Support heapq on typed arrays?

[da]

BPO	33593
Nosy	@rhettinger, @serhiy-storchaka

^{Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.}

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GitHub fields:

assignee = None
closed_at = <Date 2018-05-22.19:09:53.137>
created_at = <Date 2018-05-21.21:11:04.036>
labels = ['3.8', 'type-feature', 'library']
title = 'Support heapq on typed arrays?'
updated_at = <Date 2018-05-22.19:09:53.135>
user = 'https://bugs.python.org/da'

bugs.python.org fields:

activity = <Date 2018-05-22.19:09:53.135>
actor = 'rhettinger'
assignee = 'none'
closed = True
closed_date = <Date 2018-05-22.19:09:53.137>
closer = 'rhettinger'
components = ['Library (Lib)']
creation = <Date 2018-05-21.21:11:04.036>
creator = 'da'
dependencies = []
files = []
hgrepos = []
issue_num = 33593
keywords = []
message_count = 6.0
messages = ['317250', '317303', '317311', '317315', '317317', '317327']
nosy_count = 3.0
nosy_names = ['rhettinger', 'serhiy.storchaka', 'da']
pr_nums = []
priority = 'normal'
resolution = 'rejected'
stage = 'resolved'
status = 'closed'
superseder = None
type = 'enhancement'
url = 'https://bugs.python.org/issue33593'
versions = ['Python 3.8']

[da]

It'd be really great if we could have support for using the heapq module on typed arrays from array. For example:

import array
import heapq
import random

a = array.array('I', (random.randrange(10) for _ in range(10)))
heapq.heapify(a)

Right now this code throws a TypeError:

TypeError: heap argument must be a list

I suppose I could use bisect to insert items one by one but I imagine a single call to heapify() would be more efficient, especially if I'm loading the array from a byte string.

From what I can tell the problem lies in the C implementation, since removing the _heapq imports at the end of the heapq module (in 3.6) makes it work.

[rhettinger]

I don't think we should go down this path. The efficiency of the C implementation depends on it being tightly coupled to lists. This tool is used in the schedulers of various async tools (such as Tornando), used for merge(), nsmallest(), and nlargest() all of which depend on this foundational tool being very fast.

Also, I question whether it makes sense at all to be heapifying numpy arrays using standard library tooling. It numpy arrays actually needed this and needed for it to be efficient, it would need to be implemented natively in numpy.

[da]

I was referring to the C arrays in the Python standard library: https://docs.python.org/3/library/array.html

[da]

However I do see your point about the speed.

[serhiy-storchaka]

Workaround:

alist = list(a)
heapq.heapify(alist)
a[:] = alist

And it should be not much slower than using heapq.heapify() directly if it could support general sequences. Using it with array.array would add significant overhead due to boxing.

[rhettinger]

As noted by Serhiy, the interaction with the Array type would incur significant overhead. Your fastest approach will be to follow his suggest to first convert to a list and then perform heap manipulations.

Marking this as closed. Thank you for the suggestion.