Spark SQL 用户自定义函数UDF、用户自定义聚合函数UDAF 教程（Java踩坑教学版）

在Spark中，也支持Hive中的自定义函数。自定义函数大致可以分为三种：

UDF(User-Defined-Function)，即最基本的自定义函数，类似to_char,to_date等
UDAF（User- Defined Aggregation Funcation），用户自定义聚合函数，类似在group by之后使用的sum,avg等
UDTF(User-Defined Table-Generating Functions),用户自定义生成函数，有点像stream里面的flatMap

本篇就手把手教你如何编写UDF和UDAF

先来个简单的UDF

场景：

我们有这样一个文本文件：

1^^d

2^b^d

3^c^d

4^^d

在读取数据的时候，第二列的数据如果为空，需要显示'null'，不为空就直接输出它的值。定义完成后，就可以直接在SparkSQL中使用了。

代码为：

package test;

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.JavaRDD;

import org.apache.spark.api.java.JavaSparkContext;

import org.apache.spark.sql.DataFrame;

import org.apache.spark.sql.Row;

import org.apache.spark.sql.RowFactory;

import org.apache.spark.sql.SQLContext;

import org.apache.spark.sql.types.DataTypes;

import org.apache.spark.sql.types.StructField;

import org.apache.spark.sql.types.StructType;

import java.util.ArrayList;

import java.util.List;

/**

 * Created by xinghailong on 2017/2/23.

 */

public class test3 {

    public static void main(String[] args) {

        //创建spark的运行环境

        SparkConf sparkConf = new SparkConf();

        sparkConf.setMaster("local[2]");

        sparkConf.setAppName("test-udf");

        JavaSparkContext sc = new JavaSparkContext(sparkConf);

        SQLContext sqlContext = new SQLContext(sc);

        //注册自定义方法

        sqlContext.udf().register("isNull", (String field,String defaultValue)->field==null?defaultValue:field, DataTypes.StringType);

        //读取文件

        JavaRDD<String> lines = sc.textFile( "C:\\test-udf.txt" );

        JavaRDD<Row> rows = lines.map(line-> RowFactory.create(line.split("\\^")));

        List<StructField> structFields = new ArrayList<StructField>();

        structFields.add(DataTypes.createStructField( "a", DataTypes.StringType, true ));

        structFields.add(DataTypes.createStructField( "b", DataTypes.StringType, true ));

        structFields.add(DataTypes.createStructField( "c", DataTypes.StringType, true ));

        StructType structType = DataTypes.createStructType( structFields );

        DataFrame test = sqlContext.createDataFrame( rows, structType);

        test.registerTempTable("test");

        sqlContext.sql("SELECT con_join(c,b) FROM test GROUP BY a").show();

        sc.stop();

    }

}

输出内容为：

+---+----+---+

|  a| _c1|  c|

+---+----+---+

|  1|null|  d|

|  2|   b|  d|

|  3|   c|  d|

|  4|null|  d|

+---+----+---+

其中比较关键的就是这句:

sqlContext.udf().register("isNull", (String field,String defaultValue)->field==null?defaultValue:field, DataTypes.StringType);

这里我直接用的java8的语法写的，如果是java8之前的版本，需要使用Function2创建匿名函数。

再来个自定义的UDAF—求平均数

先来个最简单的UDAF，求平均数。类似这种的操作有很多，比如最大值，最小值，累加，拼接等等，都可以采用相同的思路来做。

首先是需要定义UDAF函数

package test;

import org.apache.spark.sql.Row;

import org.apache.spark.sql.expressions.MutableAggregationBuffer;

import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;

import org.apache.spark.sql.types.DataType;

import org.apache.spark.sql.types.DataTypes;

import org.apache.spark.sql.types.StructField;

import org.apache.spark.sql.types.StructType;

import java.util.ArrayList;

import java.util.List;

/**

 * Created by xinghailong on 2017/2/23.

