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Wes McKinney commented on ARROW-1017: ------------------------------------- Update. The following leaks memory: {code:language=python} import pyarrow as pa import numpy as np def leak(): data = [pa.array(np.concatenate([np.random.randn(1000000)] * 100))] table = pa.Table.from_arrays(data, ['foo']) while True: table.to_pandas() {code} this does not: {code:language=python} def leak(): data = [pa.array(np.concatenate([np.random.randn(1000000)] * 100))] table = pa.Table.from_arrays(data, ['foo']) while True: table[0].to_pandas() {code} > Python: Table.to_pandas leaks memory > ------------------------------------ > > Key: ARROW-1017 > URL: https://issues.apache.org/jira/browse/ARROW-1017 > Project: Apache Arrow > Issue Type: Bug > Components: Python > Affects Versions: 0.3.0 > Reporter: James Porritt > Assignee: Wes McKinney > Fix For: 0.4.0 > > > Running the following code results in ever increasing memory usage, even > though I would expect the dataframe to be garbage collected when it goes out > of scope. For the size of my parquet file, I see the usage increasing about > 3GB per loop: > {code} > from pyarrow import HdfsClient > def read_parquet_file(client, parquet_file): > parquet = client.read_parquet(parquet_file) > df = parquet.to_pandas() > client = HdfsClient("hdfshost", 8020, "myuser", driver='libhdfs3') > parquet_file = '/my/parquet/file > while True: > read_parquet_file(client, parquet_file) > {code} > Is there a reference count issue similar to ARROW-362? -- This message was sent by Atlassian JIRA (v6.3.15#6346)