lidavidm commented on code in PR #12590: URL: https://github.com/apache/arrow/pull/12590#discussion_r860144865
########## python/pyarrow/tests/test_udf.py: ########## @@ -0,0 +1,501 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + + +import pytest + +import pyarrow as pa +from pyarrow import compute as pc + +# UDFs are all tested with a dataset scan +pytestmark = pytest.mark.dataset + + +try: + import pyarrow.dataset as ds +except ImportError: + ds = None + +global_batch_length = 10 + + +def mock_udf_context(batch_length=global_batch_length): + from pyarrow._compute import _get_scalar_udf_context + return _get_scalar_udf_context(pa.default_memory_pool(), batch_length) + + +@pytest.fixture(scope="session") +def unary_func_fixture(): + def unary_function(ctx, scalar1): + return pc.call_function("add", [scalar1, 1]) + func_name = "y=x+k" + unary_doc = {"summary": "add function", + "description": "test add function"} + pc.register_scalar_function(unary_function, + func_name, + unary_doc, + {"array": pa.int64()}, + pa.int64()) + return unary_function, func_name + + +@pytest.fixture(scope="session") +def binary_func_fixture(): + def binary_function(ctx, m, x): + return pc.call_function("multiply", [m, x]) + func_name = "y=mx" + binary_doc = {"summary": "y=mx", + "description": "find y from y = mx"} + pc.register_scalar_function(binary_function, + func_name, + binary_doc, + {"m": pa.int64(), + "x": pa.int64(), + }, + pa.int64()) + return binary_function, func_name + + +@pytest.fixture(scope="session") +def ternary_func_fixture(): + def ternary_function(ctx, m, x, c): + mx = pc.call_function("multiply", [m, x]) + return pc.call_function("add", [mx, c]) + ternary_doc = {"summary": "y=mx+c", + "description": "find y from y = mx + c"} + func_name = "y=mx+c" + pc.register_scalar_function(ternary_function, + func_name, + ternary_doc, + { + "array1": pa.int64(), + "array2": pa.int64(), + "array3": pa.int64(), + }, + pa.int64()) + return ternary_function, func_name + + +@pytest.fixture(scope="session") +def varargs_func_fixture(): + def varargs_function(ctx, *values): + base_val = values[:2] + res = pc.call_function("add", base_val) + for other_val in values[2:]: + res = pc.call_function("add", [res, other_val]) + return res + func_name = "z=ax+by+c" + varargs_doc = {"summary": "z=ax+by+c", + "description": "find z from z = ax + by + c" + } + pc.register_scalar_function(varargs_function, + func_name, + varargs_doc, + { + "array1": pa.int64(), + "array2": pa.int64(), + "array3": pa.int64(), + "array4": pa.int64(), + "array5": pa.int64(), + }, + pa.int64()) + return varargs_function, func_name + + +@pytest.fixture(scope="session") +def random_with_udf_ctx_func_fixture(): + def random_with_udf_ctx(context, one, two): + proxy_pool = pa.proxy_memory_pool(context.memory_pool) + ans = pc.add(one, two, memory_pool=proxy_pool) + res = pa.array([ans.as_py()], memory_pool=proxy_pool) + return res + in_types = {"one": pa.int64(), + "two": pa.int64(), + } + func_doc = { + "summary": "test udf context", + "description": "udf context test" + } + func_name = "test_udf_context" + pc.register_scalar_function(random_with_udf_ctx, + func_name, func_doc, + in_types, + pa.int64()) + return random_with_udf_ctx, func_name + + +@pytest.fixture(scope="session") +def output_check_func_fixture(): + # The objective of this fixture is to evaluate, + # how the UDF interface respond to unexpected + # output types. The types chosen at the test + # end are either of different Arrow data type + # or non-Arrow type. + def output_check(ctx, array): + ar = pc.call_function("add", [array, 1]) + ar = ar.cast(pa.int32()) + return ar + func_name = "test_output_value" + in_types = {"array": pa.int64()} + out_type = pa.int64() + doc = { + "summary": "add function scalar", + "description": "add function" + } + pc.register_scalar_function(output_check, func_name, doc, + in_types, out_type) + return output_check, func_name + + +@pytest.fixture(scope="session") +def nullary_check_func_fixture(): + # this needs to return array values + def nullary_check(context): + return pa.array([42] * context.batch_length, type=pa.int64(), + memory_pool=context.memory_pool) + + func_doc = { + "summary": "random function", + "description": "generates a random value" + } + func_name = "test_random_func" + pc.register_scalar_function(nullary_check, + func_name, + func_doc, + {}, + pa.int64()) + + return