Added: lucene/hadoop/trunk/site/images/hdfsarchitecture.png
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/site/images/hdfsarchitecture.png?view=auto&rev=486807
==============================================================================
Binary file - no diff available.
Propchange: lucene/hadoop/trunk/site/images/hdfsarchitecture.png
------------------------------------------------------------------------------
svn:mime-type = application/octet-stream
Added: lucene/hadoop/trunk/site/images/hdfsdatanodes.png
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/site/images/hdfsdatanodes.png?view=auto&rev=486807
==============================================================================
Binary file - no diff available.
Propchange: lucene/hadoop/trunk/site/images/hdfsdatanodes.png
------------------------------------------------------------------------------
svn:mime-type = application/octet-stream
Modified: lucene/hadoop/trunk/site/index.html
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/site/index.html?view=diff&rev=486807&r1=486806&r2=486807
==============================================================================
--- lucene/hadoop/trunk/site/index.html (original)
+++ lucene/hadoop/trunk/site/index.html Wed Dec 13 11:15:21 2006
@@ -78,6 +78,9 @@
<a href="http://wiki.apache.org/lucene-hadoop/";>Wiki</a>
</div>
<div class="menuitem">
+<a href="hdfs_design.html">Hadoop File System</a>
+</div>
+<div class="menuitem">
<a href="docs/api/">API Docs</a>
</div>
</div>
Modified: lucene/hadoop/trunk/site/issue_tracking.html
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/site/issue_tracking.html?view=diff&rev=486807&r1=486806&r2=486807
==============================================================================
--- lucene/hadoop/trunk/site/issue_tracking.html (original)
+++ lucene/hadoop/trunk/site/issue_tracking.html Wed Dec 13 11:15:21 2006
@@ -78,6 +78,9 @@
<a href="http://wiki.apache.org/lucene-hadoop/";>Wiki</a>
</div>
<div class="menuitem">
+<a href="hdfs_design.html">Hadoop File System</a>
+</div>
+<div class="menuitem">
<a href="docs/api/">API Docs</a>
</div>
</div>
Modified: lucene/hadoop/trunk/site/linkmap.html
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/site/linkmap.html?view=diff&rev=486807&r1=486806&r2=486807
==============================================================================
--- lucene/hadoop/trunk/site/linkmap.html (original)
+++ lucene/hadoop/trunk/site/linkmap.html Wed Dec 13 11:15:21 2006
@@ -78,6 +78,9 @@
<a href="http://wiki.apache.org/lucene-hadoop/";>Wiki</a>
</div>
<div class="menuitem">
+<a href="hdfs_design.html">Hadoop File System</a>
+</div>
+<div class="menuitem">
<a href="docs/api/">API Docs</a>
</div>
</div>
@@ -182,6 +185,12 @@
<ul>
<li>
<a
href="http://wiki.apache.org/lucene-hadoop/";>Wiki</a> _________________________ <em>wiki</em>
+</li>
+</ul>
+
+<ul>
+<li>
+<a href="hdfs_design.html">Hadoop File
System</a> _________________________ <em>HDFS</em>
</li>
</ul>
Modified: lucene/hadoop/trunk/site/linkmap.pdf
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/site/linkmap.pdf?view=diff&rev=486807&r1=486806&r2=486807
==============================================================================
--- lucene/hadoop/trunk/site/linkmap.pdf (original)
+++ lucene/hadoop/trunk/site/linkmap.pdf Wed Dec 13 11:15:21 2006
@@ -36,10 +36,10 @@
>>
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@@ -129,25 +129,25 @@
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<<
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@@ -155,5 +155,5 @@
/Info 4 0 R
>>
startxref
-3217
+3263
%%EOF
Modified: lucene/hadoop/trunk/site/mailing_lists.html
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/site/mailing_lists.html?view=diff&rev=486807&r1=486806&r2=486807
==============================================================================
--- lucene/hadoop/trunk/site/mailing_lists.html (original)
+++ lucene/hadoop/trunk/site/mailing_lists.html Wed Dec 13 11:15:21 2006
@@ -78,6 +78,9 @@
<a href="http://wiki.apache.org/lucene-hadoop/";>Wiki</a>
</div>
<div class="menuitem">
