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）

http://www.cnblogs.com/xing901022/p/6436161.html

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^
2^b
3^c
4^

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。

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 ));
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 分组后去重
3 累加指标值

如果不用UDAF，你要是写spark可能需要这样做:

rdd.groupBy(r->r.xxx)
.map(t2->{
HashMap<String,String> map = new HashMap<>();
for(Object p : t2._2){
map.put(xx,yyy)
}
return map.values().stream().reduce(0,Integer::sum);
});

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

Spark
Multiple Input/Output User Defined Aggregate Function (UDAF) using Java
李震的UDAF·scala版本
Spark Sql官方文档
Scala菜鸟教程
spark1.5 自定义聚合函数UDAF