useBackend: Sets the active backend driver. Functions exported by the 'ddR' package are dispatched to the backend driver. Backend-specific initialization parameters may be passed into the ellipsis (...) part of the function arguments.

Description

The default driver uses R's 'parallel' as the backend.

Usage

useBackend(driver, ...)

Arguments

driver

driver object for the backend that will be used. This object should extend class 'ddRDriver', and the S4 methods for do_dmapply, do_collect, and get_parts should be defined in the class of the driver object.

...

additional parameters to pass to the initialization function of the driver.

Details

After successfully registering a new backend with useBackend(), all subsequent dmapply, collect, and parts operations will dispatch on that driver object's class. Note that distributed objects created with a different backend prior to switching will be incompatible with these backend-specific functions of the new driver.

References

Prasad, S., Fard, A., Gupta, V., Martinez, J., LeFevre, J., Xu, V., Hsu, M., Roy, I. Large scale predictive analytics in Vertica: Fast data transfer, distributed model creation and in-database prediction. _Sigmod 2015_, 1657-1668.

Venkataraman, S., Bodzsar, E., Roy, I., AuYoung, A., and Schreiber, R. (2013) Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices. _EuroSys 2013_, 197-210.

Homepage: https://github.com/vertica/ddR

Examples

Run this code

useBackend(parallel,executors=2)
library(distributedR.ddR); useBackend(distributedR)