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From hair...@apache.org
Subject svn commit: r674588 [4/4] - in /hadoop/core/trunk: docs/ src/docs/src/documentation/content/xdocs/
Date Mon, 07 Jul 2008 18:49:23 GMT
Modified: hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_design.xml
URL: http://svn.apache.org/viewvc/hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_design.xml?rev=674588&r1=674587&r2=674588&view=diff
==============================================================================
--- hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_design.xml (original)
+++ hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_design.xml Mon Jul  7 11:49:22 2008
@@ -67,7 +67,7 @@
       <section> 
         <title> Simple Coherency Model </title>
         <p>
-        HDFS applications need a 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 throughput data access. A MapReduce application or a web crawler application fits perfectly with this model. There is a plan to support appending-writes to files in the future. 
+        HDFS applications need a 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 throughput 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 files in the future. 
         </p>
       </section>
 
@@ -90,16 +90,16 @@
 
  
     <section>
-      <title> Namenode and Datanodes </title>
+      <title> NameNode and DataNodes </title>
       <p>
-      HDFS has a master/slave architecture. An HDFS cluster consists of a single <em>Namenode</em>, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of <em>Datanodes</em>, usually 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 executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to Datanodes. The Datanodes are responsible for serving read and write requests from the file system&#x2019;s clients. The Datanodes also perform block creation, deletion, and replication upon instruction from the Namenode.
+      HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually 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 executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system&#x2019;s clients. The DataNodes also perform block creation, deletion, and replication upon instruction from the NameNode.
       </p>
       <figure alt="HDFS Architecture" src="images/hdfsarchitecture.gif"/>
       <p>
-      The Namenode and Datanode are pieces of software designed to run on commodity machines. These machines typically run a GNU/Linux operating system (<acronym title="operating system">OS</acronym>). HDFS is built using the Java language; any machine that supports Java can run the Namenode or the Datanode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has 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 rarely the case.
+      The NameNode and DataNode are pieces of software designed to run on commodity machines. These machines typically run a GNU/Linux operating system (<acronym title="operating system">OS</acronym>). HDFS is built using the Java language; any machine that supports Java can run the NameNode or the DataNode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has 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 rarely 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 <em>data</em> never flows through the Namenode.
+      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>
 
@@ -111,7 +111,7 @@
       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 or access permissions. HDFS does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features.
       </p>
       <p>
-      The Namenode maintains the file system namespace. Any change to the file system namespace or its properties is 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.
+      The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is 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>
 
@@ -123,9 +123,9 @@
       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. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. 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. Files in HDFS are write-once and have strictly one writer at any time. 
       </p>
       <p>
-      The Namenode makes all decisions regarding replication of blocks. It periodically receives a <em>Heartbeat</em> and a <em>Blockreport</em> from each of the Datanodes in the cluster. Receipt of a Heartbeat implies that the Datanode is functioning properly. A Blockreport contains a list of all blocks on a Datanode. 
+      The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode. 
     </p>
-    <figure alt="HDFS Datanodes" src="images/hdfsdatanodes.gif"/>
+    <figure alt="HDFS DataNodes" src="images/hdfsdatanodes.gif"/>
 
       <section>
         <title> Replica Placement: The First Baby Steps </title>
@@ -154,9 +154,9 @@
       </section>
 
       <section> 
-        <title> SafeMode </title>
+        <title> Safemode </title>
         <p>
-        On startup, the Namenode enters a special state called <em>Safemode</em>. Replication of data blocks does not occur when the Namenode is in the Safemode state. The Namenode receives Heartbeat and Blockreport messages from the Datanodes. A Blockreport contains the list of data blocks that a Datanode is hosting. Each block has a specified minimum number of replicas. A block is considered <em>safely replicated</em> when the minimum number of replicas of that data block has checked in with the Namenode. After 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 still have fewer than the specified number of replicas. The Namenode then replicates these blocks to other Datanodes.
+        On startup, the NameNode enters a special state called Safemode. Replication of data blocks does not occur when the NameNode is in the Safemode state. The NameNode receives Heartbeat and Blockreport messages from the DataNodes. A Blockreport contains the list of data blocks that a DataNode is hosting. 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. After 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 still have fewer than the specified number of replicas. The NameNode then replicates these blocks to other DataNodes.
         </p>
       </section>
 
@@ -165,13 +165,13 @@
     <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 <em>EditLog</em> to persistently record every change that occurs to file system <em>metadata</em>. For example, creating a new file in HDFS causes the Namenode to insert a record into the EditLog indicating this. 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 <em>local</em> host OS file system to store the EditLog. The entire file system namespace, including the mapping of blocks to files and file system properties, is stored in a file called the <em>FsImage</em>. The FsImage is stored as a file in the Namenode&#x2019;s local file system too.
+        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. 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 host OS file system to store the EditLog. The entire file system namespace, including the mapping of blocks to files and file system properties, is stored in a file called the FsImage. The FsImage is stored as a file in the NameNode&#x2019;s local file system too.
         </p>
         <p>
-        The Namenode keeps an image of the entire file system namespace and file <em>Blockmap</em> in memory. This key metadata item is designed to be compact, such that a Namenode with 4 GB of RAM is plenty to support a huge 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 to the in-memory representation of the FsImage, and flushes out this new version 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 <em>checkpoint</em>. In the current implementation, a checkpoint only occurs when the Namenode starts up. Work is in progress to support periodic checkpointing in the near future.
+        The NameNode keeps an image of the entire file system namespace and file Blockmap in memory. This key metadata item is designed to be compact, such that a NameNode with 4 GB of RAM is plenty to support a huge 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 to the in-memory representation of the FsImage, and flushes out this new version 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 only 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 in 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 and 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 is the Blockreport. 
+        The DataNode stores HDFS data in 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 and 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 is the Blockreport. 
         </p>
     </section>
 
