speed up loading of namespaces: return shallow copy in build_const_args

[magland]

Motivation and description

I am trying to speed up the loading of namespaces in pynwb. Sometimes it takes up to 6 seconds on initial load. I was tracing through the code to see what could be causing the slowness and I came across the a deepcopy in a low level function build_const_args that gets called a lot during namespace loading. I replaced this with a shallow copy and noticed a significant improvement in load time.

IMPORTANT: I am not familiar enough with the code to know whether this change is going to break anything.

This is one of two PRs I am submitting to try and speed things up.

How to test the behavior?

Run this script twice before the change and once after the change. The first time will download the needed data and will save the loaded file segments to a cache directory. The second time and third times it is run, it will not include the download time. On my machine it takes around 4 sec to load before the change and around 1.5 sec after the change.

import time
import remfile
import pynwb
import h5py


def example_slow_load_namespace():
    # https://neurosift.app/?p=/nwb&dandisetId=000409&dandisetVersion=draft&url=https://api.dandiarchive.org/api/assets/c04f6b30-82bf-40e1-9210-34f0bcd8be24/download/
    h5_url = 'https://api.dandiarchive.org/api/assets/c04f6b30-82bf-40e1-9210-34f0bcd8be24/download/'
    disk_cache = remfile.DiskCache('test_cache')
    remf = remfile.File(h5_url, disk_cache=disk_cache)
    timer = time.time()
    with h5py.File(remf, 'r') as h5f:
        with pynwb.NWBHDF5IO(file=h5f, mode='r', load_namespaces=True) as io:
            nwbfile = io.read()
            print(nwbfile)
    elapsed = time.time() - timer
    print('Elapsed time:', elapsed)


if __name__ == '__main__':
    example_slow_load_namespace()

Checklist

• Did you update CHANGELOG.md with your changes?
• Does the PR clearly describe the problem and the solution?
• Have you reviewed our Contributing Guide?
• Does the PR use "Fix #XXX" notation to tell GitHub to close the relevant issue numbered XXX when the PR is merged?

@oruebel @rly

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 88.88%. Comparing base (b0f068e) to head (da2c1d3).
Report is 30 commits behind head on dev.

Additional details and impacted files

@@            Coverage Diff             @@
##              dev    #1103      +/-   ##
==========================================
- Coverage   88.88%   88.88%   -0.01%     
==========================================
  Files          45       45              
  Lines        9836     9835       -1     
  Branches     2795     2795              
==========================================
- Hits         8743     8742       -1     
  Misses        776      776              
  Partials      317      317              

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[mavaylon1]

The change to a shallow copy should be fine. The only reason I would suspect a deepcopy is needed is if we wanted to modify an independent copy.
We do a modification here:

@classmethod
    def build_const_args(cls, spec_dict):
        ''' Build constructor arguments for this Spec class from a dictionary '''
        ret = super().build_const_args(spec_dict)
        if isinstance(ret['dtype'], dict):
            ret['dtype'] = RefSpec.build_spec(ret['dtype'])
        return ret

lines 276 -282

And also in namespace.py

if parent_cls.def_key() in spec_dict:
            spec_dict[spec_cls.def_key()] = spec_dict.pop(parent_cls.def_key())
        if parent_cls.inc_key() in spec_dict:
            spec_dict[spec_cls.inc_key()] = spec_dict.pop(parent_cls.inc_key())

I would need to dive in deeper to see if the order in which we call these methods should not conflict with us using a shallow copy. I'll tackle this next week when I am back.

[oruebel]

The only reason I would suspect a deepcopy is needed is if we wanted to modify an independent copy.

I think this will require careful testing. We should check if/where the spec object is actually being modified and why. If the spec is being modified downstream, then I'd suspect that this could lead to issues when reading multiple files where you could get undesirable side-effects where the spec if modified when reading file A and then when reading file B it would see the modifications made when reading A. I'm not sure whether that is actually the case or whether using a deepcopy was just done to be extra careful.

[mavaylon1]

The only reason I would suspect a deepcopy is needed is if we wanted to modify an independent copy.

I think this will require careful testing. We should check if/where the spec object is actually being modified and why. If the spec is being modified downstream, then I'd suspect that this could lead to issues when reading multiple files where you could get undesirable side-effects where the spec if modified when reading file A and then when reading file B it would see the modifications made when reading A. I'm not sure whether that is actually the case or whether using a deepcopy was just done to be extra careful.

Agreed.

[sneakers-the-rat]

The only reason I would suspect a deepcopy is needed is if we wanted to modify an independent copy.

I think this will require careful testing. We should check if/where the spec object is actually being modified and why. If the spec is being modified downstream, then I'd suspect that this could lead to issues when reading multiple files where you could get undesirable side-effects where the spec if modified when reading file A and then when reading file B it would see the modifications made when reading A. I'm not sure whether that is actually the case or whether using a deepcopy was just done to be extra careful.

I checked this out over here: #1152 (comment)

tl;dr the deepcopy doesn't protect from mutation anyway because of when it is called/what calls it, the main thing deepcopy seems to be doing is giving derived objects a new id