nullary_check, func_name + + +@pytest.fixture(scope="session") +def output_python_type_func_fixture(): + # This fixture helps to check the response + # when the function return value is not an Arrow + # defined data type. Instead here the returned value + # is of type int in Python. + def const_return(ctx, scalar): + return 42 + + func_name = "test_output_type" + in_types = {"array": pa.int64()} + out_type = pa.int64() + doc = { + "summary": "add function scalar", + "description": "add function" + } + pc.register_scalar_function(const_return, func_name, doc, + in_types, out_type) + return const_return, func_name + + +@pytest.fixture(scope="session") +def varargs_check_func_fixture(): + def varargs_check(ctx, *values): + base_val = values[:2] + res = pc.call_function("add", base_val) + for other_val in values[2:]: + res = pc.call_function("add", [res, other_val]) + return res + func_name = "test_varargs_function" + in_types = {"array1": pa.int64(), + "array2": pa.int64(), + } + doc = {"summary": "n add function", + "description": "add N number of arrays" + } + pc.register_scalar_function(varargs_check, func_name, doc, + in_types, pa.int64()) + + return varargs_check, func_name + + +@pytest.fixture(scope="session") +def raise_func_fixture(): + def raise_func(ctx): + raise ValueError("Test function with raise") + func_name = "test_raise" + doc = { + "summary": "test function with raise", + "description": "function with a raise" + } + pc.register_scalar_function(raise_func, func_name, doc, + {}, pa.int64()) + return raise_func, func_name + + +def check_scalar_function(func_fixture, + input, + run_in_dataset=True, + batch_length=global_batch_length): + function, name = func_fixture + expected_output = function(mock_udf_context(batch_length), *input) + func = pc.get_function(name) + assert func.name == name + + result = pc.call_function(name, input) + + assert result == expected_output + if run_in_dataset: + field_names = [f'field{index}' for index, in_arr in input] + table = pa.Table.from_arrays(input, field_names) + dataset = ds.dataset(table) + func_args = [ds.field(field_name) for field_name in field_names] + result_table = dataset.to_table( + columns={'result': ds.field('')._call(name, func_args)}) + assert result_table.column(0).chunks[0] == expected_output + + +def test_scalar_udf_array_unary(unary_func_fixture): + check_scalar_function(unary_func_fixture, + [ + pa.array([10, 20], pa.int64()) + ], + mock_udf_context() Review Comment: Hmm, wait. Why is this being passed as the third argument to `check_scalar_function`? The types don't match. ########## python/pyarrow/_compute.pyx: ########## @@ -2251,3 +2338,219 @@ cdef CExpression _bind(Expression filter, Schema schema) except *: return GetResultValue(filter.unwrap().Bind( deref(pyarrow_unwrap_schema(schema).get()))) + + +cdef class ScalarUdfContext: + """A container to hold user-defined-function related + entities. `batch_length` and `MemoryPool` are important + entities in defining functions which require these details. + + Example + ------- + + ScalarUdfContext is used with the scalar user-defined-functions. + When defining such a function, the first parameter must be a + ScalarUdfContext object. This object can be used to hold important + information. This can be further enhanced depending on the use + cases of user-defined-functions. + + >>> def random(context, one, two): + return pc.add(one, two, memory_pool=context.memory_pool) + """ + + def __init__(self): + raise TypeError("Do not call {}'s constructor directly" + .format(self.__class__.__name__)) + + cdef void init(self, const CScalarUdfContext &c_context): + self.c_context = c_context + + @property + def batch_length(self): + """ + Returns the length of the batch associated with the + user-defined-function. Useful when the batch_length + is required to do computations specially when scalars + are parameters of the user-defined-function. + + Returns + ------- + batch_length : int + The number of batches used when calling + user-defined-function. + """ + return self.c_context.batch_length + + @property + def memory_pool(self): + """ + Returns the MemoryPool associated with the + user-defined-function. An already initialized + MemoryPool can be used within the + user-defined-function. + + Returns + ------- + memory_pool : MemoryPool + MemoryPool is obtained from the KernelContext + and passed to the ScalarUdfContext. + """ + return box_memory_pool(self.c_context.pool) + + +cdef inline CFunctionDoc _make_function_doc(dict func_doc) except *: + """ + Helper function to generate the FunctionDoc + This function accepts a dictionary and expect the + summary(str), description(str) and arg_names(List[str]) keys. + """ + cdef: + CFunctionDoc f_doc + vector[c_string] c_arg_names + + f_doc.summary = tobytes(func_doc["summary"]) + f_doc.description = tobytes(func_doc["description"]) + for arg_name in func_doc["arg_names"]: + c_arg_names.push_back(tobytes(arg_name)) + f_doc.arg_names = c_arg_names + # UDFOptions integration: + # TODO: https://issues.apache.org/jira/browse/ARROW-16041 + f_doc.options_class = tobytes("") + f_doc.options_required = False + return f_doc + +cdef _scalar_udf_callback(user_function, const CScalarUdfContext& c_context, inputs): + """ + Helper callback function used to wrap the ScalarUdfContext from Python to C++ + execution. + """ + cdef ScalarUdfContext context = ScalarUdfContext.__new__(ScalarUdfContext) + context.init(c_context) + return user_function(context, *inputs) + + +def register_scalar_function(func, func_name, function_doc, in_types, + out_type): + """ + Register a user-defined scalar function. + + A scalar function is a function that executes elementwise + operations on arrays or scalars, and therefore whose results + generally do not depend on the order of the values in the Review Comment: CC @vibhatha ########## python/pyarrow/_compute.pyx: ########## @@ -2251,3 +2338,219 @@ cdef CExpression _bind(Expression filter, Schema schema) except *: return GetResultValue(filter.unwrap().Bind( deref(pyarrow_unwrap_schema(schema).get()))) + + +cdef class ScalarUdfContext: + """A container to hold user-defined-function related + entities. `batch_length` and `MemoryPool` are important + entities in defining functions which require these details. + + Example + ------- + + ScalarUdfContext is used with the scalar user-defined-functions. + When defining such a function, the first parameter must be a + ScalarUdfContext object. This object can be used to hold important + information. This can be further enhanced depending on the use + cases of user-defined-functions. + + >>> def random(context, one, two): + return pc.add(one, two, memory_pool=context.memory_pool) + """ + + def __init__(self): + raise TypeError("Do not call {}'s constructor directly" + .format(self.__class__.__name__)) + + cdef void init(self, const CScalarUdfContext &c_context): + self.c_context = c_context + + @property + def batch_length(self): + """ + Returns the length of the batch associated with the + user-defined-function. Useful when the batch_length + is required to do computations specially when scalars + are parameters of the user-defined-function. + + Returns + ------- + batch_length : int + The number of batches used when calling + user-defined-function. + """ + return self.c_context.batch_length + + @property + def memory_pool(self): + """ + Returns the MemoryPool associated with the + user-defined-function. An already initialized + MemoryPool can be used within the + user-defined-function. + + Returns + ------- + memory_pool : MemoryPool + MemoryPool is obtained from the KernelContext + and passed to the ScalarUdfContext. + """ + return box_memory_pool(self.c_context.pool) + + +cdef inline CFunctionDoc _make_function_doc(dict func_doc) except *: + """ + Helper function to generate the FunctionDoc + This function accepts a dictionary and expect the + summary(str), description(str) and arg_names(List[str]) keys. + """ + cdef: + CFunctionDoc f_doc + vector[c_string] c_arg_names + + f_doc.summary = tobytes(func_doc["summary"]) + f_doc.description = tobytes(func_doc["description"]) + for arg_name in func_doc["arg_names"]: + c_arg_names.push_back(tobytes(arg_name)) + f_doc.arg_names = c_arg_names + # UDFOptions integration: + # TODO: https://issues.apache.org/jira/browse/ARROW-16041 + f_doc.options_class = tobytes("") + f_doc.options_required = False + return f_doc + +cdef _scalar_udf_callback(user_function, const CScalarUdfContext& c_context, inputs): + """ + Helper callback function used to wrap the ScalarUdfContext from Python to C++ + execution. + """ + cdef ScalarUdfContext context = ScalarUdfContext.