src/docs/user/SqoopUserGuide.xml

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//OASIS//DTD DocBook XML V4.5//EN" "http://www.oasis-open.org/docbook/xml/4.5/docbookx.dtd">
<?asciidoc-toc?>
<?asciidoc-numbered?>

<article lang="en">
<articleinfo>
    <title>Sqoop User Guide (v1.4.6-SNAPSHOT)</title>
</articleinfo>
<screen>  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.</screen>
<section id="_introduction">
<title>Introduction</title>
<simpara>Sqoop is a tool designed to transfer data between Hadoop and
relational databases or mainframes. You can use Sqoop to import data from a
relational database management system (RDBMS) such as MySQL or Oracle or a
mainframe into the Hadoop Distributed File System (HDFS),
transform the data in Hadoop MapReduce, and then export the data back
into an RDBMS.</simpara>
<simpara>Sqoop automates most of this process, relying on the database to
describe the schema for the data to be imported. Sqoop uses MapReduce
to import and export the data, which provides parallel operation as
well as fault tolerance.</simpara>
<simpara>This document describes how to get started using Sqoop to move data
between databases and Hadoop or mainframe to Hadoop and provides reference
information for the operation of the Sqoop command-line tool suite. This
document is intended for:</simpara>
<itemizedlist>
<listitem>
<simpara>
System and application programmers
</simpara>
</listitem>
<listitem>
<simpara>
System administrators
</simpara>
</listitem>
<listitem>
<simpara>
Database administrators
</simpara>
</listitem>
<listitem>
<simpara>
Data analysts
</simpara>
</listitem>
<listitem>
<simpara>
Data engineers
</simpara>
</listitem>
</itemizedlist>
</section>
<section id="_supported_releases">
<title>Supported Releases</title>
<simpara>This documentation applies to Sqoop v1.4.6-SNAPSHOT.</simpara>
</section>
<section id="_sqoop_releases">
<title>Sqoop Releases</title>
<simpara>Sqoop is an open source software product of the Apache Software Foundation.</simpara>
<simpara>Software development for Sqoop occurs at <ulink url="http://sqoop.apache.org">http://sqoop.apache.org</ulink>
At that site you can obtain:</simpara>
<itemizedlist>
<listitem>
<simpara>
New releases of Sqoop as well as its most recent source code
</simpara>
</listitem>
<listitem>
<simpara>
An issue tracker
</simpara>
</listitem>
<listitem>
<simpara>
A wiki that contains Sqoop documentation
</simpara>
</listitem>
</itemizedlist>
</section>
<section id="_prerequisites">
<title>Prerequisites</title>
<simpara>The following prerequisite knowledge is required for this product:</simpara>
<itemizedlist>
<listitem>
<simpara>
Basic computer technology and terminology
</simpara>
</listitem>
<listitem>
<simpara>
Familiarity with command-line interfaces such as <literal>bash</literal>
</simpara>
</listitem>
<listitem>
<simpara>
Relational database management systems
</simpara>
</listitem>
<listitem>
<simpara>
Basic familiarity with the purpose and operation of Hadoop
</simpara>
</listitem>
</itemizedlist>
<simpara>Before you can use Sqoop, a release of Hadoop must be installed and
configured. Sqoop is currently supporting 4 major Hadoop releases - 0.20,
0.23, 1.0 and 2.0.</simpara>
<simpara>This document assumes you are using a Linux or Linux-like environment.
If you are using Windows, you may be able to use cygwin to accomplish
most of the following tasks. If you are using Mac OS X, you should see
few (if any) compatibility errors. Sqoop is predominantly operated and
tested on Linux.</simpara>
</section>
<section id="_basic_usage">
<title>Basic Usage</title>
<simpara>With Sqoop, you can <emphasis>import</emphasis> data from a relational database system or a
mainframe into HDFS. The input to the import process is either database table
or mainframe datasets. For databases, Sqoop will read the table row-by-row
into HDFS.  For mainframe datasets, Sqoop will read records from each mainframe
dataset into HDFS. The output of this import process is a set of files
containing a copy of the imported table or datasets.
