hive UDF
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今天有同事来问一个我写过的UDF的问题,想起之前貌似写过一篇这样的文章,草稿箱里找了下,确实有,躺了一年半了,发出来,也许对某些同学有帮助~
HIVE允许用户使用UDF(user defined function)对数据进行处理。用户可以使用‘show functions’ 查看function list,可以使用'describe function function-name'查看函数说明。- 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
hive提供的build-in函数包括以下几类:1. 关系操作符:包括 = 、 <> 、 <= 、>=等2. 算数操作符:包括 + 、 - 、 *、/等3. 逻辑操作符:包括AND 、 && 、 OR 、 || 等4. 复杂类型构造函数:包括map、struct、create_union等5. 复杂类型操作符:包括A[n]、Map[key]、S.x6. 数学操作符:包括ln(double a)、sqrt(double a)等
7. 集合操作符:包括size(Array<T>)、sort_array(Array<T>)等
8. 类型转换函数: binary(string|binary)、cast(expr as <type>)
9. 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
11. 字符串函数:包括acat(string|binary A, string|binary B...)等
12. 其他:xpath、get_json_objectscii(string str)、con编写Hive UDF有两种方式:
1. extends UDF , 重写evaluate方法
2. extends GenericUDF,重写initialize、getDisplayString、evaluate方法
编写UDF代码实例(更多例子参考https://svn.apache.org/repos/asf/hive/tags/release-0.8.1/ql/src/java/org/apache/hadoop/hive/ql/udf/
):功能:大小转小写ToLowerCase.java:- 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());
- }
- }
功能:计算array中去重后元素个数
UDFArrayUniqElementNumber .java
- 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;
-
-
-
-
-
-
- @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;
- private static final String FUNC_NAME = "ARRAY_UNIQ_ELEMENT_NUMBER";
-
- private ListObjectInspector arrayOI;
- private ObjectInspector arrayElementOI;
- private final IntWritable result = new IntWritable(-1);
-
- public ObjectInspector initialize(ObjectInspector[] arguments)
- throws UDFArgumentException {
-
-
- if (arguments.length != ARG_COUNT) {
- throw new UDFArgumentException("The function " + FUNC_NAME
- + " accepts " + ARG_COUNT + " arguments.");
- }
-
-
- 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;
- }
-
-
- 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]+ ")";
- }
- }
生成udf.jarhive有三种方法使用自定义的UDF函数
1. 临时添加UDF如下:- 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add jar并且create temporary function2. 进入会话前自动创建使用hive -i参数在进入hive时自动初始化- $ 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2和方法1本质上是相同的,区别在于方法2在会话初始化时自动完成3. 自定义UDF注册为hive内置函数可参考:hive利器 自定义UDF+重编译hive
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。