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Sqoop User Guide (v1.4.3-cdh4.7.0)


Table of Contents

1. Introduction
2. Supported Releases
3. Sqoop Releases
4. Prerequisites
5. Basic Usage
6. Sqoop Tools
6.1. Using Command Aliases
6.2. Controlling the Hadoop Installation
6.3. Using Generic and Specific Arguments
6.4. Using Options Files to Pass Arguments
6.5. Using Tools
7. sqoop-import
7.1. Purpose
7.2. Syntax
7.2.1. Connecting to a Database Server
7.2.2. Selecting the Data to Import
7.2.3. Free-form Query Imports
7.2.4. Controlling Parallelism
7.2.5. Controlling Distributed Cache
7.2.6. Controlling the Import Process
7.2.7. Controlling type mapping
7.2.8. Incremental Imports
7.2.9. File Formats
7.2.10. Large Objects
7.2.11. Importing Data Into Hive
7.2.12. Importing Data Into HBase
7.2.13. Importing Data Into Accumulo
7.2.14. Additional Import Configuration Properties
7.3. Example Invocations
8. sqoop-import-all-tables
8.1. Purpose
8.2. Syntax
8.3. Example Invocations
9. sqoop-export
9.1. Purpose
9.2. Syntax
9.3. Inserts vs. Updates
9.4. Exports and Transactions
9.5. Failed Exports
9.6. Example Invocations
10. validation
10.1. Purpose
10.2. Introduction
10.3. Syntax
10.4. Configuration
10.5. Limitations
10.6. Example Invocations
11. Saved Jobs
12. sqoop-job
12.1. Purpose
12.2. Syntax
12.3. Saved jobs and passwords
12.4. Saved jobs and incremental imports
13. sqoop-metastore
13.1. Purpose
13.2. Syntax
14. sqoop-merge
14.1. Purpose
14.2. Syntax
15. sqoop-codegen
15.1. Purpose
15.2. Syntax
15.3. Example Invocations
16. sqoop-create-hive-table
16.1. Purpose
16.2. Syntax
16.3. Example Invocations
17. sqoop-eval
17.1. Purpose
17.2. Syntax
17.3. Example Invocations
18. sqoop-list-databases
18.1. Purpose
18.2. Syntax
18.3. Example Invocations
19. sqoop-list-tables
19.1. Purpose
19.2. Syntax
19.3. Example Invocations
20. sqoop-help
20.1. Purpose
20.2. Syntax
20.3. Example Invocations
21. sqoop-version
21.1. Purpose
21.2. Syntax
21.3. Example Invocations
22. Sqoop-HCatalog Integration
22.1. HCatalog Background
22.2. Exposing HCatalog Tables to Sqoop
22.2.1. New Command Line Options
22.2.2. Supported Sqoop Hive Options
22.2.3. Direct Mode support
22.2.4. Unsupported Sqoop Options
22.2.4.1. Unsupported Sqoop Hive Import Options
22.2.4.2. Unsupported Sqoop Export and Import Options
22.2.5. Ignored Sqoop Options
22.3. Automatic Table Creation
22.4. Delimited Text Formats and Field and Line Delimiter Characters
22.5. HCatalog Table Requirements
22.6. Support for Partitioning
22.7. Schema Mapping
22.8. Support for HCatalog Data Types
22.9. Providing Hive and HCatalog Libraries for the Sqoop Job
22.10. Examples
22.11. Import
22.12. Export
23. Compatibility Notes
23.1. Supported Databases
23.2. MySQL
23.2.1. zeroDateTimeBehavior
23.2.2. UNSIGNED columns
23.2.3. BLOB and CLOB columns
23.2.4. Importing views in direct mode
23.2.5. Direct-mode Transactions
23.3. PostgreSQL
23.3.1. Importing views in direct mode
23.4. Oracle
23.4.1. Dates and Times
23.5. Schema Definition in Hive
24. Notes for specific connectors
24.1. MySQL JDBC Connector
24.1.1. Upsert functionality
24.2. Microsoft SQL Connector
24.2.1. Extra arguments
24.2.2. Schema support
24.2.3. Table hints
24.3. PostgreSQL Connector
24.3.1. Extra arguments
24.3.2. Schema support
24.4. pg_bulkload connector
24.4.1. Purpose
24.4.2. Requirements
24.4.3. Syntax
24.4.4. Data Staging
24.5. Netezza Connector
24.5.1. Extra arguments
24.5.2. Direct Mode
24.5.3. Null string handling
25. Getting Support
26. Troubleshooting
26.1. General Troubleshooting Process
26.2. Specific Troubleshooting Tips
26.2.1. Oracle: Connection Reset Errors
26.2.2. Oracle: Case-Sensitive Catalog Query Errors
26.2.3. MySQL: Connection Failure
26.2.4. Oracle: ORA-00933 error (SQL command not properly ended)
26.2.5. MySQL: Import of TINYINT(1) from MySQL behaves strangely
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1. Introduction

Sqoop is a tool designed to transfer data between Hadoop and relational databases. You can use Sqoop to import data from a relational database management system (RDBMS) such as MySQL or Oracle into the Hadoop Distributed File System (HDFS), transform the data in Hadoop MapReduce, and then export the data back into an RDBMS.

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.

This document describes how to get started using Sqoop to move data between databases and Hadoop and provides reference information for the operation of the Sqoop command-line tool suite. This document is intended for:

  • System and application programmers
  • System administrators
  • Database administrators
  • Data analysts
  • Data engineers

2. Supported Releases

This documentation applies to Sqoop v1.4.3-cdh4.7.0.

3. Sqoop Releases

Sqoop is an open source software product of the Apache Software Foundation.

Software development for Sqoop occurs at http://sqoop.apache.org At that site you can obtain:

  • New releases of Sqoop as well as its most recent source code
  • An issue tracker
  • A wiki that contains Sqoop documentation

4. Prerequisites

The following prerequisite knowledge is required for this product:

  • Basic computer technology and terminology
  • Familiarity with command-line interfaces such as bash
  • Relational database management systems
  • Basic familiarity with the purpose and operation of Hadoop

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.

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.

5. Basic Usage

With Sqoop, you can import data from a relational database system into HDFS. The input to the import process is a database table. Sqoop will read the table row-by-row into HDFS. The output of this import process is a set of files containing a copy of the imported table. 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.

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.

After manipulating the imported records (for example, with MapReduce or Hive) you may have a result data set which you can then export back to the relational database. Sqoop’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.

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 sqoop-list-databases tool) and tables within a schema (with the sqoop-list-tables tool). Sqoop also includes a primitive SQL execution shell (the sqoop-eval tool).

Most aspects of the import, code generation, and export processes can be customized. 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.

6. Sqoop Tools

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.

If Sqoop is compiled from its own source, you can run Sqoop without a formal installation process by running the bin/sqoop program. Users of a packaged deployment of Sqoop (such as an RPM shipped with Apache Bigtop) will see this program installed as /usr/bin/sqoop. The remainder of this documentation will refer to this program as sqoop. For example:

$ sqoop tool-name [tool-arguments]
[Note]Note

The following examples that begin with a $ character indicate that the commands must be entered at a terminal prompt (such as bash). The $ character represents the prompt itself; you should not start these commands by typing a $. You can also enter commands inline in the text of a paragraph; for example, sqoop help. These examples do not show a $ prefix, but you should enter them the same way. Don’t confuse the $ shell prompt in the examples with the $ that precedes an environment variable name. For example, the string literal $HADOOP_HOME includes a "$".

Sqoop ships with a help tool. To display a list of all available tools, type the following command:

$ 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
  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.

You can display help for a specific tool by entering: sqoop help (tool-name); for example, sqoop help import.

You can also add the --help argument to any command: sqoop import --help.

6.1. Using Command Aliases

In addition to typing the sqoop (toolname) syntax, you can use alias scripts that specify the sqoop-(toolname) syntax. For example, the scripts sqoop-import, sqoop-export, etc. each select a specific tool.

6.2. Controlling the Hadoop Installation

You invoke Sqoop through the program launch capability provided by Hadoop. The sqoop command-line program is a wrapper which runs the bin/hadoop script shipped with Hadoop. If you have multiple installations of Hadoop present on your machine, you can select the Hadoop installation by setting the $HADOOP_COMMON_HOME and $HADOOP_MAPRED_HOME environment variables.

For example:

$ HADOOP_COMMON_HOME=/path/to/some/hadoop \
  HADOOP_MAPRED_HOME=/path/to/some/hadoop-mapreduce \
  sqoop import --arguments...

or:

$ export HADOOP_COMMON_HOME=/some/path/to/hadoop
$ export HADOOP_MAPRED_HOME=/some/path/to/hadoop-mapreduce
$ sqoop import --arguments...

If either of these variables are not set, Sqoop will fall back to $HADOOP_HOME. If it is not set either, Sqoop will use the default installation locations for Apache Bigtop, /usr/lib/hadoop and /usr/lib/hadoop-mapreduce, respectively.

The active Hadoop configuration is loaded from $HADOOP_HOME/conf/, unless the $HADOOP_CONF_DIR environment variable is set.

6.3. Using Generic and Specific Arguments

To control the operation of each Sqoop tool, you use generic and specific arguments.

For example:

$ sqoop help import
usage: sqoop import [GENERIC-ARGS] [TOOL-ARGS]

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

[...]

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

The general command line syntax is
bin/hadoop command [genericOptions] [commandOptions]

You must supply the generic arguments -conf, -D, and so on after the tool name but before any tool-specific arguments (such as --connect). Note that generic Hadoop arguments are preceeded by a single dash character (-), whereas tool-specific arguments start with two dashes (--), unless they are single character arguments such as -P.

The -conf, -D, -fs and -jt arguments control the configuration and Hadoop server settings. For example, the -D mapred.job.name=<job_name> 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.

The -files, -libjars, and -archives arguments are not typically used with Sqoop, but they are included as part of Hadoop’s internal argument-parsing system.

6.4. Using Options Files to Pass Arguments

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.

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.

To specify an options file, simply create an options file in a convenient location and pass it to the command line via --options-file argument.

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.

For example, the following Sqoop invocation for import can be specified alternatively as shown below:

$ sqoop import --connect jdbc:mysql://localhost/db --username foo --table TEST

$ sqoop --options-file /users/homer/work/import.txt --table TEST

where the options file /users/homer/work/import.txt contains the following:

import
--connect
jdbc:mysql://localhost/db
--username
foo

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 /users/homer/work/import.txt contained the following:

#
# 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.
#

6.5. Using Tools

The following sections will describe each tool’s operation. The tools are listed in the most likely order you will find them useful.

