http://git-wip-us.apache.org/repos/asf/hbase/blob/cb77a925/src/main/docbkx/book.xml ---------------------------------------------------------------------- diff --git a/src/main/docbkx/book.xml b/src/main/docbkx/book.xml deleted file mode 100644 index ee2d7fb..0000000 --- a/src/main/docbkx/book.xml +++ /dev/null @@ -1,6107 +0,0 @@ -<?xml version="1.0" encoding="UTF-8"?> -<!-- -/** - * - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you 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. - */ ---> -<book - version="5.0" - xmlns="http://docbook.org/ns/docbook" - xmlns:xlink="http://www.w3.org/1999/xlink" - xmlns:xi="http://www.w3.org/2001/XInclude" - xmlns:svg="http://www.w3.org/2000/svg" - xmlns:m="http://www.w3.org/1998/Math/MathML" - xmlns:html="http://www.w3.org/1999/xhtml" - xmlns:db="http://docbook.org/ns/docbook" - xml:id="book"> - <info> - - <title><link - xlink:href="http://www.hbase.org"> The Apache HBase™ Reference Guide </link></title> - <subtitle><link - xlink:href="http://www.hbase.org"> - <inlinemediaobject> - <imageobject> - <imagedata - align="center" - valign="left" - fileref="hbase_logo.png" /> - </imageobject> - </inlinemediaobject> - <inlinemediaobject> - <imageobject> - <imagedata - align="center" - valign="right" - fileref="jumping-orca_rotated_25percent.png" /> - </imageobject> - </inlinemediaobject> - </link> - </subtitle> - <copyright> - <year>2014</year> - <holder>Apache Software Foundation. All Rights Reserved. Apache Hadoop, Hadoop, MapReduce, - HDFS, Zookeeper, HBase, and the HBase project logo are trademarks of the Apache Software - Foundation. </holder> - </copyright> - <abstract> - <para>This is the official reference guide of <link - xlink:href="http://www.hbase.org">Apache HBase™</link>, a distributed, versioned, big - data store built on top of <link - xlink:href="http://hadoop.apache.org/">Apache Hadoop™</link> and <link - xlink:href="http://zookeeper.apache.org/">Apache ZooKeeper™</link>. </para> - </abstract> - - <revhistory> - <revision> - <revnumber> - <?eval ${project.version}?> - </revnumber> - <date> - <?eval ${buildDate}?> - </date> - </revision> - </revhistory> - </info> - - <!--XInclude some chapters--> - <xi:include - xmlns:xi="http://www.w3.org/2001/XInclude" - href="preface.xml" /> - <xi:include - xmlns:xi="http://www.w3.org/2001/XInclude" - href="getting_started.xml" /> - <xi:include - xmlns:xi="http://www.w3.org/2001/XInclude" - href="configuration.xml" /> - <xi:include - xmlns:xi="http://www.w3.org/2001/XInclude" - href="upgrading.xml" /> - <xi:include - xmlns:xi="http://www.w3.org/2001/XInclude" - href="shell.xml" /> - - <chapter - xml:id="datamodel"> - <title>Data Model</title> - <para>In HBase, data is stored in tables, which have rows and columns. This is a terminology - overlap with relational databases (RDBMSs), but this is not a helpful analogy. Instead, it can - be helpful to think of an HBase table as a multi-dimensional map.</para> - <variablelist> - <title>HBase Data Model Terminology</title> - <varlistentry> - <term>Table</term> - <listitem> - <para>An HBase table consists of multiple rows.</para> - </listitem> - </varlistentry> - <varlistentry> - <term>Row</term> - <listitem> - <para>A row in HBase consists of a row key and one or more columns with values associated - with them. Rows are sorted alphabetically by the row key as they are stored. For this - reason, the design of the row key is very important. The goal is to store data in such a - way that related rows are near each other. A common row key pattern is a website domain. - If your row keys are domains, you should probably store them in reverse (org.apache.www, - org.apache.mail, org.apache.jira). This way, all of the Apache domains are near each - other in the table, rather than being spread out based on the first letter of the - subdomain.</para> - </listitem> - </varlistentry> - <varlistentry> - <term>Column</term> - <listitem> - <para>A column in HBase consists of a column family and a column qualifier, which are - delimited by a <literal>:</literal> (colon) character.</para> - </listitem> - </varlistentry> - <varlistentry> - <term>Column Family</term> - <listitem> - <para>Column families physically colocate a set of columns and their values, often for - performance reasons. Each column family has a set of storage properties, such as whether - its values should be cached in memory, how its data is compressed or its row keys are - encoded, and others. Each row in a table has the same column - families, though a given row might not store anything in a given column family.</para> - <para>Column families are specified when you create your table, and influence the way your - data is stored in the underlying filesystem. Therefore, the column families should be - considered carefully during schema design.</para> - </listitem> - </varlistentry> - <varlistentry> - <term>Column Qualifier</term> - <listitem> - <para>A column qualifier is added to a column family to provide the index for a given - piece of data. Given a column family <literal>content</literal>, a column qualifier - might be <literal>content:html</literal>, and another might be - <literal>content:pdf</literal>. Though column families are fixed at table creation, - column qualifiers are mutable and may differ greatly between rows.</para> - </listitem> - </varlistentry> - <varlistentry> - <term>Cell</term> - <listitem> - <para>A cell is a combination of row, column family, and column qualifier, and contains a - value and a timestamp, which represents the value's version.</para> - <para>A cell's value is an uninterpreted array of bytes.</para> - </listitem> - </varlistentry> - <varlistentry> - <term>Timestamp</term> - <listitem> - <para>A timestamp is written alongside each value, and is the identifier for a given - version of a value. By default, the timestamp represents the time on the RegionServer - when the data was written, but you can specify a different timestamp value when you put - data into the cell.</para> - <caution> - <para>Direct manipulation of timestamps is an advanced feature which is only exposed for - special cases that are deeply integrated with HBase, and is discouraged in general. - Encoding a timestamp at the application level is the preferred pattern.</para> - </caution> - <para>You can specify the maximum number of versions of a value that HBase retains, per column - family. When the maximum number of versions is reached, the oldest versions are - eventually deleted. By default, only the newest version is kept.</para> - </listitem> - </varlistentry> - </variablelist> - - <section - xml:id="conceptual.view"> - <title>Conceptual View</title> - <para>You can read a very understandable explanation of the HBase data model in the blog post <link - xlink:href="http://jimbojw.com/wiki/index.php?title=Understanding_Hbase_and_BigTable">Understanding - HBase and BigTable</link> by Jim R. Wilson. Another good explanation is available in the - PDF <link - xlink:href="http://0b4af6cdc2f0c5998459-c0245c5c937c5dedcca3f1764ecc9b2f.r43.cf2.rackcdn.com/9353-login1210_khurana.pdf">Introduction - to Basic Schema Design</link> by Amandeep Khurana. It may help to read different - perspectives to get a solid understanding of HBase schema design. The linked articles cover - the same ground as the information in this section.</para> - <para> The following example is a slightly modified form of the one on page 2 of the <link - xlink:href="http://research.google.com/archive/bigtable.html">BigTable</link> paper. There - is a table called <varname>webtable</varname> that contains two rows - (<literal>com.cnn.www</literal> - and <literal>com.example.www</literal>), three column families named - <varname>contents</varname>, <varname>anchor</varname>, and <varname>people</varname>. In - this example, for the first row (<literal>com.cnn.www</literal>), - <varname>anchor</varname> contains two columns (<varname>anchor:cssnsi.com</varname>, - <varname>anchor:my.look.ca</varname>) and <varname>contents</varname> contains one column - (<varname>contents:html</varname>). This example contains 5 versions of the row with the - row key <literal>com.cnn.www</literal>, and one version of the row with the row key - <literal>com.example.www</literal>. The <varname>contents:html</varname> column qualifier contains the entire - HTML of a given website. Qualifiers of the <varname>anchor</varname> column family each - contain the external site which links to the site represented by the row, along with the - text it used in the anchor of its link. The <varname>people</varname> column family represents - people associated with the site. - </para> - <note> - <title>Column Names</title> - <para> By convention, a column name is made of its column family prefix and a - <emphasis>qualifier</emphasis>. For example, the column - <emphasis>contents:html</emphasis> is made up of the column family - <varname>contents</varname> and the <varname>html</varname> qualifier. The colon - character (<literal>:</literal>) delimits the column family from the column family - <emphasis>qualifier</emphasis>. </para> - </note> - <table - frame="all"> - <title>Table <varname>webtable</varname></title> - <tgroup - cols="5" - align="left" - colsep="1" - rowsep="1"> - <colspec - colname="c1" /> - <colspec - colname="c2" /> - <colspec - colname="c3" /> - <colspec - colname="c4" /> - <colspec - colname="c5" /> - <thead> - <row> - <entry>Row Key</entry> - <entry>Time Stamp</entry> - <entry>ColumnFamily <varname>contents</varname></entry> - <entry>ColumnFamily <varname>anchor</varname></entry> - <entry>ColumnFamily <varname>people</varname></entry> - </row> - </thead> - <tbody> - <row> - <entry>"com.cnn.www"</entry> - <entry>t9</entry> - <entry /> - <entry><varname>anchor:cnnsi.com</varname> = "CNN"</entry> - <entry /> - </row> - <row> - <entry>"com.cnn.www"</entry> - <entry>t8</entry> - <entry /> - <entry><varname>anchor:my.look.ca</varname> = "CNN.com"</entry> - <entry /> - </row> - <row> - <entry>"com.cnn.www"</entry> - <entry>t6</entry> - <entry><varname>contents:html</varname> = "<html>..."