How R Scripts Call H2O GLM

The following diagram shows the different software layers involved when a user runs an R program that starts a GLM on H2O.

The left side shows the steps that run the the R process and the right side shows the steps that run in the H2O cloud. The top layer is the TCP/IP network code that enables the two processes to communicate with each other.

The solid line shows an R->H2O request and the dashed line shows the response for that request.

In the R program, the different components are:

  • the R script itself
  • the H2O R package
  • dependent packages (RCurl, rjson)
  • the R core runtime
../_images/start_glm_from_r.png


The following diagram shows the R program retrieving the resulting GLM model. (Not shown: the GLM model executing subtasks within H2O and depositing the result into the K/V store or R polling the /Jobs.json URL for the GLM model to complete.)

../_images/retrieve_glm_result_from_r.png


An end-to-end sequence diagram of the same transaction is below (click on the diagram to zoom). This gives a different perspective of the R and H2O interactions for the same GLM request and the resulting model.

../_images/run_glm_from_r.png


How R Expressions are Sent to H2O for Evaluation

An H2O data frame is represented in R by an S4 object of class H2OParsedData. The S4 object has a @key slot which is a reference to the big data object inside H2O.

The H2O R package overloads generic operations like ‘summary’ and ‘+’ with this new H2OParsedData class. The R core parser makes callbacks into the H2O R package, and these operations get sent to the H2O cloud over an HTTP connection.

The H2O cloud performs the big data operation (for example, ‘+’ on two columns of a dataset imported into H2O) and returns a reference to the result. This reference is stored in a new H2OParsedData S4 object inside R.