Return-Path: Delivered-To: apmail-lucene-hadoop-commits-archive@locus.apache.org Received: (qmail 14511 invoked from network); 14 Jun 2007 22:19:38 -0000 Received: from hermes.apache.org (HELO mail.apache.org) (140.211.11.2) by minotaur.apache.org with SMTP; 14 Jun 2007 22:19:38 -0000 Received: (qmail 3964 invoked by uid 500); 14 Jun 2007 22:19:40 -0000 Delivered-To: apmail-lucene-hadoop-commits-archive@lucene.apache.org Received: (qmail 3944 invoked by uid 500); 14 Jun 2007 22:19:40 -0000 Mailing-List: contact hadoop-commits-help@lucene.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: hadoop-dev@lucene.apache.org Delivered-To: mailing list hadoop-commits@lucene.apache.org Received: (qmail 3927 invoked by uid 99); 14 Jun 2007 22:19:40 -0000 Received: from herse.apache.org (HELO herse.apache.org) (140.211.11.133) by apache.org (qpsmtpd/0.29) with ESMTP; Thu, 14 Jun 2007 15:19:40 -0700 X-ASF-Spam-Status: No, hits=-97.8 required=10.0 tests=ALL_TRUSTED,NO_REAL_NAME,OBSCURED_EMAIL X-Spam-Check-By: apache.org Received: from [140.211.11.3] (HELO eris.apache.org) (140.211.11.3) by apache.org (qpsmtpd/0.29) with ESMTP; Thu, 14 Jun 2007 15:19:32 -0700 Received: by eris.apache.org (Postfix, from userid 65534) id E0EC91A981A; Thu, 14 Jun 2007 15:19:11 -0700 (PDT) Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Subject: svn commit: r547429 [1/4] - in /lucene/hadoop/trunk: ./ docs/ docs/images/ src/docs/src/documentation/ src/docs/src/documentation/content/xdocs/ src/docs/src/documentation/resources/images/ Date: Thu, 14 Jun 2007 22:19:10 -0000 To: hadoop-commits@lucene.apache.org From: cutting@apache.org X-Mailer: svnmailer-1.1.0 Message-Id: <20070614221911.E0EC91A981A@eris.apache.org> X-Virus-Checked: Checked by ClamAV on apache.org Author: cutting Date: Thu Jun 14 15:19:05 2007 New Revision: 547429 URL: http://svn.apache.org/viewvc?view=rev&rev=547429 Log: HADOOP-1438. Improve HDFS documentation, correcting typos and making images appear in PDF. Contributed by Luke Nezda. Added: lucene/hadoop/trunk/docs/images/hdfsarchitecture.gif (with props) lucene/hadoop/trunk/docs/images/hdfsdatanodes.gif (with props) lucene/hadoop/trunk/src/docs/src/documentation/resources/images/hdfsarchitecture.gif (with props) lucene/hadoop/trunk/src/docs/src/documentation/resources/images/hdfsdatanodes.gif (with props) Modified: lucene/hadoop/trunk/CHANGES.txt lucene/hadoop/trunk/docs/about.html lucene/hadoop/trunk/docs/about.pdf lucene/hadoop/trunk/docs/credits.html lucene/hadoop/trunk/docs/credits.pdf lucene/hadoop/trunk/docs/hdfs_design.html lucene/hadoop/trunk/docs/hdfs_design.pdf lucene/hadoop/trunk/docs/index.html lucene/hadoop/trunk/docs/index.pdf lucene/hadoop/trunk/docs/issue_tracking.html lucene/hadoop/trunk/docs/issue_tracking.pdf lucene/hadoop/trunk/docs/linkmap.html lucene/hadoop/trunk/docs/linkmap.pdf lucene/hadoop/trunk/docs/mailing_lists.html lucene/hadoop/trunk/docs/mailing_lists.pdf lucene/hadoop/trunk/docs/version_control.html lucene/hadoop/trunk/docs/version_control.pdf lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/hdfs_design.xml lucene/hadoop/trunk/src/docs/src/documentation/resources/images/hdfsarchitecture.odg lucene/hadoop/trunk/src/docs/src/documentation/resources/images/hdfsarchitecture.png lucene/hadoop/trunk/src/docs/src/documentation/resources/images/hdfsdatanodes.odg lucene/hadoop/trunk/src/docs/src/documentation/resources/images/hdfsdatanodes.png lucene/hadoop/trunk/src/docs/src/documentation/skinconf.xml Modified: lucene/hadoop/trunk/CHANGES.txt URL: http://svn.apache.org/viewvc/lucene/hadoop/trunk/CHANGES.txt?view=diff&rev=547429&r1=547428&r2=547429 ============================================================================== --- lucene/hadoop/trunk/CHANGES.txt (original) +++ lucene/hadoop/trunk/CHANGES.txt Thu Jun 14 15:19:05 2007 @@ -123,6 +123,10 @@ 39. HADOOP-1269. Finer grained locking in HDFS namenode. (Dhruba Borthakur via cutting) + 40. HADOOP-1438. Improve HDFS documentation, correcting typos and + making images appear in PDF. Also update copyright date for all + docs. (Luke Nezda via cutting) + Release 0.13.0 - 2007-06-08 Modified: lucene/hadoop/trunk/docs/about.html URL: http://svn.apache.org/viewvc/lucene/hadoop/trunk/docs/about.html?view=diff&rev=547429&r1=547428&r2=547429 ============================================================================== --- lucene/hadoop/trunk/docs/about.html (original) +++ lucene/hadoop/trunk/docs/about.html Thu Jun 14 15:19:05 2007 @@ -168,7 +168,7 @@