</entry> - <entry /> - <entry /> - </row> - <row> - <entry>"com.cnn.www"</entry> - <entry>t5</entry> - <entry><varname>contents:html</varname> = "<html>..."</entry> - <entry /> - <entry /> - </row> - <row> - <entry>"com.cnn.www"</entry> - <entry>t3</entry> - <entry><varname>contents:html</varname> = "<html>..."</entry> - <entry /> - <entry /> - </row> - <row> - <entry>"com.example.www"</entry> - <entry>t5</entry> - <entry><varname>contents:html</varname> = "<html>..."</entry> - <entry></entry> - <entry>people:author = "John Doe"</entry> - </row> - </tbody> - </tgroup> - </table> - <para>Cells in this table that appear to be empty do not take space, or in fact exist, in - HBase. This is what makes HBase "sparse." A tabular view is not the only possible way to - look at data in HBase, or even the most accurate. The following represents the same - information as a multi-dimensional map. This is only a mock-up for illustrative - purposes and may not be strictly accurate.</para> - <programlisting><![CDATA[ -{ - "com.cnn.www": { - contents: { - t6: contents:html: "<html>..." - t5: contents:html: "<html>..." - t3: contents:html: "<html>..." - } - anchor: { - t9: anchor:cnnsi.com = "CNN" - t8: anchor:my.look.ca = "CNN.com" - } - people: {} - } - "com.example.www": { - contents: { - t5: contents:html: "<html>..." - } - anchor: {} - people: { - t5: people:author: "John Doe" - } - } -} - ]]></programlisting> - - </section> - <section - xml:id="physical.view"> - <title>Physical View</title> - <para> Although at a conceptual level tables may be viewed as a sparse set of rows, they are - physically stored by column family. A new column qualifier (column_family:column_qualifier) - can be added to an existing column family at any time.</para> - <table - frame="all"> - <title>ColumnFamily <varname>anchor</varname></title> - <tgroup - cols="3" - align="left" - colsep="1" - rowsep="1"> - <colspec - colname="c1" /> - <colspec - colname="c2" /> - <colspec - colname="c3" /> - <thead> - <row> - <entry>Row Key</entry> - <entry>Time Stamp</entry> - <entry>Column Family <varname>anchor</varname></entry> - </row> - </thead> - <tbody> - <row> - <entry>"com.cnn.www"</entry> - <entry>t9</entry> - <entry><varname>anchor:cnnsi.com</varname> = "CNN"</entry> - </row> - <row> - <entry>"com.cnn.www"</entry> - <entry>t8</entry> - <entry><varname>anchor:my.look.ca</varname> = "CNN.com"</entry> - </row> - </tbody> - </tgroup> - </table> - <table - frame="all"> - <title>ColumnFamily <varname>contents</varname></title> - <tgroup - cols="3" - align="left" - colsep="1" - rowsep="1"> - <colspec - colname="c1" /> - <colspec - colname="c2" /> - <colspec - colname="c3" /> - <thead> - <row> - <entry>Row Key</entry> - <entry>Time Stamp</entry> - <entry>ColumnFamily "contents:"</entry> - </row> - </thead> - <tbody> - <row> - <entry>"com.cnn.www"</entry> - <entry>t6</entry> - <entry><varname>contents:html</varname> = "<html>..."</entry> - </row> - <row> - <entry>"com.cnn.www"</entry> - <entry>t5</entry> - <entry><varname>contents:html</varname> = "<html>..."</entry> - </row> - <row> - <entry>"com.cnn.www"</entry> - <entry>t3</entry> - <entry><varname>contents:html</varname> = "<html>..."</entry> - </row> - </tbody> - </tgroup> - </table> - <para>The empty cells shown in the - conceptual view are not stored at all. - Thus a request for the value of the <varname>contents:html</varname> column at time stamp - <literal>t8</literal> would return no value. Similarly, a request for an - <varname>anchor:my.look.ca</varname> value at time stamp <literal>t9</literal> would - return no value. However, if no timestamp is supplied, the most recent value for a - particular column would be returned. Given multiple versions, the most recent is also the - first one found, since timestamps - are stored in descending order. Thus a request for the values of all columns in the row - <varname>com.cnn.www</varname> if no timestamp is specified would be: the value of - <varname>contents:html</varname> from timestamp <literal>t6</literal>, the value of - <varname>anchor:cnnsi.com</varname> from timestamp <literal>t9</literal>, the value of - <varname>anchor:my.look.ca</varname> from timestamp <literal>t8</literal>. </para> - <para>For more information about the internals of how Apache HBase stores data, see <xref - linkend="regions.arch" />. </para> - </section> - - <section - xml:id="namespace"> - <title>Namespace</title> - <para> A namespace is a logical grouping of tables analogous to a database in relation - database systems. This abstraction lays the groundwork for upcoming multi-tenancy related - features: <itemizedlist> - <listitem> - <para>Quota Management (HBASE-8410) - Restrict the amount of resources (ie regions, - tables) a namespace can consume.</para> - </listitem> - <listitem> - <para>Namespace Security Administration (HBASE-9206) - provide another level of security - administration for tenants.</para> - </listitem> - <listitem> - <para>Region server groups (HBASE-6721) - A namespace/table can be pinned onto a subset - of regionservers thus guaranteeing a course level of isolation.</para> - </listitem> - </itemizedlist> - </para> - <section - xml:id="namespace_creation"> - <title>Namespace management</title> - <para> A namespace can be created, removed or altered. Namespace membership is determined - during table creation by specifying a fully-qualified table name of the form:</para> - - <programlisting language="xml"><![CDATA[<table namespace>:<table qualifier>]]></programlisting> - - - <example> - <title>Examples</title> - - <programlisting language="bourne"> -#Create a namespace -create_namespace 'my_ns' - </programlisting> - <programlisting language="bourne"> -#create my_table in my_ns namespace -create 'my_ns:my_table', 'fam' - </programlisting> - <programlisting language="bourne"> -#drop namespace -drop_namespace 'my_ns' - </programlisting> - <programlisting language="bourne"> -#alter namespace -alter_namespace 'my_ns', {METHOD => 'set', 'PROPERTY_NAME' => 'PROPERTY_VALUE'} - </programlisting> - </example> - </section> - <section - xml:id="namespace_special"> - <title>Predefined namespaces</title> - <para> There are two predefined special namespaces: </para> - <itemizedlist> - <listitem> - <para>hbase - system namespace, used to contain hbase internal tables</para> - </listitem> - <listitem> - <para>default - tables with no explicit specified namespace will automatically fall into - this namespace.</para> - </listitem> - </itemizedlist> - <example> - <title>Examples</title> - - <programlisting language="bourne"> -#namespace=foo and table qualifier=bar -create 'foo:bar', 'fam' - -#namespace=default and table qualifier=bar -create 'bar', 'fam' -</programlisting> - </example> - </section> - </section> - - <section - xml:id="table"> - <title>Table</title> - <para> Tables are declared up front at schema definition time. </para> - </section> - - <section - xml:id="row"> - <title>Row</title> - <para>Row keys are uninterrpreted bytes. Rows are lexicographically sorted with the lowest - order appearing first in a table. The empty byte array is used to denote both the start and - end of a tables' namespace.</para> - </section> - - <section - xml:id="columnfamily"> - <title>Column Family<indexterm><primary>Column Family</primary></indexterm></title> - <para> Columns in Apache HBase are grouped into <emphasis>column families</emphasis>. All - column members of a column family have the same prefix. For example, the columns - <emphasis>courses:history</emphasis> and <emphasis>courses:math</emphasis> are both - members of the <emphasis>courses</emphasis> column family. The colon character - (<literal>:</literal>) delimits the column family from the <indexterm><primary>column - family qualifier</primary><secondary>Column Family Qualifier</secondary></indexterm>. - The column family prefix must be composed of <emphasis>printable</emphasis> characters. The - qualifying tail, the column family <emphasis>qualifier</emphasis>, can be made of any - arbitrary bytes. Column families must be declared up front at schema definition time whereas - columns do not need to be defined at schema time but can be conjured on the fly while the - table is up an running.</para> - <para>Physically, all column family members are stored together on the filesystem. Because - tunings and storage specifications are done at the column family level, it is advised that - all column family members have the same general access pattern and size - characteristics.