innerjoin between two large pandas dataframe using dask
You might try using smaller partitions.  Recall that the memory use of joins depend on how many shared rows there are.  Depending on your data the memory use of an output partition may be much larger than the memory use of your input partitions.
I have two large tables with one of them is relatively small ~8Million rows and one column. Other is large 173Million rows and one column. The index of the first data frame is IntervalIndex (eg (0,13], (13, 20], (20, 23], ...) and the second one is ordered numbers (1,2,3, ...). Both DataFrame are sorted so 
DF1     category
(0,13]  1
(13 20] 2
....
Df2 Value
1   5.2
2   3.4
3   7.8
Desired
Df3
index value  category
1  5.2      1
2  3.4      1
3 7.8       1
I want two obtain inner join (faster algorithm) that returns inner join similar to MySQL on data_frame2.index

I would like to be able to perform it in an elaborate way in a cluster because when I PRODUCED THE INNERJOIN WITH SMALLER SECOND DATASET THE RESULT ARE SO MEMORY CONSUMING IMAGINE 105MEGABYTE for 10 rows using map_partitions.
Another problem is that I cannot use scatter twice, given if first DaskDF=client.scatter(dataframe2) followed by DaskDF=client.submit(fun1,DaskDF) I am unable to do sth like client.submit(fun2,DaskDF).