+<a href="hdfs_design.html">Hadoop File System</a>
+</div>
+<div class="menuitem">
<a href="docs/api/">API Docs</a>
</div>
</div>
Modified: lucene/hadoop/trunk/site/version_control.html
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/site/version_control.html?view=diff&rev=486807&r1=486806&r2=486807
==============================================================================
--- lucene/hadoop/trunk/site/version_control.html (original)
+++ lucene/hadoop/trunk/site/version_control.html Wed Dec 13 11:15:21 2006
@@ -78,6 +78,9 @@
<a href="http://wiki.apache.org/lucene-hadoop/";>Wiki</a>
</div>
<div class="menuitem">
+<a href="hdfs_design.html">Hadoop File System</a>
+</div>
+<div class="menuitem">
<a href="docs/api/">API Docs</a>
</div>
</div>
Added:
lucene/hadoop/trunk/src/site/src/documentation/content/xdocs/hdfs_design.xml
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/src/site/src/documentation/content/xdocs/hdfs_design.xml?view=auto&rev=486807
==============================================================================
---
lucene/hadoop/trunk/src/site/src/documentation/content/xdocs/hdfs_design.xml
(added)
+++
lucene/hadoop/trunk/src/site/src/documentation/content/xdocs/hdfs_design.xml
Wed Dec 13 11:15:21 2006
@@ -0,0 +1,350 @@
+<?xml version="1.0"?>
+
+<!DOCTYPE document PUBLIC "-//APACHE//DTD Documentation V2.0//EN"
+ "http://forrest.apache.org/dtd/document-v20.dtd";>
+
+
+<document>
+
+ <header>
+ <title>
+ The Hadoop Distributed File System: Architecture and Design
+ </title>
+ <authors>
+ <person name="Dhruba Borthakur" email="[EMAIL PROTECTED]"/>
+ </authors>
+ </header>
+
+ <body>
+ <section>
+ <title> Introduction </title>
+ <p>
+ The Hadoop File System (HDFS) is as a distributed file system running
+ on commodity hardware. It has many similarities with existing
+ distributed file systems. However, the differences from other
+ distributed file systems are significant. HDFS is highly fault-tolerant
+ and can be deployed on low-cost hardware. HDFS provides high
+ throughput access to application data and is suitable for applications
+ that have large datasets. HDFS relaxes a few POSIX requirements to
+ enable streaming access to file system data.
+
+ HDFS was originally built as infrastructure for the open source web
+ crawler Apache Nutch project. HDFS is part of the Hadoop Project,
+ which is part of the Lucene Apache Project. The Project URL is
+ <a href="http://projects.apache.org/projects/hadoop.html";>here</a>.
+ </p>
+ </section>
+
+ <section>
+ <title> Assumptions and Goals </title>
+
+ <section>
+ <title>Hardware Failure</title>
+ <p>
+ Hardware Failure is the norm rather than the exception. The entire
HDFS file system may consist of hundreds or thousands of server machines that
stores pieces of file system data. The fact that there are a huge number of
components and that each component has a non-trivial probability of failure
means that some component of HDFS is always non-functional. Therefore,
detection of faults and automatically recovering quickly from those faults are
core architectural goals of HDFS.
+ </p>
+ </section>
+
+
+ <section>
+ <title> Streaming Data Access </title>
+ <p>
+ Applications that run on HDFS need streaming access to their data
sets. They are not general purpose applications that typically run on a general
purpose file system. HDFS is designed more for batch processing rather than
interactive use by users. The emphasis is on throughput of data access rather
than latency of data access. POSIX imposes many hard requirements that are not
needed for applications that are targeted for HDFS. POSIX semantics in a few
key areas have been traded off to further enhance data throughout rates.
+ </p>
+ </section>
+
+ <section>
+ <title> Large Data Sets </title>
+ <p>
+ Applications that run on HDFS have large data sets. This means that a
typical file in HDFS is gigabytes to terabytes in size. Thus, HDFS is tuned to
support large files. It should provide high aggregate data bandwidth and should
scale to hundreds of nodes in a single cluster. It should support tens of
millions of files in a single cluster.
