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Hey all - creating an API workflow for auto-generating dashboards. Context: ML model monitoring for our enterprise. Problem: Currently stuck on generating a query_context for a POST api/v1/chart/ request, based off of a saved_query.
Is there a defined way of generating this query_context? Or example repos containing a programatic workflow that I could refer to instead?
__init__
() got an unexpected keyword argument 'user'."
Hi there, we are looking into working ob the current Alert/Reports area to
1) Use the Alert/Report Owner to generate the Data within the Celery Worker Code as we are using Row Level Security for Multi Tentant Type Integration
2) Add a S3 Data Drop as an addition to the already available Email and Slack
3) Use the Chart Data API to use a configured Chart Context instead of the pre-set Chart Context
We want to do this based on 1.5.0 Code at this time.
I have set a macro in my superset.py like this :
def get_graindate(col):
return "(case "+'{{ time_grain }}'+" when 'P1Y' then year("+col+") when 'P1M' then month("+col+") when 'P1D' then day("+col+") end)"
JINJA_CONTEXT_ADDONS = {
'graindate': lambda col: get_graindate(col),
}
but '{{ time_grain }}' is alway undefined.
if i do select '{{ time_grain }}' it give me 'P1Y',
is it possible to pass '{{ time_grain }}' in parameter like this
graindate('date_time', '{{ time_grain }}')` <= (i'v tried this but no luck) ?
Thanks for your help, it make me crazy 😝