Copyright © - 2005 The Apache Software Foundation. + 2007 The Apache Software Foundation.
Modified: lucene/hadoop/trunk/docs/about.pdf URL: http://svn.apache.org/viewvc/lucene/hadoop/trunk/docs/about.pdf?view=diff&rev=547429&r1=547428&r2=547429 ============================================================================== --- lucene/hadoop/trunk/docs/about.pdf (original) +++ lucene/hadoop/trunk/docs/about.pdf Thu Jun 14 15:19:05 2007 @@ -8,7 +8,7 @@ << /Length 358 /Filter [ /ASCII85Decode /FlateDecode ] >> stream -Gaua8_/=lZ%#44r$6S4\0+"#]Z(4oi]1g[ogNK5$=sQQScrPMj&R(HA0 +Gaua8_/=lZ%#44r$6S4\0+"#]Z(4oi]1g[ogNK5$=sQQScrPMj&R(HA0@fTP0B=~> endstream endobj 6 0 obj @@ -39,7 +39,7 @@ << /Length 1390 /Filter [ /ASCII85Decode /FlateDecode ] >> stream -Gat=+a`?E"&A@B[E3uZ^Qoh;-3"sO^Q-*^p1-%TF6m4o!$j`AeU4A!40,c@@342[uH]t<<_k1.@s[.gMbbi'']N<6RtW$XKkeQ]J,7q.,a;m%-!d8`VUL!>@FS%MUks@GPJQBRSJj2Op[RE,UlhuRiSZF!HbA6o,qbpjn-i7p(o307hp,!E/C,a%WaBIB_1D@7RT,Oe_/a*Iba]ib5^>umbJq@+K:cl[+A]d@qB*sD"dQJ&JNCMdVu92APM(E.4t[0L4gEu2\R5DGqui%C6k`0+9`"0"(Z,hQ:Y"fLI_FA-u@h^ll!fZm8K-8Jbp`$I_YB-6&6P7P8$(e@E\"TXAS9edYc>%h2[X^F([Mr),+T@SY5;JUVFAqGW1Xd$QOS9X,'J%"QS)gRk.V4Slq8d_:8g$/CI3MNO5"NLB.shtcgt2:#JtAdQFrDKcWGr&fK41;nD2?Id6:`Y_>!A(iP3t"l*2WGhqR5:GVS#EIss^G?[iQ<8BVl,\41o_j():oaMV/NU)lHle&Hc8Q>"[`LbPk,U=5`AZO+oDI[3^fc.TNeh%F*#/4@=5+PSOG@IMT$B+7?jBV2@6K5mSG4d;Vf65.Uh`Ib0u=c8/o.#gZN:.pM$9*]!8JS'^2n7YKM.#r$h_\p1pS sMo4S]E,a7JCmZK,lWgdMKmIO5qWMts6$(YHZ+f"@rci\aEk=Vn`7[,F-s^M!R\?&P@KMJ-u#!]ar_NN.m>raD#`V4eR1.!GO[2T6(sh_s?Q7I6IC-cmrM*J\is+R\7!/`qu<#O-g)'!)!-@gd,_G"_eZ<[aP?(5)E,PC"&NrC@476i^9L(>-NGgB@Y?"^pJ.o*A>M3\6,n.KClMfZ!4H0XaYfK!ZU%q%gb"O2u~> +Gat=+a`?E"&A@B[E3uZ^Qoh;-3"sO^Q-*^p1-%TF6m4o!$j`AeU4A!40,c@@342[uH]t<<_k1.@s[.gMbbi'']N<6RtW$XKkeQ]J,7q.,a;m%-!d8`VUL!>@FS%MUks@GPJQBRSJj2Op[RE,UlhuRiSZF!HbA6o,qbpjn-i7p(o307hp,!E/C,a%WaBIB_1D@7RT,Oe_/a*Iba]ib5^>umbJq@+K:cl[+A]d@qB*sD"dQJ&JNCMdVu92APM(E.4t[0L4gEu2\R5DGqui%C6k`0+9`"0"(Z,hQ:Y"fLI_FA-u@h^ll!fZm8K-8Jbp`$I_YB-6&6P7P8$(e@E\"TXAS9edYc>%h2[X^F([Mr),+T@SY5;JUVFAqGW1Xd$QOS9X,'J%"QS)gRk.V4Slq8d_:8g$/CI3MNO5"NLB.shtcgt2:#JtAdQFrDKcWGr&fK41;nD2?Id6:`Y_>!A(iP3t"l*2WGhqR5:GVS#EIss^G?