+ </p>
+ </section>
+
+
+ <section>
+ <title> Simple Coherency Model </title>
+ <p>
+ Most HDFS applications need write-once-read-many access model for
files. A file once created, written and closed need not be changed. This
assumption simplifies data coherency issues and enables high throughout data
access. A Map-Reduce application or a Web-Crawler application fits perfectly
with this model. There is a plan to support appending-writes to a file in
future.
+ </p>
+ </section>
+
+
+ <section>
+ <title> Moving computation is cheaper than moving data </title>
+ <p>
+ A computation requested by an application is most optimal if the
computation can be done near where the data is located. This is especially true
when the size of the data set is huge. This eliminates network congestion and
increase overall throughput of the system. The assumption is that it is often
better to migrate the computation closer to where the data is located rather
than moving the data to where the application is running. HDFS provides
interfaces for applications to move themselves closer to where the data is
located.
+ </p>
+ </section>
+
+
+ <section>
+ <title> Portability across Heterogeneous Hardware and Software
Platforms </title>
+ <p>
+ HDFS should be designed in such a way that it is easily portable from
one platform to another. This facilitates widespread adoption of HDFS as a
platform of choice for a large set of applications.
+ </p>
+ </section>
+ </section>
+
+
+ <section>
+ <title> Namenode and Datanode </title>
+ <p>
+ HDFS has a master/slave architecture. An HDFS cluster 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. HDFS exposes a file system namespace and allows user
data to be stored in files. Internally, a file is split into one or more blocks
and these blocks are stored in a set of Datanodes. The Namenode makes
filesystem namespace operations like opening, closing, renaming etc. of files
and directories. It also determines the mapping of blocks to Datanodes. The
Datanodes are responsible for serving read and write requests from filesystem
clients. The Datanodes also perform block creation, deletion, and replication
upon instruction from the Namenode.
+ </p>
+ <p> <img alt="" src="images/hdfsarchitecture.png"/> </p>
+ <p>
+ The Namenode and Datanode are pieces of software that run on commodity
machines. These machines are typically commodity Linux machines. HDFS is built
using the Java language; any machine that support Java can run the Namenode or
the Datanode. Usage of the highly portable Java language means that HDFS can be
deployed on a wide range of machines. A typical deployment could have a
dedicated machine that runs only the Namenode software. Each of the other
machines in the cluster runs one instance of the Datanode software. The
architecture does not preclude running multiple Datanodes on the same machine
but in a real-deployment that is never the case.
+ </p>
+ <p>
+The existence of a single Namenode in a cluster greatly simplifies the
architecture of the system. The Namenode is the arbitrator and repository for
all HDFS metadata. The system is designed in such a way that user data never
flows through the Namenode.
+ </p>
+ </section>
+
+
+
+ <section>
+ <title> The File System Namespace </title>
+ <p>
+ HDFS supports a traditional hierarchical file organization. A user or an
application can create directories and store files inside these directories.
The file system namespace hierarchy is similar to most other existing file
systems. One can create and remove files, move a file from one directory to
another, or rename a file. HDFS does not yet implement user quotas and access
permissions. HDFS does not support hard links and soft links. However, the HDFS
architecture does not preclude implementing these features at a later time.
+ </p>
+ <p>
+ The Namenode maintains the file system namespace. Any change to the file
system namespace and properties are recorded by the Namenode. An application
can specify the number of replicas of a file that should be maintained by HDFS.
The number of copies of a file is called the replication factor of that file.
This information is stored by the Namenode.
+ </p>
+ </section>
+
+
+
+ <section>
+ <title> Data Replication </title>
+ <p>
+HDFS is designed to reliably store very large files across machines in a large
cluster. It 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. An application can specify the number of replicas of a
file. The replication factor can be specified at file creation time and can be
changed later.
+ </p>
+ <p>
+ The Namenode makes all decisions regarding replication of blocks. It
periodically receives Heartbeat and a Blockreport from each of the Datanodes in
the cluster. A receipt of a heartbeat implies that the Datanode is in good
health and is serving data as desired. A Blockreport contains a list of all
blocks on that Datanode.