7. sqoop-import

7.1. Purpose

The import 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.

7.2. Syntax

$ sqoop import (generic-args) (import-args)
$ sqoop-import (generic-args) (import-args)

While the Hadoop generic arguments must precede any import arguments, you can type the import arguments in any order with respect to one another.

[Note]Note

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.

Table 1. Common arguments

Argument Description
--connect <jdbc-uri> Specify JDBC connect string
--connection-manager <class-name> Specify connection manager class to use
--driver <class-name> Manually specify JDBC driver class to use
--hadoop-mapred-home <dir> Override $HADOOP_MAPRED_HOME
--help Print usage instructions
--password-file Set path for a file containing the authentication password
-P Read password from console
--password <password> Set authentication password
--username <username> Set authentication username
--verbose Print more information while working
--connection-param-file <filename> Optional properties file that provides connection parameters

7.2.1. Connecting to a Database Server

Sqoop is designed to import tables from a database into HDFS. To do so, you must specify a connect string that describes how to connect to the database. The connect string is similar to a URL, and is communicated to Sqoop with the --connect argument. This describes the server and database to connect to; it may also specify the port. For example:

$ sqoop import --connect jdbc:mysql://database.example.com/employees

This string will connect to a MySQL database named employees on the host database.example.com. It’s important that you do not use the URL localhost 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 localhost, 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.

You might need to authenticate against the database before you can access it. You can use the --username 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.

Secure way of supplying password to the database. 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 --password-file 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:

$ sqoop import --connect jdbc:mysql://database.example.com/employees \
    --username venkatesh --passwordFile ${user.home}/.password

Another way of supplying passwords is using the -P argument which will read a password from a console prompt.

[Warning]Warning

The --password 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 ps. The -P argument is the preferred method over using the --password argument. Credentials may still be transferred between nodes of the MapReduce cluster using insecure means. For example:

$ sqoop import --connect jdbc:mysql://database.example.com/employees \
    --username aaron --password 12345

Sqoop automatically supports several databases, including MySQL. Connect strings beginning with jdbc:mysql:// 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.)

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 $SQOOP_HOME/lib directory on your client machine. (This will be /usr/lib/sqoop/lib if you installed from an RPM or Debian package.) Each driver .jar file also has a specific driver class which defines the entry-point to the driver. For example, MySQL’s Connector/J library has a driver class of com.mysql.jdbc.Driver. Refer to your database vendor-specific documentation to determine the main driver class. This class must be provided as an argument to Sqoop with --driver.

For example, to connect to a SQLServer database, first download the driver from microsoft.com and install it in your Sqoop lib path.

Then run Sqoop. For example:

$ sqoop import --driver com.microsoft.jdbc.sqlserver.SQLServerDriver \
    --connect <connect-string> ...

When connecting to a database using JDBC, you can optionally specify extra JDBC parameters via a property file using the option --connection-param-file. The contents of this file are parsed as standard Java properties and passed into the driver while creating a connection.

[Note]Note

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.

Table 2. Validation arguments More Details

Argument Description
--validate Enable validation of data copied, supports single table copy only. --validator <class-name> Specify validator class to use.
--validation-threshold <class-name> Specify validation threshold class to use.
+--validation-failurehandler <class-name >+ Specify validation failure handler class to use.

Table 3. Import control arguments:

Argument Description
--append Append data to an existing dataset in HDFS
--as-avrodatafile Imports data to Avro Data Files
--as-sequencefile Imports data to SequenceFiles
--as-textfile Imports data as plain text (default)
--boundary-query <statement> Boundary query to use for creating splits
--columns <col,col,col…> Columns to import from table
--delete-target-dir Delete the import target directory if it exists
--direct Use direct import fast path
--direct-split-size <n> Split the input stream every n bytes when importing in direct mode
--fetch-size <n> Number of entries to read from database at once.
--inline-lob-limit <n> Set the maximum size for an inline LOB
-m,--num-mappers <n> Use n map tasks to import in parallel
-e,--query <statement> Import the results of statement.
--split-by <column-name> Column of the table used to split work units
--table <table-name> Table to read
--target-dir <dir> HDFS destination dir
--warehouse-dir <dir> HDFS parent for table destination
--where <where clause> WHERE clause to use during import
-z,--compress Enable compression
--compression-codec <c> Use Hadoop codec (default gzip)
--null-string <null-string> The string to be written for a null value for string columns
--null-non-string <null-string> The string to be written for a null value for non-string columns

The --null-string and --null-non-string arguments are optional.\ If not specified, then the string "null" will be used.

7.2.2. Selecting the Data to Import

Sqoop typically imports data in a table-centric fashion. Use the --table argument to select the table to import. For example, --table employees. This argument can also identify a VIEW or other table-like entity in a database.

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:

A1,B1,C1
A2,B2,C2
...

You can select a subset of columns and control their ordering by using the --columns argument. This should include a comma-delimited list of columns to import. For example: --columns "name,employee_id,jobtitle".

You can control which rows are imported by adding a SQL WHERE clause to the import statement. By default, Sqoop generates statements of the form SELECT <column list> FROM <table name>. You can append a WHERE clause to this with the --where argument. For example: --where "id > 400". Only rows where the id column has a value greater than 400 will be imported.

By default sqoop will use query select min(<split-by>), max(<split-by>) from <table name> 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 --boundary-query argument.

7.2.3. Free-form Query Imports

Sqoop can also import the result set of an arbitrary SQL query. Instead of using the --table, --columns and --where arguments, you can specify a SQL statement with the --query argument.

When importing a free-form query, you must specify a destination directory with --target-dir.

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 $CONDITIONS which each Sqoop process will replace with a unique condition expression. You must also select a splitting column with --split-by.

For example:

$ 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

Alternately, the query can be executed once and imported serially, by specifying a single map task with -m 1:

$ sqoop import \
  --query 'SELECT a.*, b.* FROM a JOIN b on (a.id == b.id) WHERE $CONDITIONS' \
  -m 1 --target-dir /user/foo/joinresults
[Note]Note

If you are issuing the query wrapped with double quotes ("), you will have to use \$CONDITIONS instead of just $CONDITIONS to disallow your shell from treating it as a shell variable. For example, a double quoted query may look like: "SELECT * FROM x WHERE a='foo' AND \$CONDITIONS"

[Note]Note

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 OR conditions in the WHERE clause. Use of complex queries such as queries that have sub-queries or joins leading to ambiguous projections can lead to unexpected results.

7.2.4. Controlling Parallelism

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 -m or --num-mappers 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.

When performing parallel imports, Sqoop needs a criterion by which it can split the workload. Sqoop uses a splitting column 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 id 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 SELECT * FROM sometable WHERE id >= lo AND id < hi, with (lo, hi) set to (0, 250), (250, 500), (500, 750), and (750, 1001) in the different tasks.

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 --split-by argument. For example, --split-by employee_id. 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.

7.2.5. Controlling Distributed Cache

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 --skip-dist-cache in Sqoop command when launched by Oozie will skip the step which Sqoop copies its dependencies to job cache and save massive I/O.

7.2.6. Controlling the Import Process

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 mysqldump tool which can export data from MySQL to other systems very quickly. By supplying the --direct argument, you are specifying that Sqoop should attempt the direct import channel. This channel may be higher performance than using JDBC. Currently, direct mode does not support imports of large object columns.

When importing from PostgreSQL in conjunction with direct mode, you can split the import into separate files after individual files reach a certain size. This size limit is controlled with the --direct-split-size argument.

By default, Sqoop will import a table named foo to a directory named foo inside your home directory in HDFS. For example, if your username is someuser, then the import tool will write to /user/someuser/foo/(files). You can adjust the parent directory of the import with the --warehouse-dir argument. For example:

$ sqoop import --connnect <connect-str> --table foo --warehouse-dir /shared \
    ...

This command would write to a set of files in the /shared/foo/ directory.

You can also explicitly choose the target directory, like so:

$ sqoop import --connnect <connect-str> --table foo --target-dir /dest \
    ...

This will import the files into the /dest directory. --target-dir is incompatible with --warehouse-dir.

When using direct mode, you can specify additional arguments which should be passed to the underlying tool. If the argument -- 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 mysqldump:

$ sqoop import --connect jdbc:mysql://server.foo.com/db --table bar \
    --direct -- --default-character-set=latin1

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’s contents. If you use the --append 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.

[Note]Note

When using the direct mode of import, certain database client utilities are expected to be present in the shell path of the task process. For MySQL the utilities mysqldump and mysqlimport are required, whereas for PostgreSQL the utility psql is required.

7.2.7. Controlling type mapping

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 --map-column-java (for changing mapping to Java) or --map-column-hive (for changing Hive mapping).

Table 4. Parameters for overriding mapping

Argument Description
--map-column-java <mapping> Override mapping from SQL to Java type for configured columns.
--map-column-hive <mapping> Override mapping from SQL to Hive type for configured columns.

Sqoop is expecting comma separated list of mapping in form <name of column>=<new type>. For example:

$ sqoop import ... --map-column-java id=String,value=Integer

Sqoop will rise exception in case that some configured mapping will not be used.

7.2.8. Incremental Imports

Sqoop provides an incremental import mode which can be used to retrieve only rows newer than some previously-imported set of rows.

The following arguments control incremental imports:

Table 5. Incremental import arguments:

Argument Description
--check-column (col) 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)
--incremental (mode) Specifies how Sqoop determines which rows are new. Legal values for mode include append and lastmodified.
--last-value (value) Specifies the maximum value of the check column from the previous import.

Sqoop supports two types of incremental imports: append and lastmodified. You can use the --incremental argument to specify the type of incremental import to perform.

You should specify append mode when importing a table where new rows are continually being added with increasing row id values. You specify the column containing the row’s id with --check-column. Sqoop imports rows where the check column has a value greater than the one specified with --last-value.

An alternate table update strategy supported by Sqoop is called lastmodified 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 --last-value are imported.

At the end of an incremental import, the value which should be specified as --last-value for a subsequent import is printed to the screen. When running a subsequent import, you should specify --last-value 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.

7.2.9. File Formats

You can import data in one of two file formats: delimited text or SequenceFiles.

Delimited text is the default import format. You can also specify it explicitly by using the --as-textfile 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:

1,here is a message,2010-05-01
2,happy new year!,2010-01-01
3,another message,2009-11-12

Delimited text is appropriate for most non-binary data types. It also readily supports further manipulation by other tools, such as Hive.

SequenceFiles are a binary format that store individual records in custom record-specific data types. These data types are manifested as Java classes. Sqoop will automatically generate these data types for you. This format supports exact storage of all data in binary representations, and is appropriate for storing binary data (for example, VARBINARY columns), or data that will be principly manipulated by custom MapReduce programs (reading from SequenceFiles is higher-performance than reading from text files, as records do not need to be parsed).

