A few months ago, I decided it was time to add analytics to Bizmo.site. It sounded straightforward—how hard could it be to count clicks and track user actions, right? Armed with confidence and Postgres. Spoiler: I took a wrong turn.
The Plan
My brilliant (or so I thought) plan was to use Postgres to process incoming analytics events. Every time someone clicked a button, submitted a form, or just sneezed on their screen, the event would be recorded, session time tracked, batched, and processed. Sounds efficient, doesn’t it ?
The Reality Check
At first, everything worked fine. I had some event processing going on, and it looked cool on a dashboard I whipped up. But as I started running stress tests, cracks began to show. Queries were taking forever, especially when analyzing large datasets. Every click started feeling like a weight dragging down the system.
Turns out, Postgres is great for many things, but crunching analytics data in real time isn’t one of them. My system was buckling under the load, and I realized I was trying to make Postgres something it wasn’t meant to be. I reached out to a few people who had dealt with similar issues, and they confirmed my suspicions—it was time to rethink my approach.
How is your saturday going? For me : - Solved some client DB issues :) - Connected with 2 amazing folks in @dominateXclub - Spend 7 hours choosing which chair to buy? @AdhikanshMittal one of the most amazing person i have ever met, and @sreeragh_s person who is building cool…
The Pivot
Cue the moment I learned about ClickHouse. If you’ve never heard of it, ClickHouse is a columnar database designed specifically for analytics. Think of it as the speedster of databases when it comes to querying large volumes of data.
Switching to ClickHouse meant restructuring how I was handling events. Instead of processing analytics logs in Postgres, I began dumping logs directly into ClickHouse. Almost immediately, things improved. Queries that took minutes in Postgres were done in seconds. I could finally breathe.
Lessons Learned
Here’s what I took away from this experience:
- Know your tools: Postgres is a beast, but not for everything. Use tools built for the job.
- Plan for scale: What works for 100 events per second might not work for 10,000. Test early, test often.
- Be ready to pivot: Sometimes, you just have to admit you took the wrong approach and switch gears.
Wrapping Up
Done with Analytics - âś…session duration âś…Unique visitors âś…Page Views âś…Sources âś…Demographics - country, region âś…Browser, OS, Device âś…custom Events Next Up - - Feature Flags / AB Testing - Funnels - Heatmap - Survey and feedback widgets
Building an analytics system taught me a lot—not just about databases but also about approaching problem by doing a thorough research and talking to experts. If you're planning to build something similar, save yourself some headaches and start with tools like ClickHouse. Or, if you're like me, embrace the chaos and learn as you go.