+ </p>
+ <p> <img alt="" src="images/hdfsdatanodes.png"/> </p>
+
+ <section>
+ <title> Replica Placement . The First Baby Steps </title>
+ <p>
+ The selection of placement of replicas is critical to HDFS reliability
and performance. This feature distinguishes HDFS from most other distributed
file systems. This is a feature that needs lots of tuning and experience. The
purpose of a rack-aware replica placement is to improve data reliability,
availability, and network bandwidth utilization. The current implementation for
the replica placement policy is a first effort in this direction. The
short-term goals of implementing this policy are to validate it on production
systems, learn more about its behavior and build a foundation to test and
research more sophisticated policies in the future.
+ </p>
+ <p>
+ HDFS runs on a cluster of computers that spread across many racks.
Communication between two nodes on different racks has to go through switches.
In most cases, network bandwidth between two machines in the same rack is
greater than network bandwidth between two machines on different racks.
+ </p>
+ <p>
+ At startup time, each Datanode determines the rack it belongs to and
notifies the Namenode of the rack id upon registration. HDFS provides APIs to
facilitate pluggable modules that can be used to determine the rack identity of
a machine. A simple but non-optimal policy is to place replicas across racks.
This prevents losing data when an entire rack fails and allows use of bandwidth
from multiple racks when reading data. This policy evenly distributes replicas
in the cluster and thus makes it easy to balance load on component failure.
However, this policy increases the cost of writes because a write needs to
transfer blocks to multiple racks.
+ </p>
+ <p>
+ For the most common case when the replica factor is three, HDFS.s
placement policy is to place one replica on the local node, place another
replica on a different node at the local rack, and place the last replica on
different node at a different rack. This policy cuts the inter-rack write
traffic and improves write performance. The chance of rack failure is far less
than that of node failure; this policy does not impact data reliability and
availability guarantees. But it reduces the aggregate network bandwidth when
reading data since a block is placed in only two unique racks rather than
three. The replicas of a file do not evenly distribute across the racks. One
third of replicas are on one node, two thirds of the replicas are on one rack;
the other one third of replicas is evenly distributed across all the remaining
racks. This policy improves write performance while not impacting data
reliability or read performance.
+ </p>
+ <p>
+ The implementation of the above policy is work-in-progress.
+ </p>
+ </section>
+
+ <section>
+ <title> Replica Selection </title>
+ <p>
+ HDFS tries to satisfy a read request from a replica that is closest to
the reader. If there exists a replica on the same rack as the reader node, then
that replica is preferred to satisfy the read request. If a HDFS cluster spans
multiple data centers, then a replica that is resident in the local data center
is preferred over remote replicas.
+ </p>
+ </section>
+
+ <section>
+ <title> SafeMode </title>
+ <p>
+ On startup, the Namenode enters a special state called Safemode.
Replication of data blocks does not occur when the Namenode is in Safemode
state. The Namenode receives Heartbeat and Blockreport from the Datanodes. A
Blockreport contains the list of data blocks that a Datanode reports to the
Namenode. Each block has a specified minimum number of replicas. A block is
considered safely-replicated when the minimum number of replicas of that data
block has checked in with the Namenode. When a configurable percentage of
safely-replicated data blocks checks in with the Namenode (plus an additional
30 seconds), the Namenode exits the Safemode state. It then determines the list
of data blocks (if any) that have fewer than the specified number of replicas.
The Namenode then replicates these blocks to other Datanodes.
+ </p>
+ </section>
+
+ </section>
+
+ <section>
+ <title> The Persistence of File System Metadata </title>
+ <p>
+ The HDFS namespace is stored by the Namenode. The Namenode uses a
transaction log called the EditLog to persistently record every change that
occurs to file system metadata. For example, creating a new file in HDFS causes
the Namenode to insert a record into the EditLog indicating this change.
Similarly, changing the replication factor of a file causes a new record to be
inserted into the EditLog. The Namenode uses a file in its local file system to
store the Edit Log. The entire file system namespace, the mapping of blocks to
files and filesystem properties are stored in a file called the FsImage. The
FsImage is a file in the Namenode.s local file system too.
+ </p>
+ <p>
+ The Namenode has an image of the entire file system namespace and file
Blockmap in memory. This metadata is designed to be compact, so that a 4GB
memory on the Namenode machine is plenty to support a very large number of
files and directories. When the Namenode starts up, it reads the FsImage and
EditLog from disk, applies all the transactions from the EditLog into the
in-memory representation of the FsImage and then flushes out this new metadata
into a new FsImage on disk. It can then truncate the old EditLog because its
transactions have been applied to the persistent FsImage. This process is
called a checkpoint. In the current implementation, a checkpoint occurs when
the Namenode starts up. Work is in progress to support periodic checkpointing
in the near future.
