Question 1

How does ddR compare to using the parallel package directly in R?

Accepted Answer

ddR abstracts multiple backends including parallel, allowing code to run on different engines without changes, but it adds overhead for simple tasks. The parallel package is better for straightforward multi-core processing on a single machine without distributed data structures.

Question 2

How to install and set up ddR with the Distributed R backend?

Accepted Answer

First install the ddR package, then install distributedR.ddR and load it with library(distributedR.ddR). Use useBackend(distributedR) to switch, but note it requires additional dependencies like Rcpp and XML, which can complicate setup.

Question 3

What distributed data structures does ddR support?

Accepted Answer

ddR supports distributed lists (dlist), data frames (dframe), and arrays (darray), which are automatically partitioned across nodes. These can be manipulated using dmapply and other R-style apply functions, as shown in the README examples.

Question 4

Can I use ddR with Spark for processing large datasets?

Accepted Answer

SparkR support is planned but not currently implemented in ddR, so it cannot natively run on Spark backends. For Spark-based workflows, you might need to use alternative packages or wait for future updates.

Question 5

How to debug errors in distributed applications with ddR?

Accepted Answer

Debugging can be challenging due to the distributed nature; the package provides limited tools, so it's recommended to use parts() for partition-wise inspection and rely on backend-specific logging or debugging methods from engines like Distributed R.

Question 6

Is ddR compatible with tidyverse or other popular R packages?

Accepted Answer

ddR uses its own distributed data structures, so direct integration with tidyverse functions may require data conversion to standard R objects, which can reduce performance and convenience for data manipulation workflows.

ddR

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