If you can sacrifice some precision, the HyperLogLog (HLL) probabilistic data structure is a great solution for counting unique visits because:
- It only uses 12K of memory, and those are fixed - they don't grow with the number of unique visits
- You don't need to store user data, which makes your service more privacy-oriented
The HyperLogLog algorithm is really smart, but you don't need to understand its inner workings in order to use it, some years ago Redis added it as a data structure. So all you, as a user, need to know is that with HyperLogLogs you can count unique elements (visits) in a fixed memory space of 12K, with a 0.81% margin of error.
Let's say you want to keep a count of unique visits per day; you would have to have one HyperLogLog per day, named something like cnt:page-name:20200917 and every time a user visits a page you would add them to the HLL:
> PFADD cnt:page-name:20200917 {userID}
If you add the same user multiple time, they will still only be counted once.
To get the count you run:
> PFCOUNT cnt:page-name:20200917
You can change the granularity of unique users by having different HLLs for different time intervals, for example cnt:page-name:202009 for the month of September, 2020.
This quick explainer lays it out pretty well: https://www.youtube.com/watch?v=UAL2dxl1fsE
This blog post might help too: https://redislabs.com/redis-best-practices/counting/hyperloglog/
And if you're curious about the internal implementation Antirez's release post is a great read: http://antirez.com/news/75
NOTE: Please note that with this solution you lose the information of which user visited that page, you only have the count