WIP: Use sparse_list to save memory (see #1117) #1451
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acdha
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The sparse list implementation avoids allocating the result cache elements until something triggers loading the actual content. This has a slight memory increase for very small querysets – roughly a custom class + dict() vs. a list() – but the actual result data will usually be much larger and the difference is cancelled out by the time the result cache is at a thousand items. This branch is currently broken using the released version of sparse_list – see johnsyweb/python_sparse_list#5
This bit of code really needs to be rewritten to avoid the loop but this patch at least hits the point of being testable on Python 2
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speedplane
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Nov 9, 2016
| @@ -233,7 +234,7 @@ def _fill_cache(self, start, end, **kwargs): | |||
| # an array of 100,000 ``None``s consumed less than .5 Mb, which ought | |||
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I would change this comment here explaining that a sparse list is used because the total results set can be many orders of magnitude larger than the set of results that we pull at any given time.
Following a rapid upstream release, this should get the tests in django-haystack#1117 to pass against Python 3.
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The sparse list implementation avoids allocating the result cache elements
until something triggers loading the actual content.
This has a slight memory increase for very small querysets – roughly a custom
class + dict() vs. a list() – but the actual result data will usually be much
larger and the difference is cancelled out by the time the result cache is at a
thousand items.
This branch is currently broken using the released version of sparse_list – see
johnsyweb/python_sparse_list#5