java集成hive与mahout的推荐

ymdaylpp  于 2021-06-04  发布在  Hadoop
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我想在hive中使用mahout,我将从hive中获取数据并使用数据模型来填充数据并使用mahout进行推荐。这可能吗。因为我看到mahout只为文件工作。1) 如何使用配置单元表将数据加载到mahout?2) 有没有其他方法可以将mahout推荐与hive或其他人一起使用?
这里我有一个hivejdbc结果,我想在mahout中填充到datamodel。如何填充?
我想使用数据库结果,而不是从文件中读取的mahout建议。例如:

Hive:

import java.sql.SQLException;
    import java.sql.Connection;
    import java.sql.ResultSet;
    import java.sql.Statement;
    import java.sql.DriverManager;

    public class HiveJdbcClient {
      private static String driverName = "org.apache.hive.jdbc.HiveDriver";

      /**
       * @param args
       * @throws SQLException
       */
      public static void main(String[] args) throws SQLException {
          try {
          Class.forName(driverName);
        } catch (ClassNotFoundException e) {
          // TODO Auto-generated catch block
          e.printStackTrace();
          System.exit(1);
        }
        //replace "hive" here with the name of the user the queries should run as
        Connection con = DriverManager.getConnection("jdbc:hive2://localhost:10000/default", "hive", "");
        Statement stmt = con.createStatement();
        String tableName = "testHiveDriverTable";
        stmt.execute("drop table if exists " + tableName);
        stmt.execute("create table " + tableName + " (key int, value string)");
        // show tables
        String sql = "show tables '" + tableName + "'";
        System.out.println("Running: " + sql);
        ResultSet res = stmt.executeQuery(sql);
        if (res.next()) {
          System.out.println(res.getString(1));
        }
           // describe table
        sql = "describe " + tableName;
        System.out.println("Running: " + sql);
        res = stmt.executeQuery(sql);
        while (res.next()) {
          System.out.println(res.getString(1) + "\t" + res.getString(2));
        }

        // load data into table
        // NOTE: filepath has to be local to the hive server
        // NOTE: /tmp/a.txt is a ctrl-A separated file with two fields per line
        String filepath = "/tmp/a.txt";
        sql = "load data local inpath '" + filepath + "' into table " + tableName;
        System.out.println("Running: " + sql);
        stmt.execute(sql);

        // select * query
        sql = "select * from " + tableName;
        System.out.println("Running: " + sql);
        res = stmt.executeQuery(sql);
        while (res.next()) {
          System.out.println(String.valueOf(res.getInt(1)) + "\t" + res.getString(2));
        }

        // regular hive query
        sql = "select count(1) from " + tableName;
        System.out.println("Running: " + sql);
        res = stmt.executeQuery(sql);
        while (res.next()) {
          System.out.println(res.getString(1));
        }
      }
    }

管理员:

// Create a data source from the CSV file
File userPreferencesFile = new File("data/dataset1.csv");
DataModel dataModel = new FileDataModel(userPreferencesFile);

UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(dataModel);
UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(2, userSimilarity, dataModel);

// Create a generic user based recommender with the dataModel, the userNeighborhood and the userSimilarity
Recommender genericRecommender =  new GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity);

// Recommend 5 items for each user
for (LongPrimitiveIterator iterator = dataModel.getUserIDs(); iterator.hasNext();)
{
    long userId = iterator.nextLong();

    // Generate a list of 5 recommendations for the user
    List<RecommendedItem> itemRecommendations = genericRecommender.recommend(userId, 5);

    System.out.format("User Id: %d%n", userId);

    if (itemRecommendations.isEmpty())
    {`enter code here
        System.out.println("No recommendations for this user.");
    }
    else
    {
        // Display the list of recommendations
        for (RecommendedItem recommendedItem : itemRecommendations)
        {
            System.out.format("Recommened Item Id %d. Strength of the preference: %f%n", recommendedItem.getItemID(), recommendedItem.getValue());
        }
    }
 }
9avjhtql

9avjhtql1#

mahout版本0.9为jdbc投诉数据库(如mysql/oracle/postgress等)、nosql数据库(如mongodb/hbase/cassandra)和基于文件系统的数据库(如您所述)提供了数据模型(源数据)。
在此版本中,配置单元不是100%的sql标准数据库,数据模型mysqljdbcdatamodel和sql92jdbcdatamodel不适合用于配置单元表,因为jdbc数据库中的sql语法非常不同。
对于第一个问题,您可能希望扩展abstractjdbcdatamodel并重写构造函数,以便传入配置单元数据源和配置单元特定的sql查询,以获得与abstractjdbcdatamodel构造函数中提到的查询类似的首选项、首选项时间、用户、所有用户等。
对于第二个问题,如果您使用的是非分布式算法(taste算法),则上述方法适用。如果使用分布式算法,mahout可以在hadoop上运行,以获取hive表支持的hdfs文件。请看这里关于在hadoop上运行mahout的内容

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