</para> - - </section> - <section - xml:id="cells"> - <title>Cells<indexterm><primary>Cells</primary></indexterm></title> - <para>A <emphasis>{row, column, version} </emphasis>tuple exactly specifies a - <literal>cell</literal> in HBase. Cell content is uninterrpreted bytes</para> - </section> - <section - xml:id="data_model_operations"> - <title>Data Model Operations</title> - <para>The four primary data model operations are Get, Put, Scan, and Delete. Operations are - applied via <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html">Table</link> - instances. - </para> - <section - xml:id="get"> - <title>Get</title> - <para><link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html">Get</link> - returns attributes for a specified row. Gets are executed via <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#get(org.apache.hadoop.hbase.client.Get)"> - Table.get</link>. </para> - </section> - <section - xml:id="put"> - <title>Put</title> - <para><link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Put.html">Put</link> - either adds new rows to a table (if the key is new) or can update existing rows (if the - key already exists). Puts are executed via <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#put(org.apache.hadoop.hbase.client.Put)"> - Table.put</link> (writeBuffer) or <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#batch(java.util.List, java.lang.Object[])"> - Table.batch</link> (non-writeBuffer). </para> - </section> - <section - xml:id="scan"> - <title>Scans</title> - <para><link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html">Scan</link> - allow iteration over multiple rows for specified attributes. </para> - <para>The following is an example of a Scan on a Table instance. Assume that a table is - populated with rows with keys "row1", "row2", "row3", and then another set of rows with - the keys "abc1", "abc2", and "abc3". The following example shows how to set a Scan - instance to return the rows beginning with "row".</para> -<programlisting language="java"> -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... - -Table table = ... // instantiate a Table instance - -Scan scan = new Scan(); -scan.addColumn(CF, ATTR); -scan.setRowPrefixFilter(Bytes.toBytes("row")); -ResultScanner rs = table.getScanner(scan); -try { - for (Result r = rs.next(); r != null; r = rs.next()) { - // process result... -} finally { - rs.close(); // always close the ResultScanner! -</programlisting> - <para>Note that generally the easiest way to specify a specific stop point for a scan is by - using the <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/InclusiveStopFilter.html">InclusiveStopFilter</link> - class. </para> - </section> - <section - xml:id="delete"> - <title>Delete</title> - <para><link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Delete.html">Delete</link> - removes a row from a table. Deletes are executed via <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Table.html#delete(org.apache.hadoop.hbase.client.Delete)"> - HTable.delete</link>. </para> - <para>HBase does not modify data in place, and so deletes are handled by creating new - markers called <emphasis>tombstones</emphasis>. These tombstones, along with the dead - values, are cleaned up on major compactions. </para> - <para>See <xref - linkend="version.delete" /> for more information on deleting versions of columns, and - see <xref - linkend="compaction" /> for more information on compactions. </para> - - </section> - - </section> - - - <section - xml:id="versions"> - <title>Versions<indexterm><primary>Versions</primary></indexterm></title> - - <para>A <emphasis>{row, column, version} </emphasis>tuple exactly specifies a - <literal>cell</literal> in HBase. It's possible to have an unbounded number of cells where - the row and column are the same but the cell address differs only in its version - dimension.</para> - - <para>While rows and column keys are expressed as bytes, the version is specified using a long - integer. Typically this long contains time instances such as those returned by - <code>java.util.Date.getTime()</code> or <code>System.currentTimeMillis()</code>, that is: - <quote>the difference, measured in milliseconds, between the current time and midnight, - January 1, 1970 UTC</quote>.</para> - - <para>The HBase version dimension is stored in decreasing order, so that when reading from a - store file, the most recent values are found first.</para> - - <para>There is a lot of confusion over the semantics of <literal>cell</literal> versions, in - HBase. In particular:</para> - <itemizedlist> - <listitem> - <para>If multiple writes to a cell have the same version, only the last written is - fetchable.</para> - </listitem> - - <listitem> - <para>It is OK to write cells in a non-increasing version order.</para> - </listitem> - </itemizedlist> - - <para>Below we describe how the version dimension in HBase currently works. See <link - xlink:href="https://issues.apache.org/jira/browse/HBASE-2406">HBASE-2406</link> for - discussion of HBase versions. <link - xlink:href="http://outerthought.org/blog/417-ot.html">Bending time in HBase</link> - makes for a good read on the version, or time, dimension in HBase. It has more detail on - versioning than is provided here. As of this writing, the limiitation - <emphasis>Overwriting values at existing timestamps</emphasis> mentioned in the - article no longer holds in HBase. This section is basically a synopsis of this article - by Bruno Dumon.</para> - - <section xml:id="specify.number.of.versions"> - <title>Specifying the Number of Versions to Store</title> - <para>The maximum number of versions to store for a given column is part of the column - schema and is specified at table creation, or via an <command>alter</command> command, via - <code>HColumnDescriptor.DEFAULT_VERSIONS</code>. Prior to HBase 0.96, the default number - of versions kept was <literal>3</literal>, but in 0.96 and newer has been changed to - <literal>1</literal>.</para> - <example> - <title>Modify the Maximum Number of Versions for a Column</title> - <para>This example uses HBase Shell to keep a maximum of 5 versions of column - <code>f1</code>. You could also use <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html" - >HColumnDescriptor</link>.</para> - <screen><![CDATA[hbase> alter ât1â², NAME => âf1â², VERSIONS => 5]]></screen> - </example> - <example> - <title>Modify the Minimum Number of Versions for a Column</title> - <para>You can also specify the minimum number of versions to store. By default, this is - set to 0, which means the feature is disabled. The following example sets the minimum - number of versions on field <code>f1</code> to <literal>2</literal>, via HBase Shell. - You could also use <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html" - >HColumnDescriptor</link>.</para> - <screen><![CDATA[hbase> alter ât1â², NAME => âf1â², MIN_VERSIONS => 2]]></screen> - </example> - <para>Starting with HBase 0.98.2, you can specify a global default for the maximum number of - versions kept for all newly-created columns, by setting - <option>hbase.column.max.version</option> in <filename>hbase-site.xml</filename>. See - <xref linkend="hbase.column.max.version"/>.</para> - </section> - - <section - xml:id="versions.ops"> - <title>Versions and HBase Operations</title> - - <para>In this section we look at the behavior of the version dimension for each of the core - HBase operations.</para> - - <section> - <title>Get/Scan</title> - - <para>Gets are implemented on top of Scans. The below discussion of <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html">Get</link> - applies equally to <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html">Scans</link>.</para> - - <para>By default, i.e. if you specify no explicit version, when doing a - <literal>get</literal>, the cell whose version has the largest value is returned - (which may or may not be the latest one written, see later). The default behavior can be - modified in the following ways:</para> - - <itemizedlist> - <listitem> - <para>to return more than one version, see <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Get.html#setMaxVersions()">Get.setMaxVersions()</link></para> - </listitem> - - <listitem> - <para>to return versions other than the latest, see <link - xlink:href="???">Get.setTimeRange()</link></para> - - <para>To retrieve the latest version that is less than or equal to a given value, thus - giving the 'latest' state of the record at a certain point in time, just use a range - from 0 to the desired version and set the max versions to 1.</para> - </listitem> - </itemizedlist> - - </section> - <section - xml:id="default_get_example"> - <title>Default Get Example</title> - <para>The following Get will only retrieve the current version of the row</para> - <programlisting language="java"> -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... -Get get = new Get(Bytes.toBytes("row1")); -Result r = table.get(get); -byte[] b = r.getValue(CF, ATTR); // returns current version of value -</programlisting> - </section> - <section - xml:id="versioned_get_example"> - <title>Versioned Get Example</title> - <para>The following Get will return the last 3 versions of the row.