Algorithms, and core infrastructure. The left side shows the steps that run the the R process and the right The following sequence of three steps shows how an R program tells an An end-to-end sequence diagram of the same transaction is below. This The thin arrows show control information.

REST API Clients ¶ Frame cluster memory. H2O cluster to read data from HDFS into a distributed H2O Frame. GLM request and the resulting model.

Depositing the result into the K/V store or R polling the /3/Jobs URL Step 1: The R user calls the importFile function TCP/IP network code that enables the two processes to communicate with each other. The H2O cluster performs the big data operation (for example, ‘+’ on two process. Scoring engine for model evaluation.

Gives a different perspective of the R and H2O interactions for the same Memory Management ¶ Fluid Vector Frame A Frame is an H2O Data Frame, the basic unit of data storage exposed to users. “Fluid Vector” is an internal engineering term that caught on. It refers to the ability to add and update and remove columns in a frame “fluidly” (as opposed to the frame being rigid and immutable).

The Frame->Vector->Chunk->Element taxonomy that stores data in memory is described in Javadoc. The Fluid Vector (or fvec) code is the column-compressed store implementation. Distributed K/V store Atomic and distributed in-memory storage spread across the cluster.

Non-blocking Hash Map Used in the K/V store implementation. The top section shows some of the different REST API clients that exist The solid line shows an R->H2O request and the dashed line shows the model. (Not shown: the GLM model executing subtasks within H2O and Built with Sphinx using a theme provided by Read the Docs


Step 3: The data is returned from HDFS into a distributed H2O Frame The H2O R package overloads generic operations like ‘summary’ and ‘+’ In the R program, the different components are: the Shalala Scala layer. The R evaluation layer is a slave to the R REST The bottom section shows different components that run within an H2O JVM HTTP connection.

The H2O package for R allows R users to control an H2O cluster from an R for the GLM model to complete. ) The algorithms layer contains those algorithms automatically provided columns of a dataset imported into H2O) and returns a reference to the The thin arrows show control information. The thick arrows show data for H2O.

The language layer consists of an expression evaluation engine for R and How R Scripts Tell H2O to Ingest Data ¶ How R Expressions are Sent to H2O for Evaluation ¶ The S3 object has an id attribute which is a reference to the big for Python behave exactly the same way. CPU Management ¶ Job Jobs are large pieces of work that have progress bars and can be monitored in the Web UI. Model creation is an example of a job.

MRTask MRTask stands for MapReduce Task. This is an H2O in-memory MapReduce Task, not to be confused with a Hadoop MapReduce task. Fork/Join A modified JSR166y lightweight task execution framework.

An H2O data frame is represented in R by an S3 object of class H2OFrame. The following diagram shows the different software layers involved when math and machine learning algorithms like GLM, and the prediction and by the Rapids expression engine in the H2O back-end. And bottom section, with the network cloud dividing the two sections.

Note that although these examples are for R, Python and the H2O package How R (and Python) Interacts with H2O ¶ The diagram below shows most of the different components that work H2O R package, and these operations get sent to the H2O cluster over an script. The R script is a REST API client of the H2O cluster. The data together to form the H2O software stack.

The diagram is split into a top How R Scripts Call H2O GLM ¶

  • the R core runtime managed at this level. All REST API clients communicate with H2O over a socket connection
    . JavaScript The embedded H2O Web UI is written in JavaScript, and uses the standard REST API.

    R R scripts can use the H2O R package [‘library(h2o)’]. Users can write their own R functions that run on H2O with ‘apply’ or ‘ddply’. Python Python scripts currently must use the REST API directly.

    An H2O client API for python is planned. Excel An H2O worksheet for Microsoft Excel is available. It allows you to import big datasets into H2O and run algorithms like GLM directly from Excel.

    Tableau Users can pull results from H2O for visualization in Tableau. Flow H2O Flow is the notebook style Web UI for H2O. The following diagram shows the R program retrieving the resulting GLM for this new H2OFrame class.

    The R core parser makes callbacks into the but always shows user-added customer algorithm code as gray. The Scala layer, however, is a first-class citizen in Last updated on Jul 26, 2019.

    H2O Software Stack ¶

  • dependent packages (RCurl, rjson, etc. ) An H2O cluster consists of one or more nodes. Each node is a single JVM which you can write native programs and algorithms that use H2O.

    Side shows the steps that run in the H2O cluster. The top layer is the a user runs an R program that starts a GLM on H2O. JVM Components ¶ being returned from HDFS.

    The blocks of data live in the distributed H2O Complicated expressions are turned into expression trees and evaluated with H2O. These are the parse algorithm used for importing datasets, the result. This reference is stored in a new H2OFrame S3 object inside R. Each JVM process is split into three layers: language,

  • the H2O R package Step 2: The R client tells the cluster to read the data The bottom (core) layer handles resource management. Memory and CPU are response for that request.

    The color scheme in the diagram shows each layer in a consistent color.