@@ -179,7 +179,7 @@
     <section> 
       <title> The Communication Protocols </title>
       <p>
-      All HDFS communication protocols are layered on top of the TCP/IP protocol. A client establishes a connection to a configurable <acronym title="Transmission Control Protocol">TCP</acronym> port on the Namenode machine. It talks the <em>ClientProtocol</em> with the Namenode. The Datanodes talk to the Namenode using the <em>DatanodeProtocol</em>. A Remote Procedure Call (<acronym title="Remote Procedure Call">RPC</acronym>) abstraction wraps both the ClientProtocol and the DatanodeProtocol. By design, the Namenode never initiates any RPCs. Instead, it only responds to RPC requests issued by Datanodes or clients. 
+      All HDFS communication protocols are layered on top of the TCP/IP protocol. A client establishes a connection to a configurable <acronym title="Transmission Control Protocol">TCP</acronym> port on the NameNode machine. It talks the ClientProtocol with the NameNode. The DataNodes talk to the NameNode using the DataNode Protocol. A Remote Procedure Call (<acronym title="Remote Procedure Call">RPC</acronym>) abstraction wraps both the Client Protocol and the DataNode Protocol. By design, the NameNode never initiates any RPCs. Instead, it only responds to RPC requests issued by DataNodes or clients. 
       </p>
     </section>
  
@@ -187,20 +187,20 @@
     <section> 
       <title> Robustness </title>
       <p>
-      The primary objective of HDFS is to store data reliably even in the presence of failures. The three common types of failures are Namenode failures, Datanode failures and network partitions.
+      The primary objective of HDFS is to store data reliably even in the presence of failures. The three common types of failures are NameNode failures, DataNode failures and network partitions.
       </p>
  
       <section>
         <title> Data Disk Failure, Heartbeats and Re-Replication </title>
         <p>
-        Each 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 by the absence of a Heartbeat message. The Namenode marks Datanodes without recent Heartbeats as dead and does not forward any new <acronym title="Input/Output">IO</acronym> requests to them. Any data that was registered to a dead Datanode is not available to HDFS any more. Datanode death may cause the replication factor of some blocks to fall below their specified value. The Namenode constantly tracks which blocks need to be replicated and initiates replication whenever necessary. The necessity for re-replication may arise due to many reasons: a Datanode may become unavailable, a replica may become corrupted, a hard disk on a Datanode may fail, or the replication factor of a file may be increased. 
+        Each 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 by the absence of a Heartbeat message. The NameNode marks DataNodes without recent Heartbeats as dead and does not forward any new <acronym title="Input/Output">IO</acronym> requests to them. Any data that was registered to a dead DataNode is not available to HDFS any more. DataNode death may cause the replication factor of some blocks to fall below their specified value. The NameNode constantly tracks which blocks need to be replicated and initiates replication whenever necessary. The necessity for re-replication may arise due to many reasons: a DataNode may become unavailable, a replica may become corrupted, a hard disk on a DataNode may fail, or the replication factor of a file may be increased. 
         </p>
       </section>
 
       <section>
         <title> Cluster Rebalancing </title>
         <p>
-        The HDFS architecture is compatible with <em>data rebalancing schemes</em>. A scheme might automatically move data from one Datanode to another if the free space on a Datanode falls below a certain threshold. In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster. These types of data rebalancing schemes are not yet implemented. 
+        The HDFS architecture is compatible with data rebalancing schemes. A scheme might automatically move data from one DataNode to another if the free space on a DataNode falls below a certain threshold. In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster. These types of data rebalancing schemes are not yet implemented. 
         </p>
       </section>
 
@@ -208,7 +208,7 @@
         <title> Data Integrity </title>
         <p>
         <!-- XXX "checksum checking" sounds funny -->
-        It is possible that a block of data fetched from a Datanode arrives corrupted. This corruption can occur because of faults in a storage device, network faults, or buggy software. The HDFS client software implements checksum checking on the contents of HDFS files. When a client creates an HDFS file, it computes a checksum of each block of 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 each Datanode matches the checksum stored in the associated checksum file. If not, then the client can opt to retrieve that block from another Datanode that has a replica of that block.
+        It is possible that a block of data fetched from a DataNode arrives corrupted. This corruption can occur because of faults in a storage device, network faults, or buggy software. The HDFS client software implements checksum checking on the contents of HDFS files. When a client creates an HDFS file, it computes a checksum of each block of 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 each DataNode matches the checksum stored in the associated 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>
  
