Ram, These are all pieces to a puzzle. Indexing, ORM optimization and through tables all add up to an efficient page render. Additionally, you could consider caching some of your results, using a key like ‘postal code’ - some sort of constant that you could use as a metric/pivot. Use a cron job to populate your through tables and cache the results for a faster return.
Benny On Jan 16, 2021, at 5:47 PM, Ram <ram.mullap...@gmail.com> wrote: Hi, We have a similar type of challenge in our project. We will definitely try indexing. Basically we are currently doing like this. 1. Registered users can post an ad in Classifieds by uploading 4 - 6 images per ad. All the ads are placed in one postgres db table. (classifieds table) 2. Same user opens the Classifieds page where we list all the Ads posted by this user as well as other users. This page loading is taking a lot of time. 3. Each ad will display a thumbnail image, title of the ad, 2 line description, price value, distance value from nearby zip code, age of the ad, zip code 4. Since we are calculating the distance between zip codes each Ad using a third party API, I think page loading is taking a lot of time. 4.1. I've dumb idea here where I can add a. new table, 'nearby_ads table'. As soon as the ad was submitted successfully, process the ads distance and add all the nearby ads into this table. When the classifieds page is launched, only nearby ads will be displayed. First of all I'm not sure whether this is a good idea because we are going to have two tables, one is classifieds table and the other one is a nearby_ads table. Second of all I'm not sure what is the limit that I can set limit on how far I could calculate nearby ads ( like 10 miles or 30 miles or 50 miles radius). 5. Our goal is to present all the nearby ads based on the logged in user zip code and if a visitor opens this Classifieds page, we have to capture nearby location of the visitor and present the nearby ads. 6. Additionally registered users and visitors should be able to filter the ads based on different zip code though both are from different location Please let me know if you recommend any advanced packages or libraries in Django or Python to achieve the above. Or indexing is the only option to reduce the page loading time? I appreciate it if you can share any recommendations and pointers. Best Regards ~Ram On Fri, Jan 15, 2021 at 12:51 AM Benjamin Schollnick <bscholln...@schollnick.net<mailto:bscholln...@schollnick.net>> wrote: Also don’t forget to include Indexing. As a general rule, any field that you perform any query against, should be indexed. Yes, it costs a “bit” for disk space, but indexing can dramatically increase your query speed. - Benjamin On Jan 14, 2021, at 10:44 PM, Benny M <chibe...@outlook.com<mailto:chibe...@outlook.com>> wrote: Slow loading can be sometimes be caused by non-optimized or overly complex queries. Look into prefetch_related and select_related for starters. https://docs.djangoproject.com/en/3.1/ref/models/querysets/ Django Debug Toolbar is also a great packages for examining the interaction between Django’s ORM and your database. Best, Benny On Jan 14, 2021, at 9:18 PM, Salima Begum <salim...@rohteksolutions.com<mailto:salim...@rohteksolutions.com>> wrote: Hi all, We are building a Web application, We have a classifieds page that is loading too slow. How to fix this issue. how to reduce loading time of classifieds page. Thanks ~Salima -- 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 django-users+unsubscr...@googlegroups.com<mailto:django-users+unsubscr...@googlegroups.com>. To view this discussion on the web visit https://groups.google.com/d/msgid/django-users/CAMSz6bnvP51oD_dfhY0YEWiGHGq3DbZ04u%2Bz6Vg_9sBPGvjgOw%40mail.gmail.com<https://groups.google.com/d/msgid/django-users/CAMSz6bnvP51oD_dfhY0YEWiGHGq3DbZ04u%2Bz6Vg_9sBPGvjgOw%40mail.gmail.com?utm_medium=email&utm_source=footer>. -- You received this message because you are subscribed to the Google Groups "Django users" group. 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