</para> - <programlisting language="java"> -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... -Get get = new Get(Bytes.toBytes("row1")); -get.setMaxVersions(3); // will return last 3 versions of row -Result r = table.get(get); -byte[] b = r.getValue(CF, ATTR); // returns current version of value -List<KeyValue> kv = r.getColumn(CF, ATTR); // returns all versions of this column -</programlisting> - </section> - - <section> - <title>Put</title> - - <para>Doing a put always creates a new version of a <literal>cell</literal>, at a certain - timestamp. By default the system uses the server's <literal>currentTimeMillis</literal>, - but you can specify the version (= the long integer) yourself, on a per-column level. - This means you could assign a time in the past or the future, or use the long value for - non-time purposes.</para> - - <para>To overwrite an existing value, do a put at exactly the same row, column, and - version as that of the cell you would overshadow.</para> - <section - xml:id="implicit_version_example"> - <title>Implicit Version Example</title> - <para>The following Put will be implicitly versioned by HBase with the current - time.</para> - <programlisting language="java"> -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... -Put put = new Put(Bytes.toBytes(row)); -put.add(CF, ATTR, Bytes.toBytes( data)); -table.put(put); -</programlisting> - </section> - <section - xml:id="explicit_version_example"> - <title>Explicit Version Example</title> - <para>The following Put has the version timestamp explicitly set.</para> - <programlisting language="java"> -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... -Put put = new Put( Bytes.toBytes(row)); -long explicitTimeInMs = 555; // just an example -put.add(CF, ATTR, explicitTimeInMs, Bytes.toBytes(data)); -table.put(put); -</programlisting> - <para>Caution: the version timestamp is internally by HBase for things like time-to-live - calculations. It's usually best to avoid setting this timestamp yourself. Prefer using - a separate timestamp attribute of the row, or have the timestamp a part of the rowkey, - or both. </para> - </section> - - </section> - - <section - xml:id="version.delete"> - <title>Delete</title> - - <para>There are three different types of internal delete markers. See Lars Hofhansl's blog - for discussion of his attempt adding another, <link - xlink:href="http://hadoop-hbase.blogspot.com/2012/01/scanning-in-hbase.html">Scanning - in HBase: Prefix Delete Marker</link>. </para> - <itemizedlist> - <listitem> - <para>Delete: for a specific version of a column.</para> - </listitem> - <listitem> - <para>Delete column: for all versions of a column.</para> - </listitem> - <listitem> - <para>Delete family: for all columns of a particular ColumnFamily</para> - </listitem> - </itemizedlist> - <para>When deleting an entire row, HBase will internally create a tombstone for each - ColumnFamily (i.e., not each individual column). </para> - <para>Deletes work by creating <emphasis>tombstone</emphasis> markers. For example, let's - suppose we want to delete a row. For this you can specify a version, or else by default - the <literal>currentTimeMillis</literal> is used. What this means is <quote>delete all - cells where the version is less than or equal to this version</quote>. HBase never - modifies data in place, so for example a delete will not immediately delete (or mark as - deleted) the entries in the storage file that correspond to the delete condition. - Rather, a so-called <emphasis>tombstone</emphasis> is written, which will mask the - deleted values. When HBase does a major compaction, the tombstones are processed to - actually remove the dead values, together with the tombstones themselves. If the version - you specified when deleting a row is larger than the version of any value in the row, - then you can consider the complete row to be deleted.</para> - <para>For an informative discussion on how deletes and versioning interact, see the thread <link - xlink:href="http://comments.gmane.org/gmane.comp.java.hadoop.hbase.user/28421">Put w/ - timestamp -> Deleteall -> Put w/ timestamp fails</link> up on the user mailing - list.</para> - <para>Also see <xref - linkend="keyvalue" /> for more information on the internal KeyValue format. </para> - <para>Delete markers are purged during the next major compaction of the store, unless the - <option>KEEP_DELETED_CELLS</option> option is set in the column family. To keep the - deletes for a configurable amount of time, you can set the delete TTL via the - <option>hbase.hstore.time.to.purge.deletes</option> property in - <filename>hbase-site.xml</filename>. If - <option>hbase.hstore.time.to.purge.deletes</option> is not set, or set to 0, all - delete markers, including those with timestamps in the future, are purged during the - next major compaction. Otherwise, a delete marker with a timestamp in the future is kept - until the major compaction which occurs after the time represented by the marker's - timestamp plus the value of <option>hbase.hstore.time.to.purge.deletes</option>, in - milliseconds. </para> - <note> - <para>This behavior represents a fix for an unexpected change that was introduced in - HBase 0.94, and was fixed in <link - xlink:href="https://issues.apache.org/jira/browse/HBASE-10118">HBASE-10118</link>. - The change has been backported to HBase 0.94 and newer branches.</para> - </note> - </section> - </section> - - <section> - <title>Current Limitations</title> - - <section> - <title>Deletes mask Puts</title> - - <para>Deletes mask puts, even puts that happened after the delete - was entered. See <link xlink:href="https://issues.apache.org/jira/browse/HBASE-2256" - >HBASE-2256</link>. Remember that a delete writes a tombstone, which only - disappears after then next major compaction has run. Suppose you do - a delete of everything <= T. After this you do a new put with a - timestamp <= T. This put, even if it happened after the delete, - will be masked by the delete tombstone. Performing the put will not - fail, but when you do a get you will notice the put did have no - effect. It will start working again after the major compaction has - run. These issues should not be a problem if you use - always-increasing versions for new puts to a row. But they can occur - even if you do not care about time: just do delete and put - immediately after each other, and there is some chance they happen - within the same millisecond.</para> - </section> - - <section - xml:id="major.compactions.change.query.results"> - <title>Major compactions change query results</title> - - <para><quote>...create three cell versions at t1, t2 and t3, with a maximum-versions - setting of 2. So when getting all versions, only the values at t2 and t3 will be - returned. But if you delete the version at t2 or t3, the one at t1 will appear again. - Obviously, once a major compaction has run, such behavior will not be the case - anymore...</quote> (See <emphasis>Garbage Collection</emphasis> in <link - xlink:href="http://outerthought.org/blog/417-ot.html">Bending time in - HBase</link>.)</para> - </section> - </section> - </section> - <section xml:id="dm.sort"> - <title>Sort Order</title> - <para>All data model operations HBase return data in sorted order. First by row, - then by ColumnFamily, followed by column qualifier, and finally timestamp (sorted - in reverse, so newest records are returned first). - </para> - </section> - <section xml:id="dm.column.metadata"> - <title>Column Metadata</title> - <para>There is no store of column metadata outside of the internal KeyValue instances for a ColumnFamily. - Thus, while HBase can support not only a wide number of columns per row, but a heterogenous set of columns - between rows as well, it is your responsibility to keep track of the column names. - </para> - <para>The only way to get a complete set of columns that exist for a ColumnFamily is to process all the rows. - For more information about how HBase stores data internally, see <xref linkend="keyvalue" />. - </para> - </section> - <section xml:id="joins"><title>Joins</title> - <para>Whether HBase supports joins is a common question on the dist-list, and there is a simple answer: it doesn't, - at not least in the way that RDBMS' support them (e.g., with equi-joins or outer-joins in SQL). As has been illustrated - in this chapter, the read data model operations in HBase are Get and Scan. - </para> - <para>However, that doesn't mean that equivalent join functionality can't be supported in your application, but - you have to do it yourself. The two primary strategies are either denormalizing the data upon writing to HBase, - or to have lookup tables and do the join between HBase tables in your application or MapReduce code (and as RDBMS' - demonstrate, there are several strategies for this depending on the size of the tables, e.g., nested loops vs. - hash-joins). So which is the best approach? It depends on what you are trying to do, and as such there isn't a single - answer that works for every use case. - </para> - </section> - <section xml:id="acid"><title>ACID</title> - <para>See <link xlink:href="http://hbase.apache.org/acid-semantics.html">ACID Semantics</link>. - Lars Hofhansl has also written a note on - <link xlink:href="http://hadoop-hbase.blogspot.com/2012/03/acid-in-hbase.html">ACID in HBase</link>.</para> - </section> - </chapter> <!-- data model --> - - <!