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https://issues.apache.org/jira/browse/ARROW-18400?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17638811#comment-17638811
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Alenka Frim commented on ARROW-18400:
-------------------------------------
You are correct [~westonpace]:
{code:python}
...
import tracemalloc
tracemalloc.start()
import pyarrow as pa
import pyarrow.parquet as pq
table = pa.table({'c1': nested_col})
pq.write_table(table, filename)
table_from_parquet = pq.read_table(filename, use_legacy_dataset=True)
table_from_parquet.to_pandas()
# c1
# 0 [{'a': None, 'b': None, 'c': None}, {'a': [248...
# 1 [{'a': None, 'b': None, 'c': None}, {'a': [626...
# 2 [{'a': None, 'b': None, 'c': None}, {'a': [148...
# 3 [{'a': None, 'b': None, 'c': None}, {'a': [399...
# 4 [{'a': None, 'b': None, 'c': None}, {'a': [253...
# ... ...
# 1023995 [{'a': None, 'b': None, 'c': None}, {'a': [779...
# 1023996 [{'a': None, 'b': None, 'c': None}, {'a': [309...
# 1023997 [{'a': None, 'b': None, 'c': None}, {'a': [376...
# 1023998 [{'a': None, 'b': None, 'c': None}, {'a': [391...
# 1023999 [{'a': None, 'b': None, 'c': None}, {'a': [815...
# [1024000 rows x 1 columns]
print(tracemalloc.get_traced_memory()) # in bytes
# (23087183, 4328216706)
tracemalloc.stop()
{code}
Weston, do you think this is something on the C++ side of the Dataset API or
should I look at the Python/Cython implementation?
[~adreeve] could you also try same memory profiling as you did with adding
{{use_legacy_dataset=True}} to {{{}pq.read_table{}}}?
> [Python] Quadratic memory usage of Table.to_pandas with nested data
> -------------------------------------------------------------------
>
> Key: ARROW-18400
> URL: https://issues.apache.org/jira/browse/ARROW-18400
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 10.0.1
> Environment: Python 3.10.8 on Fedora Linux 36. AMD Ryzen 9 5900 X
> with 64 GB RAM
> Reporter: Adam Reeve
> Assignee: Alenka Frim
> Priority: Critical
> Fix For: 11.0.0
>
>
> Reading nested Parquet data and then converting it to a Pandas DataFrame
> shows quadratic memory usage and will eventually run out of memory for
> reasonably small files. I had initially thought this was a regression since
> 7.0.0, but it looks like 7.0.0 has similar quadratic memory usage that kicks
> in at higher row counts.
> Example code to generate nested Parquet data:
> {code:python}
> import numpy as np
> import random
> import string
> import pandas as pd
> _characters = string.ascii_uppercase + string.digits + string.punctuation
> def make_random_string(N=10):
> return ''.join(random.choice(_characters) for _ in range(N))
> nrows = 1_024_000
> filename = 'nested.parquet'
> arr_len = 10
> nested_col = []
> for i in range(nrows):
> nested_col.append(np.array(
> [{
> 'a': None if i % 1000 == 0 else np.random.choice(10000,
> size=3).astype(np.int64),
> 'b': None if i % 100 == 0 else random.choice(range(100)),
> 'c': None if i % 10 == 0 else make_random_string(5)
> } for i in range(arr_len)]
> ))
> df = pd.DataFrame({'c1': nested_col})
> df.to_parquet(filename)
> {code}
> And then read into a DataFrame with:
> {code:python}
> import pyarrow.parquet as pq
> table = pq.read_table(filename)
> df = table.to_pandas()
> {code}
> Only reading to an Arrow table isn't a problem, it's the to_pandas method
> that exhibits the large memory usage. I haven't tested generating nested
> Arrow data in memory without writing Parquet from Pandas but I assume the
> problem probably isn't Parquet specific.
> Memory usage I see when reading different sized files on a machine with 64 GB
> RAM:
> ||Num rows||Memory used with 10.0.1 (MB)||Memory used with 7.0.0 (MB)||
> |32,000|362|361|
> |64,000|531|531|
> |128,000|1,152|1,101|
> |256,000|2,888|1,402|
> |512,000|10,301|3,508|
> |1,024,000|38,697|5,313|
> |2,048,000|OOM|20,061|
> |4,096,000| |OOM|
> With Arrow 10.0.1, memory usage approximately quadruples when row count
> doubles above 256k rows. With Arrow 7.0.0 memory usage is more linear but
> then quadruples from 1024k to 2048k rows.
> PyArrow 8.0.0 shows similar memory usage to 10.0.1 so it looks like something
> changed between 7.0.0 and 8.0.0.
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