The import process is performed in parallel. For this reason, the
output will be in multiple files. These files may be delimited text
files (for example, with commas or tabs separating each field), or
binary Avro or SequenceFiles containing serialized record data.</simpara>
<simpara>A by-product of the import process is a generated Java class which
can encapsulate one row of the imported table. This class is used
during the import process by Sqoop itself. The Java source code for
this class is also provided to you, for use in subsequent MapReduce
processing of the data. This class can serialize and deserialize data
to and from the SequenceFile format. It can also parse the
delimited-text form of a record. These abilities allow you to quickly
develop MapReduce applications that use the HDFS-stored records in
your processing pipeline. You are also free to parse the delimiteds
record data yourself, using any other tools you prefer.</simpara>
<simpara>After manipulating the imported records (for example, with MapReduce
or Hive) you may have a result data set which you can then <emphasis>export</emphasis>
back to the relational database. Sqoop&#8217;s export process will read
a set of delimited text files from HDFS in parallel, parse them into
records, and insert them as new rows in a target database table, for
consumption by external applications or users.</simpara>
<simpara>Sqoop includes some other commands which allow you to inspect the
database you are working with. For example, you can list the available
database schemas (with the <literal>sqoop-list-databases</literal> tool) and tables
within a schema (with the <literal>sqoop-list-tables</literal> tool). Sqoop also
includes a primitive SQL execution shell (the <literal>sqoop-eval</literal> tool).</simpara>
<simpara>Most aspects of the import, code generation, and export processes can
be customized. For databases, you can control the specific row range or
columns imported.  You can specify particular delimiters and escape characters
for the file-based representation of the data, as well as the file format
used.  You can also control the class or package names used in
generated code. Subsequent sections of this document explain how to
specify these and other arguments to Sqoop.</simpara>
</section>
<section id="_sqoop_tools">
<title>Sqoop Tools</title>
<simpara>Sqoop is a collection of related tools. To use Sqoop, you specify the
tool you want to use and the arguments that control the tool.</simpara>
<simpara>If Sqoop is compiled from its own source, you can run Sqoop without a formal
installation process by running the <literal>bin/sqoop</literal> program. Users
of a packaged deployment of Sqoop (such as an RPM shipped with Apache Bigtop)
will see this program installed as <literal>/usr/bin/sqoop</literal>. The remainder of this
documentation will refer to this program as <literal>sqoop</literal>. For example:</simpara>
<screen>$ sqoop tool-name [tool-arguments]</screen>
<note><simpara>The following examples that begin with a <literal>$</literal> character indicate
that the commands must be entered at a terminal prompt (such as
<literal>bash</literal>). The <literal>$</literal> character represents the prompt itself; you should
not start these commands by typing a <literal>$</literal>. You can also enter commands
inline in the text of a paragraph; for example, <literal>sqoop help</literal>. These
examples do not show a <literal>$</literal> prefix, but you should enter them the same
way.  Don&#8217;t confuse the <literal>$</literal> shell prompt in the examples with the <literal>$</literal>
that precedes an environment variable name. For example, the string
literal <literal>$HADOOP_HOME</literal> includes a "<literal>$</literal>".</simpara></note>
<simpara>Sqoop ships with a help tool. To display a list of all available
tools, type the following command:</simpara>
<screen>$ sqoop help
usage: sqoop COMMAND [ARGS]

Available commands:
  codegen            Generate code to interact with database records
  create-hive-table  Import a table definition into Hive
  eval               Evaluate a SQL statement and display the results
  export             Export an HDFS directory to a database table
  help               List available commands
  import             Import a table from a database to HDFS
  import-all-tables  Import tables from a database to HDFS
  import-mainframe   Import mainframe datasets to HDFS
  list-databases     List available databases on a server
  list-tables        List available tables in a database
  version            Display version information

See 'sqoop help COMMAND' for information on a specific command.</screen>
<simpara>You can display help for a specific tool by entering: <literal>sqoop help
(tool-name)</literal>; for example, <literal>sqoop help import</literal>.</simpara>
<simpara>You can also add the <literal>--help</literal> argument to any command: <literal>sqoop import
--help</literal>.</simpara>
<section id="_using_command_aliases">
<title>Using Command Aliases</title>
<simpara>In addition to typing the <literal>sqoop (toolname)</literal> syntax, you can use alias
scripts that specify the <literal>sqoop-(toolname)</literal> syntax. For example, the
scripts <literal>sqoop-import</literal>, <literal>sqoop-export</literal>, etc. each select a specific
tool.</simpara>
</section>
<section id="_controlling_the_hadoop_installation">
<title>Controlling the Hadoop Installation</title>
<simpara>You invoke Sqoop through the program launch capability provided by
Hadoop. The <literal>sqoop</literal> command-line program is a wrapper which runs the
<literal>bin/hadoop</literal> script shipped with Hadoop. If you have multiple
installations of Hadoop present on your machine, you can select the
Hadoop installation by setting the <literal>$HADOOP_COMMON_HOME</literal> and
<literal>$HADOOP_MAPRED_HOME</literal> environment variables.</simpara>
<simpara>For example:</simpara>
<screen>$ HADOOP_COMMON_HOME=/path/to/some/hadoop \
  HADOOP_MAPRED_HOME=/path/to/some/hadoop-mapreduce \
  sqoop import --arguments...</screen>
<simpara>or:</simpara>
<screen>$ export HADOOP_COMMON_HOME=/some/path/to/hadoop
$ export HADOOP_MAPRED_HOME=/some/path/to/hadoop-mapreduce
$ sqoop import --arguments...</screen>
<simpara>If either of these variables are not set, Sqoop will fall back to
<literal>$HADOOP_HOME</literal>. If it is not set either, Sqoop will use the default
installation locations for Apache Bigtop, <literal>/usr/lib/hadoop</literal> and
<literal>/usr/lib/hadoop-mapreduce</literal>, respectively.</simpara>
<simpara>The active Hadoop configuration is loaded from <literal>$HADOOP_HOME/conf/</literal>,
unless the <literal>$HADOOP_CONF_DIR</literal> environment variable is set.</simpara>
</section>
<section id="_using_generic_and_specific_arguments">
<title>Using Generic and Specific Arguments</title>
<simpara>To control the operation of each Sqoop tool, you use generic and
specific arguments.</simpara>
<simpara>For example:</simpara>
<screen>$ sqoop help import
usage: sqoop import [GENERIC-ARGS] [TOOL-ARGS]

Common arguments:
   --connect &lt;jdbc-uri&gt;     Specify JDBC connect string
   --connect-manager &lt;class-name&gt;     Specify connection manager class to use
   --driver &lt;class-name&gt;    Manually specify JDBC driver class to use
   --hadoop-mapred-home &lt;dir&gt;      Override $HADOOP_MAPRED_HOME
   --help                   Print usage instructions
   --password-file          Set path for file containing authentication password
   -P                       Read password from console
   --password &lt;password&gt;    Set authentication password
   --username &lt;username&gt;    Set authentication username
   --verbose                Print more information while working
   --hadoop-home &lt;dir&gt;     Deprecated. Override $HADOOP_HOME

[...]

Generic Hadoop command-line arguments:
(must preceed any tool-specific arguments)
Generic options supported are
-conf &lt;configuration file&gt;     specify an application configuration file
-D &lt;property=value&gt;            use value for given property
-fs &lt;local|namenode:port&gt;      specify a namenode
-jt &lt;local|jobtracker:port&gt;    specify a job tracker
-files &lt;comma separated list of files&gt;    specify comma separated files to be copied to the map reduce cluster
-libjars &lt;comma separated list of jars&gt;    specify comma separated jar files to include in the classpath.