Avro data files are a compact, efficient binary format that provides interoperability with applications written in other programming languages. Avro also supports versioning, so that when, e.g., columns are added or removed from a table, previously imported data files can be processed along with new ones.

By default, data is not compressed. You can compress your data by using the deflate (gzip) algorithm with the -z or --compress argument, or specify any Hadoop compression codec using the --compression-codec argument. This applies to SequenceFile, text, and Avro files.

7.2.10. Large Objects

Sqoop handles large objects (BLOB and CLOB columns) in particular ways. If this data is truly large, then these columns should not be fully materialized in memory for manipulation, as most columns are. Instead, their data is handled in a streaming fashion. Large objects can be stored inline with the rest of the data, in which case they are fully materialized in memory on every access, or they can be stored in a secondary storage file linked to the primary data storage. By default, large objects less than 16 MB in size are stored inline with the rest of the data. At a larger size, they are stored in files in the _lobs subdirectory of the import target directory. These files are stored in a separate format optimized for large record storage, which can accomodate records of up to 2^63 bytes each. The size at which lobs spill into separate files is controlled by the --inline-lob-limit argument, which takes a parameter specifying the largest lob size to keep inline, in bytes. If you set the inline LOB limit to 0, all large objects will be placed in external storage.

Table 6. Output line formatting arguments:

Argument Description
--enclosed-by <char> Sets a required field enclosing character
--escaped-by <char> Sets the escape character
--fields-terminated-by <char> Sets the field separator character
--lines-terminated-by <char> Sets the end-of-line character
--mysql-delimiters Uses MySQL’s default delimiter set: fields: , lines: \n escaped-by: \ optionally-enclosed-by: '
--optionally-enclosed-by <char> Sets a field enclosing character

When importing to delimited files, the choice of delimiter is important. Delimiters which appear inside string-based fields may cause ambiguous parsing of the imported data by subsequent analysis passes. For example, the string "Hello, pleased to meet you" should not be imported with the end-of-field delimiter set to a comma.

Delimiters may be specified as:

  • a character (--fields-terminated-by X)
  • an escape character (--fields-terminated-by \t). Supported escape characters are:

    • \b (backspace)
    • \n (newline)
    • \r (carriage return)
    • \t (tab)
    • \" (double-quote)
    • \\' (single-quote)
    • \\ (backslash)
    • \0 (NUL) - This will insert NUL characters between fields or lines, or will disable enclosing/escaping if used for one of the --enclosed-by, --optionally-enclosed-by, or --escaped-by arguments.
  • The octal representation of a UTF-8 character’s code point. This should be of the form \0ooo, where ooo is the octal value. For example, --fields-terminated-by \001 would yield the ^A character.
  • The hexadecimal representation of a UTF-8 character’s code point. This should be of the form \0xhhh, where hhh is the hex value. For example, --fields-terminated-by \0x10 would yield the carriage return character.

The default delimiters are a comma (,) for fields, a newline (\n) for records, no quote character, and no escape character. Note that this can lead to ambiguous/unparsible records if you import database records containing commas or newlines in the field data. For unambiguous parsing, both must be enabled. For example, via --mysql-delimiters.

If unambiguous delimiters cannot be presented, then use enclosing and escaping characters. The combination of (optional) enclosing and escaping characters will allow unambiguous parsing of lines. For example, suppose one column of a dataset contained the following values:

Some string, with a comma.
Another "string with quotes"

The following arguments would provide delimiters which can be unambiguously parsed:

$ sqoop import --fields-terminated-by , --escaped-by \\ --enclosed-by '\"' ...

(Note that to prevent the shell from mangling the enclosing character, we have enclosed that argument itself in single-quotes.)

The result of the above arguments applied to the above dataset would be:

"Some string, with a comma.","1","2","3"...
"Another \"string with quotes\"","4","5","6"...

Here the imported strings are shown in the context of additional columns ("1","2","3", etc.) to demonstrate the full effect of enclosing and escaping. The enclosing character is only strictly necessary when delimiter characters appear in the imported text. The enclosing character can therefore be specified as optional:

$ sqoop import --optionally-enclosed-by '\"' (the rest as above)...

Which would result in the following import:

"Some string, with a comma.",1,2,3...
"Another \"string with quotes\"",4,5,6...
[Note]Note

Even though Hive supports escaping characters, it does not handle escaping of new-line character. Also, it does not support the notion of enclosing characters that may include field delimiters in the enclosed string. It is therefore recommended that you choose unambiguous field and record-terminating delimiters without the help of escaping and enclosing characters when working with Hive; this is due to limitations of Hive’s input parsing abilities.

The --mysql-delimiters argument is a shorthand argument which uses the default delimiters for the mysqldump program. If you use the mysqldump delimiters in conjunction with a direct-mode import (with --direct), very fast imports can be achieved.

While the choice of delimiters is most important for a text-mode import, it is still relevant if you import to SequenceFiles with --as-sequencefile. The generated class' toString() method will use the delimiters you specify, so subsequent formatting of the output data will rely on the delimiters you choose.

Table 7. Input parsing arguments:

Argument Description
--input-enclosed-by <char> Sets a required field encloser
--input-escaped-by <char> Sets the input escape character
--input-fields-terminated-by <char> Sets the input field separator
--input-lines-terminated-by <char> Sets the input end-of-line character
--input-optionally-enclosed-by <char> Sets a field enclosing character

When Sqoop imports data to HDFS, it generates a Java class which can reinterpret the text files that it creates when doing a delimited-format import. The delimiters are chosen with arguments such as --fields-terminated-by; this controls both how the data is written to disk, and how the generated parse() method reinterprets this data. The delimiters used by the parse() method can be chosen independently of the output arguments, by using --input-fields-terminated-by, and so on. This is useful, for example, to generate classes which can parse records created with one set of delimiters, and emit the records to a different set of files using a separate set of delimiters.

Table 8. Hive arguments:

Argument Description
--hive-home <dir> Override $HIVE_HOME
--hive-import Import tables into Hive (Uses Hive’s default delimiters if none are set.)
--hive-overwrite Overwrite existing data in the Hive table.
--create-hive-table If set, then the job will fail if the target hive
table exits. By default this property is false.
--hive-table <table-name> Sets the table name to use when importing to Hive.
--hive-drop-import-delims Drops \n, \r, and \01 from string fields when importing to Hive.
--hive-delims-replacement Replace \n, \r, and \01 from string fields with user defined string when importing to Hive.
--hive-partition-key Name of a hive field to partition are sharded on
--hive-partition-value <v> String-value that serves as partition key for this imported into hive in this job.
--map-column-hive <map> Override default mapping from SQL type to Hive type for configured columns.

7.2.11. Importing Data Into Hive

Sqoop’s import tool’s main function is to upload your data into files in HDFS. If you have a Hive metastore associated with your HDFS cluster, Sqoop can also import the data into Hive by generating and executing a CREATE TABLE statement to define the data’s layout in Hive. Importing data into Hive is as simple as adding the --hive-import option to your Sqoop command line.

If the Hive table already exists, you can specify the --hive-overwrite option to indicate that existing table in hive must be replaced. After your data is imported into HDFS or this step is omitted, Sqoop will generate a Hive script containing a CREATE TABLE operation defining your columns using Hive’s types, and a LOAD DATA INPATH statement to move the data files into Hive’s warehouse directory.

The script will be executed by calling the installed copy of hive on the machine where Sqoop is run. If you have multiple Hive installations, or hive is not in your $PATH, use the --hive-home option to identify the Hive installation directory. Sqoop will use $HIVE_HOME/bin/hive from here.

[Note]Note

This function is incompatible with --as-avrodatafile and --as-sequencefile.

Even though Hive supports escaping characters, it does not handle escaping of new-line character. Also, it does not support the notion of enclosing characters that may include field delimiters in the enclosed string. It is therefore recommended that you choose unambiguous field and record-terminating delimiters without the help of escaping and enclosing characters when working with Hive; this is due to limitations of Hive’s input parsing abilities. If you do use --escaped-by, --enclosed-by, or --optionally-enclosed-by when importing data into Hive, Sqoop will print a warning message.

Hive will have problems using Sqoop-imported data if your database’s rows contain string fields that have Hive’s default row delimiters (\n and \r characters) or column delimiters (\01 characters) present in them. You can use the --hive-drop-import-delims option to drop those characters on import to give Hive-compatible text data. Alternatively, you can use the --hive-delims-replacement option to replace those characters with a user-defined string on import to give Hive-compatible text data. These options should only be used if you use Hive’s default delimiters and should not be used if different delimiters are specified.

Sqoop will pass the field and record delimiters through to Hive. If you do not set any delimiters and do use --hive-import, the field delimiter will be set to ^A and the record delimiter will be set to \n to be consistent with Hive’s defaults.

Sqoop will by default import NULL values as string null. Hive is however using string \N to denote NULL values and therefore predicates dealing with NULL (like IS NULL) will not work correctly. You should append parameters --null-string and --null-non-string in case of import job or --input-null-string and --input-null-non-string in case of an export job if you wish to properly preserve NULL values. Because sqoop is using those parameters in generated code, you need to properly escape value \N to \\N:

$ sqoop import  ... --null-string '\\N' --null-non-string '\\N'

The table name used in Hive is, by default, the same as that of the source table. You can control the output table name with the --hive-table option.

Hive can put data into partitions for more efficient query performance. You can tell a Sqoop job to import data for Hive into a particular partition by specifying the --hive-partition-key and --hive-partition-value arguments. The partition value must be a string. Please see the Hive documentation for more details on partitioning.

You can import compressed tables into Hive using the --compress and --compression-codec options. One downside to compressing tables imported into Hive is that many codecs cannot be split for processing by parallel map tasks. The lzop codec, however, does support splitting. When importing tables with this codec, Sqoop will automatically index the files for splitting and configuring a new Hive table with the correct InputFormat. This feature currently requires that all partitions of a table be compressed with the lzop codec.

Table 9. HBase arguments:

Argument Description
--column-family <family> Sets the target column family for the import
--hbase-create-table If specified, create missing HBase tables
--hbase-row-key <col> Specifies which input column to use as the row key
In case, if input table contains composite
key, then <col> must be in the form of a
comma-separated list of composite key
attributes
--hbase-table <table-name> Specifies an HBase table to use as the target instead of HDFS
--hbase-bulkload Enables bulk loading

7.2.12. Importing Data Into HBase

Sqoop supports additional import targets beyond HDFS and Hive. Sqoop can also import records into a table in HBase.

