2018-02-27 10:38:39 +01:00
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import numpy as np
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import pandas as pd
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import pyarrow as pa
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from . import common
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from .common import KILOBYTE, MEGABYTE
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def generate_chunks(total_size, nchunks, ncols, dtype=np.dtype('int64')):
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rowsize = total_size // nchunks // ncols
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assert rowsize % dtype.itemsize == 0
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2018-06-15 11:24:32 +02:00
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def make_column(col, chunk):
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return np.frombuffer(common.get_random_bytes(
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rowsize, seed=col + 997 * chunk)).view(dtype)
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2018-02-27 10:38:39 +01:00
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return [pd.DataFrame({
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2018-06-15 11:24:32 +02:00
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'c' + str(col): make_column(col, chunk)
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for col in range(ncols)})
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for chunk in range(nchunks)]
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2018-02-27 10:38:39 +01:00
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class StreamReader(object):
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"""
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Benchmark in-memory streaming to a Pandas dataframe.
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"""
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total_size = 64 * MEGABYTE
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ncols = 8
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chunk_sizes = [16 * KILOBYTE, 256 * KILOBYTE, 8 * MEGABYTE]
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param_names = ['chunk_size']
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params = [chunk_sizes]
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def setup(self, chunk_size):
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# Note we're careful to stream different chunks instead of
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# streaming N times the same chunk, so that we avoid operating
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# entirely out of L1/L2.
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chunks = generate_chunks(self.total_size,
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nchunks=self.total_size // chunk_size,
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ncols=self.ncols)
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batches = [pa.RecordBatch.from_pandas(df)
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for df in chunks]
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schema = batches[0].schema
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sink = pa.BufferOutputStream()
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stream_writer = pa.RecordBatchStreamWriter(sink, schema)
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for batch in batches:
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stream_writer.write_batch(batch)
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2019-02-15 00:52:04 -06:00
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self.source = sink.getvalue()
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2018-02-27 10:38:39 +01:00
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def time_read_to_dataframe(self, *args):
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reader = pa.RecordBatchStreamReader(self.source)
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table = reader.read_all()
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2018-06-15 11:24:32 +02:00
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df = table.to_pandas() # noqa
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