maven：

<!-- spark -->
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.10</artifactId>
    <version>1.6.0</version>
</dependency>
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-sql_2.10</artifactId>
    <version>1.6.0</version>
</dependency>
<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-hive_2.10</artifactId>
    <version>1.6.0</version>
</dependency>
<!-- google工具类 -->
<dependency>
    <groupId>com.google.guava</groupId>
    <artifactId>guava</artifactId>
    <version>18.0</version>
</dependency>

public class StringCount extends UserDefinedAggregateFunction {
    /**
     * inputSchema指的是输入的数据类型
     * @return
     */
    @Override
    public StructType inputSchema() {
        List<StructField> fields = Lists.newArrayList();
        fields.add(DataTypes.createStructField("str", DataTypes.StringType,true));
        return DataTypes.createStructType(fields);
    }

    /**
     * bufferSchema指的是  中间进行聚合时  所处理的数据类型
     * @return
     */
    @Override
    public StructType bufferSchema() {
        List<StructField> fields = Lists.newArrayList();
        fields.add(DataTypes.createStructField("count", DataTypes.IntegerType,true));
        return DataTypes.createStructType(fields);
    }

    /**
     * dataType指的是函数返回值的类型
     * @return
     */
    @Override
    public DataType dataType() {
        return DataTypes.IntegerType;
    }

    /**
     * 一致性检验，如果为true,那么输入不变的情况下计算的结果也是不变的。
     * @return
     */
    @Override
    public boolean deterministic() {
        return true;
    }

    /**
     * 设置聚合中间buffer的初始值，但需要保证这个语义：两个初始buffer调用下面实现的merge方法后也应该为初始buffer
     * 即如果你初始值是1，然后你merge是执行一个相加的动作，两个初始buffer合并之后等于2，
     * 不会等于初始buffer了。这样的初始值就是有问题的，所以初始值也叫"zero value"
     * @param buffer
     */
    @Override
    public void initialize(MutableAggregationBuffer buffer) {
        buffer.update(0,0);
    }

    /**
     * 用输入数据input更新buffer值,类似于combineByKey
     * @param buffer
     * @param input
     */
    @Override
    public void update(MutableAggregationBuffer buffer, Row input) {
        buffer.update(0,Integer.valueOf(buffer.getAs(0).toString())+1);
    }

    /**
     * 合并两个buffer,将buffer2合并到buffer1.在合并两个分区聚合结果的时候会被用到,类似于reduceByKey
     * 这里要注意该方法没有返回值，在实现的时候是把buffer2合并到buffer1中去，你需要实现这个合并细节
     * @param buffer1
     * @param buffer2
     */
    @Override
    public void merge(MutableAggregationBuffer buffer1, Row buffer2) {
        buffer1.update(0,Integer.valueOf(buffer1.getAs(0).toString())+Integer.valueOf(buffer2.getAs(0).toString()));
    }

    /**
     * 计算并返回最终的聚合结果
     * @param buffer
     * @return
     */
    @Override
    public Object evaluate(Row buffer) {
        return buffer.getInt(0);
    }
}

public class UDAF {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("UDAF").setMaster("local");
        JavaSparkContext sc = new JavaSparkContext(conf);
        SQLContext sqlContext = new SQLContext(sc);
        List<String> nameList = Arrays.asList("xiaoming","xiaoming", "feifei","feifei","feifei", "katong");
        //转换为javaRDD
        JavaRDD<String> nameRDD = sc.parallelize(nameList, 3);
        //转换为JavaRDD<Row>
        JavaRDD<Row> nameRowRDD = nameRDD.map(new Function<String, Row>() {
            public Row call(String name) throws Exception {
                return RowFactory.create(name);
            }
        });
        List<StructField> fields = Lists.newArrayList();
        fields.add(DataTypes.createStructField("name", DataTypes.StringType,true));
        StructType structType = DataTypes.createStructType(fields);
        DataFrame namesDF = sqlContext.createDataFrame(nameRowRDD, structType);
        //注册names表
        namesDF.registerTempTable("names");
        sqlContext.udf().register("countString",new StringCount());
        List<Row> rows = sqlContext.sql("select name,countString(name) from names group by name").javaRDD().collect();
        for (Row row : rows) {
            System.out.println(row);
        }
        sc.close();
    }
}

[feifei,3]
[xiaoming,2]
[katong,1]