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。
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
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。