 */

public class MyAvg extends UserDefinedAggregateFunction {

    @Override

    public StructType inputSchema() {

        List<StructField> structFields = new ArrayList<>();

        structFields.add(DataTypes.createStructField( "field1", DataTypes.StringType, true ));

        return DataTypes.createStructType( structFields );

    }

    @Override

    public StructType bufferSchema() {

        List<StructField> structFields = new ArrayList<>();

        structFields.add(DataTypes.createStructField( "field1", DataTypes.IntegerType, true ));

        structFields.add(DataTypes.createStructField( "field2", DataTypes.IntegerType, true ));

        return DataTypes.createStructType( structFields );

    }

    @Override

    public DataType dataType() {

        return DataTypes.IntegerType;

    }

    @Override

    public boolean deterministic() {

        return false;

    }

    @Override

    public void initialize(MutableAggregationBuffer buffer) {

        buffer.update(0,0);

        buffer.update(1,0);

    }

    @Override

    public void update(MutableAggregationBuffer buffer, Row input) {

        buffer.update(0,buffer.getInt(0)+1);

        buffer.update(1,buffer.getInt(1)+Integer.valueOf(input.getString(0)));

    }

    @Override

    public void merge(MutableAggregationBuffer buffer1, Row buffer2) {

        buffer1.update(0,buffer1.getInt(0)+buffer2.getInt(0));

        buffer1.update(1,buffer1.getInt(1)+buffer2.getInt(1));

    }

    @Override

    public Object evaluate(Row buffer) {

        return buffer.getInt(1)/buffer.getInt(0);

    }

}

使用的时候，需要先注册，然后在spark sql里面就可以直接使用了:

package test;

import com.tgou.standford.misdw.udf.MyAvg;

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.JavaRDD;

import org.apache.spark.api.java.JavaSparkContext;

import org.apache.spark.sql.DataFrame;

import org.apache.spark.sql.Row;

import org.apache.spark.sql.RowFactory;

import org.apache.spark.sql.SQLContext;

import org.apache.spark.sql.types.DataTypes;

import org.apache.spark.sql.types.StructField;

import org.apache.spark.sql.types.StructType;

import java.util.ArrayList;

import java.util.List;

/**

 * Created by xinghailong on 2017/2/23.

 */

public class test4 {

    public static void main(String[] args) {

        SparkConf sparkConf = new SparkConf();

        sparkConf.setMaster("local[2]");

        sparkConf.setAppName("test");

        JavaSparkContext sc = new JavaSparkContext(sparkConf);

        SQLContext sqlContext = new SQLContext(sc);

        sqlContext.udf().register("my_avg",new MyAvg());

        JavaRDD<String> lines = sc.textFile( "C:\\test4.txt" );

        JavaRDD<Row> rows = lines.map(line-> RowFactory.create(line.split("\\^")));

        List<StructField> structFields = new ArrayList<StructField>();

        structFields.add(DataTypes.createStructField( "a", DataTypes.StringType, true ));

        structFields.add(DataTypes.createStructField( "b", DataTypes.StringType, true ));

        StructType structType = DataTypes.createStructType( structFields );

        DataFrame test = sqlContext.createDataFrame( rows, structType);

        test.registerTempTable("test");

        sqlContext.sql("SELECT my_avg(b) FROM test GROUP BY a").show();

        sc.stop();

    }

}

计算的文本内容为：

a^3

a^6

b^2

b^4

b^6

再来个无所不能的UDAF

真正的业务场景里面，总会有千奇百怪的需求，比如：

想要按照某个字段分组，取其中的一个最大值
想要按照某个字段分组，对分组内容的数据按照特定字段统计累加
想要按照某个字段分组，针对特定的条件，拼接字符串

再比如一个场景，需要按照某个字段分组，然后分组内的数据，又需要按照某一列进行去重，最后再计算值

1 按照某个字段分组
2 分组校验条件
3 然后处理字段

如果不用UDAF，你要是写spark可能需要这样做:

rdd.groupBy(r->r.xxx)

    .map(t2->{

        HashSet<String> set = new HashSet<>();

        for(Object p : t2._2){

            if(p.getBs() > 0 ){

                map.put(xx,yyy)

            }

        }

        return StringUtils.join(set.toArray(),",");

    });

上面是一段伪码，不保证正常运行哈。

这样写，其实也能应付需求了，但是代码显得略有点丑陋。还是不如SparkSQL看的清晰明了...