__new__(ScalarUdfContext) + context.init(c_context) + return user_function(context, *inputs) + + +def register_scalar_function(func, func_name, function_doc, in_types, + out_type): + """ + Register a user-defined scalar function. + + A scalar function is a function that executes elementwise + operations on arrays or scalars, and therefore whose results + generally do not depend on the order of the values in the + arguments. Accepts and returns arrays that are all of the + same size. These functions roughly correspond to the functions + used in SQL expressions. Review Comment: CC @vibhatha ########## python/pyarrow/tests/test_udf.py: ########## @@ -0,0 +1,501 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + + +import pytest + +import pyarrow as pa +from pyarrow import compute as pc + +# UDFs are all tested with a dataset scan +pytestmark = pytest.mark.dataset + + +try: + import pyarrow.dataset as ds +except ImportError: + ds = None + +global_batch_length = 10 + + +def mock_udf_context(batch_length=global_batch_length): + from pyarrow._compute import _get_scalar_udf_context + return _get_scalar_udf_context(pa.default_memory_pool(), batch_length) + + +@pytest.fixture(scope="session") +def unary_func_fixture(): + def unary_function(ctx, scalar1): + return pc.call_function("add", [scalar1, 1]) + func_name = "y=x+k" + unary_doc = {"summary": "add function", + "description": "test add function"} + pc.register_scalar_function(unary_function, + func_name, + unary_doc, + {"array": pa.int64()}, + pa.int64()) + return unary_function, func_name + + +@pytest.fixture(scope="session") +def binary_func_fixture(): + def binary_function(ctx, m, x): + return pc.call_function("multiply", [m, x]) + func_name = "y=mx" + binary_doc = {"summary": "y=mx", + "description": "find y from y = mx"} + pc.register_scalar_function(binary_function, + func_name, + binary_doc, + {"m": pa.int64(), + "x": pa.int64(), + }, + pa.int64()) + return binary_function, func_name + + +@pytest.fixture(scope="session") +def ternary_func_fixture(): + def ternary_function(ctx, m, x, c): + mx = pc.call_function("multiply", [m, x]) + return pc.call_function("add", [mx, c]) + ternary_doc = {"summary": "y=mx+c", + "description": "find y from y = mx + c"} + func_name = "y=mx+c" + pc.register_scalar_function(ternary_function, + func_name, + ternary_doc, + { + "array1": pa.int64(), + "array2": pa.int64(), + "array3": pa.int64(), + }, + pa.int64()) + return ternary_function, func_name + + +@pytest.fixture(scope="session") +def varargs_func_fixture(): + def varargs_function(ctx, *values): + base_val = values[:2] + res = pc.call_function("add", base_val) + for other_val in values[2:]: + res = pc.call_function("add", [res, other_val]) + return res + func_name = "z=ax+by+c" + varargs_doc = {"summary": "z=ax+by+c", + "description": "find z from z = ax + by + c" + } + pc.register_scalar_function(varargs_function, + func_name, + varargs_doc, + { + "array1": pa.int64(), + "array2": pa.int64(), + "array3": pa.int64(), + "array4": pa.int64(), + "array5": pa.int64(), + }, + pa.int64()) + return varargs_function, func_name + + +@pytest.fixture(scope="session") +def random_with_udf_ctx_func_fixture(): + def random_with_udf_ctx(context, one, two): + proxy_pool = pa.proxy_memory_pool(context.memory_pool) + ans = pc.add(one, two, memory_pool=proxy_pool) + res = pa.array([ans.as_py()], memory_pool=proxy_pool) + return res + in_types = {"one": pa.int64(), + "two": pa.int64(), + } + func_doc = { + "summary": "test udf context", + "description": "udf context test" + } + func_name = "test_udf_context" + pc.register_scalar_function(random_with_udf_ctx, + func_name, func_doc, + in_types, + pa.int64()) + return random_with_udf_ctx, func_name + + +@pytest.fixture(scope="session") +def output_check_func_fixture(): + # The objective of this fixture is to evaluate, + # how the UDF interface respond to unexpected + # output types. The types chosen at the test + # end are either of different Arrow data type + # or non-Arrow type. + def output_check(ctx, array): + ar = pc.call_function("add", [array, 1]) + ar = ar.cast(pa.int32()) + return ar + func_name = "test_output_value" + in_types = {"array": pa.int64()} + out_type = pa.int64() + doc = { + "summary": "add function scalar", + "description": "add function" + } + pc.register_scalar_function(output_check, func_name, doc, + in_types, out_type) + return