If you hang around devs long enough, at some point you'll hear the great debate:
"Should I just use Postgres… or do I need something like ClickHouse?"
It usually pops up when someone's dashboard starts taking 10 seconds to load, or when your "simple analytics query" somehow decides to do a full table scan and torch your CPU.
Why This Even Matters
Most apps start with a row-based database like MySQL or Postgres. It works beautifully at first. But as the product grows and you start doing:
- funnel analysis
- event analytics
- time-series dashboards
- heavy aggregates
…that same database that handles your user logins and orders suddenly becomes the bottleneck.
You end up asking yourself:
"Why is this query slow? It's just COUNT(*)."
That question is exactly where the row-vs-column conversation begins.
Row Databases: Great for Apps, Bad for Big Analytics
Row databases store data row by row, imagine documents stacked horizontally.
So if you fetch a user, you get the whole thing in one shot. Perfect for apps.
This makes row stores great at:
- fast inserts/updates
- transactional workloads
- reading full records
- making sure your data stays consistent
Which is why Postgres/MySQL are still kings for actual application logic.
The catch?
When you run analytics, you typically only need a few columns… but the database has to scan full rows anyway. You end up reading a lot of data you don't need.
That's why reporting queries on Postgres often feel like pulling a truck with a scooter.
Column Databases: Built for Scanning a Lot, Quickly
Columnar databases flip the pattern: they store data column by column.
That sounds like a tiny detail, but it changes everything.
Because when you run analytics, you usually want:
- a count
- a sum
- a min/max
- a trend over time
And you want it across millions or billions of rows.
Column stores read only the columns you ask for, which means they can scan massive datasets in milliseconds. They also compress data like crazy because values in one column are usually similar.
ClickHouse takes this to another level. It's basically:
"What if a database had the speed of Redis, the scale of BigQuery, and the cost of two coffees a month?"
It's insanely fast for:
- dashboards
- log/event analytics
- time-series metrics
- anything where you scan a lot but update very little
So When Should You Use Postgres/MySQL?
If your workload is mostly:
- users, products, orders
- CRUD-heavy logic
- transactions
- weird edge cases
- relational integrity
- many small reads/writes
…stick with a row store. It will be faster, simpler, and much easier to maintain.
Examples:
auth tables, e-commerce carts, booking systems, money stuff, internal tools.
If it's an app, Postgres is still your hero.
When Should You Use ClickHouse?
If your workload is:
- "show me the trend over the last 30 days"
- "aggregate 200 million events by country"
- "give me a real-time dashboard"
- "I need sub-second analytics even as data explodes"
…then ClickHouse is the right tool.
ClickHouse really shines when you're doing OLAP (analytical) stuff, scanning huge tables, grouping, sorting, aggregating.
It's not meant for constant row updates. But for append-only datasets? Nothing beats it.
Quick Rule of Thumb
If you're confused, this simple rule just works:
Postgres = app database (OLTP)
ClickHouse = analytics database (OLAP)
Most real companies end up using both:
- Postgres for core application
- A pipeline (Kafka, Debezium, whatever)
- ClickHouse for analytics/dashboards
This keeps your app fast and keeps your analytics from killing the primary DB.
The "Should I Switch?" Checklist
If you answer yes to most of these, you're ready for something like ClickHouse:
- Are your analytics queries slow?
- Are dashboards causing load spikes?
- Are you storing events/logs at scale?
- Do you need real-time or near real-time insights?
- Does your Postgres look tired and stressed?
If none of these are true… stick to Postgres. Simpler is better until it's not.
Final Thoughts
Row databases and columnar databases aren't rivals, they're just built for different jobs.
- Postgres/MySQL: your app's backbone
- ClickHouse: your analytics superpower
Pick the one that matches the problem you're solving. Or pick both, that's what most modern stacks do anyway.