@@ -216,10 +216,10 @@
       <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 HDFS instance to be non-functional. For this reason, the Namenode can be configured to support maintaining 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 copies of the FsImage and EditLog may degrade the rate of namespace transactions per second that a Namenode can support. However, this degradation is acceptable because even though HDFS applications are very <em>data</em> intensive in nature, they are not <em>metadata</em> intensive. When a Namenode restarts, it selects the latest consistent FsImage and EditLog to use.
+        The FsImage and the EditLog are central data structures of HDFS. A corruption of these files can cause the HDFS instance to be non-functional. For this reason, the NameNode can be configured to support maintaining 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 copies of the FsImage and EditLog may degrade the rate of namespace transactions per second that a NameNode can support. However, this degradation is acceptable because even though HDFS applications are very data intensive in nature, they are not metadata intensive. When a NameNode restarts, it selects the latest consistent FsImage and EditLog to use.
         </p>
         <p> 
-        The Namenode machine is a single point of failure for an HDFS cluster. If the Namenode machine fails, manual intervention is necessary. Currently, automatic restart and failover of the Namenode software to another machine is not supported.
+        The NameNode machine is a single point of failure for an HDFS cluster. If the NameNode machine fails, manual intervention is necessary. Currently, automatic restart and failover of the NameNode software to another machine is not supported.
         </p>
       </section>
 
@@ -240,7 +240,7 @@
       <section>
         <title> Data Blocks </title>
         <p>
-        HDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files. A typical block size used by HDFS is 64 MB. Thus, an HDFS file is chopped up into 64 MB chunks, and if possible, each chunk will reside on a different Datanode.
+        HDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files. A typical block size used by HDFS is 64 MB. Thus, an HDFS file is chopped up into 64 MB chunks, and if possible, each chunk will reside on a different DataNode.
         </p>
       </section>
 
@@ -249,7 +249,7 @@
         <!-- XXX staging never described / referenced in its section -->
         <title> Staging </title>
         <p>
-        A client request to create a file does not reach the Namenode immediately. In fact, initially the HDFS client caches the file data into a temporary local file. Application writes are transparently redirected to this temporary local file. When the local file accumulates data worth over one 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 and the destination data block. Then 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 tells 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. 
+        A client request to create a file does not reach the NameNode immediately. In fact, initially the HDFS client caches the file data into a temporary local file. Application writes are transparently redirected to this temporary local file. When the local file accumulates data worth over one 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 and the destination data block. Then 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 tells 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. These 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 precedent. Earlier distributed file systems, e.g. <acronym title="Andrew File System">AFS</acronym>, have used client side caching to improve performance. A POSIX requirement has been relaxed to achieve higher performance of data uploads. 
@@ -259,7 +259,7 @@
       <section>
         <title> Replication Pipelining </title>
         <p>
-        When a client is writing data to an HDFS file, its data is first written to a local file as explained in the previous section. Suppose the HDFS file has a replication factor of three. When the local file accumulates a full block of user data, the client retrieves a list of Datanodes from the Namenode. This list contains 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. Finally, the third Datanode writes the data to its local repository. Thus, a Datanode can be receiving data from the previous one in the pipeline and at the same time forwarding data to the next o
 ne in the pipeline. Thus, the data is pipelined from one Datanode to the next.
+        When a client is writing data to an HDFS file, its data is first written to a local file as explained in the previous section. Suppose the HDFS file has a replication factor of three. When the local file accumulates a full block of user data, the client retrieves a list of DataNodes from the NameNode. This list contains 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. Finally, the third DataNode writes the data to its local repository. Thus, a DataNode can be receiving data from the previous one in the pipeline and at the same time forwarding data to the next o
 ne in the pipeline. Thus, the data is pipelined from one DataNode to the next.
         </p>
       </section>
 
@@ -274,9 +274,9 @@
       </p>
 
       <section>
-        <title> DFSShell </title>
+        <title> FS Shell </title>
         <p>
-        HDFS allows user data to be organized in the form of files and directories. It provides a commandline interface called <em>DFSShell</em> 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 action/command pairs:
+        HDFS allows user data to be organized in the form of files and directories. It provides a commandline interface called  FS shell 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 action/command pairs:
         </p>
         <table>
           <tr>
@@ -293,27 +293,27 @@
           </tr>
         </table>
         <p>
-        DFSShell is targeted for applications that need a scripting language to interact with the stored data.
+        FS shell is targeted for applications that need a scripting language to interact with the stored data.
         </p>
       </section>
 
       <section> 
         <title> DFSAdmin </title>
         <p>
-        The <em>DFSAdmin</em> command set is used for administering an HDFS cluster. These are commands that are used only by an HDFS administrator. Here are some sample action/command pairs:
+        The DFSAdmin command set is used for administering an HDFS cluster. These are commands that are used only by an HDFS administrator. Here are some sample action/command pairs:
         </p>
         <table>
           <tr>
             <th> Action </th><th> Command </th>
           </tr>
           <tr>
-            <td> Put a cluster in SafeMode </td> <td> <code>bin/hadoop dfsadmin -safemode enter</code> </td>
+            <td> Put the cluster in Safemode </td> <td> <code>bin/hadoop dfsadmin -safemode enter</code> </td>
           </tr>
           <tr>
-            <td> Generate a list of Datanodes </td> <td> <code>bin/hadoop dfsadmin -report</code> </td>
+            <td> Generate a list of DataNodes </td> <td> <code>bin/hadoop dfsadmin -report</code> </td>
           </tr>
           <tr>
-            <td> Decommission Datanode <code>datanodename</code> </td><td> <code>bin/hadoop dfsadmin -decommission datanodename</code> </td>
+            <td> Decommission DataNode <code>datanodename</code> </td><td> <code>bin/hadoop dfsadmin -decommission datanodename</code> </td>
           </tr>
         </table>
       </section>
@@ -333,7 +333,7 @@
       <section>
         <title> File Deletes and Undeletes </title>
         <p>
-        When a file is deleted by a user or an application, it is not immediately removed from HDFS.  Instead, HDFS first renames it to a file in the <code>/trash</code> directory. The file can be restored quickly as long as it remains in <code>/trash</code>. A file remains in <code>/trash</code> for a configurable amount of time. After the expiry of its life in <code>/trash</code>, the Namenode deletes the file from the HDFS namespace. The deletion of a file causes the blocks associated with the file to be freed. Note that 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.
+        When a file is deleted by a user or an application, it is not immediately removed from HDFS.  Instead, HDFS first renames it to a file in the <code>/trash</code> directory. The file can be restored quickly as long as it remains in <code>/trash</code>. A file remains in <code>/trash</code> for a configurable amount of time. After the expiry of its life in <code>/trash</code>, the NameNode deletes the file from the HDFS namespace. The deletion of a file causes the blocks associated with the file to be freed. Note that 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 <code>/trash</code> directory. If a user wants to undelete a file that he/she has deleted, he/she can navigate the <code>/trash</code> directory and retrieve the file. The <code>/trash</code> directory contains only the latest copy of the file that was deleted. The <code>/trash</code> 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 from <code>/trash</code> that are more than 6 hours old. In the future, this policy will be configurable through a well defined interface.
@@ -343,7 +343,7 @@
       <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. Once again, there might be a time delay between the completion of the <code>setReplication</code> API call and the appearance of free space in the cluster.
+        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. Once again, there might be a time delay between the completion of the <code>setReplication</code> API call and the appearance of free space in the cluster.
         </p>
       </section>
     </section>