By specifying --hbase-table, you instruct Sqoop to import to a table in HBase rather than a directory in HDFS. Sqoop will import data to the table specified as the argument to --hbase-table. Each row of the input table will be transformed into an HBase Put operation to a row of the output table. The key for each row is taken from a column of the input. By default Sqoop will use the split-by column as the row key column. If that is not specified, it will try to identify the primary key column, if any, of the source table. You can manually specify the row key column with --hbase-row-key. Each output column will be placed in the same column family, which must be specified with --column-family.

[Note]Note

This function is incompatible with direct import (parameter --direct).

If the input table has composite key, the --hbase-row-key must be in the form of a comma-separated list of composite key attributes. In this case, the row key for HBase row will be generated by combining values of composite key attributes using underscore as a separator. NOTE: Sqoop import for a table with composite key will work only if parameter --hbase-row-key has been specified.

If the target table and column family do not exist, the Sqoop job will exit with an error. You should create the target table and column family before running an import. If you specify --hbase-create-table, Sqoop will create the target table and column family if they do not exist, using the default parameters from your HBase configuration.

Sqoop currently serializes all values to HBase by converting each field to its string representation (as if you were importing to HDFS in text mode), and then inserts the UTF-8 bytes of this string in the target cell. Sqoop will skip all rows containing null values in all columns except the row key column.

To decrease the load on hbase, Sqoop can do bulk loading as opposed to direct writes. To use bulk loading, enable it using --hbase-bulkload.

Table 10. Accumulo arguments:

Argument Description
--accumulo-table <table-nam> Specifies an Accumulo table to use as the target instead of HDFS
--accumulo-column-family <family> Sets the target column family for the import
--accumulo-create-table If specified, create missing Accumulo tables
--accumulo-row-key <col> Specifies which input column to use as the row key
--accumulo-visibility <vis> (Optional) Specifies a visibility token to apply to all rows inserted into Accumulo. Default is the empty string.
--accumulo-batch-size <size> (Optional) Sets the size in bytes of Accumulo’s write buffer. Default is 4MB.
--accumulo-max-latency <ms> (Optional) Sets the max latency in milliseconds for the Accumulo batch writer. Default is 0.
--accumulo-zookeepers <host:port> Comma-separated list of Zookeeper servers used by the Accumulo instance
--accumulo-instance <table-name> Name of the target Accumulo instance
--accumulo-user <username> Name of the Accumulo user to import as
--accumulo-password <password> Password for the Accumulo user

7.2.13. Importing Data Into Accumulo

Sqoop supports importing records into a table in Accumulo

By specifying --accumulo-table, you instruct Sqoop to import to a table in Accumulo rather than a directory in HDFS. Sqoop will import data to the table specified as the argument to --accumulo-table. Each row of the input table will be transformed into an Accumulo Mutation operation to a row of the output table. The key for each row is taken from a column of the input. By default Sqoop will use the split-by column as the row key column. If that is not specified, it will try to identify the primary key column, if any, of the source table. You can manually specify the row key column with --accumulo-row-key. Each output column will be placed in the same column family, which must be specified with --accumulo-column-family.

[Note]Note

This function is incompatible with direct import (parameter --direct), and cannot be used in the same operation as an HBase import.

If the target table does not exist, the Sqoop job will exit with an error, unless the --accumulo-create-table parameter is specified. Otherwise, you should create the target table before running an import.

Sqoop currently serializes all values to Accumulo by converting each field to its string representation (as if you were importing to HDFS in text mode), and then inserts the UTF-8 bytes of this string in the target cell.

By default, no visibility is applied to the resulting cells in Accumulo, so the data will be visible to any Accumulo user. Use the --accumulo-visibility parameter to specify a visibility token to apply to all rows in the import job.

For performance tuning, use the optional --accumulo-buffer-size\ and --accumulo-max-latency parameters. See Accumulo’s documentation for an explanation of the effects of these parameters.

In order to connect to an Accumulo instance, you must specify the location of a Zookeeper ensemble using the --accumulo-zookeepers parameter, the name of the Accumulo instance (--accumulo-instance), and the username and password to connect with (--accumulo-user and --accumulo-password respectively).

Table 11. Code generation arguments:

Argument Description
--bindir <dir> Output directory for compiled objects
--class-name <name> Sets the generated class name. This overrides --package-name. When combined with --jar-file, sets the input class.
--jar-file <file> Disable code generation; use specified jar
--outdir <dir> Output directory for generated code
--package-name <name> Put auto-generated classes in this package
--map-column-java <m> Override default mapping from SQL type to Java type for configured columns.

As mentioned earlier, a byproduct of importing a table to HDFS is a class which can manipulate the imported data. If the data is stored in SequenceFiles, this class will be used for the data’s serialization container. Therefore, you should use this class in your subsequent MapReduce processing of the data.

The class is typically named after the table; a table named foo will generate a class named foo. You may want to override this class name. For example, if your table is named EMPLOYEES, you may want to specify --class-name Employee instead. Similarly, you can specify just the package name with --package-name. The following import generates a class named com.foocorp.SomeTable:

$ sqoop import --connect <connect-str> --table SomeTable --package-name com.foocorp

The .java source file for your class will be written to the current working directory when you run sqoop. You can control the output directory with --outdir. For example, --outdir src/generated/.

The import process compiles the source into .class and .jar files; these are ordinarily stored under /tmp. You can select an alternate target directory with --bindir. For example, --bindir /scratch.

If you already have a compiled class that can be used to perform the import and want to suppress the code-generation aspect of the import process, you can use an existing jar and class by providing the --jar-file and --class-name options. For example:

$ sqoop import --table SomeTable --jar-file mydatatypes.jar \
    --class-name SomeTableType

This command will load the SomeTableType class out of mydatatypes.jar.

7.2.14. Additional Import Configuration Properties

There are some additional properties which can be configured by modifying conf/sqoop-site.xml. Properties can be specified the same as in Hadoop configuration files, for example:

  <property>
    <name>property.name</name>
    <value>property.value</value>
  </property>

They can also be specified on the command line in the generic arguments, for example:

sqoop import -D property.name=property.value ...

Table 12. Additional import configuration properties:

Argument Description
sqoop.bigdecimal.format.string Controls how BigDecimal columns will formatted when stored as a String. A value of true (default) will use toPlainString to store them without an exponent component (0.0000001); while a value of false will use toString which may include an exponent (1E-7)
sqoop.hbase.add.row.key When set to false (default), Sqoop will not add the column used as a row key into the row data in HBase. When set to true, the column used as a row key will be added to the row data in HBase.

7.3. Example Invocations

The following examples illustrate how to use the import tool in a variety of situations.

A basic import of a table named EMPLOYEES in the corp database:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES

A basic import requiring a login:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    --username SomeUser -P
Enter password: (hidden)

Selecting specific columns from the EMPLOYEES table:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    --columns "employee_id,first_name,last_name,job_title"

Controlling the import parallelism (using 8 parallel tasks):

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    -m 8

Enabling the MySQL "direct mode" fast path:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    --direct

Storing data in SequenceFiles, and setting the generated class name to com.foocorp.Employee:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    --class-name com.foocorp.Employee --as-sequencefile

Specifying the delimiters to use in a text-mode import:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    --fields-terminated-by '\t' --lines-terminated-by '\n' \
    --optionally-enclosed-by '\"'

Importing the data to Hive:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    --hive-import

Importing only new employees:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    --where "start_date > '2010-01-01'"

Changing the splitting column from the default:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    --split-by dept_id

Verifying that an import was successful:

$ hadoop fs -ls EMPLOYEES
Found 5 items
drwxr-xr-x   - someuser somegrp          0 2010-04-27 16:40 /user/someuser/EMPLOYEES/_logs
-rw-r--r--   1 someuser somegrp    2913511 2010-04-27 16:40 /user/someuser/EMPLOYEES/part-m-00000
-rw-r--r--   1 someuser somegrp    1683938 2010-04-27 16:40 /user/someuser/EMPLOYEES/part-m-00001
-rw-r--r--   1 someuser somegrp    7245839 2010-04-27 16:40 /user/someuser/EMPLOYEES/part-m-00002
-rw-r--r--   1 someuser somegrp    7842523 2010-04-27 16:40 /user/someuser/EMPLOYEES/part-m-00003

$ hadoop fs -cat EMPLOYEES/part-m-00000 | head -n 10
0,joe,smith,engineering
1,jane,doe,marketing
...

Performing an incremental import of new data, after having already imported the first 100,000 rows of a table:

$ sqoop import --connect jdbc:mysql://db.foo.com/somedb --table sometable \
    --where "id > 100000" --target-dir /incremental_dataset --append

An import of a table named EMPLOYEES in the corp database that uses validation to validate the import using the table row count and number of rows copied into HDFS: More Details

$ sqoop import --connect jdbc:mysql://db.foo.com/corp \
    --table EMPLOYEES --validate

8. sqoop-import-all-tables

8.1. Purpose

The import-all-tables tool imports a set of tables from an RDBMS to HDFS. Data from each table is stored in a separate directory in HDFS.

For the import-all-tables tool to be useful, the following conditions must be met:

  • Each table must have a single-column primary key.
  • You must intend to import all columns of each table.
  • You must not intend to use non-default splitting column, nor impose any conditions via a WHERE clause.

8.2. Syntax

$ sqoop import-all-tables (generic-args) (import-args)
$ sqoop-import-all-tables (generic-args) (import-args)

Although the Hadoop generic arguments must preceed any import arguments, the import arguments can be entered in any order with respect to one another.

Table 13. Common arguments

Argument Description
--connect <jdbc-uri> Specify JDBC connect string
--connection-manager <class-name> Specify connection manager class to use
--driver <class-name> Manually specify JDBC driver class to use
--hadoop-mapred-home <dir> Override $HADOOP_MAPRED_HOME
--help Print usage instructions
--password-file Set path for a file containing the authentication password
-P Read password from console
--password <password> Set authentication password
--username <username> Set authentication username
--verbose Print more information while working
--connection-param-file <filename> Optional properties file that provides connection parameters

Table 14. Import control arguments:

Argument Description
--as-avrodatafile Imports data to Avro Data Files
--as-sequencefile Imports data to SequenceFiles
--as-textfile Imports data as plain text (default)
--direct Use direct import fast path
--direct-split-size <n> Split the input stream every n bytes when importing in direct mode
--inline-lob-limit <n> Set the maximum size for an inline LOB
-m,--num-mappers <n> Use n map tasks to import in parallel
--warehouse-dir <dir> HDFS parent for table destination
-z,--compress Enable compression
--compression-codec <c> Use Hadoop codec (default gzip)
--exclude-tables <tables> Comma separated list of tables to exclude from import process

These arguments behave in the same manner as they do when used for the sqoop-import tool, but the --table, --split-by, --columns, and --where arguments are invalid for sqoop-import-all-tables. The --exclude-tables argument is for +sqoop-import-all-tables only.

