Get your large SQL data in ff swiftly

The ff package is great when you are working with large data in R. Data in corporate environments are usually not that large that a Hadoop system is needed to handle it but the data are mostly large enough to make R choke on it's RAM. 

The ff package is great for this type of data. It can handle 2.14 billion elements per atomic (so 2.14 billion records). This is mostly more than enough in standard business settings nowadays.

For a while getting the data in R was quite cumbersome. The ff package now provides already nice interfaces to csv and flat files in the read.csv.ffdf and read.table.ffdf functions. To extend this towards standard SQL databases, the ETLUtils package has been put at CRAN. It contains a function called read.dbi.ffdf which can be used to extract easily data from any sql database through DBI directly into an ffdf in R.

Below, we show an example of importing a table stored at a MySQL database with 11.8 million records directly in an ffdf without having RAM issues.


R package ETLUtils @ CRAN - easy loading into ffdf

The R package ETLUtils is now available for download at it's CRAN repository.
It's a package which facilitates the ETL in situations where you need to interact with SQL databases in a corporate environment. Basically it currently focusses on the E(Extract) part of the ETL.

In the libary you'll find a function called read.dbi.ffdf which allows to extract easily large datasets from an SQL database through DBI straight into a ffdf (part of the ff package). This should ease the usage of the ff package for working with large datasets in corporate environments where you need to interact e.g. with MySQL, PostgreSQL, Oracle through the package RMySQL, RPostgreSQL or ROracle without having to worry about RAM issues when importing large datasets.