Closed huangxingyi-git closed 1 month ago
This issue is going to solved more comprehensively with https://github.com/dbt-labs/dbt-core/discussions/10672; however, I'll take a look at your PR, and assuming it doesn't hurt any other use case, I'm not against taking this in the short term.
Describe the feature
For some specific cases (eg. backfill very large amount of data), we need to execute parallel multiple
dbt run
of specific incremental(replace_where
) model in which we pass the date (or country) as var argument. For example, we have a model we run every day using Airflow for which we pass the a date relative to the Airflow scheduler. FYI https://github.com/dbt-labs/dbt-athena/pull/650/filesIf we want to process by batch of N days in parallel using Airflow concurrency, we need the tmp table create by each of the dbt run to be unique. Else, you are going to end up with N insert attempting to run with the same __dbt_tmp name, creating conflict and ultimately creating failure.
Who will this benefit?
For those who uses
repalce_where
as incremental strategy. Example Use Case: Run the same incremental model concurrently with different--vars
in order to parallelly insert multiple data partitionsAre you interested in contributing this feature?
I am interested in contributing to this feature if needed.