.setTime(format.parse(bizdate))
    c.add(Calendar.DATE, -1)
    val bizlastdate = format.format(c.getTime)
    println(s" 时间参数  ${bizdate}    ${bizlastdate}")
    // dwd_sl_user_ids 就是我们用户的各个ID ，也就是我们的数据源
    // 获取字段，这样我们就可以扩展新的ID 字段，但是不用更新代码

    val columns = spark.sql(
      s"""
         |select
         |   *
         |from
         |   itsl.dwd_sl_user_ids
         |where
         |   ds='${bizdate}'
         |limit
         |   1
         |""".stripMargin)
      .schema.fields.map(f => f.name).filterNot(e=>e.equals("ds")).toList
    println("字段信息=>"+columns)
    // 获取数据
    val dataFrame = spark.sql(
      s"""
         |select
         |   ${columns.mkString(",")}
         |from
         |   itsl.dwd_sl_user_ids
         |where
         |   ds='${bizdate}'
         |""".stripMargin
    )
    // 数据准备
    val data = dataFrame.rdd.map(row => {
      val list = new ListBuffer[String]()
      for (column <- columns) {
        val value = row.getAs[String](column)
        list.append(value)
      }
      list.toList
    })
    import spark.implicits._
    // 顶点集合
    val veritx= data.flatMap(list => {
      for (i <- 0 until columns.length if StringUtil.isNotBlank(list(i)) && (!"null".equals(list(i))))
        yield (new BigInteger(DigestUtils.md5Hex(list(i)),16).longValue, list(i))
    }).distinct
    val veritxDF=veritx.toDF("id_hashcode","id")
    veritxDF.createOrReplaceTempView("veritx")
    // 生成边的集合
    val edges = data.flatMap(list => {
      for (i <- 0 to list.length - 2 if StringUtil.isNotBlank(list(i)) && (!"null".equals(list(i)))
           ; j <- i + 1 to list.length - 1 if StringUtil.isNotBlank(list(j)) && (!"null".equals(list(j))))
        yield Edge(new BigInteger(DigestUtils.md5Hex(list(i)),16).longValue,new BigInteger(DigestUtils.md5Hex(list(j)),16).longValue, "")
    }).distinct
    // 开始使用点集合与边集合进行图计算训练
    val graph = Graph(veritx, edges)

   //计算每个顶点的连接组件成员身份，并返回具有该顶点的图值，该值包含包含该顶点的连接组件中的最低顶点id,迭代次数 控制迭代次数
        //todo.1 连通分量 无向图
    //输出每个连通子图顶点对应的最小顶点编号
//    应用场景♥♥♥
//      话单分析人物关系
//    企业信息族谱
    var vertices: DataFrame = ConnectedComponents.run(graph, 2).vertices.toDF("id_hashcode", "guid_hashcode")

    //todo.2 StronglyConnectedComponents 强连通分量 有向图
    //输出每个【强】连通子图顶点对应的最小顶点编号
//    应用场景♥♥♥
//      话单分析人物关系
//    企业信息族谱
//    var vertices: DataFrame = StronglyConnectedComponents.run(graph, 2).vertices.toDF("id_hashcode", "guid_hashcode")

    //todo.3 LabelPropagation无向图标签传播 LPA
    //从某个顶点触发，所有能够到达的顶点数量最多的，集中在一起成为一个社区，该顶点成为社区起点。
    //标签传播算法返回每个顶点对应的社区起点
    // 应用场景♥♥♥
    // 游戏通过连天记录在晚间中找代理
    // 信息传播源头推断：以消息为主题，查看消息传播的始作俑者
//    var vertices: DataFrame = LabelPropagation.run(graph, 2).vertices.toDF("id_hashcode", "guid_hashcode")

