branch-2.0.4-alpha/hadoop-hdfs-project/hadoop-hdfs/src/site/apt/HdfsUserGuide.apt.vm

~~ Licensed under the Apache License, Version 2.0 (the "License");
~~ you may not use this file except in compliance with the License.
~~ You may obtain a copy of the License at
~~
~~   http://www.apache.org/licenses/LICENSE-2.0
~~
~~ Unless required by applicable law or agreed to in writing, software
~~ distributed under the License is distributed on an "AS IS" BASIS,
~~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
~~ See the License for the specific language governing permissions and
~~ limitations under the License. See accompanying LICENSE file.

  ---
  HDFS Users Guide
  ---
  ---
  ${maven.build.timestamp}

HDFS Users Guide

%{toc|section=1|fromDepth=0}

* Purpose

   This document is a starting point for users working with Hadoop
   Distributed File System (HDFS) either as a part of a Hadoop cluster or
   as a stand-alone general purpose distributed file system. 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.

* Overview

   HDFS is the primary distributed storage used by Hadoop applications. A
   HDFS cluster primarily consists of a NameNode that manages the file
   system metadata and DataNodes that store the actual data. The HDFS
   Architecture Guide describes HDFS in detail. This user guide primarily
   deals with the interaction of users and administrators with HDFS
   clusters. The HDFS architecture diagram depicts 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.

   The following are some of the salient features that could be of
   interest to many users.

     * Hadoop, including HDFS, is well suited for distributed storage and
       distributed processing using commodity hardware. It is fault
       tolerant, scalable, and extremely simple to expand. MapReduce, well
       known for its simplicity and applicability for large set of
       distributed applications, is an integral part of Hadoop.

     * HDFS is highly configurable with a default configuration well
       suited for many installations. Most of the time, configuration
       needs to be tuned only for very large clusters.

     * Hadoop is written in Java and is supported on all major platforms.

     * Hadoop supports shell-like commands to interact with HDFS directly.

     * The NameNode and Datanodes have built in web servers that makes it
       easy to check current status of the cluster.

     * New features and improvements are regularly implemented in HDFS.
       The following is a subset of useful features in HDFS:

          * File permissions and authentication.

          * Rack awareness: to take a node's physical location into
            account while scheduling tasks and allocating storage.

          * Safemode: an administrative mode for maintenance.

          * <<<fsck>>>: a utility to diagnose health of the file system, to find
            missing files or blocks.

          * <<<fetchdt>>>: a utility to fetch DelegationToken and store it in a
            file on the local system.

          * Rebalancer: tool to balance the cluster when the data is
            unevenly distributed among DataNodes.

          * Upgrade and rollback: after a software upgrade, it is possible
            to rollback to HDFS' state before the upgrade in case of
            unexpected problems.

          * 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.

          * Checkpoint node: performs periodic checkpoints of the
            namespace and helps minimize the size of the log stored at the
            NameNode containing changes to the HDFS. Replaces the role
            previously filled by the Secondary NameNode, though is not yet
            battle hardened. The NameNode allows multiple Checkpoint nodes
            simultaneously, as long as there are no Backup nodes
            registered with the system.

          * Backup node: An extension to the Checkpoint node. In addition
            to checkpointing it also receives a stream of edits from the
            NameNode and maintains its own in-memory copy of the
            namespace, which is always in sync with the active NameNode
            namespace state. Only one Backup node may be registered with
            the NameNode at once.

* Prerequisites

   The following documents describe how to install and set up a Hadoop
   cluster:

     * {{Single Node Setup}} for first-time users.

     * {{Cluster Setup}} for large, distributed clusters.

   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.

* Web Interface

   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, the NameNode front page is at
   <<<http://namenode-name:50070/>>>. 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 page).

* Shell Commands

   Hadoop includes various shell-like commands that directly interact with
   HDFS and other file systems that Hadoop supports. The command <<<bin/hdfs dfs -help>>>
   lists the commands supported by Hadoop shell. Furthermore,
   the command <<<bin/hdfs dfs -help command-name>>> displays more detailed help
   for a command. These commands support most of the normal files system
   operations like copying files, changing file permissions, etc. It also
   supports a few HDFS specific operations like changing replication of
   files. For more information see {{{File System Shell Guide}}}.