Modified: hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_quota_admin_guide.xml
URL: http://svn.apache.org/viewvc/hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_quota_admin_guide.xml?rev=674588&r1=674587&r2=674588&view=diff
==============================================================================
--- hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_quota_admin_guide.xml (original)
+++ hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_quota_admin_guide.xml Mon Jul  7 11:49:22 2008
@@ -23,21 +23,21 @@
 
   <header>
     <title>
-      Hadoop DFS Name Space Quotas Administrator Guide
+      Name Space Quotas Administrator Guide
     </title>
   </header>
 
   <body>
       <p>
-      HDFS allows the administrator to set quotas on individual directories. 
+      The Hadoop Distributed File System (HDFS) allows the administrator to set quotas on individual directories. 
       Newly created directories have no associated quota. 
-      The largest quota is Long.Max_Value. A quota of one forces a directory 
+      The largest quota is <code>Long.Max_Value</code>. A quota of one forces a directory 
       to remain empty.
       </p>
 
       <p>
       The directory quota is a hard limit on the number of names in the tree 
-      rooted at that directory. File/directory creations fault if the quota 
+      rooted at that directory. File and directory creations fault if the quota 
       would be exceeded. Quotas stick to renamed directories; the rename 
       operation faults if operation would result in a quota violation. 
       The attempt to set a quota faults if the directory would be in violation 
@@ -46,9 +46,9 @@
 
       <p>
       Quotas are persistent with the fsimage. When starting, if the fsimage 
-      is immediately in violation of aquota (perhaps the fsimage was 
-      surreptitiously modified), startup operation fails with an error report. 
-      Setting/removing a quota creates a journal entry.
+      is immediately in violation of a quota (perhaps the fsimage was 
+      surreptitiously modified), the startup operation fails with an error report. 
+      Setting or removing a quota creates a journal entry.
       </p> 
 
       <p>
@@ -58,26 +58,26 @@
 
       <ul>
       <li>
-      <strong><em>dfsadmin -setquota &lt;N> &lt;directory>...&lt;directory></em></strong> 
+      <code>dfsadmin -setquota &lt;N> &lt;directory>...&lt;directory></code> 
       <br />
-      Set the quota <em></em> for each directory. Best effort for each directory,
-      with faults reported if <em>N</em> is not a positive long integer, 
+      Set the quota to be <code>N</code> for each directory. Best effort for each directory,
+      with faults reported if <code>N</code> is not a positive long integer, 
       the directory does not exist or it is a file, or the directory would 
       immediately exceed the new quota.
       </li>
   
       <li>
-      <strong><em>dfsadmin -clrquota &lt;directory>...&lt;director></em></strong><br /> 
+      <code>dfsadmin -clrquota &lt;directory>...&lt;director></code><br /> 
       Remove any quota for each directory. Best effort for each directory, 
       with faults reported if the directory does not exist or it is a file. 
       It is not a fault if the directory has no quota.
       </li>
   
       <li>
-      <strong><em>fs -count -q &lt;directory>...&lt;directory></em></strong><br />
-      With the <em>-q</em> option, also report the quota value set for each 
+      <code>fs -count -q &lt;directory>...&lt;directory></code><br />
+      With the <code>-q</code> option, also report the quota value set for each 
       directory, and the available quota remaining. If the directory does not have 
-      a quota set, the reported values are <em>none</em> and <em>inf</em>.
+      a quota set, the reported values are <code>none</code> and <code>inf</code>.
       </li>
       </ul>
    </body>

Modified: hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_user_guide.xml
URL: http://svn.apache.org/viewvc/hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_user_guide.xml?rev=674588&r1=674587&r2=674588&view=diff
==============================================================================
--- hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_user_guide.xml (original)
+++ hadoop/core/trunk/src/docs/src/documentation/content/xdocs/hdfs_user_guide.xml Mon Jul  7 11:49:22 2008
@@ -23,18 +23,18 @@
 
