My 2 cents...slow query log in mysql should help a lot...

Please let us know your insights at the end of your exercise... :)

On Mar 9, 3:41 pm, Gareth McCumskey <[email protected]> wrote:
> I just tried using Propel 1.3 on our application and while I would love to
> continue using it (as it seemed to produce a little more efficieny) we can't
> use it for now because the servers that the app will run on are Centos 4
> with PHP 5.1.x as its maximum version for now. The sysadmins here say that
> to force an upgrade to 5.2.x would be a hard task as to retain RedHat
> support it means they would need to upgrade to Centos 5.
>
> I am currently looking at the chapter about Optimising symfony and the
> function cache seems to be something we cna consider doing in a lot of our
> model calls from the action to help speed things up, especially for model
> methods that access historical data (i.e. stuff dated in the past that
> obviously wont change on subsequent calls) but these are relatively large
> coding changes which we will probably only do during our beta development
> phase.
>
> I am still looking through more advise recieved from this post and I have to
> thank everyone for their input. I honestly didn't expect this response and
> it has been fantastic and very helpful.
>
> On Mon, Mar 9, 2009 at 12:49 AM, Crafty_Shadow <[email protected]> wrote:
>
> > Symfony 1.1 came by default with Propel 1.2
> > You can try upgrading to 1.3 (it isn't really a trivial task, but it
> > shouldn't be a big problem)
> > There is thorough explanation on the symfony site how to do it:
> >http://www.symfony-project.org/cookbook/1_1/en/propel_13
> > It should fare a measurable increase in performance. Also, a site that
> > makes good use of cache should have caching for absolutely everything
> > not session-dependent. I find it hard to imagine a php app, no matter
> > how fast, that would run faster than symfony's cached output.
>
> > Alvaro:
> > Is your plugin based on Propel 1.3?
> > If you believe you have made significant improvements to Propel, why
> > not suggest them for version 2.0, which is still under heavy
> > development?
>
> > On Mar 8, 4:33 pm, alvaro <[email protected]> wrote:
> > > At the company I developed a symfony plugin to optimize the Propel
> > > queries and also the Propel hydrate method, improving even 5 times
> > > query speed and also memory usage.
>
> > > The plugins supports joins and thanks to PHP features the plugin
> > > returns Propel objects populated with custom AS columns.
>
> > > We are thinking on release it on the following weeks so stay tuned :)
>
> > > Regards,
>
> > > Alvaro
>
> > > On Mar 8, 2009, at 10:20 PM, Gareth McCumskey wrote:
>
> > > > We have put numerous caching techniques into effect, from Cache-
> > > > Expires headers to compression of static files like js and html
> > > > files. Currently we use symfony 1.1 and Propel as the ORM. We have
> > > > identified the bottleneck generally as being the application
> > > > processing after the db queries have run to extract the data.
>
> > > > The entire point of my question was to get some info on general tips
> > > > and tricks we can try out to see if anything helps or if perhaps we
> > > > have missed any obvious issues that may actually be the cause of the
> > > > slow performance we are getting. As it is I have gotten quite a few
> > > > and look forward to getting into the office tomorrow to try them
> > > > out. Anymore is greatly appreciated.
>
> > > > Of course I am looking through the code to see if there is anyway we
> > > > can streamline it on that end, but every little bit helps.
>
> > > > Gareth
>
> > > > On Sun, Mar 8, 2009 at 12:27 PM, Crafty_Shadow <[email protected]>
> > > > wrote:
>
> > > > Gareth, you didn't mention what version of symfony you were using,
> > > > also what ORM (if any).
> > > > The best course of optimization will depend on those. Also, as already
> > > > mentioned, caching is your best friend.
>
> > > > On Mar 8, 9:43 am, Gareth McCumskey <[email protected]> wrote:
> > > > > Well, consider a single database table that looks something like
> > > > this:
>
> > > > > From_address
> > > > > to_address (possibly multiple addresses comma-seperated)
> > > > > headers
> > > > > spam_report
> > > > > subject
>
> > > > > And we would have millions of those records in the database.
> > > > Repeated
> > > > > entries, especially on to_address, means the data is hugely
> > > > redundant. By
> > > > > normalising we are turning a text search across millions of
> > > > records with
> > > > > redundant repeated data into a text search over a unique list,
> > > > then an
> > > > > integer search over primary key (which of course is indexed).
>
> > > > > On Sun, Mar 8, 2009 at 9:37 AM, Lawrence Krubner
> > > > <[email protected]>wrote:
>
> > > > > > On Mar 8, 3:26 am, Gareth McCumskey <[email protected]> wrote:
> > > > > > > We had a speed increase because we had a lot of text searches
> > > > in the old
> > > > > > > system, all going through text fields where the same values
> > > > were repeated
> > > > > > > over and over. Its therefore a lot faster to search a much
> > > > smaller table,
> > > > > > > where the text fields are unique, and find the value once,
> > > > then use an ID
> > > > > > > comparison, being much faster to match integers than text.
>
> > > > > > In sounds like you got a speed boost from doing intelligent
> > > > indexing.
> > > > > > What you are describing sounds more like indexing than
> > > > normalization,
> > > > > > at least to me.
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