In additions to the suggestions you already received from others, have a look at django-import-export. It allows you to easily export data in various formats.
Hope that helps, Daniel Hepper https://consideratecode.com On Friday, March 10, 2017 at 12:06:13 PM UTC+1, Web Architect wrote: > > Hi James, > > Thanks for your response. Melvyn also posed a similar point of not loading > the whole records. > > But all the records are needed for reporting purposes - where the data is > read from the DB and a csv report is created. I am not quite an expert on > Django but I am not sure if there is a better way to do it. > > The scenario is as follows to make it clearer: > > Ours is an ecommerce site built on Django. Our admin/accounting team needs > to download reports now and then. We have a Django model for the line items > purchased. Now there could be 10k line items sold and each line items are > associated with other models like payments, shipments etc which is a > complex set of relations. > > We do not yet have a sophisticated reporting mechanism but was working on > building a simplistic reporting system on Django. But I am finding issues > with scaling up - as reported with CPU Usage and the amount of time taken. > If there is a way to optimise this - would be great otherwise we might not > have to look for standard methods of reporting tools. > > Would appreciate suggestions/advices on the above. > > Thanks, > > On Friday, March 10, 2017 at 2:52:50 PM UTC+5:30, James Schneider wrote: >> >> >> >> On Mar 9, 2017 9:37 PM, "Web Architect" <[email protected]> wrote: >> >> Would like to further add - the python CPU Usage is hitting almost 100 %. >> When I run a Select * query on Mysql, its quite fast and CPU is normal. I >> am not sure if anything more needs to be done in Django. >> >> >> Ironically, things being done in Django is the reason for your CPU >> utilization issue in the first place. >> >> Calling a qs.all() is NOT the same as a SELECT * statement, even more so >> when speaking to the scale of query that you mention. >> >> Your SQL query is simply listing data in a table. A very easy thing to >> do, hence the reason it runs quickly. >> >> The qs.all() call is also running the same query (probably). However, in >> addition to pulling all of the data, it is performing a transformation of >> that data in to Django model objects. If you are pulling 10K items, then >> Django is creating 10K objects, which is easily more intensive than a raw >> SQL query, even for simple model objects. >> >> In general, there's usually no practical reason to ever pull that many >> objects from a DB for display on a page. Filter down to a reasonable number >> (<100 for almost all sane cases) or implement a paging system to limit >> returned results. It's also probably using a ton of RAM only to be >> immediately thrown away at the end of the request. Browsers will >> disintegrate trying to render that many HTML elements simultaneously. >> >> Look at implementing a paging system, possibly through Django's built-in >> mechanism, or something like Datatables and the infinite scroll plugin. >> >> https://docs.djangoproject.com/en/dev/topics/pagination/ >> >> https://datatables.net/extensions/scroller/ >> >> -James >> > -- You received this message because you are subscribed to the Google Groups "Django users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/django-users. To view this discussion on the web visit https://groups.google.com/d/msgid/django-users/90218749-c5e1-4ca8-ab6c-3e191a79798f%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
