Hi James,

Thanks for the clarification. Much appreciated. Will follow your points for 
the reporting part considering the overheads in ORM. 

Thanks,

On Friday, March 10, 2017 at 4:55:35 PM UTC+5:30, James Bennett wrote:
>
> If all you need is to export data from your database (with or without 
> transforming it along the way) to a CSV, using the normal QuerySet methods 
> is probably the wrong approach; you don't need model objects to do that. 
> Some options include:
>
> * Use raw SQL to query for the data and push it to CSV (also, some 
> databases natively understand how to export query results to CSV)
> * Use the values() or values_list() methods to use lighter-weight basic 
> Python data structures (dictionaries and lists) instead of model objects
> * If you *must* instantiate model objects, use the iterator() method to 
> avoid keeping them around in-memory, and look at server-side cursors as an 
> option
> * If you're fetching related data, make sure you're eager-loading the 
> relations to avoid N+1 problems.
>
>
> On Fri, Mar 10, 2017 at 3:06 AM, Web Architect <pina...@gmail.com 
> <javascript:>> 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" <pina...@gmail.com> 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
>>>
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