-archives &lt;comma separated list of archives&gt;    specify comma separated archives to be unarchived on the compute machines.

The general command line syntax is
bin/hadoop command [genericOptions] [commandOptions]</screen>
<simpara>You must supply the generic arguments <literal>-conf</literal>, <literal>-D</literal>, and so on after the
tool name but <emphasis role="strong">before</emphasis> any tool-specific arguments (such as
<literal>--connect</literal>). Note that generic Hadoop arguments are preceeded by a
single dash character (<literal>-</literal>), whereas tool-specific arguments start
with two dashes (<literal>--</literal>), unless they are single character arguments such as <literal>-P</literal>.</simpara>
<simpara>The <literal>-conf</literal>, <literal>-D</literal>, <literal>-fs</literal> and <literal>-jt</literal> arguments control the configuration
and Hadoop server settings. For example, the <literal>-D mapred.job.name=&lt;job_name&gt;</literal> can
be used to set the name of the MR job that Sqoop launches, if not specified,
the name defaults to the jar name for the job - which is derived from the used
table name.</simpara>
<simpara>The <literal>-files</literal>, <literal>-libjars</literal>, and <literal>-archives</literal> arguments are not typically used with
Sqoop, but they are included as part of Hadoop&#8217;s internal argument-parsing
system.</simpara>
</section>
<section id="_using_options_files_to_pass_arguments">
<title>Using Options Files to Pass Arguments</title>
<simpara>When using Sqoop, the command line options that do not change from
invocation to invocation can be put in an options file for convenience.
An options file is a text file where each line identifies an option in
the order that it appears otherwise on the command line. Option files
allow specifying a single option on multiple lines by using the
back-slash character at the end of intermediate lines. Also supported
are comments within option files that begin with the hash character.
Comments must be specified on a new line and may not be mixed with
option text. All comments and empty lines are ignored when option
files are expanded. Unless options appear as quoted strings, any
leading or trailing spaces are ignored. Quoted strings if used must
not extend beyond the line on which they are specified.</simpara>
<simpara>Option files can be specified anywhere in the command line as long as
the options within them follow the otherwise prescribed rules of
options ordering. For instance, regardless of where the options are
loaded from, they must follow the ordering such that generic options
appear first, tool specific options next, finally followed by options
that are intended to be passed to child programs.</simpara>
<simpara>To specify an options file, simply create an options file in a
convenient location and pass it to the command line via
<literal>--options-file</literal> argument.</simpara>
<simpara>Whenever an options file is specified, it is expanded on the
command line before the tool is invoked. You can specify more than
one option files within the same invocation if needed.</simpara>
<simpara>For example, the following Sqoop invocation for import can
be specified alternatively as shown below:</simpara>
<screen>$ sqoop import --connect jdbc:mysql://localhost/db --username foo --table TEST

$ sqoop --options-file /users/homer/work/import.txt --table TEST</screen>
<simpara>where the options file <literal>/users/homer/work/import.txt</literal> contains the following:</simpara>
<screen>import
--connect
jdbc:mysql://localhost/db
--username
foo</screen>
<simpara>The options file can have empty lines and comments for readability purposes.
So the above example would work exactly the same if the options file
<literal>/users/homer/work/import.txt</literal> contained the following:</simpara>
<screen>#
# Options file for Sqoop import
#

# Specifies the tool being invoked
import

# Connect parameter and value
--connect
jdbc:mysql://localhost/db

# Username parameter and value
--username
foo

#
# Remaining options should be specified in the command line.
#</screen>
</section>
<section id="_using_tools">
<title>Using Tools</title>
<simpara>The following sections will describe each tool&#8217;s operation. The
tools are listed in the most likely order you will find them useful.</simpara>
</section>
</section>
<section id="_literal_sqoop_import_literal">
<title><literal>sqoop-import</literal></title>
<section id="_purpose">
<title>Purpose</title>
<simpara>The <literal>import</literal> tool imports an individual table from an RDBMS to HDFS.
Each row from a table is represented as a separate record in HDFS.
