ClickHouse is replacing Elasticsearch in this context, and is providing the same data storage, but with better compression, and not holding data in memory?
Is that correct?
Don't have a good comparison at hands though, but a few random links on the topic:
https://engineeringat.axis.com/schema-changes-clickhouse/ https://clickhouse.com/blog/contentsquare-migration-from-ela... https://clickhouse.com/blog/100x-faster-graphql-hive-migrati... https://blog.zomato.com/building-a-cost-effective-logging-pl...
We successfully replaced Elasticsearch with ClickHouse within a Year. We went from having difficulties managing 3 months of data to storing 5 year+ of data. We also had difficulties onboarding users into Kibana and Elasticsearch world, this was not such a big problem with ClickHouse since most developers feel comfortable with SQL.
This change together with Apache Superset for the BI layer made a huge impact in the amount of internal users that could extract value from the data collected. Went from around 150 to 800 internal users.
We have not yet managed to bring everything into a wide event table but as long as the logs can be joined with the metrics under an interface that the developers feel comfortable with and the solution is cost-effective enough to allow all relevant context to be collected you will get far imo.
> This change together with Apache Superset for the BI layer made a huge impact in the amount of internal users that could extract value from the data collected. Went from around 150 to 800 internal users.
Really impressive results.
And there are quite a few solutions on top of it.
A couple of examples that seem to be interesting (however I didn't use them in real life):
https://coroot.com/ https://qryn.metrico.in/#/