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docs: Example of calling Python UDF & UDAF in SQL (#258)
* Document UDF calls in SQL * Remove unnecessary imports * FIx example
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# 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. | ||
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from datafusion import udaf, SessionContext, Accumulator | ||
import pyarrow as pa | ||
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# Define a user-defined aggregation function (UDAF) | ||
class MyAccumulator(Accumulator): | ||
""" | ||
Interface of a user-defined accumulation. | ||
""" | ||
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def __init__(self): | ||
self._sum = pa.scalar(0.0) | ||
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def update(self, values: pa.Array) -> None: | ||
# not nice since pyarrow scalars can't be summed yet. This breaks on `None` | ||
self._sum = pa.scalar( | ||
self._sum.as_py() + pa.compute.sum(values).as_py() | ||
) | ||
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def merge(self, states: pa.Array) -> None: | ||
# not nice since pyarrow scalars can't be summed yet. This breaks on `None` | ||
self._sum = pa.scalar( | ||
self._sum.as_py() + pa.compute.sum(states).as_py() | ||
) | ||
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def state(self) -> pa.Array: | ||
return pa.array([self._sum.as_py()]) | ||
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def evaluate(self) -> pa.Scalar: | ||
return self._sum | ||
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my_udaf = udaf( | ||
MyAccumulator, | ||
pa.float64(), | ||
pa.float64(), | ||
[pa.float64()], | ||
"stable", | ||
# This will be the name of the UDAF in SQL | ||
# If not specified it will by default the same as accumulator class name | ||
name="my_accumulator", | ||
) | ||
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# Create a context | ||
ctx = SessionContext() | ||
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# Create a datafusion DataFrame from a Python dictionary | ||
source_df = ctx.from_pydict({"a": [1, 1, 3], "b": [4, 5, 6]}) | ||
# Dataframe: | ||
# +---+---+ | ||
# | a | b | | ||
# +---+---+ | ||
# | 1 | 4 | | ||
# | 1 | 5 | | ||
# | 3 | 6 | | ||
# +---+---+ | ||
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# Register UDF for use in SQL | ||
ctx.register_udaf(my_udaf) | ||
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# Query the DataFrame using SQL | ||
table_name = ctx.catalog().database().names().pop() | ||
result_df = ctx.sql( | ||
f"select a, my_accumulator(b) as b_aggregated from {table_name} group by a order by a" | ||
) | ||
# Dataframe: | ||
# +---+--------------+ | ||
# | a | b_aggregated | | ||
# +---+--------------+ | ||
# | 1 | 9 | | ||
# | 3 | 6 | | ||
# +---+--------------+ | ||
assert result_df.to_pydict()["a"] == [1, 3] | ||
assert result_df.to_pydict()["b_aggregated"] == [9, 6] |
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# 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. | ||
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from datafusion import udf, SessionContext | ||
import pyarrow as pa | ||
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# Define a user-defined function (UDF) | ||
def is_null(array: pa.Array) -> pa.Array: | ||
return array.is_null() | ||
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is_null_arr = udf( | ||
is_null, | ||
[pa.int64()], | ||
pa.bool_(), | ||
"stable", | ||
# This will be the name of the UDF in SQL | ||
# If not specified it will by default the same as Python function name | ||
name="is_null", | ||
) | ||
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# Create a context | ||
ctx = SessionContext() | ||
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# Create a datafusion DataFrame from a Python dictionary | ||
source_df = ctx.from_pydict({"a": [1, 2, 3], "b": [4, None, 6]}) | ||
# Dataframe: | ||
# +---+---+ | ||
# | a | b | | ||
# +---+---+ | ||
# | 1 | 4 | | ||
# | 2 | | | ||
# | 3 | 6 | | ||
# +---+---+ | ||
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# Register UDF for use in SQL | ||
ctx.register_udf(is_null_arr) | ||
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# Query the DataFrame using SQL | ||
table_name = ctx.catalog().database().names().pop() | ||
result_df = ctx.sql(f"select a, is_null(b) as b_is_null from {table_name}") | ||
# Dataframe: | ||
# +---+-----------+ | ||
# | a | b_is_null | | ||
# +---+-----------+ | ||
# | 1 | false | | ||
# | 2 | true | | ||
# | 3 | false | | ||
# +---+-----------+ | ||
assert result_df.to_pydict()["b_is_null"] == [False, True, False] |