Table 15. Output line formatting arguments:

Argument Description
--enclosed-by <char> Sets a required field enclosing character
--escaped-by <char> Sets the escape character
--fields-terminated-by <char> Sets the field separator character
--lines-terminated-by <char> Sets the end-of-line character
--mysql-delimiters Uses MySQL’s default delimiter set: fields: , lines: \n escaped-by: \ optionally-enclosed-by: '
--optionally-enclosed-by <char> Sets a field enclosing character

Table 16. Input parsing arguments:

Argument Description
--input-enclosed-by <char> Sets a required field encloser
--input-escaped-by <char> Sets the input escape character
--input-fields-terminated-by <char> Sets the input field separator
--input-lines-terminated-by <char> Sets the input end-of-line character
--input-optionally-enclosed-by <char> Sets a field enclosing character

Table 17. Hive arguments:

Argument Description
--hive-home <dir> Override $HIVE_HOME
--hive-import Import tables into Hive (Uses Hive’s default delimiters if none are set.)
--hive-overwrite Overwrite existing data in the Hive table.
--create-hive-table If set, then the job will fail if the target hive
table exits. By default this property is false.
--hive-table <table-name> Sets the table name to use when importing to Hive.
--hive-drop-import-delims Drops \n, \r, and \01 from string fields when importing to Hive.
--hive-delims-replacement Replace \n, \r, and \01 from string fields with user defined string when importing to Hive.
--hive-partition-key Name of a hive field to partition are sharded on
--hive-partition-value <v> String-value that serves as partition key for this imported into hive in this job.
--map-column-hive <map> Override default mapping from SQL type to Hive type for configured columns.

Table 18. Code generation arguments:

Argument Description
--bindir <dir> Output directory for compiled objects
--jar-file <file> Disable code generation; use specified jar
--outdir <dir> Output directory for generated code
--package-name <name> Put auto-generated classes in this package

The import-all-tables tool does not support the --class-name argument. You may, however, specify a package with --package-name in which all generated classes will be placed.

8.3. Example Invocations

Import all tables from the corp database:

$ sqoop import-all-tables --connect jdbc:mysql://db.foo.com/corp

Verifying that it worked:

$ hadoop fs -ls
Found 4 items
drwxr-xr-x   - someuser somegrp       0 2010-04-27 17:15 /user/someuser/EMPLOYEES
drwxr-xr-x   - someuser somegrp       0 2010-04-27 17:15 /user/someuser/PAYCHECKS
drwxr-xr-x   - someuser somegrp       0 2010-04-27 17:15 /user/someuser/DEPARTMENTS
drwxr-xr-x   - someuser somegrp       0 2010-04-27 17:15 /user/someuser/OFFICE_SUPPLIES

9. sqoop-export

9.1. Purpose

The export tool exports a set of files from HDFS back to an RDBMS. The target table must already exist in the database. The input files are read and parsed into a set of records according to the user-specified delimiters.

The default operation is to transform these into a set of INSERT statements that inject the records into the database. In "update mode," Sqoop will generate UPDATE statements that replace existing records in the database, and in "call mode" Sqoop will make a stored procedure call for each record.

9.2. Syntax

$ sqoop export (generic-args) (export-args)
$ sqoop-export (generic-args) (export-args)

Although the Hadoop generic arguments must preceed any export arguments, the export arguments can be entered in any order with respect to one another.

Table 19. Common arguments

Argument Description
--connect <jdbc-uri> Specify JDBC connect string
--connection-manager <class-name> Specify connection manager class to use
--driver <class-name> Manually specify JDBC driver class to use
--hadoop-mapred-home <dir> Override $HADOOP_MAPRED_HOME
--help Print usage instructions
--password-file Set path for a file containing the authentication password
-P Read password from console
--password <password> Set authentication password
--username <username> Set authentication username
--verbose Print more information while working
--connection-param-file <filename> Optional properties file that provides connection parameters

Table 20. Validation arguments More Details

Argument Description
--validate Enable validation of data copied, supports single table copy only. --validator <class-name> Specify validator class to use.
--validation-threshold <class-name> Specify validation threshold class to use.
+--validation-failurehandler <class-name >+ Specify validation failure handler class to use.

Table 21. Export control arguments:

Argument Description
--direct Use direct export fast path
--export-dir <dir> HDFS source path for the export
-m,--num-mappers <n> Use n map tasks to export in parallel
--table <table-name> Table to populate
--call <stored-proc-name> Stored Procedure to call
--update-key <col-name> Anchor column to use for updates. Use a comma separated list of columns if there are more than one column.
--update-mode <mode> Specify how updates are performed when new rows are found with non-matching keys in database.
Legal values for mode include updateonly (default) and allowinsert.
--input-null-string <null-string> The string to be interpreted as null for string columns
--input-null-non-string <null-string> The string to be interpreted as null for non-string columns
--staging-table <staging-table-name> The table in which data will be staged before being inserted into the destination table.
--clear-staging-table Indicates that any data present in the staging table can be deleted.
--batch Use batch mode for underlying statement execution.

The --export-dir argument and one of --table or --call are required. These specify the table to populate in the database (or the stored procedure to call), and the directory in HDFS that contains the source data.

You can control the number of mappers independently from the number of files present in the directory. Export performance depends on the degree of parallelism. By default, Sqoop will use four tasks in parallel for the export process. This may not be optimal; you will need to experiment with your own particular setup. Additional tasks may offer better concurrency, but if the database is already bottlenecked on updating indices, invoking triggers, and so on, then additional load may decrease performance. The --num-mappers or -m arguments control the number of map tasks, which is the degree of parallelism used.

MySQL provides a direct mode for exports as well, using the mysqlimport tool. When exporting to MySQL, use the --direct argument to specify this codepath. This may be higher-performance than the standard JDBC codepath.

[Note]Note

When using export in direct mode with MySQL, the MySQL bulk utility mysqlimport must be available in the shell path of the task process.

The --input-null-string and --input-null-non-string arguments are optional. If --input-null-string is not specified, then the string "null" will be interpreted as null for string-type columns. If --input-null-non-string is not specified, then both the string "null" and the empty string will be interpreted as null for non-string columns. Note that, the empty string will be always interpreted as null for non-string columns, in addition to other string if specified by --input-null-non-string.

Since Sqoop breaks down export process into multiple transactions, it is possible that a failed export job may result in partial data being committed to the database. This can further lead to subsequent jobs failing due to insert collisions in some cases, or lead to duplicated data in others. You can overcome this problem by specifying a staging table via the --staging-table option which acts as an auxiliary table that is used to stage exported data. The staged data is finally moved to the destination table in a single transaction.

In order to use the staging facility, you must create the staging table prior to running the export job. This table must be structurally identical to the target table. This table should either be empty before the export job runs, or the --clear-staging-table option must be specified. If the staging table contains data and the --clear-staging-table option is specified, Sqoop will delete all of the data before starting the export job.

[Note]Note

Support for staging data prior to pushing it into the destination table is not available for --direct exports. It is also not available when export is invoked using the --update-key option for updating existing data, and when stored procedures are used to insert the data.

9.3. Inserts vs. Updates

By default, sqoop-export appends new rows to a table; each input record is transformed into an INSERT statement that adds a row to the target database table. If your table has constraints (e.g., a primary key column whose values must be unique) and already contains data, you must take care to avoid inserting records that violate these constraints. The export process will fail if an INSERT statement fails. This mode is primarily intended for exporting records to a new, empty table intended to receive these results.

If you specify the --update-key argument, Sqoop will instead modify an existing dataset in the database. Each input record is treated as an UPDATE statement that modifies an existing row. The row a statement modifies is determined by the column name(s) specified with --update-key. For example, consider the following table definition:

CREATE TABLE foo(
    id INT NOT NULL PRIMARY KEY,
    msg VARCHAR(32),
    bar INT);

Consider also a dataset in HDFS containing records like these:

0,this is a test,42
1,some more data,100
...

Running sqoop-export --table foo --update-key id --export-dir /path/to/data --connect … will run an export job that executes SQL statements based on the data like so:

UPDATE foo SET msg='this is a test', bar=42 WHERE id=0;
UPDATE foo SET msg='some more data', bar=100 WHERE id=1;
...

If an UPDATE statement modifies no rows, this is not considered an error; the export will silently continue. (In effect, this means that an update-based export will not insert new rows into the database.) Likewise, if the column specified with --update-key does not uniquely identify rows and multiple rows are updated by a single statement, this condition is also undetected.

The argument --update-key can also be given a comma separated list of column names. In which case, Sqoop will match all keys from this list before updating any existing record.

Depending on the target database, you may also specify the --update-mode argument with allowinsert mode if you want to update rows if they exist in the database already or insert rows if they do not exist yet.

Table 22. Input parsing arguments:

Argument Description
--input-enclosed-by <char> Sets a required field encloser
--input-escaped-by <char> Sets the input escape character
--input-fields-terminated-by <char> Sets the input field separator
--input-lines-terminated-by <char> Sets the input end-of-line character
--input-optionally-enclosed-by <char> Sets a field enclosing character

Table 23. Output line formatting arguments:

Argument Description
--enclosed-by <char> Sets a required field enclosing character
--escaped-by <char> Sets the escape character
--fields-terminated-by <char> Sets the field separator character
--lines-terminated-by <char> Sets the end-of-line character
--mysql-delimiters Uses MySQL’s default delimiter set: fields: , lines: \n escaped-by: \ optionally-enclosed-by: '
--optionally-enclosed-by <char> Sets a field enclosing character

Sqoop automatically generates code to parse and interpret records of the files containing the data to be exported back to the database. If these files were created with non-default delimiters (comma-separated fields with newline-separated records), you should specify the same delimiters again so that Sqoop can parse your files.

If you specify incorrect delimiters, Sqoop will fail to find enough columns per line. This will cause export map tasks to fail by throwing ParseExceptions.

Table 24. Code generation arguments:

Argument Description
--bindir <dir> Output directory for compiled objects
--class-name <name> Sets the generated class name. This overrides --package-name. When combined with --jar-file, sets the input class.
--jar-file <file> Disable code generation; use specified jar
--outdir <dir> Output directory for generated code
--package-name <name> Put auto-generated classes in this package
--map-column-java <m> Override default mapping from SQL type to Java type for configured columns.

