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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.
import numpy as np
import pandas as pd
import pyarrow as pa
from . import common
from .common import KILOBYTE, MEGABYTE
def generate_chunks(total_size, nchunks, ncols, dtype=np.dtype('int64')):
rowsize = total_size // nchunks // ncols
assert rowsize % dtype.itemsize == 0
def make_column(col, chunk):
return np.frombuffer(common.get_random_bytes(
rowsize, seed=col + 997 * chunk)).view(dtype)
return [pd.DataFrame({
'c' + str(col): make_column(col, chunk)
for col in range(ncols)})
for chunk in range(nchunks)]
class StreamReader(object):
"""
Benchmark in-memory streaming to a Pandas dataframe.
"""
total_size = 64 * MEGABYTE
ncols = 8
chunk_sizes = [16 * KILOBYTE, 256 * KILOBYTE, 8 * MEGABYTE]
param_names = ['chunk_size']
params = [chunk_sizes]
def setup(self, chunk_size):
# Note we're careful to stream different chunks instead of
# streaming N times the same chunk, so that we avoid operating
# entirely out of L1/L2.
chunks = generate_chunks(self.total_size,
nchunks=self.total_size // chunk_size,
ncols=self.ncols)
batches = [pa.RecordBatch.from_pandas(df)
for df in chunks]
schema = batches[0].schema
sink = pa.BufferOutputStream()
stream_writer = pa.RecordBatchStreamWriter(sink, schema)
for batch in batches:
stream_writer.write_batch(batch)
self.source = sink.getvalue()
def time_read_to_dataframe(self, *args):
reader = pa.RecordBatchStreamReader(self.source)
table = reader.read_all()
df = table.to_pandas() # noqa
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