   <header>
     <title>
-      Hadoop DFS User Guide
+      HDFS User Guide
     </title>
   </header>
 
   <body>
     <section> <title>Purpose</title>
       <p>
- This document aims to be the starting point for users working with
+ This document is a starting point for users working with
  Hadoop Distributed File System (HDFS) either as a part of a
  <a href="http://hadoop.apache.org/">Hadoop</a>
  cluster or as a stand-alone general purpose distributed file system.
- While HDFS is designed to "just-work" in many environments, a working
+ While HDFS is designed to "just work" in many environments, a working
  knowledge of HDFS helps greatly with configuration improvements and
  diagnostics on a specific cluster.
       </p>
@@ -43,21 +43,20 @@
     <section> <title> Overview </title>
       <p>
  HDFS is the primary distributed storage used by Hadoop applications. A
- HDFS cluster primarily consists of a <em>NameNode</em> that manages the
- filesystem metadata and Datanodes that store the actual data. The
+ HDFS cluster primarily consists of a NameNode that manages the
+ file system metadata and DataNodes that store the actual data. The
  architecture of HDFS is described in detail
  <a href="hdfs_design.html">here</a>. This user guide primarily deals with 
  interaction of users and administrators with HDFS clusters. 
  The <a href="images/hdfsarchitecture.gif">diagram</a> from 
  <a href="hdfs_design.html">HDFS architecture</a> depicts 
- basic interactions among Namenode, Datanodes, and the clients. Eseentially,
- clients contact Namenode for file metadata or file modifications and perform 
- actual file I/O directly with the datanodes.
+ basic interactions among NameNode, the DataNodes, and the clients. 
+ Clients contact NameNode for file metadata or file modifications and perform 
+ actual file I/O directly with the DataNodes.
       </p>
       <p>
  The following are some of the salient features that could be of
- interest to many users. The terms in <em>italics</em>
- are described in later sections.
+ interest to many users.
       </p> 
     <ul>
     <li>
@@ -74,13 +73,13 @@
     	needs to be tuned only for very large clusters.
     </li>
     <li>
-    	It is written in Java and is supported on all major platforms.
+    	Hadoop is written in Java and is supported on all major platforms.
     </li>
     <li>
-    	Supports <em>shell like commands</em> to interact with HDFS directly.
+    	Hadoop supports shell-like commands to interact with HDFS directly.
     </li>
     <li>
-    	Namenode and Datanodes have built in web servers that makes it
+    	The NameNode and Datanodes have built in web servers that makes it
     	easy to check current status of the cluster.
     </li>
     <li>
@@ -88,33 +87,33 @@
     	The following is a subset of useful features in HDFS:
       <ul>
     	<li>
-    		<em>File permissions and authentication.</em>
+    		File permissions and authentication.
     	</li>
     	<li>
-    		<em>Rack awareness</em> : to take a node's physical location into
+    		<em>Rack awareness</em>: to take a node's physical location into
     		account while scheduling tasks and allocating storage.
     	</li>
     	<li>
-    		<em>Safemode</em> : an administrative mode for maintanance.
+    		Safemode: an administrative mode for maintenance.
     	</li>
     	<li>
-    		<em>fsck</em> : an utility to diagnose health of the filesystem, to
+    		<code>fsck</code>: a utility to diagnose health of the file system, to
     		find missing files or blocks.
     	</li>
     	<li>
-    		<em>Rebalancer</em> : tool to balance the cluster when the data is
-    		unevenly distributed among datanodes.
+    		Rebalancer: tool to balance the cluster when the data is
+    		unevenly distributed among DataNodes.
     	</li>
     	<li>
-    		<em>Upgrade and Rollback</em> : after a software upgrade, 
+    		Upgrade and rollback: after a software upgrade, 
             it is possible to
     		rollback to HDFS' state before the upgrade in case of unexpected
     		problems.
     	</li>
     	<li>
-    		<em>Secondary Namenode</em> : performs periodic checkpoints of the 
+    		Secondary NameNode: performs periodic checkpoints of the 
     		namespace and helps keep the size of file containing log of HDFS 
-    		modifications within certain limits at the Namenode.
+    		modifications within certain limits at the NameNode.
     	</li>
       </ul>
     </li>
@@ -127,7 +126,7 @@
     </p>
  	<ul>
  	<li>
- 		<a href="quickstart.html">Hadoop Quickstart</a>
+ 		<a href="quickstart.html">Hadoop Quick Start</a>
  		for first-time users.
  	</li>
  	<li>
@@ -136,56 +135,56 @@
  	</li>
     </ul>
     <p>
- 	The rest of document assumes the user is able to set up and run a
- 	HDFS with at least one Datanode. For the purpose of this document,
- 	both Namenode and Datanode could be running on the same physical
+ 	The rest of this document assumes the user is able to set up and run a
+ 	HDFS with at least one DataNode. For the purpose of this document,
+ 	both the NameNode and DataNode could be running on the same physical
  	machine.	
     </p>
      
     </section> <section> <title> Web Interface </title>
  <p>
- 	Namenode and Datanode each run an internal web server in order to
+ 	NameNode and DataNode each run an internal web server in order to
  	display basic information about the current status of the cluster.
- 	With the default configuration, namenode front page is at
- 	<code>http://namenode:50070/</code> .
- 	It lists the datanodes in the cluster and basic stats of the
+ 	With the default configuration, the NameNode front page is at
+ 	<code>http://namenode-name:50070/</code>.
+ 	It lists the DataNodes in the cluster and basic statistics of the
  	cluster. The web interface can also be used to browse the file
- 	system (using "Browse the file system" link on the Namenode front
+ 	system (using "Browse the file system" link on the NameNode front
  	page).
  </p> 
  	