output_check, func_name + + +@pytest.fixture(scope="session") +def nullary_check_func_fixture(): + # this needs to return array values + def nullary_check(context): + return pa.array([42] * context.batch_length, type=pa.int64(), + memory_pool=context.memory_pool) + + func_doc = { + "summary": "random function", + "description": "generates a random value" + } + func_name = "test_random_func" + pc.register_scalar_function(nullary_check, + func_name, + func_doc, + {}, + pa.int64()) + + return nullary_check, func_name + + +@pytest.fixture(scope="session") +def output_python_type_func_fixture(): + # This fixture helps to check the response + # when the function return value is not an Arrow + # defined data type. Instead here the returned value + # is of type int in Python. + def const_return(ctx, scalar): + return 42 + + func_name = "test_output_type" + in_types = {"array": pa.int64()} + out_type = pa.int64() + doc = { + "summary": "add function scalar", + "description": "add function" + } + pc.register_scalar_function(const_return, func_name, doc, + in_types, out_type) + return const_return, func_name + + +@pytest.fixture(scope="session") +def varargs_check_func_fixture(): + def varargs_check(ctx, *values): + base_val = values[:2] + res = pc.call_function("add", base_val) + for other_val in values[2:]: + res = pc.call_function("add", [res, other_val]) + return res + func_name = "test_varargs_function" + in_types = {"array1": pa.int64(), + "array2": pa.int64(), + } + doc = {"summary": "n add function", + "description": "add N number of arrays" + } + pc.register_scalar_function(varargs_check, func_name, doc, + in_types, pa.int64()) + + return varargs_check, func_name + + +@pytest.fixture(scope="session") +def raise_func_fixture(): + def raise_func(ctx): + raise ValueError("Test function with raise") + func_name = "test_raise" + doc = { + "summary": "test function with raise", + "description": "function with a raise" + } + pc.register_scalar_function(raise_func, func_name, doc, + {}, pa.int64()) + return raise_func, func_name + + +def check_scalar_function(func_fixture, + input, + run_in_dataset=True, + batch_length=global_batch_length): + function, name = func_fixture + expected_output = function(mock_udf_context(batch_length), *input) + func = pc.get_function(name) + assert func.name == name + + result = pc.call_function(name, input) + + assert result == expected_output + if run_in_dataset: + field_names = [f'field{index}' for index, in_arr in input] + table = pa.Table.from_arrays(input, field_names) + dataset = ds.dataset(table) + func_args = [ds.field(field_name) for field_name in field_names] + result_table = dataset.to_table( + columns={'result': ds.field('')._call(name, func_args)}) + assert result_table.column(0).chunks[0] == expected_output + + +def test_scalar_udf_array_unary(unary_func_fixture): + check_scalar_function(unary_func_fixture, + [ + pa.array([10, 20], pa.int64()) + ], + mock_udf_context() + ) + + +def test_scalar_udf_array_binary(binary_func_fixture): + check_scalar_function(binary_func_fixture, + [ + pa.array([10, 20], pa.int64()), + pa.array([2, 4], pa.int64()) + ], + mock_udf_context() + ) + + +def test_scalar_udf_array_ternary(ternary_func_fixture): + check_scalar_function(ternary_func_fixture, + [ + pa.array([10, 20], pa.int64()), + pa.array([2, 4], pa.int64()), + pa.array([5, 10], pa.int64()) + ], + mock_udf_context() + ) + + +def test_scalar_udf_array_varargs(varargs_func_fixture): + check_scalar_function(varargs_func_fixture, + [ + pa.array([2, 3], pa.int64()), + pa.array([10, 20], pa.int64()), + pa.array([3, 7], pa.int64()), + pa.array([20, 30], pa.int64()), + pa.array([5, 10], pa.int64()) + ], + mock_udf_context + ) + + +def test_registration_errors(): + # validate function name + doc = { + "summary": "test udf input", + "description": "parameters are validated" + } + in_types = {"scalar": pa.int64()} + out_type = pa.int64() + + def test_reg_function(context): + return pa.array([10]) + + with pytest.raises(TypeError): + pc.register_scalar_function(test_reg_function, + None, doc, in_types, + out_type) + + # validate function + with pytest.raises(TypeError, match="func must be a callable"): + pc.register_scalar_function(None, "test_none_function", doc, in_types, + out_type) + + # validate output type + expected_expr = "DataType expected, got <class 