Hive Functions
This documentation is for an out-of-date version of Apache Flink. We recommend you use the latest stable version.

Hive Functions #

Use Hive Built-in Functions via HiveModule #

The HiveModule provides Hive built-in functions as Flink system (built-in) functions to Flink SQL and Table API users.

For detailed information, please refer to HiveModule.

String name            = "myhive";
String version         = "2.3.4";

tableEnv.loadModue(name, new HiveModule(version));

val name            = "myhive"
val version         = "2.3.4"

tableEnv.loadModue(name, new HiveModule(version));
from pyflink.table.module import HiveModule

name = "myhive"
version = "2.3.4"

t_env.load_module(name, HiveModule(version))
   - name: core
     type: core
   - name: myhive
     type: hive
Some Hive built-in functions in older versions have thread safety issues. We recommend users patch their own Hive to fix them.

Hive User Defined Functions #

Users can use their existing Hive User Defined Functions in Flink.

Supported UDF types include:

  • UDF
  • GenericUDF
  • GenericUDTF
  • UDAF
  • GenericUDAFResolver2

Upon query planning and execution, Hive’s UDF and GenericUDF are automatically translated into Flink’s ScalarFunction, Hive’s GenericUDTF is automatically translated into Flink’s TableFunction, and Hive’s UDAF and GenericUDAFResolver2 are translated into Flink’s AggregateFunction.

To use a Hive User Defined Function, user have to

  • set a HiveCatalog backed by Hive Metastore that contains that function as current catalog of the session
  • include a jar that contains that function in Flink’s classpath
  • use Blink planner.

Using Hive User Defined Functions #

Assuming we have the following Hive functions registered in Hive Metastore:

 * Test simple udf. Registered under name 'myudf'
public class TestHiveSimpleUDF extends UDF {

	public IntWritable evaluate(IntWritable i) {
		return new IntWritable(i.get());

	public Text evaluate(Text text) {
		return new Text(text.toString());

 * Test generic udf. Registered under name 'mygenericudf'
public class TestHiveGenericUDF extends GenericUDF {

	public ObjectInspector initialize(ObjectInspector[] arguments) throws UDFArgumentException {
		checkArgument(arguments.length == 2);

		checkArgument(arguments[1] instanceof ConstantObjectInspector);
		Object constant = ((ConstantObjectInspector) arguments[1]).getWritableConstantValue();
		checkArgument(constant instanceof IntWritable);
		checkArgument(((IntWritable) constant).get() == 1);

		if (arguments[0] instanceof IntObjectInspector ||
				arguments[0] instanceof StringObjectInspector) {
			return arguments[0];
		} else {
			throw new RuntimeException("Not support argument: " + arguments[0]);

	public Object evaluate(DeferredObject[] arguments) throws HiveException {
		return arguments[0].get();

	public String getDisplayString(String[] children) {
		return "TestHiveGenericUDF";

 * Test split udtf. Registered under name 'mygenericudtf'
public class TestHiveUDTF extends GenericUDTF {

	public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {
		checkArgument(argOIs.length == 2);

		// TEST for constant arguments
		checkArgument(argOIs[1] instanceof ConstantObjectInspector);
		Object constant = ((ConstantObjectInspector) argOIs[1]).getWritableConstantValue();
		checkArgument(constant instanceof IntWritable);
		checkArgument(((IntWritable) constant).get() == 1);

		return ObjectInspectorFactory.getStandardStructObjectInspector(

	public void process(Object[] args) throws HiveException {
		String str = (String) args[0];
		for (String s : str.split(",")) {

	public void close() {

From Hive CLI, we can see they are registered:

hive> show functions;

Then, users can use them in SQL as:

Flink SQL> select mygenericudf(myudf(name), 1) as a, mygenericudf(myudf(age), 1) as b, s from mysourcetable, lateral table(myudtf(name, 1)) as T(s);