| ################################################################################ |
| # 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 datetime |
| import threading |
| import time |
| from collections.abc import Generator |
| |
| from functools import partial |
| |
| from typing import Any, Tuple, Dict, List |
| |
| from pyflink.common import Row |
| from pyflink.fn_execution import pickle |
| from pyflink.serializers import PickleSerializer |
| from pyflink.table import functions |
| from pyflink.table.udf import DelegationTableFunction, DelegatingScalarFunction, \ |
| ImperativeAggregateFunction, PandasAggregateFunctionWrapper |
| |
| _func_num = 0 |
| _constant_num = 0 |
| |
| |
| def normalize_table_function_result(it): |
| def normalize_one_row(value): |
| if isinstance(value, tuple): |
| # We assume that tuple is a single line output |
| return [*value] |
| elif isinstance(value, Row): |
| # We assume that tuple is a single line output |
| return value._values |
| else: |
| # single field value |
| return [value] |
| |
| if it is None: |
| return [] |
| |
| if isinstance(it, (list, range, Generator)): |
| def func(): |
| for item in it: |
| yield normalize_one_row(item) |
| |
| return func() |
| else: |
| return [normalize_one_row(it)] |
| |
| |
| def normalize_pandas_result(it): |
| import pandas as pd |
| arrays = [] |
| for result in it: |
| if isinstance(result, (Row, Tuple)): |
| arrays.append(pd.concat([pd.Series([item]) for item in result], axis=1)) |
| else: |
| arrays.append(pd.Series([result])) |
| return arrays |
| |
| |
| def wrap_input_series_as_dataframe(*args): |
| import pandas as pd |
| return pd.concat(args, axis=1) |
| |
| |
| def check_pandas_udf_result(f, *input_args): |
| output = f(*input_args) |
| import pandas as pd |
| assert type(output) == pd.Series or type(output) == pd.DataFrame, \ |
| "The result type of Pandas UDF '%s' must be pandas.Series or pandas.DataFrame, got %s" \ |
| % (f.__name__, type(output)) |
| assert len(output) == len(input_args[0]), \ |
| "The result length '%d' of Pandas UDF '%s' is not equal to the input length '%d'" \ |
| % (len(output), f.__name__, len(input_args[0])) |
| return output |
| |
| |
| def extract_over_window_user_defined_function(user_defined_function_proto): |
| window_index = user_defined_function_proto.window_index |
| return (*extract_user_defined_function(user_defined_function_proto, True), window_index) |
| |
| |
| def extract_user_defined_function(user_defined_function_proto, pandas_udaf=False, |
| one_arg_optimization=False)\ |
| -> Tuple[str, Dict, List]: |
| """ |
| Extracts user-defined-function from the proto representation of a |
| :class:`UserDefinedFunction`. |
| |
| :param user_defined_function_proto: the proto representation of the Python |
| :param pandas_udaf: whether the user_defined_function_proto is pandas udaf |
| :param one_arg_optimization: whether the optimization enabled |
| :class:`UserDefinedFunction` |
| """ |
| |
| def _next_func_num(): |
| global _func_num |
| _func_num = _func_num + 1 |
| return _func_num |
| |
| def _extract_input(args) -> Tuple[str, Dict, List]: |
| local_variable_dict = {} |
| local_funcs = [] |
| args_str = [] |
| for arg in args: |
| if arg.HasField("udf"): |
| # for chaining Python UDF input: the input argument is a Python ScalarFunction |
| udf_arg, udf_variable_dict, udf_funcs = extract_user_defined_function( |
| arg.udf, one_arg_optimization=one_arg_optimization) |
| args_str.append(udf_arg) |
| local_variable_dict.update(udf_variable_dict) |
| local_funcs.extend(udf_funcs) |
| elif arg.HasField("inputOffset"): |
| if one_arg_optimization: |
| args_str.append("value") |
| else: |
| # the input argument is a column of the input row |
| args_str.append("value[%s]" % arg.inputOffset) |
| else: |
| # the input argument is a constant value |
| constant_value_name, parsed_constant_value = \ |
| _parse_constant_value(arg.inputConstant) |
| args_str.append(constant_value_name) |
| local_variable_dict[constant_value_name] = parsed_constant_value |
| return ",".join(args_str), local_variable_dict, local_funcs |