    //todo.4 TriangleCount函数
    //三角计数
    //三角形：完全图（热议两点有边）
    //三角形计算：一条边的两个顶点有相同邻点，则单个点构成三角形
    //返回经过每个顶点的三角形数量
//    应用场景♥♥♥
//      社群发现：社群耦合关系紧密程度（一个人的社交网络中三角函数越多说明社交关系越稳定）
//    var vertices: DataFrame = TriangleCount.run(graph)
//      .vertices.toDF("id_hashcode", "guid_hashcode")

    //todo.5 连通节点
    // val connectedGraph = graph.connectedComponents()
    // val  vertices = connectedGraph.vertices.toDF("id_hashcode","guid_hashcode")

    vertices.createOrReplaceTempView("to_graph")
    // 加载昨日的oneid 数据 (oneid,id,id_hashcode)
    val ye_oneid = spark.sql(
      s"""
         |select
         |   oneid,id,id_hashcode
         |from
         |   itsl.dwd_patient_oneid_info_df
         |where
         |   ds='${bizlastdate}'
         |""".stripMargin
    )
    ye_oneid.createOrReplaceTempView("ye_oneid")
    // 关联获取 已经存在的 oneid，这里的min 函数就是我们说的oneid 的选择问题
    val exists_oneid=spark.sql(
      """
        |select
        |   a.guid_hashcode,min(b.oneid) as oneid
        |from
        |   to_graph a
        |inner join
        |   ye_oneid b
        |on
        |   a.id_hashcode=b.id_hashcode
        |group by
        |   a.guid_hashcode
        |""".stripMargin
    )
    exists_oneid.createOrReplaceTempView("exists_oneid")

    var result: DataFrame = spark.sql(
      s"""
         |select
         |   nvl(b.oneid,md5(cast(a.guid_hashcode as string))) as oneid,c.id,a.id_hashcode,d.id as guid,a.guid_hashcode,${bizdate} as ds
         |from
         |   to_graph a
         |left join
         |   exists_oneid b
         |on
         |   a.guid_hashcode=b.guid_hashcode
         |left join
         |   veritx c
         |on
         |   a.id_hashcode=c.id_hashcode
         |left join
         |   veritx d
         |on
         |   a.guid_hashcode=d.id_hashcode
         |""".stripMargin
    )
    // 不存在则生成 存在则取已有的 这里nvl 就是oneid  的更新逻辑，存在则获取 不存在则生成
    var resultFrame: DataFrame = result.toDF()
    resultFrame.show()
    resultFrame.write.mode(SaveMode.Append).partitionBy("ds").saveAsTable("dwd_patient_oneid_info_df")
    
    sc.stop
  }
}

③ 本地运行必须增加resources信息

3、问题解决

①Exception in thread "main" java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveSessionStateBuilder':

Caused by: java.lang.ClassNotFoundException: org.apache.spark.sql.hive.HiveSessionStateBuilder

缺少Hive相关依赖，增加

<dependency>
  <groupId>org.apache.spark</groupId>
  <artifactId>spark-hive_2.11</artifactId>
  <version>${spark.version}</version>
  <!--            <scope>provided</scope>-->
</dependency>

但其实针对odps不需要加此依赖，只需要按0步配置好环境即可

②Exception in thread "main" org.apache.spark.sql.AnalysisException: Table or view not found: itsl.dwd_sl_user_ids; line 5 pos 3;

需要按照 0 步中按照要求完成环境准备

③Exception in thread "main" org.apache.spark.sql.AnalysisException: The format of the existing table itsl.dwd_patient_oneid_info_df is OdpsTableProvider. It doesn't match the specified format ParquetFileFormat.;

解决：ALTER TABLE dwd_patient_oneid_info_df SET FILEFORMAT PARQUET;

本地读写被禁用 需要上线解决

4、打包上传

①需取消

 .master("local[1]")

②取消maven依赖

③odps.conf不能打包，建临时文件不放在resources下

本地测试时放resources下

参考用户画像之ID-Mapping_id mapping_大数据00的博客-CSDN博客

上线报

org.apache.spark.sql.AnalysisException: Table or view not found: itsl.dwd_sl_user_ids; line 5 pos 3;