Records can be stored as text files (one record per line), or in
binary representation as Avro or SequenceFiles.</simpara>
</section>
<section id="_syntax">
<title>Syntax</title>
<screen>$ sqoop import (generic-args) (import-args)
$ sqoop-import (generic-args) (import-args)</screen>
<simpara>While the Hadoop generic arguments must precede any import arguments,
you can type the import arguments in any order with respect to one
another.</simpara>
<note><simpara>In this document, arguments are grouped into collections
organized by function. Some collections are present in several tools
(for example, the "common" arguments). An extended description of their
functionality is given only on the first presentation in this
document.</simpara></note>
<table pgwide="0"
frame="topbot"
rowsep="1" colsep="1"
>
<title>Common arguments</title>
<tgroup cols="2">
<colspec colwidth="248*" align="left"/>
<colspec colwidth="230*" align="left"/>
<thead>
  <row>
    <entry>
    Argument
    </entry>
    <entry>
    Description
    </entry>
  </row>
</thead>
<tbody>
  <row>
    <entry>
    <literal>--connect &lt;jdbc-uri&gt;</literal>
    </entry>
    <entry>
    Specify JDBC connect string
    </entry>
  </row>
  <row>
    <entry>
    <literal>--connection-manager &lt;class-name&gt;</literal>
    </entry>
    <entry>
    Specify connection manager class to                                          use
    </entry>
  </row>
  <row>
    <entry>
    <literal>--driver &lt;class-name&gt;</literal>
    </entry>
    <entry>
    Manually specify JDBC driver class                                          to use
    </entry>
  </row>
  <row>
    <entry>
    <literal>--hadoop-mapred-home &lt;dir&gt;</literal>
    </entry>
    <entry>
    Override $HADOOP_MAPRED_HOME
    </entry>
  </row>
  <row>
    <entry>
    <literal>--help</literal>
    </entry>
    <entry>
    Print usage instructions
    </entry>
  </row>
  <row>
    <entry>
    <literal>--password-file</literal>
    </entry>
    <entry>
    Set path for a file containing the                                          authentication password
    </entry>
  </row>
  <row>
    <entry>
    <literal>-P</literal>
    </entry>
    <entry>
    Read password from console
    </entry>
  </row>
  <row>
    <entry>
    <literal>--password &lt;password&gt;</literal>
    </entry>
    <entry>
    Set authentication password
    </entry>
  </row>
  <row>
    <entry>
    <literal>--username &lt;username&gt;</literal>
    </entry>
    <entry>
    Set authentication username
    </entry>
  </row>
  <row>
    <entry>
    <literal>--verbose</literal>
    </entry>
    <entry>
    Print more information while working
    </entry>
  </row>
  <row>
    <entry>
    <literal>--connection-param-file &lt;filename&gt;</literal>
    </entry>
    <entry>
    Optional properties file that                                          provides connection parameters
    </entry>
  </row>
  <row>
    <entry>
    <literal>--relaxed-isolation</literal>
    </entry>
    <entry>
    Set connection transaction isolation                                          to read uncommitted for the mappers.
    </entry>
  </row>
</tbody>
</tgroup>
</table>
<section id="_connecting_to_a_database_server">
<title>Connecting to a Database Server</title>
<simpara>Sqoop is designed to import tables from a database into HDFS. To do
so, you must specify a <emphasis>connect string</emphasis> that describes how to connect to the
database. The <emphasis>connect string</emphasis> is similar to a URL, and is communicated to
Sqoop with the <literal>--connect</literal> argument. This describes the server and
database to connect to; it may also specify the port. For example:</simpara>
<screen>$ sqoop import --connect jdbc:mysql://database.example.com/employees</screen>
<simpara>This string will connect to a MySQL database named <literal>employees</literal> on the
host <literal>database.example.com</literal>. It&#8217;s important that you <emphasis role="strong">do not</emphasis> use the URL
<literal>localhost</literal> if you intend to use Sqoop with a distributed Hadoop
cluster. The connect string you supply will be used on TaskTracker nodes
throughout your MapReduce cluster; if you specify the
literal name <literal>localhost</literal>, each node will connect to a different
database (or more likely, no database at all). Instead, you should use
the full hostname or IP address of the database host that can be seen
by all your remote nodes.</simpara>
<simpara>You might need to authenticate against the database before you can
access it. You can use the <literal>--username</literal> to supply a username to the database.
Sqoop provides couple of different ways to supply a password,
secure and non-secure, to the database which is detailed below.</simpara>
<formalpara><title>Secure way of supplying password to the database</title><para>You should save the password in a file on the users home directory with 400
permissions and specify the path to that file using the <emphasis role="strong"><literal>--password-file</literal></emphasis>
argument, and is the preferred method of entering credentials. Sqoop will
then read the password from the file and pass it to the MapReduce cluster
using secure means with out exposing the password in the job configuration.
The file containing the password can either be on the Local FS or HDFS.
For example:</para></formalpara>
<screen>$ sqoop import --connect jdbc:mysql://database.example.com/employees \
    --username venkatesh --password-file ${user.home}/.password</screen>
<warning><simpara>Sqoop will read entire content of the password file and use it as
a password. This will include any trailing white space characters such as
new line characters that are added by default by most of the text editors.
You need to make sure that your password file contains only characters
that belongs to your password. On the command line you can use command
<literal>echo</literal> with switch <literal>-n</literal> to store password without any trailing white space
characters. For example to store password <literal>secret</literal> you would call
<literal>echo -n "secret" &gt; password.file</literal>.</simpara></warning>
<simpara>Another way of supplying passwords is using the <literal>-P</literal> argument which will
read a password from a console prompt.</simpara>
<formalpara><title>Protecting password from preying eyes</title><para>Hadoop 2.6.0 provides an API to separate password storage from applications.
This API is called the credential provided API and there is a new
<literal>credential</literal>  command line tool to manage passwords and their aliases.
The passwords are stored with their aliases in a keystore that is password
protected.   The keystore password can be the provided to a password prompt
on the command line, via an environment variable or defaulted to a software
defined constant.   Please check the Hadoop documentation on the usage
of this facility.</para></formalpara>
<simpara>Once the password is stored using the Credential Provider facility and
the Hadoop configuration has been suitably updated, all applications can
optionally use the alias in place of the actual password and at runtime
resolve the alias for the password to use.</simpara>
<simpara>Since the keystore or similar technology used for storing the credential
provider is shared across components, passwords for various applications,
various database and other passwords can be securely stored in them and only
the alias needs to be exposed in configuration files, protecting the password
from being visible.</simpara>
<simpara>Sqoop has been enhanced to allow usage of this funcionality if it is
available in the underlying Hadoop version being used.   One new option
has been introduced to provide the alias on the command line instead of the
actual password (--password-alias).  The argument value this option is
the alias on the storage associated with the actual password.
