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| References | <CAOF-KfjZN8Tks+Nex=E9vrFqJN_t+j8hpkhQV=BGzEtno8b0Yg@mail.gmail.com> <5008ABD5.8020407@davea.name> <CAOF-KfgUdEGZRdynU81HzRFzVuf8JfX8WkSsV6xVmXTamObukA@mail.gmail.com> <6emh0859cren5ond0k5n2f58mh36bnp9jc@invalid.netcom.com> <CAOF-KfgOkB5eWYO4y8HiPcNS1bBh5SKcpiX1Ctz6K9GRdgYgUw@mail.gmail.com> |
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| From | Ian Kelly <ian.g.kelly@gmail.com> |
| Date | 2012-07-20 12:14 -0600 |
| Subject | Re: best way to handle this in Python |
| Newsgroups | comp.lang.python |
| Message-ID | <mailman.2351.1342808103.4697.python-list@python.org> (permalink) |
On Fri, Jul 20, 2012 at 4:34 AM, Rita <rmorgan466@gmail.com> wrote: > Thats an interesting data structure Dennis. I will actually be running this > type of query many times preferable in an ad-hoc environment. That makes it > tough for sqlite3 since there will be several hundred thousand tuples. Several hundred thousand is not an enormous number. I think you're underestimating sqlite3. I just tried a test with one million tuples, six colors per tuple (six million rows altogether). Each row contains a primary key, a timestamp, a color, and a count, with an index on the timestamp column. Building the database from scratch took about a minute; adding the index took about another minute. Incremental updates would of course be much faster. Queries like "select * from data where timestamp between 500000 and 600000" return instantly (from a user perspective). Cheers, Ian
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Re: best way to handle this in Python Ian Kelly <ian.g.kelly@gmail.com> - 2012-07-20 12:14 -0600 Re: best way to handle this in Python Steven D'Aprano <steve+comp.lang.python@pearwood.info> - 2012-07-21 00:43 +0000
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