all with *python
3 replies
The PointCut object is not well documented. According the documentation:
In Python the cuts for “sales in Slovakia between June 2010 and June 2012” are defined as:
cuts = [
PointCut("geography", ["sk"]),
PointCut("date", [2010, 6], [2012, 6])
]But:
def __init__(self, dimension, path, hierarchy=None, invert=False,
hidden=False):The signature doesn't accept multiple paths, and sees [2012, 6] as a hierarchy, causing an exception.
So, how to slice on a range ?
for the yesterday' question i managed to reorganize the data on the ETL side, splitting wins and losses on different measures, instead of keeping the "is win ? profit_net_norm" approach. haven't managed the slicing yet, instead
I don't know what the API expects to receive.
Are you sure it's a PointCut?
a-ah
thank you!
Yes, some constraints are caused by the very fact of being a snowflake/star schema, but nowadays analytic systems come with more horsepower.
I'm personally not maintaining CubesViewer. I still use CubesViewer/CubETL with Cubes sometimes but...
(by the way, a little bit of off-topic spam... I have just published http://www.RopeCow.com)
I'd go with Elastic for this kind of service too, I guess.
In that regard, I use some other tool of mine (CubETL) to do ETL onto Elastic. But it's also unmaintained.
for the ETL part I'm on Redis+Asyncio+Supervisor, this way it scales well as long as you partition the queues
I don't know Supervisor... is it a ETL tool?
it's an event driven system. the "orchestrator" process check the db for the missing data, ask to the workers, they push it back into a queue managed by the orchestrator. while consuming the queue the process write data onto the "online db" and emit related events into another redis queues :).
other processes consume the queues, handle events and make projections into a star-schema modeled pgsql.
this is where I wanted to use cubes, to spawn other process and do real-time aggregations of the projected data (composite events).
other processes consume the queues, handle events and make projections into a star-schema modeled pgsql.
this is where I wanted to use cubes, to spawn other process and do real-time aggregations of the projected data (composite events).
no supervisor is a processes manager, it comes with a nice frontend.
but then you write your orchestration process yourself?
I hope you can find your ideal pipeline 8-)
lol the game is fun
:D Thanks :) I've been working on it for a few months but I'll get back to a more business-oriented job soon hopefully :)
Elastic is a "necessary evil". I personally hate it, its syntax is really complicated... but it does a good job.
I like more.. simple stuff.. if you haven't you may want to take a look at RediSearch. The downside is it's ram based, so if you have large datasets it hardly can be an option without spending a lot, but it's really really good.
I like more.. simple stuff.. if you haven't you may want to take a look at RediSearch. The downside is it's ram based, so if you have large datasets it hardly can be an option without spending a lot, but it's really really good.
also see here: https://help.tableau.com/current/pro/desktop/en-us/cubes.htm
based on this I would say "no, Tableau does not support
cubes"