**  DFSAdmin Command

   The <<<bin/hadoop dfsadmin>>> command supports a few HDFS administration
   related operations. The <<<bin/hadoop dfsadmin -help>>> command lists all the
   commands currently supported. For e.g.:

     * <<<-report>>>: reports basic statistics of HDFS. Some of this
       information is also available on the NameNode front page.

     * <<<-safemode>>>: though usually not required, an administrator can
       manually enter or leave Safemode.

     * <<<-finalizeUpgrade>>>: removes previous backup of the cluster made
       during last upgrade.

     * <<<-refreshNodes>>>: Updates the namenode with the set of datanodes
       allowed to connect to the namenode. Namenodes re-read datanode
       hostnames in the file defined by <<<dfs.hosts>>>, <<<dfs.hosts.exclude>>>.
       Hosts defined in <<<dfs.hosts>>> are the datanodes that are part of the
       cluster. If there are entries in <<<dfs.hosts>>>, only the hosts in it
       are allowed to register with the namenode. Entries in
       <<<dfs.hosts.exclude>>> are datanodes that need to be decommissioned.
       Datanodes complete decommissioning when all the replicas from them
       are replicated to other datanodes. Decommissioned nodes are not
       automatically shutdown and are not chosen for writing for new
       replicas.

     * <<<-printTopology>>> : Print the topology of the cluster. Display a tree
       of racks and datanodes attached to the tracks as viewed by the
       NameNode.

   For command usage, see {{{dfsadmin}}}.

* Secondary NameNode

   The NameNode stores modifications to the file system as a log appended
   to a native file system file, edits. When a NameNode starts up, it
   reads HDFS state from an image file, fsimage, and then applies edits
   from the edits log file. It then writes new HDFS state to the fsimage
   and starts normal operation with an empty edits file. Since NameNode
   merges fsimage and edits files only during start up, 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.

   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 start of the checkpoint process on the secondary NameNode is
   controlled by two configuration parameters.

     * <<<dfs.namenode.checkpoint.period>>>, set to 1 hour by default, specifies
       the maximum delay between two consecutive checkpoints, and

     * <<<dfs.namenode.checkpoint.txns>>>, set to 40000 default, defines the
       number of uncheckpointed transactions on the NameNode which will
       force an urgent checkpoint, even if the checkpoint period has not
       been reached.

   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.

   For command usage, see {{{secondarynamenode}}}.

* Checkpoint Node

   NameNode persists its namespace using two files: fsimage, which is the
   latest checkpoint of the namespace and edits, a journal (log) of
   changes to the namespace since the checkpoint. When a NameNode starts
   up, it merges the fsimage and edits journal to provide an up-to-date
   view of the file system metadata. The NameNode then overwrites fsimage
   with the new HDFS state and begins a new edits journal.

   The Checkpoint node periodically creates checkpoints of the namespace.
   It downloads fsimage and edits from the active NameNode, merges them
   locally, and uploads the new image back to the active NameNode. The
   Checkpoint node usually runs on a different machine than the NameNode
   since its memory requirements are on the same order as the NameNode.
   The Checkpoint node is started by bin/hdfs namenode -checkpoint on the
   node specified in the configuration file.

   The location of the Checkpoint (or Backup) node and its accompanying
   web interface are configured via the <<<dfs.namenode.backup.address>>> and
   <<<dfs.namenode.backup.http-address>>> configuration variables.

   The start of the checkpoint process on the Checkpoint node is
   controlled by two configuration parameters.

     * <<<dfs.namenode.checkpoint.period>>>, set to 1 hour by default, specifies
       the maximum delay between two consecutive checkpoints

     * <<<dfs.namenode.checkpoint.txns>>>, set to 40000 default, defines the
       number of uncheckpointed transactions on the NameNode which will
       force an urgent checkpoint, even if the checkpoint period has not
       been reached.

   The Checkpoint node stores the latest checkpoint in a directory that is
   structured the same as the NameNode's directory. This allows the
   checkpointed image to be always available for reading by the NameNode
   if necessary. See Import checkpoint.

   Multiple checkpoint nodes may be specified in the cluster configuration
   file.

   For command usage, see {{{namenode}}}.