原因是本节③

5、运行及结果

结果

oneid id id_hashcode guid guid_hashcode ds
598e7008ffc3c6adeebd4d619e2368f3 耀总_app 8972546956853102969 133890 -9124021106546307510 20230818
598e7008ffc3c6adeebd4d619e2368f3 310322 1464684454693316922 133890 -9124021106546307510 20230818
598e7008ffc3c6adeebd4d619e2368f3 zhi333 6097391781232248718 133890 -9124021106546307510 20230818
598e7008ffc3c6adeebd4d619e2368f3 3 2895972726640982771 133890 -9124021106546307510 20230818
598e7008ffc3c6adeebd4d619e2368f3 耀总_小程序 -6210536828479319643 133890 -9124021106546307510 20230818
598e7008ffc3c6adeebd4d619e2368f3 zhi3333 -2388340305120644671 133890 -9124021106546307510 20230818
598e7008ffc3c6adeebd4d619e2368f3 133890 -9124021106546307510 133890 -9124021106546307510 20230818
598e7008ffc3c6adeebd4d619e2368f3 耀总_官网 -9059665468531982172 133890 -9124021106546307510 20230818
598e7008ffc3c6adeebd4d619e2368f3 U120311 -2948409726589830290 133890 -9124021106546307510 20230818
d39364f7fb05a0729646a766d6d43340 U140888 -8956123177900303496 U140888 -8956123177900303496 20230818
d39364f7fb05a0729646a766d6d43340 大神_官网 7742134357614280661 U140888 -8956123177900303496 20230818
d39364f7fb05a0729646a766d6d43340 220317 4342975012645585979 U140888 -8956123177900303496 20230818
d39364f7fb05a0729646a766d6d43340 device_b 934146606527688393 U140888 -8956123177900303496 20230818
d39364f7fb05a0729646a766d6d43340 165133 -8678359668161914326 U140888 -8956123177900303496 20230818
d39364f7fb05a0729646a766d6d43340 大神_app 3787345307522484927 U140888 -8956123177900303496 20230818
d39364f7fb05a0729646a766d6d43340 大神_小程序 8356079890110865354 U140888 -8956123177900303496 20230818
d39364f7fb05a0729646a766d6d43340 2 8000222017881409068 U140888 -8956123177900303496 20230818
d39364f7fb05a0729646a766d6d43340 zhi2222 8743693657758842828 U140888 -8956123177900303496 20230818
34330e92b91e164549cf750e428ba9cd 130826 -5006751273669536424 大法_app -7101862661925406891 20230818
34330e92b91e164549cf750e428ba9cd device_a -3383445179222035358 大法_app -7101862661925406891 20230818
34330e92b91e164549cf750e428ba9cd 1 994258241967195291 大法_app -7101862661925406891 20230818
34330e92b91e164549cf750e428ba9cd device_x 3848069073815866650 大法_app -7101862661925406891 20230818
34330e92b91e164549cf750e428ba9cd zhi1111 7020506831794259850 大法_app -7101862661925406891 20230818
34330e92b91e164549cf750e428ba9cd 185133 -2272106561927942561 大法_app -7101862661925406891 20230818
34330e92b91e164549cf750e428ba9cd 大法_app -7101862661925406891 大法_app -7101862661925406891 20230818
34330e92b91e164549cf750e428ba9cd U130311 5694117693724929174 大法_app -7101862661925406891 20230818
34330e92b91e164549cf750e428ba9cd 大法_官网 -4291733115832359573 大法_app -7101862661925406891 20230818
34330e92b91e164549cf750e428ba9cd 大法_小程序 -5714002662175910850 大法_app -7101862661925406891 20230818

6、思考

如果联通图是循环的怎么处理呢？A是B的朋友，B是C的朋友，C是A的朋友

标签： spark 大数据 分布式
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