{"id":10455,"date":"2018-03-10T11:54:05","date_gmt":"2018-03-10T11:54:05","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=10455"},"modified":"2018-03-10T11:54:05","modified_gmt":"2018-03-10T11:54:05","slug":"hive-udf","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/hive-udf\/","title":{"rendered":"Hive UDF – User Defined Function with Example"},"content":{"rendered":"

In the last hive tutorial, we studied the Hive View & Index<\/strong>. In this blog, we will learn the whole concept of\u00a0Apache Hive<\/strong> UDF (User-Defined Function)<\/span>. Also, we will learn Hive UDF example as well as be testing to understand Hive user-defined function well.<\/span><\/p>\n

What is Hive UDF?<\/span><\/h2>\n

Basically, we can use two different interfaces for writing Apache Hive User Defined Functions. <\/span><\/p>\n

    \n
  1. Simple API <\/span><\/li>\n
  2. Complex API <\/span><\/li>\n<\/ol>\n

    As long as our function reads and returns primitive types, we can use the simple API (org.apache.hadoop.hive.ql.exec.UDF). In other words, it means basic Hadoop<\/strong> & Hive writable types. Such as Text, IntWritable, LongWritable, DoubleWritable, etc.<\/span><\/p>\n

    Before we proceed, let’s discuss Hive Features & Limitations<\/strong> in detail.
    \nSo, let\u2019s discuss each Hive UDF API in detail:<\/span><\/p>\n

    a. Simple API<\/span><\/h3>\n

    Basically, with the simpler UDF API, building a Hive User Defined Function involves little more than writing a class with one function (evaluate). However, let\u2019s see an example to understand it well:<\/span>
    \nSimple API – Hive UDF Example<\/strong><\/p>\n

