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From Apache Wiki <wikidi...@apache.org>
Subject [Hadoop Wiki] Update of "HadoopVsGridGain" by OwenOMalley
Date Sun, 20 Apr 2008 17:04:39 GMT
Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Hadoop Wiki" for change notification.

The following page has been changed by OwenOMalley:
http://wiki.apache.org/hadoop/HadoopVsGridGain

New page:
= Comparing GridGain to Hadoop =

This comparison based on my knowledge of [http://hadoop.apache.org/core/ Hadoop] 
and the !JavaDocs for [http://www.gridgain.com/ GridGain] 2.0.2. With 
respect to names, Hadoop jobs are broken down into many tasks, while 
!GridGain reverses that. For this discussion, I'll stick to the Hadoop 
meanings to avoid confusion.

The primary difference is that Hadoop is designed to work with large
data sets (100's of TB in a single job) and !GridGain is not. !GridGain's
jobs have a single reducer and it is given all of the values in a 
java.util.List. Therefore, the !GridGain's jobs are limited to what can
fit in a single jvm's heap.

In !GridGain's system each map and reduce returns a single value. To
support map/reduce like semantics, each value would need to be a
list. In Hadoop, each map and reduce may generate zero or more
key/value pairs that are serialized as they are generated.

!GridGain provides only distributed computation support and doesn't have
a distributed file system. Hadoop provides HDFS that provides reliable
scalable store to 1000's of nodes and PB of data.

!GridGain's framework does not sort the data between the maps and the
reduce. However, since it is passed as a java.util.List to the reduce,
it is pretty easy for the application to sort it as desired. Hadoop
provides an automatic distributed sort of the data between the maps
and reduces.

!GridGain does not support combiners, or counters. Combiners are an
optional pass that reduces the values out of each map to shrink the
amount of data that needs to be shuffled. Counters are user and
system defined events that are counted and are used to track the
progress of the job as it runs.

!GridGain uses java.io serialization, while Hadoop uses a much more
flexible serialization, which can use either java.io or user-defined
serialization.

Hadoop provides a web interface to track the job's progress and I
don't see any similar functionality in !GridGain.

!GridGain's model is more flexible in that the job can create arbitrary
tasks, each with different input and code to run. The application gets
a callback when each task finishes and can easily cancel the rest of
the job's tasks.

!GridGain doesn't have any support for non-Java applications, while
Hadoop supports both C++ and text-based applications.

!GridGain's map method returns a Map<Task, Node>, which allows it to do
task locality. However, the locality is not a suggestion, but an
order. To handle over-subscribed nodes, !GridGain supports application
collision strategies which can accept, queue, or reject tasks sent to
the node. Hadoop's !InputSplits define a list of nodes that it would
prefer to run on, but it will run on the first otherwise idle node.

And of course, Hadoop on 10,000 cpus is free and !GridGain on 6 cpus is 
$20,000/ year.

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