Wes McKinney created ARROW-4099:
-----------------------------------
Summary: [Python] Pretty printing very large ChunkedArray objects
can use unbounded memory
Key: ARROW-4099
URL: https://issues.apache.org/jira/browse/ARROW-4099
Project: Apache Arrow
Issue Type: Improvement
Components: Python
Reporter: Wes McKinney
Fix For: 0.13.0
In working on ARROW-2970, I have the following dataset:
{code}
values = [b'x'] + [
b'x' * (1 << 20)
] * 2 * (1 << 10)
arr = np.array(values)
arrow_arr = pa.array(arr)
{code}
The object {{arrow_arr}} has 129 chunks, each element of which is 1MB of
binary. The repr for this object is over 600MB:
{code}
In [10]: rep = repr(arrow_arr)
In [11]: len(rep)
Out[11]: 637536258
{code}
There's probably a number of failsafes we can implement to avoid badness in
these pathological cases (which may not happen often, but given the kinds of
bug reports we are seeing, people do have datasets that look like this)
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