This is really cool! I wonder what was the initial drive for this new feature?
Is this meant to be a "short-cut" to express complicated SQL queries, or is this meant to adding new semantics beyond SQL? While I like the idea of custom data types with dataflow-like syntax, implementing a whole new query processing engine for the new data type seems like a lot of engineering work. Also you now have to handle many edge cases such as very very large time series -- I wonder if you have efficient lookup mechanisms on timevectors yet, and various timestamp and value types. If all these new syntax can actually be expressed using SQL, however complex, I think a "lazier" approach is to write a "translator" that rewrites the new syntax into good old SQLs or add a translator at planning stage. This way you can take advantage of Postgres' optimizer and let it do the rest of heavy lifting.
This is really cool! I wonder what was the initial drive for this new feature?
Is this meant to be a "short-cut" to express complicated SQL queries, or is this meant to adding new semantics beyond SQL? While I like the idea of custom data types with dataflow-like syntax, implementing a whole new query processing engine for the new data type seems like a lot of engineering work. Also you now have to handle many edge cases such as very very large time series -- I wonder if you have efficient lookup mechanisms on timevectors yet, and various timestamp and value types. If all these new syntax can actually be expressed using SQL, however complex, I think a "lazier" approach is to write a "translator" that rewrites the new syntax into good old SQLs or add a translator at planning stage. This way you can take advantage of Postgres' optimizer and let it do the rest of heavy lifting.
Kusto has a similar data type "series" that is also created from aggregating over some columns (https://docs.microsoft.com/en-us/azure/data-explorer/kusto/q...).