Clickhouse has a unique performance gain when you have a system that isn’t operational data that is normalized and updated often. But rather tables of timeseries data being ingested for write only.
An example, stock prices or order books in real-time. Tens of thousands per second. Clickhouse can write, merge, aggregate records really nicely.
Then selects against ordered data with aggregates are lightning fast. It has lots of nuances to learn and has really powerful capability, but only for this type of use case.
It doesn’t have atomic transactions. Updates and deletes are very poor performing.
For high ingestion (really high) you have to start sharding. It’s nice to have a DB that can do that natively, MongoDB and Influx are very popular, depending on the exact application.
If you can, share your experience!
I also do finance, so if there is anything more to explore, I’m here to listen and learn.
Clickhouse has a unique performance gain when you have a system that isn’t operational data that is normalized and updated often. But rather tables of timeseries data being ingested for write only.
An example, stock prices or order books in real-time. Tens of thousands per second. Clickhouse can write, merge, aggregate records really nicely.
Then selects against ordered data with aggregates are lightning fast. It has lots of nuances to learn and has really powerful capability, but only for this type of use case.
It doesn’t have atomic transactions. Updates and deletes are very poor performing.
For high ingestion (really high) you have to start sharding. It’s nice to have a DB that can do that natively, MongoDB and Influx are very popular, depending on the exact application.