+ </p>
+ <p>
+ The Datanode stores HDFS data into files in its local file system. The
Datanode has no knowledge about HDFS files. It stores each block of HDFS data
in a separate file in its local file system. The Datanode does not create all
files in the same directory. Instead, it uses a heuristic to determine the
optimal number of files per directory. It creates subdirectories appropriately.
It is not optimal to create all local files in the same directory because the
local file system might not be able to efficiently support a huge number of
files in a single directory. When a Datanode starts up, it scans through its
local file system, generates a list of all HDFS data blocks that correspond to
each of these local files and sends this report to the Namenode. This report is
called the Blockreport.
+ </p>
+ </section>
+
+
+ <section>
+ <title> The Communication Protocol </title>
+ <p>
+ All communication protocols are layered on top of the TCP/IP protocol. A
client establishes a connection to a well-defined and configurable port on the
Namenode machine. It talks the ClientProtocol with the Namenode. The Datanodes
talk to the Namenode using the DatanodeProtocol. The details on these protocols
will be explained later on. A Remote Procedure Call (RPC) abstraction wraps the
ClientProtocol and the DatanodeProtocol. By design, the Namenode never
initiates an RPC. It responds to RPC requests issued by a Datanode or a client.
+ </p>
+ </section>
+
+
+ <section>
+ <title> Robustness </title>
+ <p>
+The primary objective of HDFS is to store data reliably even in the presence
of failures. The three types of common failures are Namenode failures, Datanode
failures and network partitions.
+ </p>
+
+ <section>
+ <title> Data Disk Failure, Heartbeats and Re-Replication </title>
+ <p>
+ A Datanode sends a heartbeat message to the Namenode periodically. A
network partition can cause a subset of Datanodes to lose connectivity with the
Namenode. The Namenode detects this condition be a lack of heartbeat message.
The Namenode marks these Datanodes as dead and does not forward any new IO
requests to these Datanodes. The data that was residing on those Datanodes are
not available to HDFS any more. This may cause the replication factor of some
blocks to fall below their specified value. The Namenode determines all the
blocks that need to be replicated and starts replicating them to other
Datanodes. The necessity for re-replication may arise due to many reasons: a
Datanode becoming unavailable, a corrupt replica, a bad disk on the Datanode or
an increase of the replication factor of a file.
+ </p>
+ </section>
+
+ <section>
+ <title> Cluster Rebalancing </title>
+ <p>
+ The HDFS architecture is compatible with data rebalancing schemes. It
is possible that data may move automatically from one Datanode to another if
the free space on a Datanode falls below a certain threshold. Also, a sudden
high demand for a particular file can dynamically cause creation of additional
replicas and rebalancing of other data in the cluster. These types of
rebalancing schemes are not yet implemented.
+ </p>
+ </section>
+
+ <section>
+ <title> Data Correctness </title>
+ <p>
+ It is possible that a block of data fetched from a Datanode is
corrupted. This corruption can occur because of faults in the storage device, a
bad network or buggy software. The HDFS client implements checksum checking on
the contents of a HDFS file. When a client creates a HDFS file, it computes a
checksum of each block on the file and stores these checksums in a separate
hidden file in the same HDFS namespace. When a client retrieves file contents
it verifies that the data it received from a Datanode satisfies the checksum
stored in the checksum file. If not, then the client can opt to retrieve that
block from another Datanode that has a replica of that block.
+ </p>
+ </section>
+
+
+ <section>
+ <title> Metadata Disk Failure </title>
+ <p>
+ The FsImage and the EditLog are central data structures of HDFS. A
corruption of these files can cause the entire cluster to be non-functional.
For this reason, the Namenode can be configured to support multiple copies of
the FsImage and EditLog. Any update to either the FsImage or EditLog causes
each of the FsImages and EditLogs to get updated synchronously. This
synchronous updating of multiple EditLog may degrade the rate of namespace
transactions per second that a Namenode can support. But this degradation is
acceptable because HDFS applications are very data intensive in nature; they
are not metadata intensive. A Namenode, when it restarts, selects the latest
consistent FsImage and EditLog to use.
