I’ve been using ClickHouse too and it’s significantly faster than Postgres for certain analytical workloads. I benchmarked it and while Postgres took 47 seconds, ClickHouse finished within 700ms when performing a query on the OpenFoodFacts dataset (~9GB). Interestingly enough TimescaleDB (Postgres extension) took 6 seconds.
Updates and deletes don’t work as well and not being able to perform an upsert can be quite annoying. However, I found the ReplacingMergeTree and AggregatingMergeTree table engines to be good replacements so far.
I’ve been using ClickHouse too and it’s significantly faster than Postgres for certain analytical workloads. I benchmarked it and while Postgres took 47 seconds, ClickHouse finished within 700ms when performing a query on the OpenFoodFacts dataset (~9GB). Interestingly enough TimescaleDB (Postgres extension) took 6 seconds.
All actions were performed through Datagrip
1 Insertion speed is influenced by reduced networking overhead due to the databases being in-process.
Updates and deletes don’t work as well and not being able to perform an upsert can be quite annoying. However, I found the ReplacingMergeTree and AggregatingMergeTree table engines to be good replacements so far.
Also there’s !clickhouse@programming.dev
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