99 lines
2.7 KiB
Python
99 lines
2.7 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Tests multithreading behaviour for reading and
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parsing files for each parser defined in parsers.py
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"""
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from __future__ import division
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from multiprocessing.pool import ThreadPool
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import numpy as np
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import pandas as pd
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import pandas.util.testing as tm
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from pandas import DataFrame
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from pandas.compat import BytesIO, range
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def _construct_dataframe(num_rows):
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df = DataFrame(np.random.rand(num_rows, 5), columns=list('abcde'))
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df['foo'] = 'foo'
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df['bar'] = 'bar'
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df['baz'] = 'baz'
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df['date'] = pd.date_range('20000101 09:00:00',
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periods=num_rows,
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freq='s')
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df['int'] = np.arange(num_rows, dtype='int64')
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return df
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class MultithreadTests(object):
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def _generate_multithread_dataframe(self, path, num_rows, num_tasks):
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def reader(arg):
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start, nrows = arg
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if not start:
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return self.read_csv(path, index_col=0, header=0,
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nrows=nrows, parse_dates=['date'])
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return self.read_csv(path,
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index_col=0,
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header=None,
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skiprows=int(start) + 1,
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nrows=nrows,
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parse_dates=[9])
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tasks = [
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(num_rows * i // num_tasks,
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num_rows // num_tasks) for i in range(num_tasks)
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]
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pool = ThreadPool(processes=num_tasks)
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results = pool.map(reader, tasks)
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header = results[0].columns
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for r in results[1:]:
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r.columns = header
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final_dataframe = pd.concat(results)
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return final_dataframe
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def test_multithread_stringio_read_csv(self):
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# see gh-11786
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max_row_range = 10000
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num_files = 100
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bytes_to_df = [
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'\n'.join(
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['%d,%d,%d' % (i, i, i) for i in range(max_row_range)]
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).encode() for j in range(num_files)]
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files = [BytesIO(b) for b in bytes_to_df]
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# read all files in many threads
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pool = ThreadPool(8)
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results = pool.map(self.read_csv, files)
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first_result = results[0]
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for result in results:
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tm.assert_frame_equal(first_result, result)
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def test_multithread_path_multipart_read_csv(self):
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# see gh-11786
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num_tasks = 4
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file_name = '__threadpool_reader__.csv'
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num_rows = 100000
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df = _construct_dataframe(num_rows)
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with tm.ensure_clean(file_name) as path:
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df.to_csv(path)
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final_dataframe = self._generate_multithread_dataframe(
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path, num_rows, num_tasks)
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tm.assert_frame_equal(df, final_dataframe)
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