+ </p>
+ <p>
+ The Namenode machine is a single point of failure for the HDFS
cluster. If a Namenode machine fails, manual intervention is necessary.
Currently, automatic restart and failover of the Namenode software to another
machine is not supported.
+ </p>
+ </section>
+
+ <section>
+ <title> Snapshots </title>
+ <p>
+ Snapshots support storing a copy of data at a particular instant of
time. One usage of the snapshot-feature may be to roll back a corrupted cluster
to a previously known good point in time. HDFS current does not support
snapshots but it will be supported it in future release.
+ </p>
+ </section>
+
+ </section>
+
+
+ <section>
+ <title> Data Organization </title>
+
+ <section>
+ <title> Data Blocks </title>
+ <p>
+ HDFS is designed to support large files. Applications that are
compatible with HDFS are those that deal with large data sets. These
applications write the data only once; they read the data one or more times and
require that reads are satisfied at streaming speeds. HDFS supports
write-once-read-many semantics on files. A typical block size used by HDFS is
64 MB. Thus, a HDFS file is chopped up into 128MB chunks, and each chunk could
reside in different Datanodes.
+ </p>
+ </section>
+
+
+ <section>
+ <title> Staging </title>
+ <p>
+ A client-request to create a file does not reach the Namenode
immediately. In fact, the HDFS client caches the file data into a temporary
local file. An application-write is transparently redirected to this temporary
local file. When the local file accumulates data worth over a HDFS block size,
the client contacts the Namenode. The Namenode inserts the file name into the
file system hierarchy and allocates a data block for it. The Namenode responds
to the client request with the identity of the Datanode(s) and the destination
data block. The client flushes the block of data from the local temporary file
to the specified Datanode. When a file is closed, the remaining un-flushed data
in the temporary local file is transferred to the Datanode. The client then
instructs the Namenode that the file is closed. At this point, the Namenode
commits the file creation operation into a persistent store. If the Namenode
dies before the file is closed, the file is lost.
+ </p>
+ <p>
+ The above approach has been adopted after careful consideration of
target applications that run on HDFS. Applications need streaming writes to
files. If a client writes to a remote file directly without any client side
buffering, the network speed and the congestion in the network impacts
throughput considerably. This approach is not without precedence either.
Earlier distributed file system, e.g. AFS have used client side caching to
improve performance. A POSIX requirement has been relaxed to achieve higher
performance of data uploads.
+ </p>
+ </section>
+
+ <section>
+ <title> Pipelining </title>
+ <p>
+ When a client is writing data to a HDFS file, its data is first
written to a local file as explained above. Suppose the HDFS file has a
replication factor of three. When the local file accumulates a block of user
data, the client retrieves a list of Datanodes from the Namenode. This list
represents the Datanodes that will host a replica of that block. The client
then flushes the data block to the first Datanode. The first Datanode starts
receiving the data in small portions (4 KB), writes each portion to its local
repository and transfers that portion to the second Datanode in the list. The
second Datanode, in turn, starts receiving each portion of the data block,
writes that portion to its repository and then flushes that portion to the
third Datanode. The third Datanode writes the data to its local repository. A
Datanode could be receiving data from the previous one in the pipeline and at
the same time it could be forwarding data to the next one in the pipeline. Th
us, the data is pipelined from one Datanode to the next.
+ </p>
+ </section>
+
+ </section>
+
+ <section>
+ <title> Accessibility </title>
+ <p>
+ HDFS can be accessed by application by many different ways. Natively,
HDFS provides a Java API for applications to use. A C language wrapper for this
Java API is available. A HTTP browser can also be used to browse the file in
HDFS. Work is in progress to expose a HDFS content repository through the
WebDAV Protocol.