Example usage is as follows:</simpara>
<screen>$ sqoop import --connect jdbc:mysql://database.example.com/employees \
    --username dbuser --password-alias mydb.password.alias</screen>
<simpara>Similarly, if the command line option is not preferred, the alias can be saved
in the file provided with --password-file option.  Along with this, the
Sqoop configuration parameter org.apache.sqoop.credentials.loader.class
should be set to the classname that provides the alias resolution:
<literal>org.apache.sqoop.util.password.CredentialProviderPasswordLoader</literal></simpara>
<simpara>Example usage is as follows (assuming .password.alias has the alias for
the real password) :</simpara>
<screen>$ sqoop import --connect jdbc:mysql://database.example.com/employees \
    --username dbuser --password-file ${user.home}/.password-alias</screen>
<warning><simpara>The <literal>--password</literal> parameter is insecure, as other users may
be able to read your password from the command-line arguments via
the output of programs such as <literal>ps</literal>. The <emphasis role="strong"><literal>-P</literal></emphasis> argument is the preferred
method over using the <literal>--password</literal> argument. Credentials may still be
transferred between nodes of the MapReduce cluster using insecure means.
For example:</simpara></warning>
<screen>$ sqoop import --connect jdbc:mysql://database.example.com/employees \
    --username aaron --password 12345</screen>
<simpara>Sqoop automatically supports several databases, including MySQL.  Connect
strings beginning with <literal>jdbc:mysql://</literal> are handled automatically in Sqoop.  (A
full list of databases with built-in support is provided in the "Supported
Databases" section. For some, you may need to install the JDBC driver
yourself.)</simpara>
<simpara>You can use Sqoop with any other
JDBC-compliant database. First, download the appropriate JDBC
driver for the type of database you want to import, and install the .jar
file in the <literal>$SQOOP_HOME/lib</literal> directory on your client machine. (This will
be <literal>/usr/lib/sqoop/lib</literal> if you installed from an RPM or Debian package.)
Each driver <literal>.jar</literal> file also has a specific driver class which defines
the entry-point to the driver. For example, MySQL&#8217;s Connector/J library has
a driver class of <literal>com.mysql.jdbc.Driver</literal>. Refer to your database
vendor-specific documentation to determine the main driver class.
This class must be provided as an argument to Sqoop with <literal>--driver</literal>.</simpara>
<simpara>For example, to connect to a SQLServer database, first download the driver from
microsoft.com and install it in your Sqoop lib path.</simpara>
<simpara>Then run Sqoop. For example:</simpara>
<screen>$ sqoop import --driver com.microsoft.jdbc.sqlserver.SQLServerDriver \
    --connect &lt;connect-string&gt; ...</screen>
<simpara>When connecting to a database using JDBC, you can optionally specify extra
JDBC parameters via a property file using the option
<literal>--connection-param-file</literal>. The contents of this file are parsed as standard
Java properties and passed into the driver while creating a connection.</simpara>
<note><simpara>The parameters specified via the optional property file are only
applicable to JDBC connections. Any fastpath connectors that use connections
other than JDBC will ignore these parameters.</simpara></note>
<table pgwide="0"
frame="topbot"
rowsep="1" colsep="1"
>
<title>Validation arguments <link linkend="validation">More Details</link></title>
<tgroup cols="2">
<colspec colwidth="267*" align="left"/>
<colspec colwidth="230*" align="left"/>
<thead>
  <row>
    <entry>
    Argument
    </entry>
    <entry>
    Description
    </entry>
  </row>
</thead>
<tbody>
  <row>
    <entry>
    <literal>--validate</literal>
    </entry>
    <entry>
    Enable validation of data copied,                                             supports single table copy only.
    </entry>
  </row>
  <row>
    <entry>
    <literal>--validator &lt;class-name&gt;</literal>
    </entry>
    <entry>
    Specify validator class to use.
    </entry>
  </row>
  <row>
    <entry>
    <literal>--validation-threshold &lt;class-name&gt;</literal>
    </entry>
    <entry>
    Specify validation threshold class                                             to use.
    </entry>
  </row>
  <row>
    <entry>
    <literal>--validation-failurehandler &lt;class-name&gt;</literal>
    </entry>
    <entry>
    Specify validation failure                                             handler class to use.
    </entry>
  </row>
</tbody>
</tgroup>
</table>
<table pgwide="0"
frame="topbot"
rowsep="1" colsep="1"
>
<title>Import control arguments:</title>
<tgroup cols="2">
<colspec colwidth="206*" align="left"/>
<colspec colwidth="236*" align="left"/>
<thead>
  <row>
    <entry>
    Argument
    </entry>
    <entry>
    Description
    </entry>
  </row>
</thead>
<tbody>
  <row>
    <entry>
    <literal>--append</literal>
    </entry>
    <entry>
    Append data to an existing dataset                                  in HDFS
    </entry>
  </row>
  <row>
    <entry>
    <literal>--as-avrodatafile</literal>
    </entry>
    <entry>
    Imports data to Avro Data Files
    </entry>
  </row>
  <row>
    <entry>
    <literal>--as-sequencefile</literal>
    </entry>
    <entry>
    Imports data to SequenceFiles
    </entry>
  </row>
  <row>
    <entry>
    <literal>--as-textfile</literal>
    </entry>
    <entry>
    Imports data as plain text (default)
    </entry>
  </row>
  <row>
    <entry>
    <literal>--as-parquetfile</literal>
    </entry>
    <entry>
    Imports data to Parquet Files
    </entry>
  </row>
  <row>
    <entry>
    <literal>--boundary-query &lt;statement&gt;</literal>
    </entry>
    <entry>
    Boundary query to use for creating splits
    </entry>
  </row>
  <row>
    <entry>
    <literal>--columns &lt;col,col,col&#8230;&gt;</literal>
    </entry>
    <entry>
    Columns to import from table
    </entry>
  </row>
  <row>
    <entry>
    <literal>--delete-target-dir</literal>
    </entry>
    <entry>
    Delete the import target directory                                  if it exists
    </entry>
  </row>
  <row>
    <entry>
    <literal>--direct</literal>
    </entry>
    <entry>
    Use direct connector if exists for the database
    </entry>
  </row>
  <row>
    <entry>
    <literal>--fetch-size &lt;n&gt;</literal>
    </entry>
    <entry>
    Number of entries to read from database                                  at once.