'NoneType'>" + with pytest.raises(TypeError, match=expected_expr): + pc.register_scalar_function(test_reg_function, + "test_output_function", doc, in_types, + None) + + # validate input type + expected_expr = r'in_types must be a dictionary of DataType' + with pytest.raises(TypeError, match=expected_expr): + pc.register_scalar_function(test_reg_function, + "test_input_function", doc, None, + out_type) + + # register an already registered function + # first registration + pc.register_scalar_function(test_reg_function, + "test_reg_function", doc, {}, + out_type) + # second registration + expected_expr = "Already have a function registered with name:" \ + + " test_reg_function" + with pytest.raises(pa.lib.ArrowKeyError, match=expected_expr): + pc.register_scalar_function(test_reg_function, + "test_reg_function", doc, {}, + out_type) + + +def test_varargs_function_validation(varargs_check_func_fixture): + _function, func_name = varargs_check_func_fixture Review Comment: ```suggestion _, func_name = varargs_check_func_fixture ``` is what's usually done to ignore things in unpacking ########## python/pyarrow/tests/test_udf.py: ########## @@ -0,0 +1,545 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + + +import pytest + +import pyarrow as pa +from pyarrow import compute as pc + + +# Marks all of the tests in this module +# Ignore these with pytest ... -m 'not udf' +# pytestmark = pytest.mark.udf + +unary_doc = {"summary": "add function", + "description": "test add function"} + + +@pytest.fixture(scope="session") +def udf_context(): + return pc._get_scalar_udf_context(pa.default_memory_pool(), 0) + + +@pytest.fixture(scope="session") +def unary_func_fixture(): + def unary_function(udf_context, scalar1): + return pc.call_function("add", [scalar1, 1]) + return unary_function + + +binary_doc = {"summary": "y=mx", + "description": "find y from y = mx"} + + +@pytest.fixture(scope="session") +def binary_func_fixture(): + def binary_function(ctx, m, x): + return pc.call_function("multiply", [m, x]) + return binary_function + + +ternary_doc = {"summary": "y=mx+c", + "description": "find y from y = mx + c"} + + +@pytest.fixture(scope="session") +def ternary_func_fixture(): + def ternary_function(ctx, m, x, c): + mx = pc.call_function("multiply", [m, x]) + return pc.call_function("add", [mx, c]) + return ternary_function + + +varargs_doc = {"summary": "z=ax+by+c", + "description": "find z from z = ax + by + c" + } + + +@pytest.fixture(scope="session") +def varargs_func_fixture(): + def varargs_function(ctx, *values): + base_val = values[:2] + res = pc.call_function("add", base_val) + for other_val in values[2:]: + res = pc.call_function("add", [res, other_val]) + return res + return varargs_function + + +@pytest.fixture(scope="session") +def random_with_udf_ctx_func_fixture(): + def random_with_udf_ctx(context, one, two): + old_pool = pa.default_memory_pool() + proxy_pool = pa.proxy_memory_pool(context.memory_pool) + pa.set_memory_pool(proxy_pool) + try: + ans = pc.add(one, two, memory_pool=proxy_pool) + allocated_before = proxy_pool.bytes_allocated() + # allocating 64 bytes + res = pa.array([ans.as_py()], memory_pool=proxy_pool) + allocated_after = proxy_pool.bytes_allocated() + finally: + pa.set_memory_pool(old_pool) + assert allocated_before == 0 + assert allocated_after == 64 + assert context.batch_length == 2 + return res Review Comment: CC @vibhatha ########## python/pyarrow/_compute.pyx: ########## @@ -2275,3 +2279,205 @@ cdef CExpression _bind(Expression filter, Schema schema) except *: return GetResultValue(filter.unwrap().Bind( deref(pyarrow_unwrap_schema(schema).get()))) + + +cdef class ScalarUdfContext: + """ + Per-invocation function context/state. + + This object will always be the first argument to a user-defined + function. It should not be used outside of a call to the function. + """ + + def __init__(self): + raise TypeError("Do not call {}'s constructor directly" + .format(self.__class__.