[iQ<8BVl,\41o_j():oaMV/NU)lHle&Hc8Q>"[`LbPk,U=5`AZO+oDI[3^fc.TNeh%F*#/4@=5+PSOG@IMT$B+7?jBV2@6K5mSG4d;Vf65.Uh`Ib0u=c8/o.#gZN:.pM$9*]!8JS'^2n7YKM.#r$h_\p1pS sMo4S]E,a7JCmZK,lWgdMKmIO5qWMts6$(YHZ+f"@rci\aEk=Vn`7[,F-s^M!R\?&P@KMJ-u#!]ar_NN.m>raD#`V4eR1.!GO[2T6(sh_s?Q7I6IC-cmrM*J\is+R\7!/`qu<#O-g)'!)!-@gd,_G"_eZ<[aP?(5)E,PC"$82C@476i^9L(>-NGgB@Y?"^pJ.o*A>M3\6,n.KClMfZ!4H0XaYfK!ZU%q%gk"N3W~> endstream endobj 11 0 obj Modified: lucene/hadoop/trunk/docs/credits.html URL: http://svn.apache.org/viewvc/lucene/hadoop/trunk/docs/credits.html?view=diff&rev=547429&r1=547428&r2=547429 ============================================================================== --- lucene/hadoop/trunk/docs/credits.html (original) +++ lucene/hadoop/trunk/docs/credits.html Thu Jun 14 15:19:05 2007 @@ -203,7 +203,7 @@
Copyright © - 2005 The Apache Software Foundation. + 2007 The Apache Software Foundation.
Modified: lucene/hadoop/trunk/docs/credits.pdf URL: http://svn.apache.org/viewvc/lucene/hadoop/trunk/docs/credits.pdf?view=diff&rev=547429&r1=547428&r2=547429 ============================================================================== --- lucene/hadoop/trunk/docs/credits.pdf (original) +++ lucene/hadoop/trunk/docs/credits.pdf Thu Jun 14 15:19:05 2007 @@ -8,7 +8,7 @@ << /Length 421 /Filter [ /ASCII85Decode /FlateDecode ] >> stream -Gaua::K)m"&B4*cMETC?C]:Pk*j&qmn`DYeomdTWAm0e:3duD^AX_+A]epef-(DCA+E+*OgU.0H+9PnIW$@U&Zk_D[$\!^?b3cM]^9cB("4lKH:+C@S3Jg)a;PgV]^+#1JQ+VOi%70k+(5dL?5^\BHKMMO?e9Ae(im?#?&$lG/uqW$?NW05aP+AeKBW4/h#C[d!2Ps7B"P??!dTe.4V:Qm+K.KJS=>+IU*^:_7^QWouHH\YtD7pL0EqPu*QA:5$fT?n%45#JL*#lKrKYBmg`7]b69d=*eMl\81[[e/MU$hg$$^XR0Y!k!Nu)%(>Qt?;/'05b$VP?`$5PrX3@>n(%~> +Gaua::K)m"&B4*cMETC?C]:Pk*j&qmn`DYeomdTWAm0e:3duD^AX_+A]epef-(DCA+E+*OgU.0H+9PnIW$@U&Zk_D[$\!^?b3cM]^9cB("4lKH:+C@S3Jg)a;PgV]^+#1JQ+VOi%70k+(5dL?5^\BHKMMO?e9Ae(im?#?&$lG/uqW$?NW05aP+AeKBW4/h#C[d!2Ps7B"P??!dTe.4V:Qm+K.KJS=>+IU*^:_7^QWouHH\YtD7pL0EqPu*QA:5$fT?n%45#JL*#lKrKYBmg`7]b68QYOp+bES'>AW&M.'^@NtE=kh9"b_On1(i7sq]pO-?JMt4+^.aA)r>0-Hn(7~> endstream endobj 6 0 obj @@ -61,7 +61,7 @@ << /Length 1030 /Filter [ /ASCII85Decode /FlateDecode ] >> stream -Gat=*=``:N&:XAW:nL?l)F]gklS04?1$JitR8239@t\i3ChfIql*'Oo+#8TX0Kea^Dnji3]38JLSVt3MeADCu-OrG96P4kk7@Ja&GV5kr5f4K9fq\)F^YC"FdOa+Te^"!1k_BQcV5aB7AStY9H1h_,JiOl!gJ'X.re5bpqfVd,FoFIXa2:oc)+cV\B8fLKqm]n%VX3Cm6YD1Bd"'ED"8I#MZ\9Kp:KQu(n4f(IZ'dNm/5dD^4g0?iJ0WtWGEY.LF'bZkkr>6lfgmR6G%Z-5I=h`>,1-L%W&)HT5-pQBG"cl3"Y]UnI"@*;dW>D^)-pZ"UkmI=I@@Y_D]0e&/RTXe%h3)NmU1jA7>STRQ:7TFE>Hb$Tf\/PGO_chuD!