If the records to be exported were generated as the result of a previous import, then the original generated class can be used to read the data back. Specifying --jar-file and --class-name obviate the need to specify delimiters in this case.

The use of existing generated code is incompatible with --update-key; an update-mode export requires new code generation to perform the update. You cannot use --jar-file, and must fully specify any non-default delimiters.

9.4. Exports and Transactions

Exports are performed by multiple writers in parallel. Each writer uses a separate connection to the database; these have separate transactions from one another. Sqoop uses the multi-row INSERT syntax to insert up to 100 records per statement. Every 100 statements, the current transaction within a writer task is committed, causing a commit every 10,000 rows. This ensures that transaction buffers do not grow without bound, and cause out-of-memory conditions. Therefore, an export is not an atomic process. Partial results from the export will become visible before the export is complete.

9.5. Failed Exports

Exports may fail for a number of reasons:

  • Loss of connectivity from the Hadoop cluster to the database (either due to hardware fault, or server software crashes)
  • Attempting to INSERT a row which violates a consistency constraint (for example, inserting a duplicate primary key value)
  • Attempting to parse an incomplete or malformed record from the HDFS source data
  • Attempting to parse records using incorrect delimiters
  • Capacity issues (such as insufficient RAM or disk space)

If an export map task fails due to these or other reasons, it will cause the export job to fail. The results of a failed export are undefined. Each export map task operates in a separate transaction. Furthermore, individual map tasks commit their current transaction periodically. If a task fails, the current transaction will be rolled back. Any previously-committed transactions will remain durable in the database, leading to a partially-complete export.

9.6. Example Invocations

A basic export to populate a table named bar:

$ sqoop export --connect jdbc:mysql://db.example.com/foo --table bar  \
    --export-dir /results/bar_data

This example takes the files in /results/bar_data and injects their contents in to the bar table in the foo database on db.example.com. The target table must already exist in the database. Sqoop performs a set of INSERT INTO operations, without regard for existing content. If Sqoop attempts to insert rows which violate constraints in the database (for example, a particular primary key value already exists), then the export fails.

Another basic export to populate a table named bar with validation enabled: More Details

$ sqoop export --connect jdbc:mysql://db.example.com/foo --table bar  \
    --export-dir /results/bar_data --validate

An export that calls a stored procedure named barproc for every record in /results/bar_data would look like:

$ sqoop export --connect jdbc:mysql://db.example.com/foo --call barproc \
    --export-dir /results/bar_data

10. validation

10.1. Purpose

Validate the data copied, either import or export by comparing the row counts from the source and the target post copy.

10.2. Introduction

There are 3 basic interfaces: ValidationThreshold - Determines if the error margin between the source and target are acceptable: Absolute, Percentage Tolerant, etc. Default implementation is AbsoluteValidationThreshold which ensures the row counts from source and targets are the same.

ValidationFailureHandler - Responsible for handling failures: log an error/warning, abort, etc. Default implementation is LogOnFailureHandler that logs a warning message to the configured logger.

Validator - Drives the validation logic by delegating the decision to ValidationThreshold and delegating failure handling to ValidationFailureHandler. The default implementation is RowCountValidator which validates the row counts from source and the target.

10.3. Syntax

$ sqoop import (generic-args) (import-args)
$ sqoop export (generic-args) (export-args)

Validation arguments are part of import and export arguments.

10.4. Configuration

The validation framework is extensible and pluggable. It comes with default implementations but the interfaces can be extended to allow custom implementations by passing them as part of the command line arguments as described below.

Validator. 

Property:         validator
Description:      Driver for validation,
                  must implement org.apache.sqoop.validation.Validator
Supported values: The value has to be a fully qualified class name.
Default value:    org.apache.sqoop.validation.RowCountValidator

Validation Threshold. 

Property:         validation-threshold
Description:      Drives the decision based on the validation meeting the
                  threshold or not. Must implement
                  org.apache.sqoop.validation.ValidationThreshold
Supported values: The value has to be a fully qualified class name.
Default value:    org.apache.sqoop.validation.AbsoluteValidationThreshold

Validation Failure Handler. 

Property:         validation-failurehandler
Description:      Responsible for handling failures, must implement
                  org.apache.sqoop.validation.ValidationFailureHandler
Supported values: The value has to be a fully qualified class name.
Default value:    org.apache.sqoop.validation.AbortOnFailureHandler

10.5. Limitations

Validation currently only validates data copied from a single table into HDFS. The following are the limitations in the current implementation:

  • all-tables option
  • free-form query option
  • Data imported into Hive, HBase or Accumulo
  • table import with --where argument
  • incremental imports

10.6. Example Invocations

A basic import of a table named EMPLOYEES in the corp database that uses validation to validate the row counts:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp  \
    --table EMPLOYEES --validate

A basic export to populate a table named bar with validation enabled:

$ sqoop export --connect jdbc:mysql://db.example.com/foo --table bar  \
    --export-dir /results/bar_data --validate

Another example that overrides the validation args:

$ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
    --validate --validator org.apache.sqoop.validation.RowCountValidator \
    --validation-threshold \
          org.apache.sqoop.validation.AbsoluteValidationThreshold \
    --validation-failurehandler \
          org.apache.sqoop.validation.AbortOnFailureHandler

11. Saved Jobs

Imports and exports can be repeatedly performed by issuing the same command multiple times. Especially when using the incremental import capability, this is an expected scenario.

Sqoop allows you to define saved jobs which make this process easier. A saved job records the configuration information required to execute a Sqoop command at a later time. The section on the sqoop-job tool describes how to create and work with saved jobs.

By default, job descriptions are saved to a private repository stored in $HOME/.sqoop/. You can configure Sqoop to instead use a shared metastore, which makes saved jobs available to multiple users across a shared cluster. Starting the metastore is covered by the section on the sqoop-metastore tool.

12. sqoop-job

12.1. Purpose

The job tool allows you to create and work with saved jobs. Saved jobs remember the parameters used to specify a job, so they can be re-executed by invoking the job by its handle.

If a saved job is configured to perform an incremental import, state regarding the most recently imported rows is updated in the saved job to allow the job to continually import only the newest rows.

12.2. Syntax

$ sqoop job (generic-args) (job-args) [-- [subtool-name] (subtool-args)]
$ sqoop-job (generic-args) (job-args) [-- [subtool-name] (subtool-args)]

Although the Hadoop generic arguments must preceed any job arguments, the job arguments can be entered in any order with respect to one another.

Table 25. Job management options:

Argument Description
--create <job-id> Define a new saved job with the specified job-id (name). A second Sqoop command-line, separated by a -- should be specified; this defines the saved job.
--delete <job-id> Delete a saved job.
--exec <job-id> Given a job defined with --create, run the saved job.
--show <job-id> Show the parameters for a saved job.
--list List all saved jobs

Creating saved jobs is done with the --create action. This operation requires a -- followed by a tool name and its arguments. The tool and its arguments will form the basis of the saved job. Consider:

$ sqoop job --create myjob -- import --connect jdbc:mysql://example.com/db \
    --table mytable

This creates a job named myjob which can be executed later. The job is not run. This job is now available in the list of saved jobs:

$ sqoop job --list
Available jobs:
  myjob

We can inspect the configuration of a job with the show action:

 $ sqoop job --show myjob
 Job: myjob
 Tool: import
 Options:
 ----------------------------
 direct.import = false
 codegen.input.delimiters.record = 0
 hdfs.append.dir = false
 db.table = mytable
 ...

And if we are satisfied with it, we can run the job with exec:

$ sqoop job --exec myjob
10/08/19 13:08:45 INFO tool.CodeGenTool: Beginning code generation
...

The exec action allows you to override arguments of the saved job by supplying them after a --. For example, if the database were changed to require a username, we could specify the username and password with:

$ sqoop job --exec myjob -- --username someuser -P
Enter password:
...

Table 26. Metastore connection options:

Argument Description
--meta-connect <jdbc-uri> Specifies the JDBC connect string used to connect to the metastore

By default, a private metastore is instantiated in $HOME/.sqoop. If you have configured a hosted metastore with the sqoop-metastore tool, you can connect to it by specifying the --meta-connect argument. This is a JDBC connect string just like the ones used to connect to databases for import.

In conf/sqoop-site.xml, you can configure sqoop.metastore.client.autoconnect.url with this address, so you do not have to supply --meta-connect to use a remote metastore. This parameter can also be modified to move the private metastore to a location on your filesystem other than your home directory.

If you configure sqoop.metastore.client.enable.autoconnect with the value false, then you must explicitly supply --meta-connect.

Table 27. Common options:

Argument Description
--help Print usage instructions
--verbose Print more information while working

12.3. Saved jobs and passwords

The Sqoop metastore is not a secure resource. Multiple users can access its contents. For this reason, Sqoop does not store passwords in the metastore. If you create a job that requires a password, you will be prompted for that password each time you execute the job.

You can enable passwords in the metastore by setting sqoop.metastore.client.record.password to true in the configuration.

Note that you have to set sqoop.metastore.client.record.password to true if you are executing saved jobs via Oozie because Sqoop cannot prompt the user to enter passwords while being executed as Oozie tasks.

12.4. Saved jobs and incremental imports

Incremental imports are performed by comparing the values in a check column against a reference value for the most recent import. For example, if the --incremental append argument was specified, along with --check-column id and --last-value 100, all rows with id > 100 will be imported. If an incremental import is run from the command line, the value which should be specified as --last-value in a subsequent incremental import will be printed to the screen for your reference. If an incremental import is run from a saved job, this value will be retained in the saved job. Subsequent runs of sqoop job --exec someIncrementalJob will continue to import only newer rows than those previously imported.

13. sqoop-metastore

13.1. Purpose

The metastore tool configures Sqoop to host a shared metadata repository. Multiple users and/or remote users can define and execute saved jobs (created with sqoop job) defined in this metastore.

Clients must be configured to connect to the metastore in sqoop-site.xml or with the --meta-connect argument.

13.2. Syntax

$ sqoop metastore (generic-args) (metastore-args)
$ sqoop-metastore (generic-args) (metastore-args)

Although the Hadoop generic arguments must preceed any metastore arguments, the metastore arguments can be entered in any order with respect to one another.

Table 28. Metastore management options:

Argument Description
--shutdown Shuts down a running metastore instance on the same machine.

Running sqoop-metastore launches a shared HSQLDB database instance on the current machine. Clients can connect to this metastore and create jobs which can be shared between users for execution.

The location of the metastore’s files on disk is controlled by the sqoop.metastore.server.location property in conf/sqoop-site.xml. This should point to a directory on the local filesystem.