| |
| variable_dict = {} |
| user_defined_funcs = [] |
| |
| user_defined_func = pickle.loads(user_defined_function_proto.payload) |
| if pandas_udaf: |
| user_defined_func = PandasAggregateFunctionWrapper(user_defined_func) |
| func_name = 'f%s' % _next_func_num() |
| if isinstance(user_defined_func, DelegatingScalarFunction) \ |
| or isinstance(user_defined_func, DelegationTableFunction): |
| if user_defined_function_proto.is_pandas_udf: |
| variable_dict[func_name] = partial(check_pandas_udf_result, user_defined_func.func) |
| else: |
| variable_dict[func_name] = user_defined_func.func |
| else: |
| variable_dict[func_name] = user_defined_func.eval |
| user_defined_funcs.append(user_defined_func) |
| |
| func_args, input_variable_dict, input_funcs = _extract_input(user_defined_function_proto.inputs) |
| variable_dict.update(input_variable_dict) |
| user_defined_funcs.extend(input_funcs) |
| if user_defined_function_proto.takes_row_as_input: |
| if input_variable_dict: |
| # for constant or other udfs as input arguments. |
| func_str = "%s(%s)" % (func_name, func_args) |
| elif user_defined_function_proto.is_pandas_udf or pandas_udaf: |
| # for pandas udf/udaf, the input data structure is a List of Pandas.Series |
| # we need to merge these Pandas.Series into a Pandas.DataFrame |
| variable_dict['wrap_input_series_as_dataframe'] = wrap_input_series_as_dataframe |
| func_str = "%s(wrap_input_series_as_dataframe(%s))" % (func_name, func_args) |
| else: |
| # directly use `value` as input argument |
| # e.g. |
| # lambda value: Row(value[0], value[1]) |
| # can be optimized to |
| # lambda value: value |
| func_str = "%s(value)" % func_name |
| else: |
| func_str = "%s(%s)" % (func_name, func_args) |
| return func_str, variable_dict, user_defined_funcs |
| |
| |
| def _parse_constant_value(constant_value) -> Tuple[str, Any]: |
| j_type = constant_value[0] |
| serializer = PickleSerializer() |
| pickled_data = serializer.loads(constant_value[1:]) |
| # the type set contains |
| # TINYINT,SMALLINT,INTEGER,BIGINT,FLOAT,DOUBLE,DECIMAL,CHAR,VARCHAR,NULL,BOOLEAN |
| # the pickled_data doesn't need to transfer to anther python object |
| if j_type == 0: |
| parsed_constant_value = pickled_data |
| # the type is DATE |
| elif j_type == 1: |
| parsed_constant_value = \ |
| datetime.date(year=1970, month=1, day=1) + datetime.timedelta(days=pickled_data) |
| # the type is TIME |
| elif j_type == 2: |
| seconds, milliseconds = divmod(pickled_data, 1000) |
| minutes, seconds = divmod(seconds, 60) |
| hours, minutes = divmod(minutes, 60) |
| parsed_constant_value = datetime.time(hours, minutes, seconds, milliseconds * 1000) |
| # the type is TIMESTAMP |
| elif j_type == 3: |
| parsed_constant_value = \ |
| datetime.datetime(year=1970, month=1, day=1, hour=0, minute=0, second=0) \ |
| + datetime.timedelta(milliseconds=pickled_data) |
| else: |
| raise Exception("Unknown type %s, should never happen" % str(j_type)) |
| |
| def _next_constant_num(): |
| global _constant_num |
| _constant_num = _constant_num + 1 |
| return _constant_num |
| |
| constant_value_name = 'c%s' % _next_constant_num() |
| return constant_value_name, parsed_constant_value |
| |
| |
| def extract_user_defined_aggregate_function( |
| current_index, |
| user_defined_function_proto, |
| distinct_info_dict: Dict[Tuple[List[str]], Tuple[List[int], List[int]]]): |
| user_defined_agg = load_aggregate_function(user_defined_function_proto.payload) |
| assert isinstance(user_defined_agg, ImperativeAggregateFunction) |
| args_str = [] |
| local_variable_dict = {} |
| for arg in user_defined_function_proto.inputs: |
| if arg.HasField("inputOffset"): |
| # the input argument is a column of the input row |
| args_str.append("value[%s]" % arg.inputOffset) |
| else: |
| # the input argument is a constant value |
| constant_value_name, parsed_constant_value = \ |
| _parse_constant_value(arg.inputConstant) |
| for key, value in local_variable_dict.items(): |
| if value == parsed_constant_value: |
| constant_value_name = key |
| break |
| if constant_value_name not in local_variable_dict: |
