OOM when loading large JSON files in v2

[anand-bala]

Overview

I am trying to parse a ~2.5GB JSON data file containing a list of lists of data (think Array of Array of Structs). Using the recommended approach of model_validate_json(f.read()) results in the OS SIGKILL-ing the process due to it running out of memory. In comparison, Python's json module parses it effortlessly.

For a bit of detail, I profiled the code using the below snippets using memray and am attaching the HTML flame graph files as TXT for ease of use (and because Github doesn't allow HTML files as attachments but allows PPTX..).

I wasn't able to dig deeper into the issue (due to lack of time) but it is possible that the issue is related to #843, but I could be very wrong (hence the new issue).

Vanilla json

import json

with open("dataset.json") as f:
    data = json.load(f)

memray-flamegraph-test-json.py.107113.html.txt

This approach just uses about 8.8G of memory: ~6 for parsing and the rest for the string data buffer.

Pydantic recommended API

from titanium_data import Data

with open("dataset.json") as f:
    data = Data.model_validate_json(f)

memray-flamegraph-test-pydantic.py.131233.html.txt

This gets SIGKILLed by the OS after consuming ~23G to parse the 2.5GB file.

Pydantic second approach

This uses the "non-recommended" approach from pydantic/pydantic#7323

import json

from titanium_data import Data

with open("./data/trajectories/scenario1/dataset.json") as f:
    data = json.load(f)
data = Data.model_validate(data)

memray-flamegraph-test-pydantic2.py.130581.html.txt

Interestingly enough, this method successfully parses the dataset, and much faster than the direct approach of using model_validate_json.

System Information

uname -srvmo

• Linux 5.15.0-84-generic #93~20.04.1-Ubuntu SMP Wed Sep 6 16:15:40 UTC 2023 x86_64 GNU/Linux

Pydantic versions:

• pydantic==2.3.0
• -e git+https://github.com/pydantic/pydantic-core@c086caec1a200417f19850244282c06b5d4d1650#egg=pydantic_core
  • Equivalent to ==2.6.3

[adriangb]

Can you share the dataset, or a similar dataset, and the model in question?

[anand-bala]

I am not sure if I can, but I can check and let you know.

[samuelcolvin]

Ye, I've seen this too, a partial answer will be the new jiter JSON parser, but even that right now requires you to read the entire JSON into memory, but maybe just one or two copies in memory is fine.

[davidhewitt]

This is very likely also related to PyO3/pyo3#3382 / PyO3/pyo3#1056

[lattwood]

@sydney-runkle err, the memray flamegraphs make it pretty apparent this is an issue?

[davidhewitt]

We have long updated to PyO3 0.21; we don't expect there to be a significant issue here any more. I'll close this one; @lattwood if you have a new example / repro, can you please open a new issue?