Dataflows : c=a*b versus c=join(a*b)

Etienne CAUSSE

Hi everyone,

I see in an application developed by consultants here, that some dataflows are using a c=join(a*b) formula, and I can't find the difference with "c=a*b" when I test on my dataflows.

In some cases, "JOIN" appears in the db log as the algorithm, and some other time it's HBMP.

Does anyone knows what is the intended use of join algorithm ? What's the impact on the result ? on performance ?

 

Thanks,
Etienne 

unknown

Hi Etienne,

 

the JOIN algorithm is a new kind of dataflow syntax that has not been inserted yet in the official documentation.
You should not be using it as a standard development method as not officially enabled yet - it is not active by default and certain options need to be enabled in order for it to work.. It will likely be available in future BOARD versions as a standard dataflow method.

Used in a standard a*b dataflow with heterogeneous cubes structures, it won't differ from a standard HBMP execution.

 

To give a bit more of context, the JOIN  dataflow main use case is when you need to expand a cube adding an entity to it, through a matrix.
The most common scenario is when having a sparsity of 2+ elements, and you need to expand the cube adding one entity to the existing sparsity.The JOIN dataflow is able to expand the cube in a more efficient way that the standard HBMP dataflow, and it properly creates the new sparsity in the process.

Example:
a) Source cube: Month (D), Entity 1 (S), Entity 2 (S), Entity 3 (D)
b) Matrix cube: Entity 2 (S), Entity 4 (S)
c) Target cube: Month (D), Entity 1 (S), Entity 2 (S), Entity 4 (S), Entity 3 (D)

 

c = JOIN(a*b)