Question

I’ve got a web app that tracks page views. The app checks the IP address of the end-user and maps it to a Country. Everything works here so far.

However, I am realizing that this table (called views) is getting populated very fast, since it tracks every page view. On some pages reaching 500k, 1M, etc. Which could potentially store over millions of records on this table within a short time.

I thought about aggregating the results every 24 hours into a new table (called country_views) and then deleting the records in views.

So for example. My views table could look like:

page_id | country_id | created_at | updated_at
======= | ========== |=========== | ==========
1       | 1          | 2011-11... | 2011-11...
1       | 2          | 2011-11... | 2011-11...
1       | 1          | 2011-11... | 2011-11...
1       | 1          | 2011-11... | 2011-11...
2       | 4          | 2011-11... | 2011-11...
2       | 3          | 2011-11... | 2011-11...

After 24 hours. I count the results from this table and put them into a new table called country_views. Which could look like:

page_id | country_id | total | created_at | updated_at
======= | ========== | ===== | ========== | ==========
1       | 1          | 3     | 2011-11... | 2011-11...
1       | 2          | 1     | 2011-11... | 2011-11...
2       | 4          | 1     | 2011-11... | 2011-11...
2       | 3          | 1     | 2011-11... | 2011-11...

On the same web app. I plan on showing this data to certain users as a pie chart to display which countries are viewing certain pages.

Is this a sensible approach? Is there a better way to go about this?

Answer 1

Why don’t you just keep a count in views?

You only want to count which page was viewed by which country so just add a count column and increment it if the record already exists otherwise add a new row 🙂