     </section> <section> <title>Shell Commands</title>
  	<p>
-      Hadoop includes various "shell-like" commands that directly
+      Hadoop includes various shell-like commands that directly
       interact with HDFS and other file systems that Hadoop supports.
       The command
       <code>bin/hadoop fs -help</code>
       lists the commands supported by Hadoop
-      shell. Further,
-      <code>bin/hadoop fs -help command</code>
-      displays more detailed help on a command. The commands support
-      most of the normal filesystem operations like copying files,
+      shell. Furthermore, the command
+      <code>bin/hadoop fs -help command-name</code>
+      displays more detailed help for a command. These commands support
+      most of the normal files ystem operations like copying files,
       changing file permissions, etc. It also supports a few HDFS
       specific operations like changing replication of files.
      </p>
 
    <section> <title> DFSAdmin Command </title>
    <p>
-   	<code>'bin/hadoop dfsadmin'</code>
+   	The <code>bin/hadoop dfsadmin</code>
    	command supports a few HDFS administration related operations.
-   	<code>bin/hadoop dfsadmin -help</code>
+   	The <code>bin/hadoop dfsadmin -help</code> command
    	lists all the commands currently supported. For e.g.:
    </p>  
    	<ul>
    	<li>
    	    <code>-report</code>
-   	    : reports basic stats of HDFS. Some of this information is
-   	    also available on the Namenode front page.
+   	    : reports basic statistics of HDFS. Some of this information is
+   	    also available on the NameNode front page.
    	</li>
    	<li>
    		<code>-safemode</code>
    		: though usually not required, an administrator can manually enter
-   		or leave <em>safemode</em>.
+   		or leave Safemode.
    	</li>
    	<li>
    		<code>-finalizeUpgrade</code>
@@ -197,88 +196,88 @@
    	</p>  
    </section>
    
-   </section> <section> <title> Secondary Namenode </title>
+   </section> <section> <title> Secondary NameNode </title>
    <p>
-     Namenode stores modifications to the file system as a log
+     The NameNode stores modifications to the file system as a log
      appended to a native file system file (<code>edits</code>). 
-   	When a Namenode starts up, it reads HDFS state from an image
-   	file (<code>fsimage</code>) and then applies <em>edits</em> from 
-    edits log file. It then writes new HDFS state to (<code>fsimage</code>)
+   	When a NameNode starts up, it reads HDFS state from an image
+   	file (<code>fsimage</code>) and then applies edits from the
+    edits log file. It then writes new HDFS state to the <code>fsimage</code>
     and starts normal
-   	operation with an empty edits file. Since namenode merges
+   	operation with an empty edits file. Since NameNode merges
    	<code>fsimage</code> and <code>edits</code> files only during start up, 
-    edits file could get very large over time on a large cluster. 
-    Another side effect of larger edits file is that next 
-    restart of Namenade takes longer.
+    the edits log file could get very large over time on a busy cluster. 
+    Another side effect of a larger edits file is that next 
+    restart of NameNode takes longer.
    </p>
    <p>
-     The secondary namenode merges fsimage and edits log periodically
-     and keeps edits log size with in a limit. It is usually run on a
-     different machine than the primary Namenode since its memory requirements
-     are on the same order as the primary namemode. The secondary
-     namenode is started by <code>bin/start-dfs.sh</code> on the nodes 
+     The secondary NameNode merges the fsimage and the edits log files periodically
+     and keeps edits log size within a limit. It is usually run on a
+     different machine than the primary NameNode since its memory requirements
+     are on the same order as the primary NameNode. The secondary
+     NameNode is started by <code>bin/start-dfs.sh</code> on the nodes 
      specified in <code>conf/masters</code> file.
    </p>
    <p>
-     The start of the checkpoint process on the secondary name-node is 
+     The start of the checkpoint process on the secondary NameNode is 
      controlled by two configuration parameters.
    </p>
    <ul>
       <li>
         <code>fs.checkpoint.period</code>, set to 1 hour by default, specifies
-        the maximal delay between two consecutive checkpoints, and 
+        the maximum delay between two consecutive checkpoints, and 
       </li>
       <li>
         <code>fs.checkpoint.size</code>, set to 64MB by default, defines the
         size of the edits log file that forces an urgent checkpoint even if 
-        the maximal checkpoint delay is not reached.
+        the maximum checkpoint delay is not reached.
       </li>
    </ul>
    <p>
-     The secondary name-node stores the latest checkpoint in a storage 
-     directory, which is structured the same way as the primary name-node's
-     storage directory. So that the checkpointed image is always ready to be
-     read by the primary name-node if necessary.
+     The secondary NameNode stores the latest checkpoint in a  
+     directory which is structured the same way as the primary NameNode's
+     directory. So that the check pointed image is always ready to be
+     read by the primary NameNode if necessary.
    </p>
    <p>
-     The latest checkpoint can be imported to the primary name-node if
+     The latest checkpoint can be imported to the primary NameNode if
      all other copies of the image and the edits files are lost.
      In order to do that one should:
    </p>
    <ul>
       <li>
-        create an empty storage directory specified in the 
+        Create an empty directory specified in the 
         <code>dfs.name.dir</code> configuration variable;
       </li>
       <li>
-        specify the location of the checkpoint storage directory in the 
+        Specify the location of the checkpoint directory in the 
         configuration variable <code>fs.checkpoint.dir</code>;
       </li>
       <li>
-        and start the name-node with <code>-importCheckpoint</code> option.
+        and start the NameNode with <code>-importCheckpoint</code> option.
       </li>
    </ul>
    <p>
-     The name-node will upload the checkpoint from the 
-     <code>fs.checkpoint.dir</code> directory and then save it to the name-node
-     storage directory(s) set in <code>dfs.name.dir</code>.
-     The name-node will fail if a legal image is contained in 
+     The NameNode will upload the checkpoint from the 
+     <code>fs.checkpoint.dir</code> directory and then save it to the NameNode
+     directory(s) set in <code>dfs.name.dir</code>.
+     The NameNode will fail if a legal image is contained in 
      <code>dfs.name.dir</code>.
-     The name-node verifies that the image in <code>fs.checkpoint.dir</code> is
+     The NameNode verifies that the image in <code>fs.checkpoint.dir</code> is
      consistent, but does not modify it in any way.
    </p>
    <p>
-     For command usage, see <a href="commands_manual.html#secondarynamenode">secondarynamenode command</a>.
+     For command usage, see <a href="commands_manual.html#secondarynamenode"><code>secondarynamenode</code> command</a>.
    </p>
    
