User-defined Functions #

PyFlink Table API empowers users to do data transformations with Python user-defined functions.

Currently, it supports two kinds of Python user-defined functions: the general Python user-defined functions which process data one row at a time and vectorized Python user-defined functions which process data one batch at a time.

Bundling UDFs #

To run Python UDFs (as well as Pandas UDFs) in any non-local mode, it is strongly recommended bundling your Python UDF definitions using the config option python-files, if your Python UDFs live outside the file where the main() function is defined. Otherwise, you may run into ModuleNotFoundError: No module named 'my_udf' if you define Python UDFs in a file called my_udf.py.

Loading resources in UDFs #

There are scenarios when you want to load some resources in UDFs first, then running computation (i.e., eval) over and over again, without having to re-load the resources. For example, you may want to load a large deep learning model only once, then run batch prediction against the model multiple times.

Overriding the open method of UserDefinedFunction is exactly what you need.

class Predict(ScalarFunction):
    def open(self, function_context):
        import pickle

        with open("resources.zip/resources/model.pkl", "rb") as f:
            self.model = pickle.load(f)

    def eval(self, x):
        return self.model.predict(x)

predict = udf(Predict(), result_type=DataTypes.DOUBLE(), func_type="pandas")

Accessing job parameters #

The open() method provides a FunctionContext that contains information about the context in which user-defined functions are executed, such as the metric group, the global job parameters, etc.

The following information can be obtained by calling the corresponding methods of FunctionContext:

Method Description
get_metric_group() Metric group for this parallel subtask.
get_job_parameter(name, default_value) Global job parameter value associated with given key.
class HashCode(ScalarFunction):

    def open(self, function_context: FunctionContext):
        # access the global "hashcode_factor" parameter
        # "12" would be the default value if the parameter does not exist
        self.factor = int(function_context.get_job_parameter("hashcode_factor", "12"))

    def eval(self, s: str):
        return hash(s) * self.factor

hash_code = udf(HashCode(), result_type=DataTypes.INT())
TableEnvironment t_env = TableEnvironment.create(...)
t_env.get_config().set('pipeline.global-job-parameters', 'hashcode_factor:31')
t_env.create_temporary_system_function("hashCode", hash_code)
t_env.sql_query("SELECT myField, hashCode(myField) FROM MyTable")

Testing User-Defined Functions #

Suppose you have defined a Python user-defined function as following:

add = udf(lambda i, j: i + j, result_type=DataTypes.BIGINT())

To unit test it, you need to extract the original Python function using ._func and then unit test it:

f = add._func
assert f(1, 2) == 3