自定义函数
为了满足客户个性化的需求，
- 文件格式：Text File，Sequence File
- 内存中的数据格式
: Java Integer/String, Hadoop IntWritable/Text - 用户提供的
map/reduce 脚本：不管什么语言，利用stdin/stdout 传输数据 - 用户自定义函数
User Defined Function
用户可以使用‘show functions’ 查看
hive> show functions;
OK
!
!=
......
Time taken: 0.275 seconds
hive> desc function substr;
OK
substr(str, pos[, len]) - returns the substring of str that starts at pos and is of length len orsubstr(bin, pos[, len]) - returns the slice of byte array that starts at pos and is of length len
Time taken: 0.095 seconds
- 关系操作符：包括
= 、<>、<=、>= 等 - 算数操作符：包括
+ 、-、*、／等 - 逻辑操作符：包括
AND 、&&、OR、|| 等 - 复杂类型构造函数：包括
map 、struct、create_union 等 - 复杂类型操作符：包括
A[n] 、Map[key]、S.x - 数学操作符：包括
ln(double a) 、sqrt(double a) 等 - 集合操作符：包括
size(Array ) 、sort_array(Array ) 等 - 类型转换函数
: binary(string|binary) 、cast(expr as) - 日期函数：包括
from_unixtime(bigint unixtime[, string format]) 、unix_timestamp() 等10. 条件函数：包括if(boolean testCondition, T valueTrue, T valueFalseOrNull) 等 - 字符串函数：包括
acat(string|binary A, string|binary B …) 等 - 其他：xpath、get_json_objectscii(string str)、con
Definition
编写
- extends UDF，重写
evaluate 方法 - extends GenericUDF，重写
initialize 、getDisplayString、evaluate 方法
Extends UDF
如下的用法是将大写转化为小写
package test.udf;
import org.apache.hadoop.hive.ql.exec.UDF;
import org.apache.hadoop.io.Text;
public class ToLowerCase extends UDF {
public Text evaluate(final Text s) {
if (s == null) { return null; }
return new Text(s.toString().toLowerCase());
}
}
Extends GenericUDF
计算
package test.udf;
import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
import org.apache.hadoop.hive.ql.exec.UDFArgumentTypeException;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.udf.generic.GenericUDF;
import org.apache.hadoop.hive.serde2.objectinspector.ListObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector.Category;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
import org.apache.hadoop.io.IntWritable;
/**
* UDF:
* Get nubmer of objects with duplicate elements eliminated
*/
@Description(name = "array_uniq_element_number", value = "_FUNC_(array) - Returns nubmer of objects with duplicate elements eliminated.", extended = "Example:\n"
+ " > SELECT _FUNC_(array(1, 2, 2, 3, 3)) FROM src LIMIT 1;\n" + " 3")
public class UDFArrayUniqElementNumber extends GenericUDF {
private static final int ARRAY_IDX = 0;
private static final int ARG_COUNT = 1; // Number of arguments to this UDF
private static final String FUNC_NAME = "ARRAY_UNIQ_ELEMENT_NUMBER"; // External Name
private ListObjectInspector arrayOI;
private ObjectInspector arrayElementOI;
private final IntWritable result = new IntWritable(-1);
public ObjectInspector initialize(ObjectInspector[] arguments)
throws UDFArgumentException {
// Check if two arguments were passed
if (arguments.length != ARG_COUNT) {
throw new UDFArgumentException("The function " + FUNC_NAME
+ " accepts " + ARG_COUNT + " arguments.");
}
// Check if ARRAY_IDX argument is of category LIST
if (!arguments[ARRAY_IDX].getCategory().equals(Category.LIST)) {
throw new UDFArgumentTypeException(ARRAY_IDX, "\""
+ org.apache.hadoop.hive.serde.Constants.LIST_TYPE_NAME
+ "\" " + "expected at function ARRAY_CONTAINS, but "
+ "\"" + arguments[ARRAY_IDX].getTypeName() + "\" "
+ "is found");
}
arrayOI = (ListObjectInspector) arguments[ARRAY_IDX];
arrayElementOI = arrayOI.getListElementObjectInspector();
return PrimitiveObjectInspectorFactory.writableIntObjectInspector;
}
public IntWritable evaluate(DeferredObject[] arguments)
throws HiveException {
result.set(0);
Object array = arguments[ARRAY_IDX].get();
int arrayLength = arrayOI.getListLength(array);
if (arrayLength <= 1) {
result.set(arrayLength);
return result;
}
//element compare; Algorithm complexity: O(N^2)
int num = 1;
int i, j;
for(i = 1; i < arrayLength; i++)
{
Object listElement = arrayOI.getListElement(array, i);
for(j = i - 1; j >= 0; j--)
{
if (listElement != null) {
Object tmp = arrayOI.getListElement(array, j);
if (ObjectInspectorUtils.compare(tmp, arrayElementOI, listElement,
arrayElementOI) == 0) {
break;
}
}
}
if(-1 == j)
{
num++;
}
}
result.set(num);
return result;
}
public String getDisplayString(String[] children) {
assert (children.length == ARG_COUNT);
return "array_uniq_element_number(" + children[ARRAY_IDX]+ ")";
}
}
Usage
临时添加UDF
hive> select * from test;
OK
Hello
wORLD
ZXM
ljz
Time taken: 13.76 seconds
hive> add jar /home/work/udf.jar;
Added /home/work/udf.jar to class path
Added resource: /home/work/udf.jar
hive> create temporary function mytest as 'test.udf.ToLowerCase';
OK
Time taken: 0.103 seconds
hive> show functions;
......
mytest
......
hive> select mytest(test.name) from test;
......
OK
hello
world
zxm
ljz
Time taken: 38.218 seconds
这种方式在会话结束后，函数自动销毁，因此每次打开新的会话，都需要重新
进入会话前自动创建
使用
$ cat hive_init
add jar /home/work/udf.jar;
create temporary function mytest as 'test.udf.ToLowerCase';
$ hive -i hive_init
Logging initialized using configuration in file:/home/work/hive/hive-0.8.1/conf/hive-log4j.properties
Hive history file=/tmp/work/hive_job_log_work_201209200147_1951517527.txt
hive> show functions;
......
mytest
......
hive> select mytest(test.name) from test;
......
OK
hello
world
zxm
ljz
方法
注册为内部函数
和前两者相比，第三种方式直接将用户的自定义函数作为注册为内置函数，未来使用起来非常简单，但这种方式也非常危险，一旦出错，将是灾难性的，因此，建议如果不是特别通用，并且固化下来的函数，还是使用前两种方式比较靠谱。
UDAF
public static class AvgEvaluator implements UDAFEvaluator { } public void init() {} public boolean iterate(Double o) {} public AvgState terminatePartial() {} public boolean terminatePartial(Double o) { } public Double terminate() {}
}
UDTF
•
代码示例 public class MyUDTF extends GenericUDTF{ public StructObjectInspector initialize(ObjectInspector[] args) {} public void process(Object[] args) throws HiveException { } }