The metastore is available over TCP/IP. The port is controlled by the sqoop.metastore.server.port configuration parameter, and defaults to 16000.

Clients should connect to the metastore by specifying sqoop.metastore.client.autoconnect.url or --meta-connect with the value jdbc:hsqldb:hsql://<server-name>:<port>/sqoop. For example, jdbc:hsqldb:hsql://metaserver.example.com:16000/sqoop.

This metastore may be hosted on a machine within the Hadoop cluster, or elsewhere on the network.

14. sqoop-merge

14.1. Purpose

The merge tool allows you to combine two datasets where entries in one dataset should overwrite entries of an older dataset. For example, an incremental import run in last-modified mode will generate multiple datasets in HDFS where successively newer data appears in each dataset. The merge tool will "flatten" two datasets into one, taking the newest available records for each primary key.

14.2. Syntax

$ sqoop merge (generic-args) (merge-args)
$ sqoop-merge (generic-args) (merge-args)

Although the Hadoop generic arguments must preceed any merge arguments, the job arguments can be entered in any order with respect to one another.

Table 29. Merge options:

Argument Description
--class-name <class> Specify the name of the record-specific class to use during the merge job.
--jar-file <file> Specify the name of the jar to load the record class from.
--merge-key <col> Specify the name of a column to use as the merge key.
--new-data <path> Specify the path of the newer dataset.
--onto <path> Specify the path of the older dataset.
--target-dir <path> Specify the target path for the output of the merge job.

The merge tool runs a MapReduce job that takes two directories as input: a newer dataset, and an older one. These are specified with --new-data and --onto respectively. The output of the MapReduce job will be placed in the directory in HDFS specified by --target-dir.

When merging the datasets, it is assumed that there is a unique primary key value in each record. The column for the primary key is specified with --merge-key. Multiple rows in the same dataset should not have the same primary key, or else data loss may occur.

To parse the dataset and extract the key column, the auto-generated class from a previous import must be used. You should specify the class name and jar file with --class-name and --jar-file. If this is not availab,e you can recreate the class using the codegen tool.

The merge tool is typically run after an incremental import with the date-last-modified mode (sqoop import --incremental lastmodified …).

Supposing two incremental imports were performed, where some older data is in an HDFS directory named older and newer data is in an HDFS directory named newer, these could be merged like so:

$ sqoop merge --new-data newer --onto older --target-dir merged \
    --jar-file datatypes.jar --class-name Foo --merge-key id

This would run a MapReduce job where the value in the id column of each row is used to join rows; rows in the newer dataset will be used in preference to rows in the older dataset.

This can be used with both SequenceFile-, Avro- and text-based incremental imports. The file types of the newer and older datasets must be the same.

15. sqoop-codegen

15.1. Purpose

The codegen tool generates Java classes which encapsulate and interpret imported records. The Java definition of a record is instantiated as part of the import process, but can also be performed separately. For example, if Java source is lost, it can be recreated. New versions of a class can be created which use different delimiters between fields, and so on.

15.2. Syntax

$ sqoop codegen (generic-args) (codegen-args)
$ sqoop-codegen (generic-args) (codegen-args)

Although the Hadoop generic arguments must preceed any codegen arguments, the codegen arguments can be entered in any order with respect to one another.

Table 30. Common arguments

Argument Description
--connect <jdbc-uri> Specify JDBC connect string
--connection-manager <class-name> Specify connection manager class to use
--driver <class-name> Manually specify JDBC driver class to use
--hadoop-mapred-home <dir> Override $HADOOP_MAPRED_HOME
--help Print usage instructions
--password-file Set path for a file containing the authentication password
-P Read password from console
--password <password> Set authentication password
--username <username> Set authentication username
--verbose Print more information while working
--connection-param-file <filename> Optional properties file that provides connection parameters

Table 31. Code generation arguments:

Argument Description
--bindir <dir> Output directory for compiled objects
--class-name <name> Sets the generated class name. This overrides --package-name. When combined with --jar-file, sets the input class.
--jar-file <file> Disable code generation; use specified jar
--outdir <dir> Output directory for generated code
--package-name <name> Put auto-generated classes in this package
--map-column-java <m> Override default mapping from SQL type to Java type for configured columns.

Table 32. Output line formatting arguments:

Argument Description
--enclosed-by <char> Sets a required field enclosing character
--escaped-by <char> Sets the escape character
--fields-terminated-by <char> Sets the field separator character
--lines-terminated-by <char> Sets the end-of-line character
--mysql-delimiters Uses MySQL’s default delimiter set: fields: , lines: \n escaped-by: \ optionally-enclosed-by: '
--optionally-enclosed-by <char> Sets a field enclosing character

Table 33. Input parsing arguments:

Argument Description
--input-enclosed-by <char> Sets a required field encloser
--input-escaped-by <char> Sets the input escape character
--input-fields-terminated-by <char> Sets the input field separator
--input-lines-terminated-by <char> Sets the input end-of-line character
--input-optionally-enclosed-by <char> Sets a field enclosing character

Table 34. Hive arguments:

Argument Description
--hive-home <dir> Override $HIVE_HOME
--hive-import Import tables into Hive (Uses Hive’s default delimiters if none are set.)
--hive-overwrite Overwrite existing data in the Hive table.
--create-hive-table If set, then the job will fail if the target hive
table exits. By default this property is false.
--hive-table <table-name> Sets the table name to use when importing to Hive.
--hive-drop-import-delims Drops \n, \r, and \01 from string fields when importing to Hive.
--hive-delims-replacement Replace \n, \r, and \01 from string fields with user defined string when importing to Hive.
--hive-partition-key Name of a hive field to partition are sharded on
--hive-partition-value <v> String-value that serves as partition key for this imported into hive in this job.
--map-column-hive <map> Override default mapping from SQL type to Hive type for configured columns.

If Hive arguments are provided to the code generation tool, Sqoop generates a file containing the HQL statements to create a table and load data.

15.3. Example Invocations

Recreate the record interpretation code for the employees table of a corporate database:

$ sqoop codegen --connect jdbc:mysql://db.example.com/corp \
    --table employees

16. sqoop-create-hive-table

16.1. Purpose

The create-hive-table tool populates a Hive metastore with a definition for a table based on a database table previously imported to HDFS, or one planned to be imported. This effectively performs the "--hive-import" step of sqoop-import without running the preceeding import.

If data was already loaded to HDFS, you can use this tool to finish the pipeline of importing the data to Hive. You can also create Hive tables with this tool; data then can be imported and populated into the target after a preprocessing step run by the user.

16.2. Syntax

$ sqoop create-hive-table (generic-args) (create-hive-table-args)
$ sqoop-create-hive-table (generic-args) (create-hive-table-args)

Although the Hadoop generic arguments must preceed any create-hive-table arguments, the create-hive-table arguments can be entered in any order with respect to one another.

Table 35. Common arguments

Argument Description
--connect <jdbc-uri> Specify JDBC connect string
--connection-manager <class-name> Specify connection manager class to use
--driver <class-name> Manually specify JDBC driver class to use
--hadoop-mapred-home <dir> Override $HADOOP_MAPRED_HOME
--help Print usage instructions
--password-file Set path for a file containing the authentication password
-P Read password from console
--password <password> Set authentication password
--username <username> Set authentication username
--verbose Print more information while working
--connection-param-file <filename> Optional properties file that provides connection parameters

Table 36. Hive arguments:

Argument Description
--hive-home <dir> Override $HIVE_HOME
--hive-overwrite Overwrite existing data in the Hive table.
--create-hive-table If set, then the job will fail if the target hive
table exits. By default this property is false.
--hive-table <table-name> Sets the table name to use when importing to Hive.
--table The database table to read the definition from.

Table 37. Output line formatting arguments:

Argument Description
--enclosed-by <char> Sets a required field enclosing character
--escaped-by <char> Sets the escape character
--fields-terminated-by <char> Sets the field separator character
--lines-terminated-by <char> Sets the end-of-line character
--mysql-delimiters Uses MySQL’s default delimiter set: fields: , lines: \n escaped-by: \ optionally-enclosed-by: '
--optionally-enclosed-by <char> Sets a field enclosing character

Do not use enclosed-by or escaped-by delimiters with output formatting arguments used to import to Hive. Hive cannot currently parse them.

16.3. Example Invocations

Define in Hive a table named emps with a definition based on a database table named employees:

$ sqoop create-hive-table --connect jdbc:mysql://db.example.com/corp \
    --table employees --hive-table emps

17. sqoop-eval

17.1. Purpose

The eval tool allows users to quickly run simple SQL queries against a database; results are printed to the console. This allows users to preview their import queries to ensure they import the data they expect.

[Warning]Warning

The eval tool is provided for evaluation purpose only. You can use it to verify database connection from within the Sqoop or to test simple queries. It’s not suppose to be used in production workflows.

17.2. Syntax

$ sqoop eval (generic-args) (eval-args)
$ sqoop-eval (generic-args) (eval-args)

Although the Hadoop generic arguments must preceed any eval arguments, the eval arguments can be entered in any order with respect to one another.

Table 38. Common arguments

Argument Description
--connect <jdbc-uri> Specify JDBC connect string
--connection-manager <class-name> Specify connection manager class to use
--driver <class-name> Manually specify JDBC driver class to use
--hadoop-mapred-home <dir> Override $HADOOP_MAPRED_HOME
--help Print usage instructions
--password-file Set path for a file containing the authentication password
-P Read password from console
--password <password> Set authentication password
--username <username> Set authentication username
--verbose Print more information while working
--connection-param-file <filename> Optional properties file that provides connection parameters

Table 39. SQL evaluation arguments:

Argument Description
-e,--query <statement> Execute statement in SQL.

17.3. Example Invocations

Select ten records from the employees table:

$ sqoop eval --connect jdbc:mysql://db.example.com/corp \
    --query "SELECT * FROM employees LIMIT 10"

Insert a row into the foo table:

$ sqoop eval --connect jdbc:mysql://db.example.com/corp \
    -e "INSERT INTO foo VALUES(42, 'bar')"

18. sqoop-list-databases

18.1. Purpose

List database schemas present on a server.

18.2. Syntax

$ sqoop list-databases (generic-args) (list-databases-args)
$ sqoop-list-databases (generic-args) (list-databases-args)

Although the Hadoop generic arguments must preceed any list-databases arguments, the list-databases arguments can be entered in any order with respect to one another.