* Backup Node

   The Backup node provides the same checkpointing functionality as the
   Checkpoint node, as well as maintaining an in-memory, up-to-date copy
   of the file system namespace that is always synchronized with the
   active NameNode state. Along with accepting a journal stream of file
   system edits from the NameNode and persisting this to disk, the Backup
   node also applies those edits into its own copy of the namespace in
   memory, thus creating a backup of the namespace.

   The Backup node does not need to download fsimage and edits files from
   the active NameNode in order to create a checkpoint, as would be
   required with a Checkpoint node or Secondary NameNode, since it already
   has an up-to-date state of the namespace state in memory. The Backup
   node checkpoint process is more efficient as it only needs to save the
   namespace into the local fsimage file and reset edits.

   As the Backup node maintains a copy of the namespace in memory, its RAM
   requirements are the same as the NameNode.

   The NameNode supports one Backup node at a time. No Checkpoint nodes
   may be registered if a Backup node is in use. Using multiple Backup
   nodes concurrently will be supported in the future.

   The Backup node is configured in the same manner as the Checkpoint
   node. It is started with <<<bin/hdfs namenode -backup>>>.

   The location of the Backup (or Checkpoint) node and its accompanying
   web interface are configured via the <<<dfs.namenode.backup.address>>> and
   <<<dfs.namenode.backup.http-address>>> configuration variables.

   Use of a Backup node provides the option of running the NameNode with
   no persistent storage, delegating all responsibility for persisting the
   state of the namespace to the Backup node. To do this, start the
   NameNode with the <<<-importCheckpoint>>> option, along with specifying no
   persistent storage directories of type edits <<<dfs.namenode.edits.dir>>> for
   the NameNode configuration.

   For a complete discussion of the motivation behind the creation of the
   Backup node and Checkpoint node, see {{{https://issues.apache.org/jira/browse/HADOOP-4539}HADOOP-4539}}.
   For command usage, see {{{namenode}}}.

* Import Checkpoint

   The latest checkpoint can be imported to the NameNode if all other
   copies of the image and the edits files are lost. In order to do that
   one should:

     * Create an empty directory specified in the <<<dfs.namenode.name.dir>>>
       configuration variable;

     * Specify the location of the checkpoint directory in the
       configuration variable <<<dfs.namenode.checkpoint.dir>>>;

     * and start the NameNode with <<<-importCheckpoint>>> option.

   The NameNode will upload the checkpoint from the
   <<<dfs.namenode.checkpoint.dir>>> directory and then save it to the NameNode
   directory(s) set in <<<dfs.namenode.name.dir>>>. The NameNode will fail if a
   legal image is contained in <<<dfs.namenode.name.dir>>>. The NameNode
   verifies that the image in <<<dfs.namenode.checkpoint.dir>>> is consistent,
   but does not modify it in any way.

   For command usage, see {{{namenode}}}.

* Rebalancer

   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 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:

     * Policy to keep one of the replicas of a block on the same node as
       the node that is writing the block.

     * Need to spread different replicas of a block across the racks so
       that cluster can survive loss of whole rack.

     * One of the replicas is usually placed on the same rack as the node
       writing to the file so that cross-rack network I/O is reduced.

     * Spread HDFS data uniformly across the DataNodes in the cluster.

   Due to multiple competing considerations, data might not be uniformly
   placed across the DataNodes. HDFS provides a tool for administrators
   that analyzes block placement and rebalanaces data across the DataNode.
   A brief administrator's guide for rebalancer as a PDF is attached to
   {{{https://issues.apache.org/jira/browse/HADOOP-1652}HADOOP-1652}}.

   For command usage, see {{{balancer}}}.

* Rack Awareness

   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 multiple racks for improved fault
   tolerance. Hadoop lets the cluster administrators decide which rack a
   node belongs to through configuration variable
   <<<net.topology.script.file.name>>>. When this script is configured, each
   node runs the script to determine its rack id. A default installation
   assumes all the nodes belong to the same rack. This feature and
   configuration is further described in PDF attached to
   {{{https://issues.apache.org/jira/browse/HADOOP-692}HADOOP-692}}.

* Safemode

   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 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 <<<bin/hadoop dfsadmin -safemode>>>
   command. NameNode front page shows whether Safemode is on or off. A
   more detailed description and configuration is maintained as JavaDoc
   for <<<setSafeMode()>>>.