*7elB_-V=7_*5Th6U!>"Ya@2MR?K"5Dr2jmbXk3E9tGh=8S4sbRW=(T&uD=EQ#p6#[K+^gFN\8ldV!9Ae@_'Ob)J>f33LLa@r[\R0A9c6/MJ!_2PHM0Ksm4qY,b0:")&Prf@8-LRd[#'/!lnLK1'd9?P(59buub[u&-gW3eTh95"a6fRZEKGqc3oBkT&B7VS\;cY6ldYW&+OlW#CP;";3PkVl;J:jS-IZlEYg"MXWNBf[oSOqMOZ)!V9m)+XL3r@hZZ'I=Ab?3s&X],u-`=IgqqR^2-p=1Ll>gJ:k/?iA?QnZc-KPZ4R#MFgq'2LET?%H2^/If_![2:(B>/R>E`oYtdg@cY[j@a`s=\h8 1:<%`(V$V%Uin,/"mR>;W$^:NH@r/RM%rW.s%K~> +Gat=*=``:N&:XAW:nL?l)F]gklS04?1$JitR8239@t\i3ChfIql*'Oo+#8TX0Kea^Dnji3]38JLSVt3MeADCu-OrG96P4kk7@Ja&GV5kr5f4K9fq\)F^YC"FdOa+Te^"!1k_BQcV5aB7AStY9H1h_,JiOl!gJ'X.re5bpqfVd,FoFIXa2:oc)+cV\B8fLKqm]n%VX3Cm6YD1Bd"'ED"8I#MZ\9Kp:KQu(n4f(IZ'dNm/5dD^4g0?iJ0WtWGEY.LF'bZkkr>6lfgmR6G%Z-5I=h`>,1-L%W&)HT5-pQBG"cl3"Y]UnI"@*;dW>D^)-pZ"UkmI=I@@Y_D]0e&/RTXe%h3)NmU1jA7>STRQ:7TFE>Hb$Tf\/PGO_chuD!*7elB_-V=7_*5Th6U!>"Ya@2MR?K"5Dr2jmbXk3E9tGh=8S4sbRW=(T&uD=EQ#p6#[K+^gFN\8ldV!9Ae@_'Ob)J>f33LLa@r[\R0A9c6/MJ!_2PHM0Ksm4qY,b0:")&Prf@8-LRd[#'/!lnLK1'd9?P(59buub[u&-gW3eTh95"a6fRZEKGqc3oBkT&B7VS\;cY6ldYW&+OlW#CP;";3PkVl;J:jS-IZlEYg"MXWNBf[oSOqMOZ)!V9m)+XL3r@hZZ'I=Ab?3s&X],u-`=IgqqR^2-p=1Ll>gJ:k/?iA?QnZc-KPZ4R#MFgq'2LET?%H2^/If_![2:(8>/R>E`oYtdg@cY[j@a`s=\h8 1:<%`(V$V%Uin,/"mR>;W$^:NH@r/RM%r`.r&-~> endstream endobj 15 0 obj Modified: lucene/hadoop/trunk/docs/hdfs_design.html URL: http://svn.apache.org/viewvc/lucene/hadoop/trunk/docs/hdfs_design.html?view=diff&rev=547429&r1=547428&r2=547429 ============================================================================== --- lucene/hadoop/trunk/docs/hdfs_design.html (original) +++ lucene/hadoop/trunk/docs/hdfs_design.html Thu Jun 14 15:19:05 2007 @@ -135,7 +135,7 @@ Assumptions and Goals
  • - Namenode and Datanode + Namenode and Datanodes
  • The File System Namespace @@ -164,7 +164,7 @@ Data Replication
  • - Data Organization + Data Organization
    • @@ -232,7 +232,7 @@ Space Reclamation
    • - File Deletes and Undelete + File Deletes and Undeletes
    • Decrease Replication Factor @@ -249,301 +249,326 @@

