guillem/blog
1
0
Fork 0
Code Issues Pull requests Packages Projects Releases Wiki Activity

Update 'How fast are really the analytical DBMS?'

Guillem Borrell Nogueras 2023-02-19 09:55:42 +01:00
parent 8ef9c5a3ad
commit 92b720ed90
1 changed files with 3 additions and 1 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
How-fast-are-really-the-analytical-DBMS%3F.md

@ -55,3 +55,5 @@ And finally clickhouse
Additionally, clickhouse used rougly 3.5 GB of memory to execute the query, while duckdb ran with a limitation of 4GB RAM memory. The limitation was followed, since the container used for these tests would have crashed if it weren't.
Clickhouse was 88 faster than PostgreSQL.
This two-order-of-magnitude-faster operation is an obvious enabler. This means that someone can attach gigabyte-sized datasets to dashboards without the need of pre-aggregating data. I've needed to create hundreds of ETL processes during my career for the sole purpose of feeding some dashboard that otherwise would have taken hours to plot the results. You can still do that with cloud-hosted distributed databases like Snowflake or Redshift but at a relevant cost.