Spark Java API 计算 Levenshtein 距离

Spark Java API 计算 Levenshtein 距离

在上一篇文章中，完成了Spark开发环境的搭建，最终的目标是对用户昵称信息做聚类分析，找出违规的昵称。聚类分析需要一个距离，用来衡量两个昵称之间的相似度。这里采用levenshtein距离。现在就来开始第一个小目标，用Spark JAVA API 计算字符串之间的Levenshtein距离。

1. 数据准备

样本数据如下：

{"name":"Michael", "nick":"Mich","age":50}

把数据保存成文件并上传到hdfs上：./bin/hdfs dfs -put levestein.json /user/panda

2. 代码实现

定义一个类表示样本数据：

    public static class User{
        private String name;
        private String nick;
        private int age;

        public String getName() {
            return name;
        }

        public void setName(String name) {
            this.name = name;
        }

        public String getNick() {
            return nick;
        }

        public void setNick(String nick) {
            this.nick = nick;
        }

        public int getAge() {
            return age;
        }

        public void setAge(int age) {
            this.age = age;
        }
    }

创建SparkSession

SparkSession sparkSession = SparkSession.builder()
                .appName("levenshtein example")
                .master("spark://172.25.129.170:7077")
                .config("spark.some.config.option", "some-value")
                .getOrCreate();

在Spark命令行./bin/pyspark 启动Spark时，会默认创建一个名称为 spark 的SparkSession。而这里是写代码，也需要创建SparkSession对象。

The SparkSession instance is the way Spark executes user-defined
manipulations across the cluster. There is a one-to-one correspondence between a SparkSession and
a Spark Application.

定义数据类型

Encoder<User> userEncoder = Encoders.bean(User.class);

JAVA里面定义了一套数据类型，比如java.util.String是字符串类型；类似地，Spark也有自己的数据类型，因此Encoder就定义了如何将Java对象映射成Spark里面的对象。

Used to convert a JVM object of type T to and from the internal Spark SQL representation.

To efficiently support domain-specific objects, an Encoder is required. The encoder maps the domain specific type T to Spark's internal type system. For example, given a class Person with two fields, name (string) and age (int), an encoder is used to tell Spark to generate code at runtime to serialize the Person object into a binary structure. This binary structure often has much lower memory footprint as well as are optimized for efficiency in data processing (e.g. in a columnar format). To understand the internal binary representation for data, use the schema function.

构建Dataset：

Dataset<User> userDataset = sparkSession.read().json(path).as(userEncoder);

说明一下Dataset与DataFrame区别，Dataset是针对Scala和JAVA特有的。Dataset是有类型的，Dataset的每一行是某种类型的数据，比如上面的User类型。

A Dataset is a strongly typed collection of domain-specific objects that can be transformed in parallel using functional or relational operations. Each Dataset also has an untyped view called a DataFrame, which is a Dataset of Row.

而DataFrame的每一行的类型是Row（看官方文档，我就这样理解了，哈哈。。）

DataFrame is represented by a Dataset of Row。While, in Java API, users need to use Dataset<Row> to represent a DataFrame.

这个图就很好地解释了DataFrame和Dataset的区别。

计算levenshtein距离，将之 transform 成一个新DataFrame中：

Column lev_res = functions.levenshtein(userDataset.col("name"), userDataset.col("nick"));
Dataset<Row> leveDataFrame = userDataset.withColumn("distance", lev_res);

完整代码

import org.apache.spark.sql.*;

public class LevenstenDistance {
    public static class User{
        private String name;
        private String nick;
        private int age;

        public String getName() {
            return name;
        }

        public void setName(String name) {
            this.name = name;
        }

        public String getNick() {
            return nick;
        }

        public void setNick(String nick) {
            this.nick = nick;
        }

        public int getAge() {
            return age;
        }

        public void setAge(int age) {
            this.age = age;
        }
    }

    public static void main(String[] args) {
        SparkSession sparkSession = SparkSession.builder()
                .appName("levenshtein example")
                .master("spark://172.25.129.170:7077")
                .config("spark.some.config.option", "some-value")
                .getOrCreate();
        String path = "hdfs://172.25.129.170:9000/user/panda/levestein.json";
        Encoder<User> userEncoder = Encoders.bean(User.class);
        Dataset<User> userDataset = sparkSession.read().json(path).as(userEncoder);
        userDataset.show();

        Column lev_res = functions.levenshtein(userDataset.col("name"), userDataset.col("nick"));
        Dataset<Row> leveDataFrame = userDataset.withColumn("distance", lev_res);
//        userDataset.show();
        leveDataFrame.show();
        System.out.println(lev_res.toString());
    }
}

原来的Dataset：

计算Levenshtein距离后的得到的DataFrame：

根据上面的示例，下面来演示一下一个更实际点的例子：计算昵称和签名之间的levenshtein距离，若levenshtein距离相同，就代表该用户的 昵称 和 签名 是相同的：

数据格式如下：

{"nick":"赖求","uid":123456}

1. 加载数据

           Dataset<Row> dataset = spark.read().format("json")
                   .option("header", "false")
                   .load("hdfs://172.25.129.170:9000/user/panda/profile_noempty.json");
   

   ​

2. 取出昵称和签名

           //空字符串 与 null 是不同的
           Dataset<Row> nickSign = dataset.filter(col("nick").isNotNull())
                   .filter(col("signature").isNotNull())
                   .select(col("nick"), col("signature"), col("uid"));
   

   ​

3. 计算昵称和签名的Levenshtein距离

   Column lev_distance = functions.levenshtein(nickSign.col("nick"), nickSign.col("signature"));
           Dataset<Row> nickSignDistance = nickSign.withColumn("distance", lev_distance);
   

   ​

4. 按距离进行过滤

   Dataset<Row> sameNickSign = nickSignDistance.filter("distance = 0");
   

   ​

这样就能找出昵称和签名完全一样的用户了。

原文：https://www.cnblogs.com/hapjin/p/9954191.html