Bug#577612: hadoop: [debconf_rewrite] Debconf templates and debian/control review
Christian Perrier
bubulle at debian.org
Tue Apr 13 05:17:53 UTC 2010
Package: hadoop
Version: N/A
Severity: normal
Tags: patch
Dear Debian maintainer,
On Friday, March 26, 2010, I notified you of the beginning of a review process
concerning debconf templates for hadoop.
The debian-l10n-english contributors have now reviewed these templates,
and the proposed changes are attached to this bug report.
Please review the suggested changes, and if you have any
objections, let me know in the next 3 days.
However, please try to avoid uploading hadoop with these changes
right now.
The second phase of this process will begin on Friday, April 16, 2010, when I will
coordinate updates to translations of debconf templates.
The existing translators will be notified of the changes: they will
receive an updated PO file for their language.
Simultaneously, a general call for new translations will be sent to
the debian-i18n mailing list.
Both these calls for translations will request updates to be sent as
individual bug reports. That will probably trigger a lot of bug
reports against your package, but these should be easier to deal with.
The call for translation updates and new translations will run until
about Friday, May 07, 2010. Please avoid uploading a package with fixed or changed
debconf templates and/or translation updates in the meantime. Of
course, other changes are safe.
Please note that this is an approximative delay, which depends on my
own availability to process this work and is influenced by the fact
that I simultaneously work on many packages.
Around Saturday, May 08, 2010, I will contact you again and will send a final patch
summarizing all the updates (changes to debconf templates,
updates to debconf translations and new debconf translations).
Again, thanks for your attention and cooperation.
-- System Information:
Debian Release: squeeze/sid
APT prefers unstable
APT policy: (500, 'unstable')
Architecture: i386 (i686)
Kernel: Linux 2.6.32-3-686 (SMP w/2 CPU cores)
Locale: LANG=C, LC_CTYPE=C (charmap=ANSI_X3.4-1968)
Shell: /bin/sh linked to /bin/dash
-------------- next part --------------
--- hadoop.old/debian/hadoop-namenoded.templates 2010-03-22 09:56:11.717948376 +0100
+++ hadoop/debian/hadoop-namenoded.templates 2010-04-13 07:16:51.506026053 +0200
@@ -1,17 +1,25 @@
+# These templates have been reviewed by the debian-l10n-english
+# team
+#
+# If modifications/additions/rewording are needed, please ask
+# debian-l10n-english at lists.debian.org for advice.
+#
+# Even minor modifications require translation updates and such
+# changes should be coordinated with translators and reviewers.
+
Template: hadoop-namenoded/format
Type: boolean
Default: false
-_Description: Should the namenode's filesystem be formatted now?
- The namenode manages the Hadoop Distributed FileSystem (HDFS). Like a
- normal filesystem, it needs to be formatted prior to first use. If the
- HDFS filesystem is not formatted, the namenode daemon will fail to
+_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 the "normal" filesystem on this
- computer. If you're using HDFS for the first time and don't have data
- from previous installations on this computer, it should be save to
- proceed with yes.
+ 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.
.
- You can later on format the filesystem yourself with
- .
- su -c"hadoop namenode -format" hadoop
+ 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".
--- hadoop.old/debian/control 2010-03-22 09:56:11.717948376 +0100
+++ hadoop/debian/control 2010-03-31 06:59:53.610677495 +0200
@@ -29,7 +29,7 @@
Package: libhadoop-java
Architecture: all
-Depends: ${misc:Depends},
+Depends: ${misc:Depends},
libcommons-cli-java,
libcommons-codec-java,
libcommons-el-java,
@@ -44,20 +44,46 @@
libslf4j-java,
libxmlenc-java
Suggests: libhsqldb-java
-Description: software platform for processing vast amounts of data
- This package contains the core java libraries.
+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: Hadoop contrib to create lucene indexes
- This contrib package provides a utility to build or update an index
- using Map/Reduce.
- .
- A distributed "index" is partitioned into "shards". Each shard corresponds
- to a Lucene instance. org.apache.hadoop.contrib.index.main.UpdateIndex
- contains the main() method which uses a Map/Reduce job to analyze documents
+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
@@ -65,9 +91,9 @@
Architecture: all
Depends: ${misc:Depends}, libhadoop-java (= ${binary:Version}),
default-jre-headless | java6-runtime-headless
-Description: software platform for processing vast amounts of data
- Hadoop is a software platform that lets one easily write and
- run applications that process vast amounts of data.
+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.
@@ -86,33 +112,73 @@
nodes around the cluster. MapReduce can then process the data where it is
located.
.
- This package contains the hadoop shell interface. See the packages hadoop-.*d
- for the hadoop daemons.
+ 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: Creates user and directories for hadoop daemons
- Prepares some common things for all hadoop daemon packages:
- * creates the user hadoop
- * creates data and log directories owned by the hadoop user
- * manages the update-alternatives mechanism for hadoop configuration
- * brings in the common dependencies
+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: Contains the javadoc for hadoop
- contains the api documentation of hadoop
+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: Task Tracker for Hadoop
+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.
@@ -121,34 +187,90 @@
Section: misc
Architecture: all
Depends: ${misc:Depends}, hadoop-daemons-common (= ${binary:Version})
-Description: Job Tracker for Hadoop
- The jobtracker is a central service which is responsible for managing
- the tasktracker services running on all nodes in a Hadoop Cluster.
- The jobtracker allocates work to the tasktracker nearest to the data
+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: Name Node for Hadoop
- The Hadoop Distributed Filesystem (HDFS) requires one unique server, the
- namenode, which manages the block locations of files on the filesystem.
+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: Secondary Name Node for Hadoop
- The Secondary Name Node is responsible for checkpointing file system images.
- It is _not_ a failover pair for the namenode, and may safely be run on the
- same machine.
+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 Node for Hadoop
+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 Filesystem
+ blocks of data over the network to Hadoop Distributed File System
(HDFS) clients.
More information about the pkg-java-maintainers
mailing list