      Introduction

      - 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 - here. + The Hadoop Distributed File System (HDFS) is a distributed file system designed to run 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 is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data. HDFS was originally built as infrastructure for the Apache Nutch web search engine project. HDFS is part of the Apache Hadoop project, which is part of the Apache Lucene project. The project URL is http://projects.apache.org/projects/hadoop.html.

      - +

      Assumptions and Goals

      - -

      Hardware Failure

      + +

      Hardware Failure

      - 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. + Hardware failure is the norm rather than the exception. An HDFS instance may consist of hundreds or thousands of server machines, each storing part of the file system’s 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 quick, automatic recovery from them is a core architectural goal of HDFS.

      - +

      Streaming Data Access

      - 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. + Applications that run on HDFS need streaming access to their data sets. They are not general purpose applications that typically run on general purpose file systems. HDFS is designed more for batch processing rather than interactive use by users. The emphasis is on high throughput of data access rather than low 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 has been traded to increase data throughput rates.

      - +

      Large Data Sets

      - 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. + Applications that run on HDFS have large data sets. 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 scale to hundreds of nodes in a single cluster. It should support tens of millions of files in a single instance.

      - +

      Simple Coherency Model

      - 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. + 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.

      - -

      Moving computation is cheaper than moving data

      + +

      “Moving Computation is Cheaper than Moving Data”

      - 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. + A computation requested by an application is much more efficient if it is executed near the data it operates on. This is especially true when the size of the data set is huge. This minimizes network congestion and increases the 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.

      - -

      Portability across Heterogeneous Hardware and Software Platforms

      + +

      Portability Across Heterogeneous Hardware and Software Platforms

      - 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. + HDFS has been designed to be easily portable from one platform to another. This facilitates widespread adoption of HDFS as a platform of choice for a large set of applications.

      - -

      Namenode and Datanode

      + +

      Namenode and Datanodes

      - 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. + 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’s clients. The Datanodes also perform block creation, deletion, and replication upon instruction from the Namenode.

      -

      -

      +
      +HDFS Architecture

      - 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. + The Namenode and Datanode are pieces of software designed to run on commodity machines. These machines typically run a GNU/Linux operating system (OS). 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 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. + 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.

      - +

      The File System Namespace

      - 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. + 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.

      - 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. + 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.

      - +

      Data Replication

      -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. + 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.

      - 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. + 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.

      -

      -

      - -

      Replica Placement . The First Baby Steps

      +
      +HDFS Datanodes
      + +

      Replica Placement: The First Baby Steps

      - 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. + The placement of replicas is critical to HDFS reliability and performance. Optimizing replica placement 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 policy 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.

      - 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. + Large HDFS instances run on a cluster of computers that commonly spread across many racks. Communication between two nodes in different racks has to go through switches. In most cases, network bandwidth between machines in the same rack is greater than network bandwidth between machines in different racks.

      - 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. + At startup time, each Datanode determines the rack it belongs to and notifies the Namenode of its rack id upon registration. HDFS provides APIs to facilitate pluggable modules that can be used to determine the rack id of a machine. A simple but non-optimal policy is to place replicas on unique 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 which 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.

      - 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. + For the common case, when the replication factor is three, HDFS’s placement policy is to put one replica on one node in the local rack, another on a different node in the local rack, and the last on a different node in a different rack. This policy cuts the inter-rack write traffic which generally 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. However, it does reduce the aggregate network bandwidth used when reading data since a block is placed in only two unique racks rather than three. With this policy, the replicas of a file do not evenly distribute across the racks. One third of replicas are on one node, two thirds of replicas are on one rack, and the other third are evenly distributed across the remaining racks. This policy improves write performance without compromising data reliability or read performance.

      - The implementation of the above policy is work-in-progress. + The current, default replica placement policy described here is a work in progress.

      - +

      Replica Selection

      - 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. + To minimize global bandwidth consumption and read latency, 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 angg/ HDFS cluster spans multiple data centers, then a replica that is resident in the local data center is preferred over any remote replica.

      - +

      SafeMode

      - 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. + 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.

      - +

      The Persistence of File System Metadata

      - 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. + 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’s local file system too.

      - 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. + 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.

      - 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. + 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 Communication Protocol

      + +

      The Communication Protocols

      - 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. + All HDFS communication protocols are layered on top of the TCP/IP protocol. A client establishes a connection to a configurable TCP port on the Namenode machine. It talks the ClientProtocol with the Namenode. The Datanodes talk to the Namenode using the DatanodeProtocol. A Remote Procedure Call (RPC) 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.

