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From Apache Wiki <wikidi...@apache.org>
Subject [Lucene-hadoop Wiki] Update of "FrontPage" by JimKellerman
Date Tue, 13 Feb 2007 21:38:03 GMT
Dear Wiki user,

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

The following page has been changed by JimKellerman:
http://wiki.apache.org/lucene-hadoop/FrontPage

The comment on the change is:
Front page reorganization. move links above the fold

------------------------------------------------------------------------------
- = Introduction =
- 
  [http://lucene.apache.org/hadoop/ Hadoop] is a framework for running applications on large
clusters built of commodity hardware. The Hadoop framework transparently provides applications
both reliability and data motion. Hadoop implements a computational paradigm named [:HadoopMapReduce:
Map/Reduce], where the application is divided into many small fragments of work, each of which
may be executed or reexecuted on any node in the cluster. In addition, it provides a distributed
file system that stores data on the compute nodes, providing very high aggregate bandwidth
across the cluster. Both Map/Reduce and the distributed file system are designed so that node
failures are automatically handled by the framework.
  
+ See the [:Description: Project Description] for more details.
- The intent is to scale Hadoop up to handling thousand of computers. The current high water
marks that have been reported are:
-  * Nodes in a single file system cluster (!DataNodes): 902
-  * Nodes in a single map/reduce cluster (!TaskTrackers): 902
- 
- Hadoop was originally built as infrastructure for the [http://lucene.apache.org/nutch/ Nutch]
project, which crawls the web and builds a search engine index for the crawled pages. Both
Hadoop and Nutch are part of the [http://lucene.apache.org/java/docs/index.html Lucene] [http://www.apache.org/
Apache] project.
- 
- == Hadoop Map/Reduce ==
- 
- === Programming model and execution framework ===
- 
- Map/Reduce is a programming paradigm that expresses a large distributed computation as a
- sequence of distributed operations on data sets of key/value pairs. The Hadoop Map/Reduce
- framework harnesses a cluster of machines and executes user defined Map/Reduce jobs across
- the nodes in the cluster. A Map/Reduce computation has two phases, a ''map'' phase and a
''reduce''
- phase. The input to the computation is a data set of key/value pairs.
- 
- In the map phase, the framework splits the input data set into a large number of fragments
- and assigns each fragment to a ''map task''. The framework also distributes the many map
tasks
- across the cluster of nodes on which it operates. Each map task consumes key/value pairs
- from its assigned fragment and produces a set of intermediate key/value pairs. For each
- input key/value pair ''(K,V)'', the map task invokes a user defined ''map function'' that
transmutes
- the input into a different key/value pair ''(K',V')''.
- 
- Following the map phase the framework sorts the intermediate data set by key and produces
- a set of ''(K',V'*)'' tuples so that all the values associated with a particular key appear
- together. It also partitions the set of tuples into a number of fragments equal to the
- number of reduce tasks.
- 
- In the reduce phase, each ''reduce task'' consumes the fragment of ''(K',V'*)'' tuples assigned
to it.
- For each such tuple it invokes a user-defined ''reduce function'' that transmutes the tuple
into
- an output key/value pair ''(K,V)''. Once again, the framework distributes the many reduce
- tasks across the cluster of nodes and deals with shipping the appropriate fragment of
- intermediate data to each reduce task.
- 
- Tasks in each phase are executed in a fault-tolerant manner, if node(s) fail in the middle
- of a computation the tasks assigned to them are re-distributed among the remaining nodes.
- Having many map and reduce tasks enables good load balancing and allows failed tasks to
be
- re-run with small runtime overhead.
- 
- === Architecture ===
- 
- The Hadoop Map/Reduce framework has a master/slave architecture. It has a single master
- server or ''jobtracker'' and several slave servers or ''tasktrackers'', one per node in
the cluster.
- The ''jobtracker'' is the point of interaction between users and the framework. Users submit
- map/reduce jobs to the ''jobtracker'', which puts them in a queue of pending jobs and executes
- them on a first-come/first-served basis. The ''jobtracker'' manages the assignment of map
and
- reduce tasks to the ''tasktrackers''. The ''tasktrackers'' execute tasks upon instruction
from the
- jobtracker and also handle data motion between the map and reduce phases.
- 
- 
- == Hadoop DFS ==
- 
- Hadoop's Distributed File System is designed to reliably store very large files across
- machines in a large cluster.  It is inspired by the
- [http://labs.google.com/papers/gfs.html Google File System]. Hadoop DFS stores each file
- as a sequence of blocks, all blocks in a file except the last block are the same size.
- Blocks belonging to a file are replicated for fault tolerance. The block size and replication
- factor are configurable per file. Files in HDFS are "write once" and have strictly one writer
- at any time.
- 
- === Architecture ===
- 
- Like Hadoop Map/Reduce, HDFS follows a master/slave architecture. An HDFS installation
- consists of a single ''Namenode'', a master server that manages the filesystem namespace
- and regulates access to files by clients. In addition, there are a number of ''Datanodes'',
- one per node in the cluster, which manage storage attached to the nodes that they run on.
- The ''Namenode'' makes filesystem namespace operations like opening, closing, renaming etc.
- of files and directories available via an RPC interface. It also determines the mapping
of
- blocks to ''Datanodes''. The ''Datanodes'' are responsible for serving read and write
- requests from filesystem clients, they also perform block creation, deletion, and replication
- upon instruction from the ''Namenode''.
- 
  
  == General Information ==
   * [http://lucene.apache.org/hadoop/ Hadoop Website ]

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