__name__)) + + cdef void init(self, const CScalarUdfContext &c_context): + self.c_context = c_context + + @property + def batch_length(self): + """ + The common length of all input arguments (int). + + In the case that all arguments are scalars, this value + is used to pass the "actual length" of the arguments, + e.g. because the scalar values are encoding a column + with a constant value. + """ + return self.c_context.batch_length + + @property + def memory_pool(self): + """ + A memory pool for allocations (:class:`MemoryPool`). + """ + return box_memory_pool(self.c_context.pool) + + +cdef inline CFunctionDoc _make_function_doc(dict func_doc) except *: + """ + Helper function to generate the FunctionDoc + This function accepts a dictionary and expect the + summary(str), description(str) and arg_names(List[str]) keys. + """ + cdef: + CFunctionDoc f_doc + vector[c_string] c_arg_names + + f_doc.summary = tobytes(func_doc["summary"]) + f_doc.description = tobytes(func_doc["description"]) + for arg_name in func_doc["arg_names"]: + c_arg_names.push_back(tobytes(arg_name)) + f_doc.arg_names = c_arg_names + # UDFOptions integration: + # TODO: https://issues.apache.org/jira/browse/ARROW-16041 + f_doc.options_class = b"" + f_doc.options_required = False + return f_doc + + +cdef object box_scalar_udf_context(const CScalarUdfContext& c_context): + cdef ScalarUdfContext context = ScalarUdfContext.__new__(ScalarUdfContext) + context.init(c_context) + return context + + +cdef _scalar_udf_callback(user_function, const CScalarUdfContext& c_context, inputs): + """ + Helper callback function used to wrap the ScalarUdfContext from Python to C++ + execution. + """ + context = box_scalar_udf_context(c_context) + return user_function(context, *inputs) + + +def _get_scalar_udf_context(memory_pool, batch_length): + cdef CScalarUdfContext c_context + c_context.pool = maybe_unbox_memory_pool(memory_pool) + c_context.batch_length = batch_length + context = box_scalar_udf_context(c_context) + return context + + +def register_scalar_function(func, func_name, function_doc, in_types, + out_type): + """ + Register a user-defined scalar function. + + A scalar function is a function that executes elementwise + operations on arrays or scalars, and therefore whose results + generally do not depend on the order of the values in the + arguments. Accepts and returns arrays that are all of the + same size. These functions roughly correspond to the functions + used in SQL expressions. + + Parameters + ---------- + func : callable + A callable implementing the user-defined function. + It must take arguments equal to the number of + in_types defined. It must return an Array or Scalar + matching the out_type. It must return a Scalar if + all arguments are scalar, else it must return an Array. + + To define a varargs function, pass a callable that takes + varargs. The last in_type will be the type of the all + varargs arguments. + func_name : str + Name of the function. This name must be globally unique. + function_doc : dict + A dictionary object with keys "summary" (str), + and "description" (str). + in_types : Dict[str, DataType] + A dictionarym mapping function argument names to + their respective DataType. + The argument names will be used to generate + documentation for the function. The number of + arguments specified here determines the function + arity. + out_type : DataType + Output type of the function. + + Examples + -------- + + >>> import pyarrow.compute as pc + >>> + >>> func_doc = {} + >>> func_doc["summary"] = "simple udf" + >>> func_doc["description"] = "add a constant to a scalar" + >>> + >>> def add_constant(ctx, array): + ... return pc.add(array, 1) + >>> + >>> func_name = "py_add_func" + >>> in_types = {"array": pa.int64()} + >>> out_type = pa.int64() + >>> pc.register_scalar_function(add_constant, func_name, func_doc, + ... in_types, out_type) + >>> + >>> func = pc.get_function(func_name) + >>> func.name + 'py_add_func' + >>> answer = pc.call_function(func_name, [pa.array([20])]) + >>> answer + <pyarrow.lib.Int64Array object at 0x10c22e700> + [ + 21 + ] + """ + cdef: + c_string c_func_name + CArity c_arity + CFunctionDoc c_func_doc + vector[shared_ptr[CDataType]] c_in_types + PyObject* c_function + shared_ptr[CDataType] c_out_type + CScalarUdfOptions c_options + + c_func_name = tobytes(func_name) + + if callable(func): + c_function = <PyObject*>func + else: + raise TypeError("func must be a callable") + + func_spec = inspect.getfullargspec(func) + num_args = -1 + if isinstance(in_types, dict): + for in_type in in_types.values(): + if isinstance(in_type, DataType): Review Comment: CC @vibhatha -- This is an automated message from the Apache Git Service. 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