* fsck

   HDFS supports the fsck command to check for various inconsistencies. It
   it is designed for reporting problems with various files, for example,
   missing blocks for a file or under-replicated blocks. Unlike a
   traditional fsck 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 all files during reporting. The HDFS
   fsck command is not a Hadoop shell command. It can be run as
   <<<bin/hadoop fsck>>>. For command usage, see {{{fsck}}}. fsck can be run on the
   whole file system or on a subset of files.

* fetchdt

   HDFS supports the fetchdt command to fetch Delegation Token and store
   it in a file on the local system. This token can be later used to
   access secure server (NameNode for example) from a non secure client.
   Utility uses either RPC or HTTPS (over Kerberos) to get the token, and
   thus requires kerberos tickets to be present before the run (run kinit
   to get the tickets). The HDFS fetchdt command is not a Hadoop shell
   command. It can be run as <<<bin/hadoop fetchdt DTfile>>>. After you got
   the token you can run an HDFS command without having Kerberos tickets,
   by pointing <<<HADOOP_TOKEN_FILE_LOCATION>>> environmental variable to the
   delegation token file. For command usage, see {{{fetchdt}}} command.

* Recovery Mode

   Typically, you will configure multiple metadata storage locations.
   Then, if one storage location is corrupt, you can read the metadata
   from one of the other storage locations.

   However, what can you do if the only storage locations available are
   corrupt? In this case, there is a special NameNode startup mode called
   Recovery mode that may allow you to recover most of your data.

   You can start the NameNode in recovery mode like so: <<<namenode -recover>>>

   When in recovery mode, the NameNode will interactively prompt you at
   the command line about possible courses of action you can take to
   recover your data.

   If you don't want to be prompted, you can give the <<<-force>>> option. This
   option will force recovery mode to always select the first choice.
   Normally, this will be the most reasonable choice.

   Because Recovery mode can cause you to lose data, you should always
   back up your edit log and fsimage before using it.

* Upgrade and Rollback

   When Hadoop is upgraded on an existing cluster, as with any software
   upgrade, it is possible there are new bugs or incompatible changes that
   affect existing applications and were 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 rollback the cluster to the
   state it was in before the upgrade. HDFS upgrade is described in more
   detail in {{{Hadoop Upgrade}}} Wiki page. HDFS can have one such backup at a
   time. Before upgrading, administrators need to remove existing backup
   using bin/hadoop dfsadmin <<<-finalizeUpgrade>>> command. The following
   briefly describes the typical upgrade procedure:

     * Before upgrading Hadoop software, finalize if there an existing
       backup. <<<dfsadmin -upgradeProgress>>> status can tell if the cluster
       needs to be finalized.

     * Stop the cluster and distribute new version of Hadoop.

     * Run the new version with <<<-upgrade>>> option (<<<bin/start-dfs.sh -upgrade>>>).

     * Most of the time, cluster works just fine. Once the new HDFS is
       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.

     * If there is a need to move back to the old version,

          * stop the cluster and distribute earlier version of Hadoop.

          * start the cluster with rollback option. (<<<bin/start-dfs.h -rollback>>>).

* File Permissions and Security

   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 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
   Permissions Guide.

* Scalability

   Hadoop currently runs on clusters with thousands of nodes. The
   {{{PoweredBy}}} Wiki page 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
   limitation. On very large clusters, increasing average size of files
   stored in HDFS helps with increasing cluster size without increasing
   memory requirements on NameNode. The default configuration may not
   suite very large clustes. The {{{FAQ}}} Wiki page lists suggested
   configuration improvements for large Hadoop clusters.

* Related Documentation

   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 list is a starting
   point for further exploration:

     * {{{Hadoop Site}}}: The home page for the Apache Hadoop site.

     * {{{Hadoop Wiki}}}: The home page (FrontPage) for the Hadoop Wiki. Unlike
       the released documentation, which is part of Hadoop source tree,
       Hadoop Wiki is regularly edited by Hadoop Community.

     * {{{FAQ}}}: The FAQ Wiki page.

     * {{{Hadoop JavaDoc API}}}.

     * {{{Hadoop User Mailing List}}}: core-user[at]hadoop.apache.org.

     * Explore {{{src/hdfs/hdfs-default.xml}}}. It includes brief description of
       most of the configuration variables available.

     * {{{Hadoop Commands Guide}}}: Hadoop commands usage.