kzk2000

+1 this would allow computation of delta time between rows and then allow for computing things like the time-weighted averages of price (TWAP), bid-ask spread, or mid-quotes on server side.

One extra note here for clarification: this operator should ideally behave like pandas’ shift operator, ie df[“column_name”].shift(i) where i < 0 gets you data from future rows i > 0...

+1 Any update here? Is there an ETA for as-of join support?

@runekaagaard thanks, so no ETA? Just to be crystal clear: We are referring to **as-of joins** and **not** AS OF for data versioning. Good example for AS OF JOINS is...

FWIW, if your data is already on Kafka, it's trivial to sync - **Kafka to Clickhouse**: https://clickhouse.com/docs/en/integrations/kafka#clickhouse-to-kafka - **Kafka from Clickhouse**: https://clickhouse.com/docs/en/integrations/kafka#clickhouse-to-kafka that said, syncing Arroyo stream to Clickhouse without...

Clickhouse has various integrations for data ingestions, Kafka as mentioned above is just one of them. I'm no expert but maybe any of these https://clickhouse.com/docs/en/integrations -> search for "Data ingestion"...