datafusion/__init__.py

# 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.

from abc import ABCMeta, abstractmethod
from typing import List

try:
    import importlib.metadata as importlib_metadata
except ImportError:
    import importlib_metadata

import pyarrow as pa

from ._internal import (
    AggregateUDF,
    Config,
    DataFrame,
    SessionContext,
    SessionConfig,
    RuntimeConfig,
    ScalarUDF,
)

from .common import (
    DFField,
    DFSchema,
)

from .expr import (
    Expr,
    Projection,
    TableScan,
)

__version__ = importlib_metadata.version(__name__)

__all__ = [
    "Config",
    "DataFrame",
    "SessionContext",
    "SessionConfig",
    "RuntimeConfig",
    "Expr",
    "AggregateUDF",
    "ScalarUDF",
    "column",
    "literal",
    "TableScan",
    "Projection",
    "DFSchema",
    "DFField",
]


class Accumulator(metaclass=ABCMeta):
    @abstractmethod
    def state(self) -> List[pa.Scalar]:
        pass

    @abstractmethod
    def update(self, values: pa.Array) -> None:
        pass

    @abstractmethod
    def merge(self, states: pa.Array) -> None:
        pass

    @abstractmethod
    def evaluate(self) -> pa.Scalar:
        pass


def column(value):
    return Expr.column(value)


col = column


def literal(value):
    if not isinstance(value, pa.Scalar):
        value = pa.scalar(value)
    return Expr.literal(value)


lit = literal


def udf(func, input_types, return_type, volatility, name=None):
    """
    Create a new User Defined Function
    """
    if not callable(func):
        raise TypeError("`func` argument must be callable")
    if name is None:
        name = func.__qualname__
    return ScalarUDF(
        name=name,
        func=func,
        input_types=input_types,
        return_type=return_type,
        volatility=volatility,
    )


def udaf(accum, input_type, return_type, state_type, volatility, name=None):
    """
    Create a new User Defined Aggregate Function
    """
    if not issubclass(accum, Accumulator):
        raise TypeError(
            "`accum` must implement the abstract base class Accumulator"
        )
    if name is None:
        name = accum.__qualname__
    return AggregateUDF(
        name=name,
        accumulator=accum,
        input_type=input_type,
        return_type=return_type,
        state_type=state_type,
        volatility=volatility,
    )