[LCFC] templates://hadoop/{hadoop-namenoded.templates}
Christian PERRIER
bubulle at debian.org
Sat Apr 10 05:04:18 UTC 2010
This is the last call for comments for the review of debconf
templates for hadoop.
The reviewed templates will be sent on Monday, April 12, 2010 to the package
maintainer as a bug report and a mail will be sent to this list with
"[BTS]" as a subject tag.
--
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Template: hadoop-namenoded/format
Type: boolean
Default: false
_Description: Should namenoded's file system be formatted?
The Name Node daemon manages the Hadoop Distributed File System (HDFS).
Like a normal file system, it needs to be formatted prior to first use.
If the HDFS file system is not formatted, the Name Node will fail to
start.
.
This operation does not affect other file systems on this
computer. You can safely choose to format the file system if you're
using HDFS for the first time and don't have data from previous
installations on this computer.
.
If you choose not to format the file system right now, you can do it
later by executing "hadoop namenode -format" as the user "hadoop".
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Source: hadoop
Section: java
Priority: optional
Maintainer: Debian Java Maintainers <pkg-java-maintainers at lists.alioth.debian.org>
Uploaders: Thomas Koch <thomas.koch at ymc.ch>
Homepage: http://hadoop.apache.org
Vcs-Browser: http://git.debian.org/?p=pkg-java/hadoop.git
Vcs-Git: git://git.debian.org/pkg-java/hadoop.git
Standards-Version: 3.8.4
Build-Depends: debhelper (>= 7.4.11), default-jdk, ant (>= 1.6.0), javahelper (>= 0.28),
po-debconf,
libcommons-cli-java,
libcommons-codec-java,
libcommons-el-java,
libcommons-httpclient-java,
libcommons-io-java,
libcommons-logging-java,
libcommons-net-java,
libtomcat6-java,
libjetty-java (>>6),
libservlet2.5-java,
liblog4j1.2-java,
libslf4j-java,
libxmlenc-java,
liblucene2-java,
libhsqldb-java,
ant-optional,
javacc
Package: libhadoop-java
Architecture: all
Depends: ${misc:Depends},
libcommons-cli-java,
libcommons-codec-java,
libcommons-el-java,
libcommons-httpclient-java,
libcommons-io-java,
libcommons-logging-java,
libcommons-net-java,
libtomcat6-java,
libjetty-java (>>6),
libservlet2.5-java,
liblog4j1.2-java,
libslf4j-java,
libxmlenc-java
Suggests: libhsqldb-java
Description: data-intensive clustering framework - Java libraries
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
This package contains the core Java libraries.
Package: libhadoop-index-java
Architecture: all
Depends: ${misc:Depends}, libhadoop-java (= ${binary:Version}),
liblucene2-java
Description: data-intensive clustering framework - Lucene index support
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
The org.apache.hadoop.contrib.index.main.UpdateIndex library provides
support for managing an index using MapReduce. A distributed "index" is
partitioned into "shards", each corresponding to a Lucene instance.
This library's main() method uses a MapReduce job to analyze documents
and update Lucene instances in parallel.
Package: hadoop-bin
Section: misc
Architecture: all
Depends: ${misc:Depends}, libhadoop-java (= ${binary:Version}),
default-jre-headless | java6-runtime-headless
Description: data-intensive clustering framework - tools
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
Hadoop implements MapReduce, using the Hadoop Distributed File System (HDFS).
MapReduce divides applications into many small blocks of work. HDFS creates
multiple replicas of data blocks for reliability, placing them on compute
nodes around the cluster. MapReduce can then process the data where it is
located.
.
This package provides the hadoop command line interface. See the hadoop-.*d
packages for the Hadoop daemons.
Package: hadoop-daemons-common
Section: misc
Architecture: all
Depends: ${misc:Depends}, hadoop-bin (= ${binary:Version}), daemon, adduser,
lsb-base (>= 3.2-14)
Description: data-intensive clustering framework - common files
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
This package provides infrastructure for the Hadoop daemon packages,
creating the hadoop user (with data and log directories) and maintaining
the update-alternatives mechanism for hadoop configuration.
Package: libhadoop-java-doc
Section: doc
Architecture: all
Depends: ${misc:Depends}, libhadoop-java (= ${binary:Version})
Description: data-intensive clustering framework - Java documentation
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
This package provides the API documentation of Hadoop.
Package: hadoop-tasktrackerd
Section: misc
Architecture: all
Depends: ${misc:Depends}, hadoop-daemons-common (= ${binary:Version})
Description: data-intensive clustering framework - Task Tracker
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
The Task Tracker is the Hadoop service that accepts MapReduce tasks and
computes results. Each node in a Hadoop cluster that should be doing
computation should run a Task Tracker.
Package: hadoop-jobtrackerd
Section: misc
Architecture: all
Depends: ${misc:Depends}, hadoop-daemons-common (= ${binary:Version})
Description: data-intensive clustering framework - Job Tracker
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
The Job Tracker is a central service which is responsible for managing
the Task Tracker services running on all nodes in an Hadoop Cluster.
The Job Tracker allocates work to the Task Tracker nearest to the data
with an available work slot.
Package: hadoop-namenoded
Section: misc
Architecture: all
Depends: ${misc:Depends}, hadoop-daemons-common (= ${binary:Version})
Description: data-intensive clustering framework - Name Node
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
The Hadoop Distributed File System (HDFS) requires one unique server, the
Name Node, which manages the block locations of files on the file system.
Package: hadoop-secondarynamenoded
Section: misc
Architecture: all
Depends: ${misc:Depends}, hadoop-daemons-common (= ${binary:Version})
Description: data-intensive clustering framework - secondary Name Node
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
The secondary Name Node is responsible for checkpointing file system images.
It is _not_ a failover partner for the name node, and may safely be run on
the same machine.
Package: hadoop-datanoded
Section: misc
Architecture: all
Depends: ${misc:Depends}, hadoop-daemons-common (= ${binary:Version})
Description: data-intensive clustering framework - Data Node
Hadoop is a software platform for writing and running applications
that process vast amounts of data on a distributed file system.
.
Here's what makes Hadoop especially useful:
* Scalable: Hadoop can reliably store and process petabytes.
* Economical: It distributes the data and processing across clusters
of commonly available computers. These clusters can number
into the thousands of nodes.
* Efficient: By distributing the data, Hadoop can process it in parallel
on the nodes where the data is located. This makes it
extremely rapid.
* Reliable: Hadoop automatically maintains multiple copies of data and
automatically redeploys computing tasks based on failures.
.
The Data Nodes in the Hadoop Cluster are responsible for serving up
blocks of data over the network to Hadoop Distributed File System
(HDFS) clients.
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