| #!/usr/bin/env python |
| |
| # 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 pyarrow as pa |
| import numpy as np |
| import pandas as pd |
| import pandas.util.testing as tm |
| import memory_profiler |
| import gc |
| import io |
| |
| MEGABYTE = 1 << 20 |
| |
| |
| def assert_does_not_leak(f, iterations=10, check_interval=1, tolerance=5): |
| gc.collect() |
| baseline = memory_profiler.memory_usage()[0] |
| for i in range(iterations): |
| f() |
| if i % check_interval == 0: |
| gc.collect() |
| usage = memory_profiler.memory_usage()[0] |
| diff = usage - baseline |
| print("{0}: {1}\r".format(i, diff), end="") |
| if diff > tolerance: |
| raise Exception("Memory increased by {0} megabytes after {1} " |
| "iterations".format(diff, i + 1)) |
| gc.collect() |
| usage = memory_profiler.memory_usage()[0] |
| diff = usage - baseline |
| print("\nMemory increased by {0} megabytes after {1} " |
| "iterations".format(diff, iterations)) |
| |
| |
| def test_leak1(): |
| data = [pa.array(np.concatenate([np.random.randn(100000)] * 1000))] |
| table = pa.Table.from_arrays(data, ['foo']) |
| |
| def func(): |
| table.to_pandas() |
| assert_does_not_leak(func) |
| |
| |
| def test_leak2(): |
| data = [pa.array(np.concatenate([np.random.randn(100000)] * 10))] |
| table = pa.Table.from_arrays(data, ['foo']) |
| |
| def func(): |
| df = table.to_pandas() |
| |
| batch = pa.RecordBatch.from_pandas(df) |
| |
| sink = io.BytesIO() |
| writer = pa.RecordBatchFileWriter(sink, batch.schema) |
| writer.write_batch(batch) |
| writer.close() |
| |
| buf_reader = pa.BufferReader(sink.getvalue()) |
| reader = pa.open_file(buf_reader) |
| reader.read_all() |
| |
| assert_does_not_leak(func, iterations=50, tolerance=50) |
| |
| |
| def test_leak3(): |
| import pyarrow.parquet as pq |
| |
| df = pd.DataFrame({'a{0}'.format(i): [1, 2, 3, 4] |
| for i in range(50)}) |
| table = pa.Table.from_pandas(df, preserve_index=False) |
| |
| writer = pq.ParquetWriter('leak_test_' + tm.rands(5) + '.parquet', |
| table.schema) |
| |
| def func(): |
| writer.write_table(table, row_group_size=len(table)) |
| |
| # This does not "leak" per se but we do want to have this use as little |
| # memory as possible |
| assert_does_not_leak(func, iterations=500, |
| check_interval=50, tolerance=20) |
| |
| |
| if __name__ == '__main__': |
| test_leak3() |