Overview of RevoScaleR

R is a very popular programming language whose rich set of features and packages make it ideal for data analysis and modeling. Traditionally, R works by loading every object (including datasets) as a memory object. This means that large data sets can quickly surpass the amount of available space in the memory. This is especially problematic when many users are working on the same R server, where free memory can quickly turn into a scarce resource. Over time, many R packages have surfaced attempting to address this limitation. Some propose a way to more efficiently load and process the data, which would in turn make it possible to work with larger data sizes. However, this approach can only take us so far, since efficiency eventually hits a wall. Being efficient in R also requires more sophisticated knowledge about programming that many R users lack.

Microsoft R Server (MSR) on the other hand takes a different approach. MRS's RevoScaleR package stores the dataset on the disk (hard drive) and loads it only a chunks at a time (where each chunk is a certain number of rows) for processing. Once the chunk is processed, it then loads the next chunk of the data. By default, the chunk size is set to 500K rows, but we can change it to a lower number when dealing with wider datasets (lots of columns), and a larger number when dealing with longer data sets (few columns).

Data in RevoScaleR is external (because it's stored on disk) and inherently distributed (because we process it chunk-wise). This means we are no longer bound by memory when dealing with data: Our data can be as large as we have space on the hard-disk to store it. Since at every point in time we only load one chunk of the data as a memory object (an R list object to be specific), we never overexert the system's memory. All this is of course happening behind the scene with minimal input form the user.

But there is no such thing as a free lunch, so there is also a cost to pay when working with distributed data: Since most open-source R algorithms for data processing and analysis (including most third-party packages) rely on the whole dataset to be loaded into the R session as a data.frame object, they no longer work directly with distributed data. But as we will see,

  • Most data-processing steps (cleaning data, creating new columns or modifying existing ones) can still indirectly (and relatively easily) be used by RevoScaleR to process the distributed data, so that we can still leverage a great deal of our R code. What we mean by indirectly will become clear as we cover a wide range of examples.
  • On the other hand, some data processing steps (such as merging data or sorting data) and most analysis and statistical algorithms (such as the lm function used to build linear models) have their RevoScaleR counterparts which mirror the way they work, but work on a distributed data set in addition to a data.frame. For example, RevoScaleR has an rxLinMod function which replicates what lm does, but because rxLinMod is a distributed algorithm it runs both on a data.frame (where it far outperforms lm if the data.frame in question is large), and on a distributed dataset.

Using RevoScaleR we can both leverage existing R functionality (such as what's offered by R's rich set of third-party packages) and use what RevoScaleR offers through its own set of distributed functions. One last advantage that RevoScaleR's distributed functions offer is code portability:

  • Because open-source R's analytics functions are generally not parallel, using these algorithms in an inherently distributed environment can be a challenge. For example, deploying our code to Hadoop means having to rewrite our R code as mappers and reducers that Hadoop understands, which can be a daunting task. The inherently parallel data processing and analysis functions in RevoScaleR on the other hand make them ideal for porting our code from MRS running on a single machine to MRS on a Hadoop cluster or other inherently distributed environments.


Let's review what the RevoScaleR package offers:

  1. When our data is large, but still small enough to fit in the memory as a data.frame, we can still use RevoScaleR's parallel algorithms to run models on the data much faster than their open-source counterparts (such as using rxLinMod instead of lm).
  2. When the data is too large to fit in available memory, we can work dircetly with data on disk (such as flat files) or convert the data to an external and distributed format called XDF. RevoScaleR's data-processing and analysis functions work with such data in addition to a data.frame.
  3. When the data is saved in a distributed environment such as HDFS or SQL Server, which is often the case in production, with some minor adjustments we can deploy our code in such environments, reducing the hurdle of going from development to production.

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