    </entry>
  </row>
  <row>
    <entry>
    <literal>--inline-lob-limit &lt;n&gt;</literal>
    </entry>
    <entry>
    Set the maximum size for an inline LOB
    </entry>
  </row>
  <row>
    <entry>
    <literal>-m,--num-mappers &lt;n&gt;</literal>
    </entry>
    <entry>
    Use <emphasis>n</emphasis> map tasks to import in parallel
    </entry>
  </row>
  <row>
    <entry>
    <literal>-e,--query &lt;statement&gt;</literal>
    </entry>
    <entry>
    Import the results of <emphasis><literal>statement</literal></emphasis>.
    </entry>
  </row>
  <row>
    <entry>
    <literal>--split-by &lt;column-name&gt;</literal>
    </entry>
    <entry>
    Column of the table used to split work                                  units
    </entry>
  </row>
  <row>
    <entry>
    <literal>--table &lt;table-name&gt;</literal>
    </entry>
    <entry>
    Table to read
    </entry>
  </row>
  <row>
    <entry>
    <literal>--target-dir &lt;dir&gt;</literal>
    </entry>
    <entry>
    HDFS destination dir
    </entry>
  </row>
  <row>
    <entry>
    <literal>--warehouse-dir &lt;dir&gt;</literal>
    </entry>
    <entry>
    HDFS parent for table destination
    </entry>
  </row>
  <row>
    <entry>
    <literal>--where &lt;where clause&gt;</literal>
    </entry>
    <entry>
    WHERE clause to use during import
    </entry>
  </row>
  <row>
    <entry>
    <literal>-z,--compress</literal>
    </entry>
    <entry>
    Enable compression
    </entry>
  </row>
  <row>
    <entry>
    <literal>--compression-codec &lt;c&gt;</literal>
    </entry>
    <entry>
    Use Hadoop codec (default gzip)
    </entry>
  </row>
  <row>
    <entry>
    <literal>--null-string &lt;null-string&gt;</literal>
    </entry>
    <entry>
    The string to be written for a null                                  value for string columns
    </entry>
  </row>
  <row>
    <entry>
    <literal>--null-non-string &lt;null-string&gt;</literal>
    </entry>
    <entry>
    The string to be written for a null                                  value for non-string columns
    </entry>
  </row>
</tbody>
</tgroup>
</table>
<simpara>The <literal>--null-string</literal> and <literal>--null-non-string</literal> arguments are optional.\
If not specified, then the string "null" will be used.</simpara>
</section>
<section id="_selecting_the_data_to_import">
<title>Selecting the Data to Import</title>
<simpara>Sqoop typically imports data in a table-centric fashion. Use the
<literal>--table</literal> argument to select the table to import. For example, <literal>--table
employees</literal>. This argument can also identify a <literal>VIEW</literal> or other table-like
entity in a database.</simpara>
<simpara>By default, all columns within a table are selected for import.
Imported data is written to HDFS in its "natural order;" that is, a
table containing columns A, B, and C result in an import of data such
as:</simpara>
<screen>A1,B1,C1
A2,B2,C2
...</screen>
<simpara>You can select a subset of columns and control their ordering by using
the <literal>--columns</literal> argument. This should include a comma-delimited list
of columns to import. For example: <literal>--columns "name,employee_id,jobtitle"</literal>.</simpara>
<simpara>You can control which rows are imported by adding a SQL <literal>WHERE</literal> clause
to the import statement. By default, Sqoop generates statements of the
form <literal>SELECT &lt;column list&gt; FROM &lt;table name&gt;</literal>. You can append a
<literal>WHERE</literal> clause to this with the <literal>--where</literal> argument. For example: <literal>--where
"id &gt; 400"</literal>. Only rows where the <literal>id</literal> column has a value greater than
400 will be imported.</simpara>
<simpara>By default sqoop will use query <literal>select min(&lt;split-by&gt;), max(&lt;split-by&gt;) from
&lt;table name&gt;</literal> to find out boundaries for creating splits. In some cases this query
is not the most optimal so you can specify any arbitrary query returning two
numeric columns using <literal>--boundary-query</literal> argument.</simpara>
</section>
<section id="_free_form_query_imports">
<title>Free-form Query Imports</title>
<simpara>Sqoop can also import the result set of an arbitrary SQL query. Instead of
using the <literal>--table</literal>, <literal>--columns</literal> and <literal>--where</literal> arguments, you can specify
a SQL statement with the <literal>--query</literal> argument.</simpara>
<simpara>When importing a free-form query, you must specify a destination directory
with <literal>--target-dir</literal>.</simpara>
<simpara>If you want to import the results of a query in parallel, then each map task
will need to execute a copy of the query, with results partitioned by bounding
conditions inferred by Sqoop. Your query must include the token <literal>$CONDITIONS</literal>
which each Sqoop process will replace with a unique condition expression.
You must also select a splitting column with <literal>--split-by</literal>.</simpara>
<simpara>For example:</simpara>
<screen>$ sqoop import \
  --query 'SELECT a.*, b.* FROM a JOIN b on (a.id == b.id) WHERE $CONDITIONS' \
  --split-by a.id --target-dir /user/foo/joinresults</screen>
<simpara>Alternately, the query can be executed once and imported serially, by
specifying a single map task with <literal>-m 1</literal>:</simpara>
<screen>$ sqoop import \
  --query 'SELECT a.*, b.* FROM a JOIN b on (a.id == b.id) WHERE $CONDITIONS' \
  -m 1 --target-dir /user/foo/joinresults</screen>
<note><simpara>If you are issuing the query wrapped with double quotes ("),
you will have to use <literal>\$CONDITIONS</literal> instead of just <literal>$CONDITIONS</literal>
to disallow your shell from treating it as a shell variable.