-- schema design --> - <xi:include xmlns:xi="http://www.w3.org/2001/XInclude" href="schema_design.xml"/> - - <chapter - xml:id="mapreduce"> - <title>HBase and MapReduce</title> - <para>Apache MapReduce is a software framework used to analyze large amounts of data, and is - the framework used most often with <link - xlink:href="http://hadoop.apache.org/">Apache Hadoop</link>. MapReduce itself is out of the - scope of this document. A good place to get started with MapReduce is <link - xlink:href="http://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html" />. MapReduce version - 2 (MR2)is now part of <link - xlink:href="http://hadoop.apache.org/docs/r2.3.0/hadoop-yarn/hadoop-yarn-site/">YARN</link>. </para> - - <para> This chapter discusses specific configuration steps you need to take to use MapReduce on - data within HBase. In addition, it discusses other interactions and issues between HBase and - MapReduce jobs. - <note> - <title>mapred and mapreduce</title> - <para>There are two mapreduce packages in HBase as in MapReduce itself: <filename>org.apache.hadoop.hbase.mapred</filename> - and <filename>org.apache.hadoop.hbase.mapreduce</filename>. The former does old-style API and the latter - the new style. The latter has more facility though you can usually find an equivalent in the older - package. Pick the package that goes with your mapreduce deploy. When in doubt or starting over, pick the - <filename>org.apache.hadoop.hbase.mapreduce</filename>. In the notes below, we refer to - o.a.h.h.mapreduce but replace with the o.a.h.h.mapred if that is what you are using. - </para> - </note> - </para> - - <section - xml:id="hbase.mapreduce.classpath"> - <title>HBase, MapReduce, and the CLASSPATH</title> - <para>By default, MapReduce jobs deployed to a MapReduce cluster do not have access to either - the HBase configuration under <envar>$HBASE_CONF_DIR</envar> or the HBase classes.</para> - <para>To give the MapReduce jobs the access they need, you could add - <filename>hbase-site.xml</filename> to the - <filename><replaceable>$HADOOP_HOME</replaceable>/conf/</filename> directory and add the - HBase JARs to the <filename><replaceable>HADOOP_HOME</replaceable>/conf/</filename> - directory, then copy these changes across your cluster. You could add hbase-site.xml to - $HADOOP_HOME/conf and add HBase jars to the $HADOOP_HOME/lib. You would then need to copy - these changes across your cluster or edit - <filename><replaceable>$HADOOP_HOME</replaceable>conf/hadoop-env.sh</filename> and add - them to the <envar>HADOOP_CLASSPATH</envar> variable. However, this approach is not - recommended because it will pollute your Hadoop install with HBase references. It also - requires you to restart the Hadoop cluster before Hadoop can use the HBase data.</para> - <para> Since HBase 0.90.x, HBase adds its dependency JARs to the job configuration itself. The - dependencies only need to be available on the local CLASSPATH. The following example runs - the bundled HBase <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/RowCounter.html">RowCounter</link> - MapReduce job against a table named <systemitem>usertable</systemitem> If you have not set - the environment variables expected in the command (the parts prefixed by a - <literal>$</literal> sign and curly braces), you can use the actual system paths instead. - Be sure to use the correct version of the HBase JAR for your system. The backticks - (<literal>`</literal> symbols) cause ths shell to execute the sub-commands, setting the - CLASSPATH as part of the command. This example assumes you use a BASH-compatible shell. </para> - <screen language="bourne">$ <userinput>HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar rowcounter usertable</userinput></screen> - <para>When the command runs, internally, the HBase JAR finds the dependencies it needs for - zookeeper, guava, and its other dependencies on the passed <envar>HADOOP_CLASSPATH</envar> - and adds the JARs to the MapReduce job configuration. See the source at - TableMapReduceUtil#addDependencyJars(org.apache.hadoop.mapreduce.Job) for how this is done. </para> - <note> - <para> The example may not work if you are running HBase from its build directory rather - than an installed location. You may see an error like the following:</para> - <screen>java.lang.RuntimeException: java.lang.ClassNotFoundException: org.apache.hadoop.hbase.mapreduce.RowCounter$RowCounterMapper</screen> - <para>If this occurs, try modifying the command as follows, so that it uses the HBase JARs - from the <filename>target/</filename> directory within the build environment.</para> - <screen language="bourne">$ <userinput>HADOOP_CLASSPATH=${HBASE_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar:`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar rowcounter usertable</userinput></screen> - </note> - <caution> - <title>Notice to Mapreduce users of HBase 0.96.1 and above</title> - <para>Some mapreduce jobs that use HBase fail to launch. The symptom is an exception similar - to the following:</para> - <screen> -Exception in thread "main" java.lang.IllegalAccessError: class - com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass - com.google.protobuf.LiteralByteString - at java.lang.ClassLoader.defineClass1(Native Method) - at java.lang.ClassLoader.defineClass(ClassLoader.java:792) - at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142) - at java.net.URLClassLoader.defineClass(URLClassLoader.java:449) - at java.net.URLClassLoader.access$100(URLClassLoader.java:71) - at java.net.URLClassLoader$1.run(URLClassLoader.java:361) - at java.net.URLClassLoader$1.run(URLClassLoader.java:355) - at java.security.AccessController.doPrivileged(Native Method) - at java.net.URLClassLoader.findClass(URLClassLoader.java:354) - at java.lang.ClassLoader.loadClass(ClassLoader.java:424) - at java.lang.ClassLoader.loadClass(ClassLoader.java:357) - at - org.apache.hadoop.hbase.protobuf.ProtobufUtil.toScan(ProtobufUtil.java:818) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.convertScanToString(TableMapReduceUtil.java:433) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:186) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:147) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:270) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:100) -... -</screen> - <para>This is caused by an optimization introduced in <link - xlink:href="https://issues.apache.org/jira/browse/HBASE-9867">HBASE-9867</link> that - inadvertently introduced a classloader dependency. </para> - <para>This affects both jobs using the <code>-libjars</code> option and "fat jar," those - which package their runtime dependencies in a nested <code>lib</code> folder.</para> - <para>In order to satisfy the new classloader requirements, hbase-protocol.jar must be - included in Hadoop's classpath. See <xref - linkend="hbase.mapreduce.classpath" /> for current recommendations for resolving - classpath errors. The following is included for historical purposes.</para> - <para>This can be resolved system-wide by including a reference to the hbase-protocol.jar in - hadoop's lib directory, via a symlink or by copying the jar into the new location.</para> - <para>This can also be achieved on a per-job launch basis by including it in the - <code>HADOOP_CLASSPATH</code> environment variable at job submission time. When - launching jobs that package their dependencies, all three of the following job launching - commands satisfy this requirement:</para> - <screen language="bourne"> -$ <userinput>HADOOP_CLASSPATH=/path/to/hbase-protocol.jar:/path/to/hbase/conf hadoop jar MyJob.jar MyJobMainClass</userinput> -$ <userinput>HADOOP_CLASSPATH=$(hbase mapredcp):/path/to/hbase/conf hadoop jar MyJob.jar MyJobMainClass</userinput> -$ <userinput>HADOOP_CLASSPATH=$(hbase classpath) hadoop jar MyJob.jar MyJobMainClass</userinput> - </screen> - <para>For jars that do not package their dependencies, the following command structure is - necessary:</para> - <screen language="bourne"> -$ <userinput>HADOOP_CLASSPATH=$(hbase mapredcp):/etc/hbase/conf hadoop jar MyApp.jar MyJobMainClass -libjars $(hbase mapredcp | tr ':' ',')</userinput> ... - </screen> - <para>See also <link - xlink:href="https://issues.apache.org/jira/browse/HBASE-10304">HBASE-10304</link> for - further discussion of this issue.</para> - </caution> - </section> - - <section> - <title>MapReduce Scan Caching</title> - <para>TableMapReduceUtil now restores the option to set scanner caching (the number of rows - which are cached before returning the result to the client) on the Scan object that is - passed in. This functionality was lost due to a bug in HBase 0.95 (<link - xlink:href="https://issues.apache.org/jira/browse/HBASE-11558">HBASE-11558</link>), which - is fixed for HBase 0.98.5 and 0.96.3. The priority order for choosing the scanner caching is - as follows:</para> - <orderedlist> - <listitem> - <para>Caching settings which are set on the scan object.</para> - </listitem> - <listitem> - <para>Caching settings which are specified via the configuration option - <option>hbase.client.scanner.caching</option>, which can either be set manually in - <filename>hbase-site.xml</filename> or via the helper method - <code>TableMapReduceUtil.setScannerCaching()</code>.</para> - </listitem> - <listitem> - <para>The default value <code>HConstants.DEFAULT_HBASE_CLIENT_SCANNER_CACHING</code>, which is set to - <literal>100</literal>.</para> - </listitem> - </orderedlist> - <para>Optimizing the caching settings is a balance between the time the client waits for a - result and the number of sets of results the client needs to receive. If the caching setting - is too large, the client could end up waiting for a long time or the request could even time - out. If the setting is too small, the scan needs to return results in several pieces. - If you think of the scan as a shovel, a bigger cache setting is analogous to a bigger - shovel, and a smaller cache setting is equivalent to more shoveling in order to fill the - bucket.