    </section> <section> <title> Rebalancer </title>
     <p>
       HDFS data might not always be be placed uniformly across the
-      datanode. One common reason is addition of new datanodes to an
-      existing cluster. While placing new <em>blocks</em> (data for a file is
-      stored as a series of blocks), Namenode considers various
-      parameters before choosing the datanodes to receive these blocks.
-      Some of the considerations are : 
+      DataNode. One common reason is addition of new DataNodes to an
+      existing cluster. While placing new blocks (data for a file is
+      stored as a series of blocks), NameNode considers various
+      parameters before choosing the DataNodes to receive these blocks.
+      Some of the considerations are: 
     </p>
       <ul>
       <li>
@@ -295,15 +294,15 @@
         reduced.
       </li>
       <li>
-        Spread HDFS data uniformly across the datanodes in the cluster.
+        Spread HDFS data uniformly across the DataNodes in the cluster.
       </li>
       </ul>
     <p>
       Due to multiple competing considerations, data might not be
-      uniformly placed across the datanodes.
+      uniformly placed across the DataNodes.
       HDFS provides a tool for administrators that analyzes block
-      placement and relanaces data across the datnodes. A brief
-      adminstrator's guide for rebalancer as a
+      placement and rebalanaces data across the DataNode. A brief
+      administrator's guide for rebalancer as a
       <a href="http://issues.apache.org/jira/secure/attachment/12368261/RebalanceDesign6.pdf">PDF</a>
       is attached to
       <a href="http://issues.apache.org/jira/browse/HADOOP-1652">HADOOP-1652</a>.
@@ -314,15 +313,15 @@
     
    </section> <section> <title> Rack Awareness </title>
     <p>
-      Typically large Hadoop clusters are arranged in <em>racks</em> and
+      Typically large Hadoop clusters are arranged in racks and
       network traffic between different nodes with in the same rack is
       much more desirable than network traffic across the racks. In
-      addition Namenode tries to place replicas of block on
+      addition NameNode tries to place replicas of block on
       multiple racks for improved fault tolerance. Hadoop lets the
-      cluster administrators decide which <em>rack</em> a node belongs to
+      cluster administrators decide which rack a node belongs to
       through configuration variable <code>dfs.network.script</code>. When this
       script is configured, each node runs the script to determine its
-      <em>rackid</em>. A default installation assumes all the nodes belong to
+      rack id. A default installation assumes all the nodes belong to
       the same rack. This feature and configuration is further described
       in <a href="http://issues.apache.org/jira/secure/attachment/12345251/Rack_aware_HDFS_proposal.pdf">PDF</a>
       attached to 
@@ -331,36 +330,37 @@
 
    </section> <section> <title> Safemode </title>
     <p>
-      During start up Namenode loads the filesystem state from
-      <em>fsimage</em> and <em>edits</em> log file. It then waits for datanodes
+      During start up the NameNode loads the file system state from the
+      fsimage  and the edits log file. It then waits for DataNodes
       to report their blocks so that it does not prematurely start
       replicating the blocks though enough replicas already exist in the
-      cluster. During this time Namenode stays in <em>safemode</em>. A 
-      <em>Safemode</em>
-      for Namenode is essentially a read-only mode for the HDFS cluster,
-      where it does not allow any modifications to filesystem or blocks.
-      Normally Namenode gets out of safemode automatically at
-      the beginning. If required, HDFS could be placed in safemode explicitly
-      using <code>'bin/hadoop dfsadmin -safemode'</code> command. Namenode front
-      page shows whether safemode is on or off. A more detailed
+      cluster. During this time NameNode stays in Safemode. 
+      Safemode
+      for the NameNode is essentially a read-only mode for the HDFS cluster,
+      where it does not allow any modifications to file system or blocks.
+      Normally the NameNode leaves Safemode automatically after the DataNodes
+      have reported that most file system blocks are available. 
+      If required, HDFS could be placed in Safemode explicitly
+      using <code>'bin/hadoop dfsadmin -safemode'</code> command. NameNode front
+      page shows whether Safemode is on or off. A more detailed
       description and configuration is maintained as JavaDoc for
       <a href="http://hadoop.apache.org/core/docs/current/api/org/apache/hadoop/dfs/NameNode.html#setSafeMode(org.apache.hadoop.dfs.FSConstants.SafeModeAction)"><code>setSafeMode()</code></a>.
     </p>
     