+ </p>
+
+ <section>
+ <title> DFSShell </title>
+ <p>
+ HDFS allows user data to be organized in the form of files and
directories. It provides an interface called DFSShell that lets a user interact
with the data in HDFS. The syntax of this command set is similar to other
shells (e.g. bash, csh) that users are already familiar with. Here are some
sample commands:
+ </p>
+ <p>
+ Create a directory named /foodir : hadoop dfs -mkdir /foodir
+ </p>
+ <p>
+ View a file /foodir/myfile.txt : hadoop dfs -cat
/foodir/myfile.txt
+ </p>
+ <p>
+ Delete a file /foodir/myfile.txt : hadoop dfs -rm /foodir
myfile.txt
+ </p>
+ <p>
+ The command syntax for DFSShell is targeted for applications that need
a scripting language to interact with the stored data.
+ </p>
+ </section>
+
+ <section>
+ <title> DFSAdmin </title>
+ <p>
+ The DFSAdmin command set is used for administering a dfs cluster.
These are commands that are used only by a HDFS administrator. Here are some
sample commands:
+ </p>
+ <p>
+ Put a cluster in Safe Mode : bin/hadoop dfsadmin -safemode
enter
+ </p>
+ <p>
+ Generate a list of Datanodes : bin/hadoop dfsadmin -report
+ </p>
+ <p>
+ Decommission a Datanode : bin/hadoop dfsadmin -decommission
datanodename
+ </p>
+ </section>
+
+ <section>
+ <title> Browser Interface </title>
+ <p>
+ A typical HDFS install configures a web-server to expose the HDFS
namespace through a configurable port. This allows a Web browser to navigate
the HDFS namespace and view contents of a HDFS file.
+ </p>
+ </section>
+
+ </section>
+
+ <section>
+ <title> Space Reclamation </title>
+
+ <section>
+ <title> File Deletes and Undelete </title>
+ <p>
+ When a file is deleted by a user or an application, it is not
immediately removed from HDFS. HDFS renames it to a file in the /trash
directory. The file can be restored quickly as long as it remains in /trash. A
file remains in /trash for a configurable amount of time. After the expiry of
its life in /trash, the Namenode deletes the file from the HDFS namespace. The
deletion of the file causes the blocks associated with the file to be freed.
There could be an appreciable time delay between the time a file is deleted by
a user and the time of the corresponding increase in free space in HDFS.
+ </p>
+ <p>
+A user can Undelete a file after deleting it as long as it remains in the
/trash directory. If a user wants to undelete a file that he/she has deleted,
he/she can navigate the /trash directory and retrieve the file. The /trash
directory contains only the latest copy of the file that was deleted. The
/trash directory is just like any other directory with one special feature:
HDFS applies specified policies to automatically delete files from this
directory. The current default policy is to delete files that are older than 6
hours. In future, this policy will be configurable through a well defined
interface.
+ </p>
+ </section>
+
+ <section>
+ <title> Decrease Replication Factor </title>
+ <p>
+ When the replication factor of a file is reduced, the Namenode selects
excess replicas that can be deleted. The next Heartbeat transfers this
information to the Datanode. The Datanode then removes the corresponding blocks
and the corresponding free space appears in the cluster. The point to note here
is that there might be a time delay between the completion of the
setReplication API and the appearance of free space in the cluster.
+ </p>
+ </section>
+ </section>
+
+
+ <section>
+ <title> References </title>
+ <p>
+ <a href="http://lucene.apache.org/hadoop/docs/api/";>
+ Browse the HDFS Java Interface
+ </a>
+ </p>
+ <p>
+ <a href= "http://lucene.apache.org/hadoop/version_control.html";>
+ Download the HDFS source code
+ </a>
+ </p>
+ </section>
+
+ </body>
+</document>
+
Modified: lucene/hadoop/trunk/src/site/src/documentation/content/xdocs/site.xml
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/src/site/src/documentation/content/xdocs/site.xml?view=diff&rev=486807&r1=486806&r2=486807
==============================================================================
--- lucene/hadoop/trunk/src/site/src/documentation/content/xdocs/site.xml
(original)
+++ lucene/hadoop/trunk/src/site/src/documentation/content/xdocs/site.xml Wed
Dec 13 11:15:21 2006
@@ -26,6 +26,7 @@
<docs label="Documentation">
<faq label="FAQ" href="ext:faq" />
<wiki label="Wiki" href="ext:wiki" />
+ <HDFS label="Hadoop File System" href="hdfs_design.html" />
<apidocs label="API Docs" href="docs/api/" />
</docs>
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