| local_variable_dict[constant_value_name] = parsed_constant_value |
| args_str.append(constant_value_name) |
| |
| if user_defined_function_proto.distinct: |
| if tuple(args_str) in distinct_info_dict: |
| distinct_info_dict[tuple(args_str)][0].append(current_index) |
| distinct_info_dict[tuple(args_str)][1].append(user_defined_function_proto.filter_arg) |
| distinct_index = distinct_info_dict[tuple(args_str)][0][0] |
| else: |
| distinct_info_dict[tuple(args_str)] = \ |
| ([current_index], [user_defined_function_proto.filter_arg]) |
| distinct_index = current_index |
| else: |
| distinct_index = -1 |
| if user_defined_function_proto.takes_row_as_input and not local_variable_dict: |
| # directly use `value` as input argument |
| # e.g. |
| # lambda value: Row(value[0], value[1]) |
| # can be optimized to |
| # lambda value: value |
| func_str = "lambda value : [value]" |
| else: |
| func_str = "lambda value : (%s,)" % ",".join(args_str) |
| return user_defined_agg, \ |
| eval(func_str, local_variable_dict) \ |
| if args_str else lambda v: tuple(), \ |
| user_defined_function_proto.filter_arg, \ |
| distinct_index |
| |
| |
| def is_built_in_function(payload): |
| # The payload may be a pickled bytes or the class name of the built-in functions. |
| # If it represents a built-in function, it will start with 0x00. |
| # If it is a pickled bytes, it will start with 0x80. |
| return payload[0] == 0 |
| |
| |
| def load_aggregate_function(payload): |
| if is_built_in_function(payload): |
| built_in_function_class_name = payload[1:].decode("utf-8") |
| cls = getattr(functions, built_in_function_class_name) |
| return cls() |
| else: |
| return pickle.loads(payload) |
| |
| |
| def parse_function_proto(proto): |
| from pyflink.fn_execution import flink_fn_execution_pb2 |
| serialized_fn = flink_fn_execution_pb2.UserDefinedFunctions() |
| serialized_fn.ParseFromString(proto) |
| return serialized_fn |
| |
| |
| def deserialized_operation_from_serialized_bytes(b): |
| import cloudpickle |
| return cloudpickle.loads(b) |
| |
| |
| def create_scalar_operation_from_proto(proto, one_arg_optimization=False, |
| one_result_optimization=False): |
| from pyflink.fn_execution.table.operations import ScalarFunctionOperation |
| |
| serialized_fn = parse_function_proto(proto) |
| scalar_operation = ScalarFunctionOperation( |
| serialized_fn, one_arg_optimization, one_result_optimization) |
| return scalar_operation |
| |
| |
| def create_serialized_scalar_operation_from_proto(proto, one_arg_optimization=False, |
| one_result_optimization=False): |
| """ |
| The CPython extension included in proto does not support initialization multiple times, so we |
| choose the only interpreter process to be responsible for initialization and proto parsing. The |
| only interpreter parses the proto and serializes function operations with cloudpickle. |
| """ |
| |
| import cloudpickle |
| |
| scalar_operation = create_scalar_operation_from_proto( |
| bytes(b % 256 for b in proto), one_arg_optimization, one_result_optimization) |
| return cloudpickle.dumps(scalar_operation) |
| |
| |
| class PeriodicThread(threading.Thread): |
| """Call a function periodically with the specified number of seconds""" |
| |
| def __init__(self, |
| interval, |
| function, |
| args=None, |
| kwargs=None |
| ) -> None: |
| threading.Thread.__init__(self) |
| self._interval = interval |
| self._function = function |
| self._args = args if args is not None else [] |
| self._kwargs = kwargs if kwargs is not None else {} |
| self._finished = threading.Event() |
| |
| def run(self) -> None: |
| now = time.time() |
| next_call = now + self._interval |
| while (next_call <= now and not self._finished.is_set()) or \ |
| (next_call > now and not self._finished.wait(next_call - now)): |
| if next_call <= now: |
| next_call = now + self._interval |
| else: |
| next_call = next_call + self._interval |
| self._function(*self._args, **self._kwargs) |
| now = time.time() |
| |
| def cancel(self) -> None: |
| """Stop the thread if it hasn't finished yet.""" |
| self._finished.set() |