</para> - <para>The list of priorities mentioned above allows you to set a reasonable default, and - override it for specific operations.</para> - <para>See the API documentation for <link - xlink:href="https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html" - >Scan</link> for more details.</para> - </section> - - <section> - <title>Bundled HBase MapReduce Jobs</title> - <para>The HBase JAR also serves as a Driver for some bundled mapreduce jobs. To learn about - the bundled MapReduce jobs, run the following command.</para> - - <screen language="bourne">$ <userinput>${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar</userinput> -<computeroutput>An example program must be given as the first argument. -Valid program names are: - copytable: Export a table from local cluster to peer cluster - completebulkload: Complete a bulk data load. - export: Write table data to HDFS. - import: Import data written by Export. - importtsv: Import data in TSV format. - rowcounter: Count rows in HBase table</computeroutput> - </screen> - <para>Each of the valid program names are bundled MapReduce jobs. To run one of the jobs, - model your command after the following example.</para> - <screen language="bourne">$ <userinput>${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar rowcounter myTable</userinput></screen> - </section> - - <section> - <title>HBase as a MapReduce Job Data Source and Data Sink</title> - <para>HBase can be used as a data source, <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html">TableInputFormat</link>, - and data sink, <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableOutputFormat.html">TableOutputFormat</link> - or <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/MultiTableOutputFormat.html">MultiTableOutputFormat</link>, - for MapReduce jobs. Writing MapReduce jobs that read or write HBase, it is advisable to - subclass <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableMapper.html">TableMapper</link> - and/or <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableReducer.html">TableReducer</link>. - See the do-nothing pass-through classes <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/IdentityTableMapper.html">IdentityTableMapper</link> - and <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/IdentityTableReducer.html">IdentityTableReducer</link> - for basic usage. For a more involved example, see <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/RowCounter.html">RowCounter</link> - or review the <code>org.apache.hadoop.hbase.mapreduce.TestTableMapReduce</code> unit test. </para> - <para>If you run MapReduce jobs that use HBase as source or sink, need to specify source and - sink table and column names in your configuration.</para> - - <para>When you read from HBase, the <code>TableInputFormat</code> requests the list of regions - from HBase and makes a map, which is either a <code>map-per-region</code> or - <code>mapreduce.job.maps</code> map, whichever is smaller. If your job only has two maps, - raise <code>mapreduce.job.maps</code> to a number greater than the number of regions. Maps - will run on the adjacent TaskTracker if you are running a TaskTracer and RegionServer per - node. When writing to HBase, it may make sense to avoid the Reduce step and write back into - HBase from within your map. This approach works when your job does not need the sort and - collation that MapReduce does on the map-emitted data. On insert, HBase 'sorts' so there is - no point double-sorting (and shuffling data around your MapReduce cluster) unless you need - to. If you do not need the Reduce, you myour map might emit counts of records processed for - reporting at the end of the jobj, or set the number of Reduces to zero and use - TableOutputFormat. If running the Reduce step makes sense in your case, you should typically - use multiple reducers so that load is spread across the HBase cluster.</para> - - <para>A new HBase partitioner, the <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/HRegionPartitioner.html">HRegionPartitioner</link>, - can run as many reducers the number of existing regions. The HRegionPartitioner is suitable - when your table is large and your upload will not greatly alter the number of existing - regions upon completion. Otherwise use the default partitioner. </para> - </section> - - <section> - <title>Writing HFiles Directly During Bulk Import</title> - <para>If you are importing into a new table, you can bypass the HBase API and write your - content directly to the filesystem, formatted into HBase data files (HFiles). Your import - will run faster, perhaps an order of magnitude faster. For more on how this mechanism works, - see <xref - linkend="arch.bulk.load" />.</para> - </section> - - <section> - <title>RowCounter Example</title> - <para>The included <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/RowCounter.html">RowCounter</link> - MapReduce job uses <code>TableInputFormat</code> and does a count of all rows in the specified - table. To run it, use the following command: </para> - <screen language="bourne">$ <userinput>./bin/hadoop jar hbase-X.X.X.jar</userinput></screen> - <para>This will - invoke the HBase MapReduce Driver class. Select <literal>rowcounter</literal> from the choice of jobs - offered. This will print rowcouner usage advice to standard output. Specify the tablename, - column to count, and output - directory. If you have classpath errors, see <xref linkend="hbase.mapreduce.classpath" />.</para> - </section> - - <section - xml:id="splitter"> - <title>Map-Task Splitting</title> - <section - xml:id="splitter.default"> - <title>The Default HBase MapReduce Splitter</title> - <para>When <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html">TableInputFormat</link> - is used to source an HBase table in a MapReduce job, its splitter will make a map task for - each region of the table. Thus, if there are 100 regions in the table, there will be 100 - map-tasks for the job - regardless of how many column families are selected in the - Scan.</para> - </section> - <section - xml:id="splitter.custom"> - <title>Custom Splitters</title> - <para>For those interested in implementing custom splitters, see the method - <code>getSplits</code> in <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormatBase.html">TableInputFormatBase</link>. - That is where the logic for map-task assignment resides. </para> - </section> - </section> - <section - xml:id="mapreduce.example"> - <title>HBase MapReduce Examples</title> - <section - xml:id="mapreduce.example.read"> - <title>HBase MapReduce Read Example</title> - <para>The following is an example of using HBase as a MapReduce source in read-only manner. - Specifically, there is a Mapper instance but no Reducer, and nothing is being emitted from - the Mapper. There job would be defined as follows...</para> - <programlisting language="java"> -Configuration config = HBaseConfiguration.create(); -Job job = new Job(config, "ExampleRead"); -job.setJarByClass(MyReadJob.class); // class that contains mapper - -Scan scan = new Scan(); -scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs -scan.setCacheBlocks(false); // don't set to true for MR jobs -// set other scan attrs -... - -TableMapReduceUtil.initTableMapperJob( - tableName, // input HBase table name - scan, // Scan instance to control CF and attribute selection - MyMapper.class, // mapper - null, // mapper output key - null, // mapper output value - job); -job.setOutputFormatClass(NullOutputFormat.class); // because we aren't emitting anything from mapper - -boolean b = job.waitForCompletion(true); -if (!b) { - throw new IOException("error with job!"); -} - </programlisting> - <para>...and the mapper instance would extend <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableMapper.html">TableMapper</link>...</para> - <programlisting language="java"> -public static class MyMapper extends TableMapper<Text, Text> { - - public void map(ImmutableBytesWritable row, Result value, Context context) throws InterruptedException, IOException { - // process data for the row from the Result instance. - } -} - </programlisting> - </section> - <section - xml:id="mapreduce.example.readwrite"> - <title>HBase MapReduce Read/Write Example</title> - <para>The following is an example of using HBase both as a source and as a sink with - MapReduce. This example will simply copy data from one table to another.