先来个简单的UDF
场景：
我们有这样一个文本文件：
1^^d2^b^d3^c^d4^^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^1111^2a^1111^2a^1111^2a^1111^2a^1111^2a^2222^0a^3333^1b^4444^0b^5555^3c^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。
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