For example, a double quoted query may look like:
<literal>"SELECT * FROM x WHERE a='foo' AND \$CONDITIONS"</literal></simpara></note>
<note><simpara>The facility of using free-form query in the current version of Sqoop
is limited to simple queries where there are no ambiguous projections and
no <literal>OR</literal> conditions in the <literal>WHERE</literal> clause. Use of complex queries such as
queries that have sub-queries or joins leading to ambiguous projections can
lead to unexpected results.</simpara></note>
</section>
<section id="_controlling_parallelism">
<title>Controlling Parallelism</title>
<simpara>Sqoop imports data in parallel from most database sources. You can
specify the number
of map tasks (parallel processes) to use to perform the import by
using the <literal>-m</literal> or <literal>--num-mappers</literal> argument. Each of these arguments
takes an integer value which corresponds to the degree of parallelism
to employ. By default, four tasks are used. Some databases may see
improved performance by increasing this value to 8 or 16. Do not
increase the degree of parallelism greater than that available within
your MapReduce cluster; tasks will run serially and will likely
increase the amount of time required to perform the import. Likewise,
do not increase the degree of parallism higher than that which your
database can reasonably support. Connecting 100 concurrent clients to
your database may increase the load on the database server to a point
where performance suffers as a result.</simpara>
<simpara>When performing parallel imports, Sqoop needs a criterion by which it
can split the workload. Sqoop uses a <emphasis>splitting column</emphasis> to split the
workload. By default, Sqoop will identify the primary key column (if
present) in a table and use it as the splitting column. The low and
high values for the splitting column are retrieved from the database,
and the map tasks operate on evenly-sized components of the total
range. For example, if you had a table with a primary key column of
<literal>id</literal> whose minimum value was 0 and maximum value was 1000, and Sqoop
was directed to use 4 tasks, Sqoop would run four processes which each
execute SQL statements of the form <literal>SELECT * FROM sometable WHERE id
&gt;= lo AND id &lt; hi</literal>, with <literal>(lo, hi)</literal> set to (0, 250), (250, 500),
(500, 750), and (750, 1001) in the different tasks.</simpara>
<simpara>If the actual values for the primary key are not uniformly distributed
across its range, then this can result in unbalanced tasks. You should
explicitly choose a different column with the <literal>--split-by</literal> argument.
For example, <literal>--split-by employee_id</literal>. Sqoop cannot currently split on
multi-column indices. If your table has no index column, or has a
multi-column key, then you must also manually choose a splitting
column.</simpara>
</section>
<section id="_controlling_distributed_cache">
<title>Controlling Distributed Cache</title>
<simpara>Sqoop will copy the jars in $SQOOP_HOME/lib folder to job cache every
time when start a Sqoop job. When launched by Oozie this is unnecessary
since Oozie use its own Sqoop share lib which keeps Sqoop dependencies
in the distributed cache. Oozie will do the localization on each
worker node for the Sqoop dependencies only once during the first Sqoop
job and reuse the jars on worker node for subsquencial jobs. Using
option <literal>--skip-dist-cache</literal> in Sqoop command when launched by Oozie will
skip the step which Sqoop copies its dependencies to job cache and save
massive I/O.</simpara>
</section>
<section id="_controlling_the_import_process">
<title>Controlling the Import Process</title>
<simpara>By default, the import process will use JDBC which provides a
reasonable cross-vendor import channel. Some databases can perform
imports in a more high-performance fashion by using database-specific
data movement tools. For example, MySQL provides the <literal>mysqldump</literal> tool
which can export data from MySQL to other systems very quickly. By
supplying the <literal>--direct</literal> argument, you are specifying that Sqoop
should attempt the direct import channel. This channel may be
higher performance than using JDBC.</simpara>
<simpara>Details about use of direct mode with each specific RDBMS, installation requirements, available
options and limitations can be found in <xref linkend="connectors"/>.</simpara>
<simpara>By default, Sqoop will import a table named <literal>foo</literal> to a directory named
<literal>foo</literal> inside your home directory in HDFS. For example, if your
username is <literal>someuser</literal>, then the import tool will write to
<literal>/user/someuser/foo/(files)</literal>. You can adjust the parent directory of
the import with the <literal>--warehouse-dir</literal> argument. For example:</simpara>
<screen>$ sqoop import --connnect &lt;connect-str&gt; --table foo --warehouse-dir /shared \
    ...</screen>
<simpara>This command would write to a set of files in the <literal>/shared/foo/</literal> directory.</simpara>
<simpara>You can also explicitly choose the target directory, like so:</simpara>
<screen>$ sqoop import --connnect &lt;connect-str&gt; --table foo --target-dir /dest \
    ...</screen>
<simpara>This will import the files into the <literal>/dest</literal> directory. <literal>--target-dir</literal> is
incompatible with <literal>--warehouse-dir</literal>.</simpara>
<simpara>When using direct mode, you can specify additional arguments which
should be passed to the underlying tool. If the argument
<literal>--</literal> is given on the command-line, then subsequent arguments are sent
directly to the underlying tool. For example, the following adjusts
the character set used by <literal>mysqldump</literal>:</simpara>
<screen>$ sqoop import --connect jdbc:mysql://server.foo.com/db --table bar \
    --direct -- --default-character-set=latin1</screen>
<simpara>By default, imports go to a new target location. If the destination directory
already exists in HDFS, Sqoop will refuse to import and overwrite that
directory&#8217;s contents. If you use the <literal>--append</literal> argument, Sqoop will import
data to a temporary directory and then rename the files into the normal
target directory in a manner that does not conflict with existing filenames
in that directory.</simpara>
</section>
<section id="_controlling_transaction_isolation">
<title>Controlling transaction isolation</title>
<simpara>By default, Sqoop uses the read committed transaction isolation in the mappers
to import data.  This may not be the ideal in all ETL workflows and it may
desired to reduce the isolation guarantees.   The <literal>--relaxed-isolation</literal> option
can be used to instruct Sqoop to use read uncommitted isolation level.</simpara>
<simpara>The <literal>read-uncommitted</literal> isolation level is not supported on all databases
(for example, Oracle), so specifying the option <literal>--relaxed-isolation</literal>
may not be supported on all databases.</simpara>
</section>
<section id="_controlling_type_mapping">
<title>Controlling type mapping</title>
<simpara>Sqoop is preconfigured to map most SQL types to appropriate Java or Hive
representatives. However the default mapping might not be suitable for
everyone and might be overridden by <literal>--map-column-java</literal> (for changing
mapping to Java) or <literal>--map-column-hive</literal> (for changing Hive mapping).</simpara>
<table pgwide="0"
frame="topbot"
rowsep="1" colsep="1"
>
<title>Parameters for overriding mapping</title>
<tgroup cols="2">
<colspec colwidth="206*" align="left"/>
<colspec colwidth="236*" align="left"/>
<thead>
  <row>
    <entry>
    Argument
    </entry>
    <entry>
    Description
    </entry>
  </row>
</thead>
<tbody>
  <row>
    <entry>
    <literal>--map-column-java &lt;mapping&gt;</literal>
    </entry>
    <entry>
    Override mapping from SQL to Java type                                  for configured columns.