-   </section> <section> <title> Fsck </title>
+   </section> <section> <title> fsck </title>
      <p>    
-      HDFS supports <code>fsck</code> command to check for various 
+      HDFS supports the <code>fsck</code> command to check for various 
       inconsistencies.
       It it is designed for reporting problems with various
-      files, for e.g. missing blocks for a file or under replicated
-      blocks. Unlike a traditional fsck utility for native filesystems,
-      this command does not correct the errors it detects. Normally Namenode
+      files, for example, missing blocks for a file or under-replicated
+      blocks. Unlike a traditional <code>fsck</code> utility for native file systems,
+      this command does not correct the errors it detects. Normally NameNode
       automatically corrects most of the recoverable failures. By default
-      fsck ignores open files but provides an option to select during reporting.
-      HDFS' fsck is not a
+      <code>fsck</code> ignores open files but provides an option to select all files during reporting.
+      The HDFS <code>fsck</code> command is not a
       Hadoop shell command. It can be run as '<code>bin/hadoop fsck</code>'.
-      For command usage, see <a href="commands_manual.html#fsck">fsck command</a>. 
-      Fsck can be run on the whole filesystem or on a subset of files.
+      For command usage, see <a href="commands_manual.html#fsck"><code>fsck</code> command</a>. 
+      <code>fsck</code> can be run on the whole file system or on a subset of files.
      </p>
      
    </section> <section> <title> Upgrade and Rollback </title>
@@ -371,14 +371,14 @@
       not discovered earlier. In any non-trivial HDFS installation, it
       is not an option to loose any data, let alone to restart HDFS from
       scratch. HDFS allows administrators to go back to earlier version
-      of Hadoop and <em>roll back</em> the cluster to the state it was in 
+      of Hadoop and rollback the cluster to the state it was in 
       before
       the upgrade. HDFS upgrade is described in more detail in 
       <a href="http://wiki.apache.org/hadoop/Hadoop%20Upgrade">upgrade wiki</a>.
       HDFS can have one such backup at a time. Before upgrading,
       administrators need to remove existing backup using <code>bin/hadoop
       dfsadmin -finalizeUpgrade</code> command. The following
-      briefly describes typical upgrade procedure : 
+      briefly describes the typical upgrade procedure: 
      </p>
       <ul>
       <li>
@@ -397,7 +397,7 @@
         considered working well (may be after a few days of operation),
         finalize the upgrade. Note that until the cluster is finalized,
         deleting the files that existed before the upgrade does not free
-        up real disk space on the datanodes.
+        up real disk space on the DataNodes.
       </li>
       <li>
         If there is a need to move back to the old version,
@@ -414,23 +414,23 @@
      <p>           
       The file permissions are designed to be similar to file permissions on
       other familiar platforms like Linux. Currently, security is limited
-      to simple file permissions. The user that starts Namenode is
-      treated as the <em>super user</em> for HDFS. Future versions of HDFS will
+      to simple file permissions. The user that starts NameNode is
+      treated as the superuser for HDFS. Future versions of HDFS will
       support network authentication protocols like Kerberos for user
       authentication and encryption of data transfers. The details are discussed in the 
-      <a href="hdfs_permissions_guide.html"><em>Permissions User and Administrator Guide</em></a>.
+      <a href="hdfs_permissions_guide.html">Permissions User and Administrator Guide</a>.
      </p>
      
    </section> <section> <title> Scalability </title>
      <p>
       Hadoop currently runs on clusters with thousands of nodes.
-      <a href="http://wiki.apache.org/hadoop/PoweredBy">PoweredBy Hadoop</a>
+      <a href="http://wiki.apache.org/hadoop/PoweredBy">Powered By Hadoop</a>
       lists some of the organizations that deploy Hadoop on large
-      clusters. HDFS has one Namenode for each cluster. Currently
-      the total memory available on Namenode is the primary scalability
+      clusters. HDFS has one NameNode for each cluster. Currently
+      the total memory available on NameNode is the primary scalability
       limitation. On very large clusters, increasing average size of
       files stored in HDFS helps with increasing cluster size without
-      increasing memory requirements on Namenode.
+      increasing memory requirements on NameNode.
    
       The default configuration may not suite very large clustes.
       <a href="http://wiki.apache.org/hadoop/FAQ">Hadoop FAQ</a> page lists
@@ -439,15 +439,14 @@
      
    </section> <section> <title> Related Documentation </title>          
       <p>
-      This user guide is intended to be a good starting point for
-      working with HDFS. While it continues to improve,
+      This user guide is a good starting point for
+      working with HDFS. While the user guide continues to improve,
       there is a large wealth of documentation about Hadoop and HDFS.
-      The following lists starting points for further exploration :
+      The following list is a starting point for further exploration:
       </p>
       <ul>
       <li>
-        <a href="http://hadoop.apache.org/">Hadoop Home Page</a>
-        : the start page for everything Hadoop.
+        <a href="http://hadoop.apache.org/">Hadoop Home Page</a>: The start page for everything Hadoop.
       </li>
       <li>
         <a href="http://wiki.apache.org/hadoop/FrontPage">Hadoop Wiki</a>



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