如何编写自定义hive UDF函数

Hive可以允许用户编写自己定义的函数UDF，来在查询中使用。Hive中有3种UDF：

UDF：操作单个数据行，产生单个数据行；

UDAF：操作多个数据行，产生一个数据行。

UDTF：操作一个数据行，产生多个数据行一个表作为输出。

用户构建的UDF使用过程如下：

第一步：继承UDF或者UDAF或者UDTF，实现特定的方法。

UDF实例参见http://svn.apache.org/repos/asf/hive/trunk/contrib/src/java/org/apache/hadoop/hive/contrib/udf/example/UDFExampleAdd.java

package org.apache.hadoop.hive.contrib.udf.example;

import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDF;

/**
 * UDFExampleAdd.
 *
 */
//UDF是作用于单个数据行，产生一个数据行 
//用户必须要继承UDF，且必须至少实现一个evalute方法，该方法并不在UDF中 
//但是Hive会检查用户的UDF是否拥有一个evalute方法
@Description(name = "example_add", value = "_FUNC_(expr) - Example UDAF that returns the sum")
public class UDFExampleAdd extends UDF {

//实现具体逻辑
  public Integer evaluate(Integer... a) {
    int total = 0;
    for (Integer element : a) {
      if (element != null) {
        total += element;
      }
    }
    return total;
  }

  public Double evaluate(Double... a) {
    double total = 0;
    for (Double element : a) {
      if (element != null) {
        total += element;
      }
    }
    return total;
  }

}

UDAF实例参见

http://svn.apache.org/repos/asf/hive/trunk/contrib/src/java/org/apache/hadoop/hive/contrib/udaf/example/UDAFExampleAvg.java

package org.apache.hadoop.hive.contrib.udaf.example;

import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDAF;
import org.apache.hadoop.hive.ql.exec.UDAFEvaluator;

/**
 * This is a simple UDAF that calculates average.
 * 
 * It should be very easy to follow and can be used as an example for writing
 * new UDAFs.
 * 
 * Note that Hive internally uses a different mechanism (called GenericUDAF) to
 * implement built-in aggregation functions, which are harder to program but
 * more efficient.
 * 
 */
//UDAF是输入多个数据行，产生一个数据行 
//用户自定义的UDAF必须是继承了UDAF，且内部包含多个实现了exec的静态类
@Description(name = "example_avg",
value = "_FUNC_(col) - Example UDAF to compute average")
public final class UDAFExampleAvg extends UDAF {

  /**
   * The internal state of an aggregation for average.
   * 
   * Note that this is only needed if the internal state cannot be represented
   * by a primitive.
   * 
   * The internal state can also contains fields with types like
   * ArrayList<String> and HashMap<String,Double> if needed.
   */
  public static class UDAFAvgState {
    private long mCount;
    private double mSum;
  }

  /**
   * The actual class for doing the aggregation. Hive will automatically look
   * for all internal classes of the UDAF that implements UDAFEvaluator.
   */
  public static class UDAFExampleAvgEvaluator implements UDAFEvaluator {

    UDAFAvgState state;

    public UDAFExampleAvgEvaluator() {
      super();
      state = new UDAFAvgState();
      init();
    }

    /**
     * Reset the state of the aggregation.
	 * 重置聚合过程的状态
     */
    public void init() {
      state.mSum = 0;
      state.mCount = 0;
    }

    /**
     * Iterate through one row of original data.
     * 
     * The number and type of arguments need to the same as we call this UDAF
     * from Hive command line.
     * 
     * This function should always return true.
	 * 在原始值的一行数据上进行迭代
     * 参数的个数和类型需与hive命令行中调用该UDF的参数相同。
	 * 这个函数应当总是返回true
     */
    public boolean iterate(Double o) {
      if (o != null) {
        state.mSum += o;
        state.mCount++;
      }
      return true;
    }

    /**
     * Terminate a partial aggregation and return the state. If the state is a
     * primitive, just return primitive Java classes like Integer or String.
     */
	//Hive需要部分聚集结果的时候会调用该方法 
    //会返回一个封装了聚集计算当前状态的对象
    public UDAFAvgState terminatePartial() {
      // This is SQL standard - average of zero items should be null.
      return state.mCount == 0 ? null : state;
    }

    /**
     * Merge with a partial aggregation.
     * 
     * This function should always have a single argument which has the same
     * type as the return value of terminatePartial().
     */
	//合并两个部分聚集值会调用这个方法
    public boolean merge(UDAFAvgState o) {
      if (o != null) {
        state.mSum += o.mSum;
        state.mCount += o.mCount;
      }
      return true;
    }

    /**
     * Terminates the aggregation and return the final result.
	 * 终止聚合过程，返回最终结果
     */
	//Hive需要最终聚集结果时候会调用该方法
    public Double terminate() {
      // This is SQL standard - average of zero items should be null.
      return state.mCount == 0 ? null : Double.valueOf(state.mSum
          / state.mCount);
    }
  }

  private UDAFExampleAvg() {
    // prevent instantiation
  }

}

第二步：将写好的UDF函数注册到Hive中，具体有下面两种方法。

方法一

（1）将写好的类打包为jar。

（2）进入到Hive外壳环境中，利用add jar 注册该jar文件

（3）为该类起一个别名，用于查询使用。

参考命令见下：

add jar UDFExample.jar //注册jar
create temporary function my_add as 'org.apache.hadoop.hive.contrib.udf.example. UDFExampleAdd';  // UDF只是为这个Hive会话临时定义的
create temporary function my_avg as 'org.apache.hadoop.hive.contrib.udaf.example. UDAFExampleAvg'; 

但这种方法注册的UDF只有在当前Hive会话中生效。如果想永久生效，可在Hive源码中注册该UDF函数，具体见方法二　

方法二

（1）在org.apache.hadoop.hive.ql.exec.FunctionRegistry中注册UDF函数

registerUDF("my_add", UDFExampleAdd.class, false);
registerUDAF("my_avg", UDAFExampleAvg.class);

（2）打包编译Hive源码包

（3）部署Hive包和UDF包，将UDF包放在Hive的ClassPath中即可。