所以我们再尝试用SparkSql中的UDAF来一版！

首先需要创建UDAF类

import org.apache.commons.lang.StringUtils;

import org.apache.spark.sql.Row;

import org.apache.spark.sql.expressions.MutableAggregationBuffer;

import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;

import org.apache.spark.sql.types.*;

import java.util.*;

/**

 *

 * Created by xinghailong on 2017/2/23.

 */

public class ConditionJoinUDAF extends UserDefinedAggregateFunction {

    @Override

    public StructType inputSchema() {

        List<StructField> structFields = new ArrayList<>();

        structFields.add(DataTypes.createStructField( "field1", DataTypes.IntegerType, true ));

        structFields.add(DataTypes.createStructField( "field2", DataTypes.StringType, true ));

        return DataTypes.createStructType( structFields );

    }

    @Override

    public StructType bufferSchema() {

        List<StructField> structFields = new ArrayList<>();

        structFields.add(DataTypes.createStructField( "field", DataTypes.StringType, true ));

        return DataTypes.createStructType( structFields );

    }

    @Override

    public DataType dataType() {

        return DataTypes.StringType;

    }

    @Override

    public boolean deterministic() {//是否强制每次执行的结果相同

        return false;

    }

    @Override

    public void initialize(MutableAggregationBuffer buffer) {//初始化

        buffer.update(0,"");

    }

    @Override

    public void update(MutableAggregationBuffer buffer, Row input) {//相同的executor间的数据合并

        Integer bs = input.getInt(0);

        String field = buffer.getString(0);

        String in = input.getString(1);

        if(bs > 0 && !"".equals(in) && !field.contains(in)){

            field += ","+in;

        }

        buffer.update(0,field);

    }

    @Override

    public void merge(MutableAggregationBuffer buffer1, Row buffer2) {//不同excutor间的数据合并

        String field1 = buffer1.getString(0);

        String field2 = buffer2.getString(0);

        if(!"".equals(field2)){

            field1 += ","+field2;

        }

        buffer1.update(0,field1);

    }

    @Override

    public Object evaluate(Row buffer) {//根据Buffer计算结果

        return StringUtils.join(Arrays.stream(buffer.getString(0).split(",")).filter(line->!line.equals("")).toArray(),",");

    }

}

拿一个例子坐下实验：

a^1111^2

a^1111^2

a^1111^2

a^1111^2

a^1111^2

a^2222^0

a^3333^1

b^4444^0

b^5555^3

c^6666^0

按照第一列进行分组，不同的第三列值，进行拼接。

package test;

import test.ConditionJoinUDAF;

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.JavaRDD;

import org.apache.spark.api.java.JavaSparkContext;

import org.apache.spark.sql.DataFrame;

import org.apache.spark.sql.Row;

import org.apache.spark.sql.RowFactory;

import org.apache.spark.sql.SQLContext;

import org.apache.spark.sql.types.DataTypes;

import org.apache.spark.sql.types.StructField;

import org.apache.spark.sql.types.StructType;

import java.util.ArrayList;

import java.util.List;

/**

 * Created by xinghailong on 2017/2/23.

 */

public class test2 {

    public static void main(String[] args) {

        SparkConf sparkConf = new SparkConf();

        sparkConf.setMaster("local[2]");

        sparkConf.setAppName("test");

        JavaSparkContext sc = new JavaSparkContext(sparkConf);

        SQLContext sqlContext = new SQLContext(sc);

        sqlContext.udf().register("con_join",new ConditionJoinUDAF());

        JavaRDD<String> lines = sc.textFile( "C:\\test2.txt" );

        JavaRDD<Row> rows = lines.map(line-> RowFactory.create(line.split("\\^")));

        List<StructField> structFields = new ArrayList<StructField>();

        structFields.add(DataTypes.createStructField( "a", DataTypes.StringType, true ));

        structFields.add(DataTypes.createStructField( "b", DataTypes.StringType, true ));

        structFields.add(DataTypes.createStructField( "c", DataTypes.StringType, true ));

        StructType structType = DataTypes.createStructType( structFields );

        DataFrame test = sqlContext.createDataFrame( rows, structType);

        test.registerTempTable("test");

        sqlContext.sql("SELECT con_join(c,b) FROM test GROUP BY a").show();

        sc.stop();

    }

}

这样SQL简洁明了，就能表达意思了。

参考