    class SimpleUDFExample extends UDF\n      {\n         \u00a0public Text evaluate(Text input)\n               {\n \u00a0\u00a0                 \u00a0return new Text(\"Hello \" + input.toString());\n\u00a0              }\n      }<\/pre>\n
    i. TESTING SIMPLE Hive UDF<\/strong><\/h4>\n
    Moreover, we can test it with regular testing tools, like JUnit, since the Hive UDF is simple one function.<\/span><\/p>\n
    public class SimpleUDFExampleTest\n    { \u00a0\n\u00a0      @Test\n      \u00a0public void testUDF()\n          {\n\u00a0\u00a0\u00a0          SimpleUDFExample example = new SimpleUDFExample();\n\u00a0\u00a0          \u00a0Assert.assertEquals(\"Hello world\", example.evaluate(new Text(\"world\")).toString());\n\u00a0         }\n   }<\/pre>\n
    b. Complex API<\/span><\/h3>\n
    However, to write code for objects that are not writable types. Like struct, map and array types. Hence the org.apache.hadoop.hive.ql.udf.generic. GenericUDF API offers a way.<\/span><\/p>\n
    In addition, for the function arguments, it needs us to manually manage object inspectors. Also, to verify the number and types of the arguments we receive. To be more specific, an object inspector offers a consistent interface for underlying object types.<\/span><\/p>\n
     Hence, that different object implementation can all be accessed in a consistent way from within hive. For example, we could implement a struct as a Map so long as you provide a corresponding object inspector.<\/span><\/p>\n
    Moreover, with this API we need to implement three methods:<\/span><\/p>\n
    \/\/ this is like the evaluate method of the simple API. It takes the actual arguments and returns the result\nabstract Object evaluate(GenericUDF.DeferredObject[] arguments);\n\/\/ Doesn't really matter, we can return anything but should be a string representation of the function.\nabstract String getDisplayString(String[] children);\n\/\/ called once, before any evaluate() calls. You receive an array of object inspectors that represent the arguments of the function\n\/\/ this is where you validate that the function is receiving the correct argument types and the correct number of arguments.\nabstract ObjectInspector initialize(ObjectInspector[] arguments);<\/pre>\n
    To understand this properly, \u00a0let\u2019s take an example.<\/span>
    \nComplex API – Apache Hive UDF Example<\/strong>
    \nBasically, here the creation of a function called containsString. However, it takes two arguments:<\/span><\/p>\n
      \n
    1.  A list of Strings:<\/span><\/li>\n
    2.  A String<\/span><\/li>\n<\/ol>\n
      Further, it returns true\/false on whether the list contains the string that we offer, for example:<\/span>
      \nLet’s learn Apache Hive Operators<\/strong> in detail.
      \ncontainsString(List(“a”, “b”, “c”), “b”); \/\/ true<\/span>
      \ncontainsString(List(“a”, “b”, “c”), “d”); \/\/ false<\/span><\/p>\n
      i.\u00a0GenericUDF API<\/h4>\n
      However, the GenericUDF API needs a little more boilerplate, unlike with Hive UDF API:<\/span><\/p>\n
      class ComplexUDFExample extends GenericUDF\n   {\n     \u00a0ListObjectInspector listOI;\n     \u00a0StringObjectInspector elementOI;\n\u00a0     @Override\n\u00a0     public String getDisplayString(String[] arg0)\n         {\n\u00a0\u00a0\u00a0         return \"arrayContainsExample()\"; \/\/ this should probably be better\n\u00a0        }\n@Override\n\u00a0    public ObjectInspector initialize(ObjectInspector[] arguments) throws UDFArgumentException\n        {\n\u00a0\u00a0\u00a0       if (arguments.length != 2)\n              {\n\u00a0\u00a0\u00a0\u00a0\u00a0              throw new UDFArgumentLengthException(\"arrayContainsExample only takes 2 arguments: List<T>, T\");\n\u00a0\u00a0\u00a0           }<\/pre>\n
      \/\/ 1. Check we received the right object types.\n\u00a0\u00a0\u00a0ObjectInspector a = arguments[0];\n\u00a0\u00a0\u00a0ObjectInspector b = arguments[1];\n\u00a0\u00a0\u00a0if (!(a instanceof ListObjectInspector) || !(b instanceof StringObjectInspector))\n      {\n\u00a0\u00a0\u00a0\u00a0\u00a0    throw new UDFArgumentException(\"first argument must be a list \/ array, second argument must be a string\");\n\u00a0\u00a0\u00a0   }\n\u00a0\u00a0\u00a0this.listOI = (ListObjectInspector) a;\n\u00a0\u00a0\u00a0this.elementOI = (StringObjectInspector) b;\n\/\/ 2. Check that the list contains strings\n\u00a0\u00a0\u00a0if(!(listOI.getListElementObjectInspector() instanceof StringObjectInspector))\n      {\n\u00a0\u00a0\u00a0\u00a0\u00a0    throw new UDFArgumentException(\"first argument must be a list of strings\");\n\u00a0\u00a0\u00a0   }\u00a0\n\/\/ the return type of our function is a boolean, so we provide the correct object inspector\n \u00a0\u00a0\u00a0return PrimitiveObjectInspectorFactory.javaBooleanObjectInspector;\n    \u00a0}\n\u00a0@Override\n\u00a0 public Object evaluate(DeferredObject[] arguments) throws HiveException\n     { \u00a0\n\/\/ get the list and string from the deferred objects using the object inspectors\n    \u00a0\u00a0\u00a0List<String> list = (List<String>) this.listOI.getList(arguments[0].get());\n\u00a0\u00a0\u00a0    String arg = elementOI.getPrimitiveJavaObject(arguments[1].get());\u00a0\u00a0\n\/\/ check for nulls\n\u00a0\u00a0\u00a0   if (list == null || arg == null)\n        {\n\u00a0\u00a0\u00a0\u00a0\u00a0     return null;\n\u00a0\u00a0     \u00a0}\u00a0\n\/\/ see if our list contains the value we need\n      \u00a0for(String s: list)\n          {\n\u00a0\u00a0\u00a0\u00a0\u00a0        if (arg.equals(s)) return new Boolean(true);\n\u00a0\u00a0\u00a0       }\n\u00a0\u00a0    \u00a0return new Boolean(false);\n\u00a0    }\n  }<\/pre>\n
      ii. TESTING\u00a0Complex Hive UDF<\/h4>\n
      However, in the setup, there is the complex part of testing the function.<\/span><\/p>\n
      public class ComplexUDFExampleTest\n   {\n\u00a0@Test\npublic void testComplexUDFReturnsCorrectValues() throws HiveException\n      {\n\/\/ set up the models we need\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ComplexUDFExample example = new ComplexUDFExample();\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ObjectInspector stringOI = PrimitiveObjectInspectorFactory.javaStringObjectInspector;\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ObjectInspector listOI = ObjectInspectorFactory.getStandardListObjectInspector(stringOI);\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0JavaBooleanObjectInspector resultInspector = (JavaBooleanObjectInspector) example.initialize(new ObjectInspector[]{listOI, stringOI});\u00a0\n\/\/ create the actual UDF arguments\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0List<String> list = new ArrayList<String>();\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0list.add(\"a\");\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0list.add(\"b\");\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0list.add(\"c\");\u00a0\u00a0\n\/\/ test our results\u00a0\n\/\/ the value exists\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Object result = example.evaluate(new DeferredObject[]{new DeferredJavaObject(list), new DeferredJavaObject(\"a\")});\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Assert.assertEquals(true, resultInspector.get(result));\u00a0\n\/\/ the value doesn't exist\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Object result2 = example.evaluate(new DeferredObject[]{new DeferredJavaObject(list), new DeferredJavaObject(\"d\")});\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Assert.assertEquals(false, resultInspector.get(result2));\u00a0\u00a0\n\/\/ arguments are null\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Object result3 = example.evaluate(new DeferredObject[]{new DeferredJavaObject(null), new DeferredJavaObject(null)});\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Assert.assertNull(result3);\n     }\n }<\/pre>\n
      So, this was all\u00a0about Hive User Defined Function Tutorial. Hope you like our explanation user-defined function in Hive.<\/p>\n
      Conclusion<\/span><\/h2>\n
      Hence, we have seen the whole concept of Apache Hive UDF and types of interfaces for writing UDF in Apache Hive: Simple API & Complex API with example. Still, if you have doubt, feel free to ask in the comment section.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
      In the last hive tutorial, we studied the Hive View & Index. In this blog, we will learn the whole concept of\u00a0Apache Hive UDF (User-Defined Function). Also, we will learn Hive UDF example as...<\/p>\n","protected":false},"author":7,"featured_media":10459,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26],"tags":[2794,2795,5055,5796,5797,5799,12873,12874,14640,14645,14646,14647],"class_list":["post-10455","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hive","tag-complex-api","tag-complex-api-example","tag-genericudf-api","tag-hive-udf","tag-hive-udf-example","tag-hive-user-defined-functions","tag-simple-api","tag-simple-api-example","tag-testing-complex-hive-udf","tag-testing-of-complex-api","tag-testing-of-simple-api","tag-testing-simple-hive-udf"],"yoast_head":"\nHive UDF - User Defined Function with Example - DataFlair<\/title>\n<meta name=\"description\" content=\"What is Apache Hive UDF,Hive UDF example,types of interfaces for writing Apache Hive User Defined Function: Simple API & Complex API with testing & example\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/hive-udf\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hive UDF - 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