Author: apalumbo
Date: Fri Apr 3 22:41:49 2015
New Revision: 1671197
URL: http://svn.apache.org/r1671197
Log:
Add H2o and Spark engine docs
Added:
mahout/site/mahout_cms/trunk/content/users/environment/h2o-internals.mdtext
mahout/site/mahout_cms/trunk/content/users/environment/spark-internals.mdtext
Added:
mahout/site/mahout_cms/trunk/content/users/environment/h2o-internals.mdtext
URL:
http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/environment/h2o-internals.mdtext?rev=1671197&view=auto
==============================================================================
--- mahout/site/mahout_cms/trunk/content/users/environment/h2o-internals.mdtext
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+# Introduction
+
+This document provides an overview of how the Mahout Scala DSL (distributed
algebraic operators) is implemented over the H2O backend engine. The document
is aimed at Mahout developers, to give a high level description of the design
so that one can explore the code inside `h2o/` with some context.
+
+## [H2O](http://h2o.ai/) Overview
+
+H2O is a distributed scalable machine learning system. Internal architecture
of H2O has a distributed math engine (h2o-core) and a separate layer on top for
algorithms and UI. The Mahout integration requires only the math engine
(h2o-core).
+
+## H2O Data Model
+
+The data model of the H2O math engine is a distributed columnar store (of
primarily numbers, but also strings). A column of numbers is called a Vector,
which is broken into Chunks (of a few thousand elements). Chunks are
distributed across the cluster based on a deterministic hash. Therefore, any
member of the cluster knows where a particular Chunk of a Vector is homed. Each
Chunk is separately compressed in memory and elements are individually
decompressed on the fly upon access with purely register operations (thereby
achieving high memory throughput). An ordered set of similarly partitioned Vecs
are composed into a Frame. A Frame is therefore a large two dimensional table
of numbers. All elements of a logical row in the Frame are guaranteed to be
homed in the same server of the cluster. Generally speaking, H2O works well on
"tall skinny" data, i.e, lots of rows (100s of millions) and modest number of
columns (10s of thousands).
+
+
+## Mahout DRM
+
+The Mahout DRM, or Distributed Row Matrix, is an abstraction for storing a
large matrix of numbers in-memory in a cluster by distributing logical rows
among servers. The DSL provides an abstract API on DRMs for backend engines to
provide implementations of this API. Examples are the Spark and H2O backend
engines. Each engine has it's own design of mapping the abstract API onto its
data model and provides implementations for algebraic operators over that
mapping.
+
+
+## H2O DSL Engine
+
+The H2O backend implements the abstract DRM as an H2O Frame. Each logical
column in the DRM is an H2O Vector. All elements of a logical DRM row are
guaranteed to be homed on the same server. A set of rows stored on a server are
presented as a read-only virtual in-core Matrix (i.e BlockMatrix) for the
closure method in the `mapBlock(...)` API.
+
+H2O provides a flexible execution framework called `MRTask`. The `MRTask`
framework typically executes over a Frame (or even a Vector), supports various
types of map() methods, can optionally modify the Frame or Vector (though this
never happens in the Mahout integration), and optionally create a new Vector or
set of Vectors (to combine them into a new Frame, and consequently a new DRM).
+
+
+## Source Layout
+
+Within mahout.git, the top level directory, `h2o/` holds all the source code
related to the H2O backend engine. Part of the code (that interfaces with the
rest of the Mahout componenets) is in Scala, and part of the code (that
interfaces with h2o-core and implements algebraic operators) is in Java. Here
is a brief overview of what functionality can be found where within `h2o/`.
+
+ h2o/ - top level directory containing all H2O related code
+
+ h2o/src/main/java/org/apache/mahout/h2obindings/ops/*.java - Physical
operator code for the various DSL algebra
+
+ h2o/src/main/java/org/apache/mahout/h2obindings/drm/*.java - DRM backing
(onto Frame) and Broadcast implementation
+
+ h2o/src/main/java/org/apache/mahout/h2obindings/H2OHdfs.java - Read / Write
between DRM (Frame) and files on HDFS
+
+ h2o/src/main/java/org/apache/mahout/h2obindings/H2OBlockMatrix.java - A
vertical block matrix of DRM presented as a virtual copy-on-write in-core
Matrix. Used in mapBlock() API
+
+ h2o/src/main/java/org/apache/mahout/h2obindings/H2OHelper.java - A
collection of various functionality and helpers. For e.g, convert between
in-core Matrix and DRM, various summary statistics on DRM/Frame.
+
+ h2o/src/main/scala/org/apache/mahout/h2obindings/H2OEngine.scala - DSL
operator graph evaluator and various abstract API implementations for a
distributed engine
+
+ h2o/src/main/scala/org/apache/mahout/h2obindings/* - Various abstract API
implementations ("glue work")
\ No newline at end of file
Added:
mahout/site/mahout_cms/trunk/content/users/environment/spark-internals.mdtext
URL:
http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/environment/spark-internals.mdtext?rev=1671197&view=auto
==============================================================================
---
mahout/site/mahout_cms/trunk/content/users/environment/spark-internals.mdtext
(added)
+++
mahout/site/mahout_cms/trunk/content/users/environment/spark-internals.mdtext
Fri Apr 3 22:41:49 2015
@@ -0,0 +1,18 @@
+# Introduction
+
+This document provides an overview of how the Mahout Scala DSL (distributed
algebraic operators) is implemented over the Spark back end engine. The
document is aimed at Mahout developers, to give a high level description of the
design.
+
+## Spark Overview
+
+## Spark Data Model
+
+
+## Mahout DRM
+
+Mahout DRM, or Distributed Row Matrix, is an abstraction for storing a large
matrix of numbers in-memory in a cluster by distributing logical rows among
servers. The DSL provides an abstract API on DRMs for backend engines to
provide implementations of this API. Examples are Spark and H2O backend
engines. Each engine has its own design of mapping the abstract API onto its
data model and provide implementations for algebraic operators over that
mapping.
+
+
+## Spark DSL Engine
+
+
+## Source Layout
