Disable automatic interning
tjreedy at udel.edu
Wed Mar 18 20:49:04 CET 2009
George Sakkis wrote:
> On Mar 18, 2:13 pm, "R. David Murray" <rdmur... at bitdance.com> wrote:
>> George Sakkis <george.sak... at gmail.com> wrote:
>>> Is there a way to turn off (either globally or explicitly per
>>> instance) the automatic interning optimization that happens for small
>>> integers and strings (and perhaps other types) ? I tried several
>>> workarounds but nothing worked:
>> No. It's an implementation detail.
And explicitly defined as such and definitely hardcoded, and used by the
interpreter itself, and for good reason. After starting up 3.0.1
Subtracting the extra two ref for each call and the two needed for the
two cached objects, that is 1200 ints *not* allocated on startup, plus
hundreds more for the other values.
>> What use case do you have for wanting to disable it?
> I'm working on some graph generation problem where the node identity
> is significant (e.g. "if node1 is node2: # do something) but ideally I
> wouldn't want to impose any constraint on what a node is (i.e. require
> a base Node class). It's not a show stopper, but it would be
> problematic if something broke when nodes happen to be (small)
> integers or strings.
I do not get this. Regardless of class, if you want to compare by
identity, each node should be a unique object with a unique value. Auto
interning makes that easier, not harder. Robust code would not,
however, depend on that help. (IE, it would explicitly make sure that
the 'equal' entries in the edge matrix or adjacency lists were identical.)
More information about the Python-list