</para> - <programlisting language="java"> -Configuration config = HBaseConfiguration.create(); -Job job = new Job(config,"ExampleReadWrite"); -job.setJarByClass(MyReadWriteJob.class); // class that contains mapper - -Scan scan = new Scan(); -scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs -scan.setCacheBlocks(false); // don't set to true for MR jobs -// set other scan attrs - -TableMapReduceUtil.initTableMapperJob( - sourceTable, // input table - scan, // Scan instance to control CF and attribute selection - MyMapper.class, // mapper class - null, // mapper output key - null, // mapper output value - job); -TableMapReduceUtil.initTableReducerJob( - targetTable, // output table - null, // reducer class - job); -job.setNumReduceTasks(0); - -boolean b = job.waitForCompletion(true); -if (!b) { - throw new IOException("error with job!"); -} - </programlisting> - <para>An explanation is required of what <classname>TableMapReduceUtil</classname> is doing, - especially with the reducer. <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableOutputFormat.html">TableOutputFormat</link> - is being used as the outputFormat class, and several parameters are being set on the - config (e.g., TableOutputFormat.OUTPUT_TABLE), as well as setting the reducer output key - to <classname>ImmutableBytesWritable</classname> and reducer value to - <classname>Writable</classname>. These could be set by the programmer on the job and - conf, but <classname>TableMapReduceUtil</classname> tries to make things easier.</para> - <para>The following is the example mapper, which will create a <classname>Put</classname> - and matching the input <classname>Result</classname> and emit it. Note: this is what the - CopyTable utility does. </para> - <programlisting language="java"> -public static class MyMapper extends TableMapper<ImmutableBytesWritable, Put> { - - public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException { - // this example is just copying the data from the source table... - context.write(row, resultToPut(row,value)); - } - - private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException { - Put put = new Put(key.get()); - for (KeyValue kv : result.raw()) { - put.add(kv); - } - return put; - } -} - </programlisting> - <para>There isn't actually a reducer step, so <classname>TableOutputFormat</classname> takes - care of sending the <classname>Put</classname> to the target table. </para> - <para>This is just an example, developers could choose not to use - <classname>TableOutputFormat</classname> and connect to the target table themselves. - </para> - </section> - <section - xml:id="mapreduce.example.readwrite.multi"> - <title>HBase MapReduce Read/Write Example With Multi-Table Output</title> - <para>TODO: example for <classname>MultiTableOutputFormat</classname>. </para> - </section> - <section - xml:id="mapreduce.example.summary"> - <title>HBase MapReduce Summary to HBase Example</title> - <para>The following example uses HBase as a MapReduce source and sink with a summarization - step. This example will count the number of distinct instances of a value in a table and - write those summarized counts in another table. - <programlisting language="java"> -Configuration config = HBaseConfiguration.create(); -Job job = new Job(config,"ExampleSummary"); -job.setJarByClass(MySummaryJob.class); // class that contains mapper and reducer - -Scan scan = new Scan(); -scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs -scan.setCacheBlocks(false); // don't set to true for MR jobs -// set other scan attrs - -TableMapReduceUtil.initTableMapperJob( - sourceTable, // input table - scan, // Scan instance to control CF and attribute selection - MyMapper.class, // mapper class - Text.class, // mapper output key - IntWritable.class, // mapper output value - job); -TableMapReduceUtil.initTableReducerJob( - targetTable, // output table - MyTableReducer.class, // reducer class - job); -job.setNumReduceTasks(1); // at least one, adjust as required - -boolean b = job.waitForCompletion(true); -if (!b) { - throw new IOException("error with job!"); -} - </programlisting> - In this example mapper a column with a String-value is chosen as the value to summarize - upon. This value is used as the key to emit from the mapper, and an - <classname>IntWritable</classname> represents an instance counter. - <programlisting language="java"> -public static class MyMapper extends TableMapper<Text, IntWritable> { - public static final byte[] CF = "cf".getBytes(); - public static final byte[] ATTR1 = "attr1".getBytes(); - - private final IntWritable ONE = new IntWritable(1); - private Text text = new Text(); - - public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException { - String val = new String(value.getValue(CF, ATTR1)); - text.set(val); // we can only emit Writables... - - context.write(text, ONE); - } -} - </programlisting> - In the reducer, the "ones" are counted (just like any other MR example that does this), - and then emits a <classname>Put</classname>. - <programlisting language="java"> -public static class MyTableReducer extends TableReducer<Text, IntWritable, ImmutableBytesWritable> { - public static final byte[] CF = "cf".getBytes(); - public static final byte[] COUNT = "count".getBytes(); - - public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { - int i = 0; - for (IntWritable val : values) { - i += val.get(); - } - Put put = new Put(Bytes.toBytes(key.toString())); - put.add(CF, COUNT, Bytes.toBytes(i)); - - context.write(null, put); - } -} - </programlisting> - </para> - </section> - <section - xml:id="mapreduce.example.summary.file"> - <title>HBase MapReduce Summary to File Example</title> - <para>This very similar to the summary example above, with exception that this is using - HBase as a MapReduce source but HDFS as the sink. The differences are in the job setup and - in the reducer. The mapper remains the same. </para> - <programlisting language="java"> -Configuration config = HBaseConfiguration.create(); -Job job = new Job(config,"ExampleSummaryToFile"); -job.setJarByClass(MySummaryFileJob.class); // class that contains mapper and reducer - -Scan scan = new Scan(); -scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs -scan.setCacheBlocks(false); // don't set to true for MR jobs -// set other scan attrs - -TableMapReduceUtil.initTableMapperJob( - sourceTable, // input table - scan, // Scan instance to control CF and attribute selection - MyMapper.class, // mapper class - Text.class, // mapper output key - IntWritable.class, // mapper output value - job); -job.setReducerClass(MyReducer.class); // reducer class -job.setNumReduceTasks(1); // at least one, adjust as required -FileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile")); // adjust directories as required - -boolean b = job.waitForCompletion(true); -if (!b) { - throw new IOException("error with job!"); -} - </programlisting> - <para>As stated above, the previous Mapper can run unchanged with this example. As for the - Reducer, it is a "generic" Reducer instead of extending TableMapper and emitting - Puts.</para> - <programlisting language="java"> - public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> { - - public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { - int i = 0; - for (IntWritable val : values) { - i += val.get(); - } - context.write(key, new IntWritable(i)); - } -} - </programlisting> - </section> - <section - xml:id="mapreduce.example.summary.noreducer"> - <title>HBase MapReduce Summary to HBase Without Reducer</title> - <para>It is also possible to perform summaries without a reducer - if you use HBase as the - reducer. </para> - <para>An HBase target table would need to exist for the job summary. The Table method - <code>incrementColumnValue</code> would be used to atomically increment values. From a - performance perspective, it might make sense to keep a Map of values with their values to - be incremeneted for each map-task, and make one update per key at during the <code> - cleanup</code> method of the mapper. However, your milage may vary depending on the - number of rows to be processed and unique keys. </para> - <para>In the end, the summary results are in HBase. </para> - </section> - <section - xml:id="mapreduce.example.summary.rdbms"> - <title>HBase MapReduce Summary to RDBMS</title> - <para>Sometimes it is more appropriate to generate summaries to an RDBMS. For these cases, - it is possible to generate summaries directly to an RDBMS via a custom reducer. The - <code>setup</code> method can connect to an RDBMS (the connection information can be - passed via custom parameters in the context) and the cleanup method can close the - connection. </para> - <para>It is critical to understand that number of reducers for the job affects the - summarization implementation, and you'll have to design this into your reducer. - Specifically, whether it is designed to run as a singleton (one reducer) or multiple - reducers. Neither is right or wrong, it depends on your use-case. Recognize that the more - reducers that are assigned to the job, the more simultaneous connections to the RDBMS will - be created - this will scale, but only to a point. </para> - <programlisting language="java"> - public static class MyRdbmsReducer extends Reducer<Text, IntWritable, Text, IntWritable> { - - private Connection c = null; - - public void setup(Context context) { - // create DB connection... - } - - public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { - // do summarization - // in this example the keys are Text, but this is just an example - } - - public void cleanup(Context context) { - // close db connection - } - -} - </programlisting> - <para>In the end, the summary results are written to your RDBMS table/s. </para> - </section> - - </section> - <!