      - +

      Robustness

      -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. + 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.

      - +

      Data Disk Failure, Heartbeats and Re-Replication

      - 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. + 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 IO 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.

      - +

      Cluster Rebalancing

      - 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. + 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.

      - -

      Data Correctness

      + +

      Data Integrity

      - 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. + + 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.

      - +

      Metadata Disk Failure

      - 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. + 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.

      - 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. + 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.

      - +

      Snapshots

      - 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. + 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 HDFS instance to a previously known good point in time. HDFS does not currently support snapshots but will in a future release.

      - -

      Data Organization

      + +

      Data Organization

      - +

      Data Blocks

      - 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. + 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.

      - +

      Staging

      - 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. + 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.

      - 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. + 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. AFS, have used client side caching to improve performance. A POSIX requirement has been relaxed to achieve higher performance of data uploads.

      - -

      Pipelining

      + +

      Replication Pipelining

      - 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. + 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.

      - +

      Accessibility

      - 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. + HDFS can be accessed from applications in many different ways. Natively, HDFS provides a Java API for applications to use. A C language wrapper for this Java API is also available. In addition, an HTTP browser can also be used to browse the files of an HDFS instance. Work is in progress to expose HDFS through the WebDAV protocol.

      - +

      DFSShell

      - 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: -

      -

      - Create a directory named /foodir : hadoop dfs -mkdir /foodir -

      -

      - View a file /foodir/myfile.txt : hadoop dfs -cat /foodir/myfile.txt -

      -

      - Delete a file /foodir/myfile.txt : hadoop dfs -rm /foodir myfile.txt + HDFS allows user data to be organized in the form of files and directories. It provides a commandline 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 action/command pairs:

      + + + + + + + + + + + + + + + + + + + + + + + + + + +
      Action Command
      Create a directory named /foodir bin/hadoop dfs -mkdir /foodir
      Create a directory named /foodir bin/hadoop dfs -mkdir /foodir
      View the contents of a file named /foodir/myfile.txt bin/hadoop dfs -cat /foodir/myfile.txt

      - The command syntax for DFSShell is targeted for applications that need a scripting language to interact with the stored data. + DFSShell is targeted for applications that need a scripting language to interact with the stored data.

      - +

      DFSAdmin

      - 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: + 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:

      -

      - Put a cluster in Safe Mode : bin/hadoop dfsadmin -safemode enter -

      -

      - Generate a list of Datanodes : bin/hadoop dfsadmin -report -

      -

      - Decommission a Datanode : bin/hadoop dfsadmin -decommission datanodename -

      - + + + + + + + + + + + + + + + + + + + + + + + + + + +
      Action Command
      Put a cluster in SafeMode bin/hadoop dfsadmin -safemode enter
      Generate a list of Datanodes bin/hadoop dfsadmin -report
      Decommission Datanode datanodename bin/hadoop dfsadmin -decommission datanodename
      +

      Browser Interface

      - 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. + A typical HDFS install configures a web server to expose the HDFS namespace through a configurable TCP port. This allows a user to navigate the HDFS namespace and view the contents of its files using a web browser.

      - +

      Space Reclamation

      - -

      File Deletes and Undelete

      + +

      File Deletes and Undeletes

      - 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. + 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 /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 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.

      -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. + 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 from /trash that are more than 6 hours old. In the future, this policy will be configurable through a well defined interface.

      - +

      Decrease Replication Factor

      - 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. + 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 setReplication API call and the appearance of free space in the cluster.

      - +

      References

      - - - Browse the HDFS Java Interface + HDFS Java API: + + http://lucene.apache.org/hadoop/api/

      - - - Download the HDFS source code + HDFS source code: + + http://lucene.apache.org/hadoop/version_control.html

      @@ -564,7 +589,7 @@
      Copyright © - 2005 The Apache Software Foundation. + 2007 The Apache Software Foundation.