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Martin wrote:
> I'd think he's talking about in memory SQLite Databases, this way you
> should be quite fast _and_ could dump all that to a persistent
> storage...
I was just talking about regular on disk SQLite databases. In terms of
priming the pum
Hi,
2008/12/24 :
> Hi Roger,
>
>> you may want to consider using SQLite
>
> Thank you for your suggestion about looking at SQLite. I haven't
> compared the performance of SQLite to Python dictionaries, but I'm
> skeptical that SQLite would be faster than in-memory Python dictionaries
> for the ty
r your help Roger!
Regards,
Malcolm
- Original message -
From: "Roger Binns"
To: python-list@python.org
Date: Wed, 24 Dec 2008 00:50:49 -0800
Subject: Re: Most efficient way to build very large dictionaries
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pyt...@bdurham.com wrote:
&g
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pyt...@bdurham.com wrote:
> Thank you for your suggestion about looking at SQLite. I haven't
> compared the performance of SQLite to Python dictionaries, but I'm
> skeptical that SQLite would be faster than in-memory Python dictionaries
> for the type
t need SQLite's
ability to work with data sets larger than my physical memory.
Regards,
Malcolm
- Original message -
From: "Roger Binns"
To: python-list@python.org
Date: Wed, 24 Dec 2008 00:19:56 -0800
Subject: Re: Most efficient way to build very large dictionaries
-BE
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pyt...@bdurham.com wrote:
> Can I take advantage of this knowledge to optimize
You do the optimization last :-) The first thing you need to do is make
sure you have a way of validating you got the correct results. With 25M
entries it would be very e
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pyt...@bdurham.com wrote:
> I would appreciate your thoughts on whether there are advantages to
> working with a pre-built dictionary and if so, what are the best ways to
> create a pre-loaded dictionary.
Based on this and your other thread, you may w