-- mr examples --> - <section - xml:id="mapreduce.htable.access"> - <title>Accessing Other HBase Tables in a MapReduce Job</title> - <para>Although the framework currently allows one HBase table as input to a MapReduce job, - other HBase tables can be accessed as lookup tables, etc., in a MapReduce job via creating - an Table instance in the setup method of the Mapper. - <programlisting language="java">public class MyMapper extends TableMapper<Text, LongWritable> { - private Table myOtherTable; - - public void setup(Context context) { - // In here create a Connection to the cluster and save it or use the Connection - // from the existing table - myOtherTable = connection.getTable("myOtherTable"); - } - - public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException { - // process Result... - // use 'myOtherTable' for lookups - } - - </programlisting> - </para> - </section> - <section - xml:id="mapreduce.specex"> - <title>Speculative Execution</title> - <para>It is generally advisable to turn off speculative execution for MapReduce jobs that use - HBase as a source. This can either be done on a per-Job basis through properties, on on the - entire cluster. Especially for longer running jobs, speculative execution will create - duplicate map-tasks which will double-write your data to HBase; this is probably not what - you want. </para> - <para>See <xref - linkend="spec.ex" /> for more information. </para> - </section> - </chapter> <!-- mapreduce --> - - <xi:include xmlns:xi="http://www.w3.org/2001/XInclude" href="security.xml" /> - - <chapter xml:id="architecture"> - <title>Architecture</title> - <section xml:id="arch.overview"> - <title>Overview</title> - <section xml:id="arch.overview.nosql"> - <title>NoSQL?</title> - <para>HBase is a type of "NoSQL" database. "NoSQL" is a general term meaning that the database isn't an RDBMS which - supports SQL as its primary access language, but there are many types of NoSQL databases: BerkeleyDB is an - example of a local NoSQL database, whereas HBase is very much a distributed database. Technically speaking, - HBase is really more a "Data Store" than "Data Base" because it lacks many of the features you find in an RDBMS, - such as typed columns, secondary indexes, triggers, and advanced query languages, etc. - </para> - <para>However, HBase has many features which supports both linear and modular scaling. HBase clusters expand - by adding RegionServers that are hosted on commodity class servers. If a cluster expands from 10 to 20 - RegionServers, for example, it doubles both in terms of storage and as well as processing capacity. - RDBMS can scale well, but only up to a point - specifically, the size of a single database server - and for the best - performance requires specialized hardware and storage devices. HBase features of note are: - <itemizedlist> - <listitem><para>Strongly consistent reads/writes: HBase is not an "eventually consistent" DataStore. This - makes it very suitable for tasks such as high-speed counter aggregation.</para> </listitem> - <listitem><para>Automatic sharding: HBase tables are distributed on the cluster via regions, and regions are - automatically split and re-distributed as your data grows.</para></listitem> - <listitem><para>Automatic RegionServer failover</para></listitem> - <listitem><para>Hadoop/HDFS Integration: HBase supports HDFS out of the box as its distributed file system.</para></listitem> - <listitem><para>MapReduce: HBase supports massively parallelized processing via MapReduce for using HBase as both - source and sink.</para></listitem> - <listitem><para>Java Client API: HBase supports an easy to use Java API for programmatic access.</para></listitem> - <listitem><para>Thrift/REST API: HBase also supports Thrift and REST for non-Java front-ends.</para></listitem> - <listitem><para>Block Cache and Bloom Filters: HBase supports a Block Cache and Bloom Filters for high volume query optimization.</para></listitem> - <listitem><para>Operational Management: HBase provides build-in web-pages for operational insight as well as JMX metrics.</para></listitem> - </itemizedlist> - </para> - </section> - - <section xml:id="arch.overview.when"> - <title>When Should I Use HBase?</title> - <para>HBase isn't suitable for every problem.</para> - <para>First, make sure you have enough data. If you have hundreds of millions or billions of rows, then - HBase is a good candidate. If you only have a few thousand/million rows, then using a traditional RDBMS - might be a better choice due to the fact that all of your data might wind up on a single node (or two) and - the rest of the cluster may be sitting idle. - </para> - <para>Second, make sure you can live without all the extra features that an RDBMS provides (e.g., typed columns, - secondary indexes, transactions, advanced query languages, etc.) An application built against an RDBMS cannot be - "ported" to HBase by simply changing a JDBC driver, for example. Consider moving from an RDBMS to HBase as a - complete redesign as opposed to a port. - </para> - <para>Third, make sure you have enough hardware. Even HDFS doesn't do well with anything less than - 5 DataNodes (due to things such as HDFS block replication which has a default of 3), plus a NameNode. - </para> - <para>HBase can run quite well stand-alone on a laptop - but this should be considered a development - configuration only. - </para> - </section> - <section xml:id="arch.overview.hbasehdfs"> - <title>What Is The Difference Between HBase and Hadoop/HDFS?</title> - <para><link xlink:href="http://hadoop.apache.org/hdfs/">HDFS</link> is a distributed file system that is well suited for the storage of large files. - Its documentation states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files. - HBase, on the other hand, is built on top of HDFS and provides fast record lookups (and updates) for large tables. - This can sometimes be a point of conceptual confusion. HBase internally puts your data in indexed "StoreFiles" that exist - on HDFS for high-speed lookups. See the <xref linkend="datamodel" /> and the rest of this chapter for more information on how HBase achieves its goals. - </para> - </section> - </section> - - <section - xml:id="arch.catalog"> - <title>Catalog Tables</title> - <para>The catalog table <code>hbase:meta</code> exists as an HBase table and is filtered out of the HBase - shell's <code>list</code> command, but is in fact a table just like any other. </para> - <section - xml:id="arch.catalog.root"> - <title>-ROOT-</title> - <note> - <para>The <code>-ROOT-</code> table was removed in HBase 0.96.0. Information here should - be considered historical.</para> - </note> - <para>The <code>-ROOT-</code> table kept track of the location of the - <code>.META</code> table (the previous name for the table now called <code>hbase:meta</code>) prior to HBase - 0.96. The <code>-ROOT-</code> table structure was as follows: </para> - <itemizedlist> - <title>Key</title> - <listitem> - <para>.META. region key (<code>.META.,,1</code>)</para> - </listitem> - </itemizedlist> - - <itemizedlist> - <title>Values</title> - <listitem> - <para><code>info:regioninfo</code> (serialized <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HRegionInfo.html">HRegionInfo</link> - instance of hbase:meta)</para> - </listitem> - <listitem> - <para><code>info:server</code> (server:port of the RegionServer holding - hbase:meta)</para> - </listitem> - <listitem> - <para><code>info:serverstartcode</code> (start-time of the RegionServer process holding - hbase:meta)</para> - </listitem> - </itemizedlist> - </section> - <section - xml:id="arch.catalog.meta"> - <title>hbase:meta</title> - <para>The <code>hbase:meta</code> table (previously called <code>.META.</code>) keeps a list - of all regions in the system. The location of <code>hbase:meta</code> was previously - tracked within the <code>-ROOT-</code> table, but is now stored in Zookeeper.</para> - <para>The <code>hbase:meta</code> table structure is as follows: </para> - <itemizedlist> - <title>Key</title> - <listitem> - <para>Region key of the format (<code>[table],[region start key],[region - id]</code>)</para> - </listitem> - </itemizedlist> - <itemizedlist> - <title>Values</title> - <listitem> - <para><code>info:regioninfo</code> (serialized <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HRegionInfo.html"> - HRegionInfo</link> instance for this region)</para> - </listitem> - <listitem> - <para><code>info:server</code> (server:port of the RegionServer containing this - region)</para> - </listitem> - <listitem> - <para><code>info:serverstartcode</code> (start-time of the RegionServer process - containing this region)</para> - </listitem> - </itemizedlist> - <para>When a table is in the process of splitting, two other columns will be created, called - <code>info:splitA</code> and <code>info:splitB</code>. These columns represent the two - daughter regions. The values for these columns are also serialized HRegionInfo instances. - After the region has been split, eventually this row will be deleted. </para> - <note> - <title>Note on HRegionInfo</title> - <para>The empty key is used to denote table start and table end. A region with an empty - start key is the first region in a table. If a region has both an empty start and an - empty end key, it is the only region in the table </para> - </note> - <para>In the (hopefully unlikely) event that programmatic processing of catalog metadata is - required, see the <link - xlink:href="http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/util/Writables.html#getHRegionInfo%28byte[]%29">Writables</link> - utility. </para> - </section> - <section - xml:id="arc
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