    </entry>
  </row>
  <row>
    <entry>
    <literal>--map-column-hive &lt;mapping&gt;</literal>
    </entry>
    <entry>
    Override mapping from SQL to Hive type                                  for configured columns.
    </entry>
  </row>
</tbody>
</tgroup>
</table>
<simpara>Sqoop is expecting comma separated list of mapping in form &lt;name of column&gt;=&lt;new type&gt;. For example:</simpara>
<screen>$ sqoop import ... --map-column-java id=String,value=Integer</screen>
<simpara>Sqoop will rise exception in case that some configured mapping will not be used.</simpara>
</section>
<section id="_incremental_imports">
<title>Incremental Imports</title>
<simpara>Sqoop provides an incremental import mode which can be used to retrieve
only rows newer than some previously-imported set of rows.</simpara>
<simpara>The following arguments control incremental imports:</simpara>
<table pgwide="0"
frame="topbot"
rowsep="1" colsep="1"
>
<title>Incremental import arguments:</title>
<tgroup cols="2">
<colspec colwidth="182*" align="left"/>
<colspec colwidth="236*" align="left"/>
<thead>
  <row>
    <entry>
    Argument
    </entry>
    <entry>
    Description
    </entry>
  </row>
</thead>
<tbody>
  <row>
    <entry>
    <literal>--check-column (col)</literal>
    </entry>
    <entry>
    Specifies the column to be examined                               when determining which rows to import.                              (the column should not be of type                               CHAR/NCHAR/VARCHAR/VARNCHAR/                              LONGVARCHAR/LONGNVARCHAR)
    </entry>
  </row>
  <row>
    <entry>
    <literal>--incremental (mode)</literal>
    </entry>
    <entry>
    Specifies how Sqoop determines which                               rows are new. Legal values for <literal>mode</literal>                              include <literal>append</literal> and <literal>lastmodified</literal>.
    </entry>
  </row>
  <row>
    <entry>
    <literal>--last-value (value)</literal>
    </entry>
    <entry>
    Specifies the maximum value of the                               check column from the previous import.
    </entry>
  </row>
</tbody>
</tgroup>
</table>
<simpara>Sqoop supports two types of incremental imports: <literal>append</literal> and <literal>lastmodified</literal>.
You can use the <literal>--incremental</literal> argument to specify the type of incremental
import to perform.</simpara>
<simpara>You should specify <literal>append</literal> mode when importing a table where new rows are
continually being added with increasing row id values. You specify the column
containing the row&#8217;s id with <literal>--check-column</literal>. Sqoop imports rows where the
check column has a value greater than the one specified with <literal>--last-value</literal>.</simpara>
<simpara>An alternate table update strategy supported by Sqoop is called <literal>lastmodified</literal>
mode. You should use this when rows of the source table may be updated, and
each such update will set the value of a last-modified column to the current
timestamp.  Rows where the check column holds a timestamp more recent than the
timestamp specified with <literal>--last-value</literal> are imported.</simpara>
<simpara>At the end of an incremental import, the value which should be specified as
<literal>--last-value</literal> for a subsequent import is printed to the screen. When running
a subsequent import, you should specify <literal>--last-value</literal> in this way to ensure
you import only the new or updated data. This is handled automatically by
creating an incremental import as a saved job, which is the preferred
mechanism for performing a recurring incremental import. See the section on
saved jobs later in this document for more information.</simpara>
</section>
<section id="_file_formats">
<title>File Formats</title>
<simpara>You can import data in one of two file formats: delimited text or
SequenceFiles.</simpara>
<simpara>Delimited text is the default import format. You can also specify it
explicitly by using the <literal>--as-textfile</literal> argument. This argument will write
string-based representations of each record to the output files, with
delimiter characters between individual columns and rows. These
delimiters may be commas, tabs, or other characters. (The delimiters
can be selected; see "Output line formatting arguments.") The
following is the results of an example text-based import:</simpara>
<screen>1,here is a message,2010-05-01
2,happy new year!,2010-01-01
3,another message,2009-11-12</screen>
<simpara>Delimited text is appropriate for most non-binary data types. It also
readily supports further manipulation by other tools, such as Hive.</simpara>