Table 40. Common arguments

Argument Description
--connect <jdbc-uri> Specify JDBC connect string
--connection-manager <class-name> Specify connection manager class to use
--driver <class-name> Manually specify JDBC driver class to use
--hadoop-mapred-home <dir> Override $HADOOP_MAPRED_HOME
--help Print usage instructions
--password-file Set path for a file containing the authentication password
-P Read password from console
--password <password> Set authentication password
--username <username> Set authentication username
--verbose Print more information while working
--connection-param-file <filename> Optional properties file that provides connection parameters

18.3. Example Invocations

List database schemas available on a MySQL server:

$ sqoop list-databases --connect jdbc:mysql://database.example.com/
information_schema
employees
[Note]Note

This only works with HSQLDB, MySQL and Oracle. When using with Oracle, it is necessary that the user connecting to the database has DBA privileges.

19. sqoop-list-tables

19.1. Purpose

List tables present in a database.

19.2. Syntax

$ sqoop list-tables (generic-args) (list-tables-args)
$ sqoop-list-tables (generic-args) (list-tables-args)

Although the Hadoop generic arguments must preceed any list-tables arguments, the list-tables arguments can be entered in any order with respect to one another.

Table 41. Common arguments

Argument Description
--connect <jdbc-uri> Specify JDBC connect string
--connection-manager <class-name> Specify connection manager class to use
--driver <class-name> Manually specify JDBC driver class to use
--hadoop-mapred-home <dir> Override $HADOOP_MAPRED_HOME
--help Print usage instructions
--password-file Set path for a file containing the authentication password
-P Read password from console
--password <password> Set authentication password
--username <username> Set authentication username
--verbose Print more information while working
--connection-param-file <filename> Optional properties file that provides connection parameters

19.3. Example Invocations

List tables available in the "corp" database:

$ sqoop list-tables --connect jdbc:mysql://database.example.com/corp
employees
payroll_checks
job_descriptions
office_supplies

In case of postgresql, list tables command with common arguments fetches only "public" schema. For custom schema, use --schema argument to list tables of particular schema Example

$ sqoop list-tables --connect jdbc:postgresql://localhost/corp  --username name -P -- --schema payrolldept
employees
expenses

20. sqoop-help

20.1. Purpose

List tools available in Sqoop and explain their usage.

20.2. Syntax

$ sqoop help [tool-name]
$ sqoop-help [tool-name]

If no tool name is provided (for example, the user runs sqoop help), then the available tools are listed. With a tool name, the usage instructions for that specific tool are presented on the console.

20.3. Example Invocations

List available tools:

$ 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

...

See 'sqoop help COMMAND' for information on a specific command.

Display usage instructions for the import tool:

$ bin/sqoop help import
usage: sqoop import [GENERIC-ARGS] [TOOL-ARGS]

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

Import control arguments:
   --as-avrodatafile             Imports data to Avro Data Files
   --as-sequencefile             Imports data to SequenceFiles
   --as-textfile                 Imports data as plain text (default)
...

21. sqoop-version

21.1. Purpose

Display version information for Sqoop.

21.2. Syntax

$ sqoop version
$ sqoop-version

21.3. Example Invocations

Display the version:

$ sqoop version
Sqoop {revnumber}
git commit id 46b3e06b79a8411320d77c984c3030db47dd1c22
Compiled by aaron@jargon on Mon May 17 13:43:22 PDT 2010

22. Sqoop-HCatalog Integration

22.1. HCatalog Background

HCatalog is a table and storage management service for Hadoop that enables users with different data processing tools Pig, MapReduce, and Hive to more easily read and write data on the grid. HCatalog’s table abstraction presents users with a relational view of data in the Hadoop distributed file system (HDFS) and ensures that users need not worry about where or in what format their data is stored: RCFile format, text files, or SequenceFiles.

HCatalog supports reading and writing files in any format for which a Hive SerDe (serializer-deserializer) has been written. By default, HCatalog supports RCFile, CSV, JSON, and SequenceFile formats. To use a custom format, you must provide the InputFormat and OutputFormat as well as the SerDe.

The ability of HCatalog to abstract various storage formats is used in providing the RCFile (and future file types) support to Sqoop.

22.2. Exposing HCatalog Tables to Sqoop

HCatalog integration with Sqoop is patterned on an existing feature set that supports Avro and Hive tables. Five new command line options are introduced, and some command line options defined for Hive are reused.

22.2.1. New Command Line Options

--hcatalog-database
Specifies the database name for the HCatalog table. If not specified, the default database name default is used. Providing the --hcatalog-database option without --hcatalog-table is an error. This is not a required option.
--hcatalog-table
The argument value for this option is the HCatalog tablename. The presence of the --hcatalog-table option signifies that the import or export job is done using HCatalog tables, and it is a required option for HCatalog jobs.
--hcatalog-home
The home directory for the HCatalog installation. The directory is expected to have a lib subdirectory and a share/hcatalog subdirectory with necessary HCatalog libraries. If not specified, the system property hcatalog.home will be checked and failing that, a system environment variable HCAT_HOME will be checked. If none of these are set, the default value will be used and currently the default is set to /usr/lib/hcatalog. This is not a required option.
--create-hcatalog-table
This option specifies whether an HCatalog table should be created automatically when importing data. By default, HCatalog tables are assumed to exist. The table name will be the same as the database table name translated to lower case. Further described in Automatic Table Creation below.
--hcatalog-storage-stanza
This option specifies the storage stanza to be appended to the table. Further described in Automatic Table Creation below.

22.2.2. Supported Sqoop Hive Options

The following Sqoop options are also used along with the --hcatalog-table option to provide additional input to the HCatalog jobs. Some of the existing Hive import job options are reused with HCatalog jobs instead of creating HCatalog-specific options for the same purpose.

--map-column-hive
This option maps a database column to HCatalog with a specific HCatalog type.
--hive-home
The Hive home location.
--hive-partition-key
Used for static partitioning filter. The partitioning key should be of type STRING. There can be only one static partitioning key.
--hive-partition-value
The value associated with the partition.

22.2.3. Direct Mode support

HCatalog integration in Sqoop has been enhanced to support direct mode connectors (which are high performance connectors specific to a database). Netezza direct mode connector has been enhanced to take advatange of this feature.

[Important]Important

Only Netezza direct mode connector is currently enabled to work with HCatalog.

22.2.4. Unsupported Sqoop Options

22.2.4.1. Unsupported Sqoop Hive Import Options

The following Sqoop Hive import options are not supported with HCatalog jobs.

  • --hive-import
  • --hive-overwrite
22.2.4.2. Unsupported Sqoop Export and Import Options

The following Sqoop export and import options are not supported with HCatalog jobs.

  • --export-dir
  • --target-dir
  • --warehouse-dir
  • --append
  • --as-sequencefile
  • --as-avrofile

22.2.5. Ignored Sqoop Options

The following options are ignored with HCatalog jobs.

  • All input delimiter options are ignored.
  • Output delimiters are generally ignored unless either --hive-drop-import-delims or --hive-delims-replacement is used. When the --hive-drop-import-delims or --hive-delims-replacement option is specified, all CHAR type database table columns will be post-processed to either remove or replace the delimiters, respectively. See Delimited Text Formats and Field and Line Delimiter Characters below. This is only needed if the HCatalog table uses text formats.

22.3. Automatic Table Creation

One of the key features of Sqoop is to manage and create the table metadata when importing into Hadoop. HCatalog import jobs also provide for this feature with the option --create-hcatalog-table. Furthermore, one of the important benefits of the HCatalog integration is to provide storage agnosticism to Sqoop data movement jobs. To provide for that feature, HCatalog import jobs provide an option that lets a user specifiy the storage format for the created table.

The option --create-hcatalog-table is used as an indicator that a table has to be created as part of the HCatalog import job. If the option --create-hcatalog-table is specified and the table exists, then the table creation will fail and the job will be aborted.

The option --hcatalog-storage-stanza can be used to specify the storage format of the newly created table. The default value for this option is stored as rcfile. The value specified for this option is assumed to be a valid Hive storage format expression. It will be appended to the create table command generated by the HCatalog import job as part of automatic table creation. Any error in the storage stanza will cause the table creation to fail and the import job will be aborted.

Any additional resources needed to support the storage format referenced in the option --hcatalog-storage-stanza should be provided to the job either by placing them in $HIVE_HOME/lib or by providing them in HADOOP_CLASSPATH and LIBJAR files.

If the option --hive-partition-key is specified, then the value of this option is used as the partitioning key for the newly created table. Only one partitioning key can be specified with this option.

Object names are mapped to the lowercase equivalents as specified below when mapped to an HCatalog table. This includes the table name (which is the same as the external store table name converted to lower case) and field names.

22.4. Delimited Text Formats and Field and Line Delimiter Characters

HCatalog supports delimited text format as one of the table storage formats. But when delimited text is used and the imported data has fields that contain those delimiters, then the data may be parsed into a different number of fields and records by Hive, thereby losing data fidelity.

For this case, one of these existing Sqoop import options can be used:

  • --hive-delims-replacement
  • --hive-drop-import-delims

If either of these options is provided for import, then any column of type STRING will be formatted with the Hive delimiter processing and then written to the HCatalog table.

22.5. HCatalog Table Requirements

The HCatalog table should be created before using it as part of a Sqoop job if the default table creation options (with optional storage stanza) are not sufficient. All storage formats supported by HCatalog can be used with the creation of the HCatalog tables. This makes this feature readily adopt new storage formats that come into the Hive project, such as ORC files.

22.6. Support for Partitioning

The Sqoop HCatalog feature supports the following table types:

  • Unpartitioned tables
  • Partitioned tables with a static partitioning key specified
  • Partitioned tables with dynamic partition keys from the database result set
  • Partitioned tables with a combination of a static key and additional dynamic partitioning keys

22.7. Schema Mapping

Sqoop currently does not support column name mapping. However, the user is allowed to override the type mapping. Type mapping loosely follows the Hive type mapping already present in Sqoop except that SQL types FLOAT and REAL are mapped to HCatalog type float. In the Sqoop type mapping for Hive, these two are mapped to double. Type mapping is primarily used for checking the column definition correctness only and can be overridden with the --map-column-hive option.

All types except binary are assignable to a String type.

Any field of number type (int, shortint, tinyint, bigint and bigdecimal, float and double) is assignable to another field of any number type during exports and imports. Depending on the precision and scale of the target type of assignment, truncations can occur.

Furthermore, date/time/timestamps are mapped to string (the full date/time/timestamp representation) or bigint (the number of milliseconds since epoch) during imports and exports.

BLOBs and CLOBs are only supported for imports. The BLOB/CLOB objects when imported are stored in a Sqoop-specific format and knowledge of this format is needed for processing these objects in a Pig/Hive job or another Map Reduce job.

Database column names are mapped to their lowercase equivalents when mapped to the HCatalog fields. Currently, case-sensitive database object names are not supported.

Projection of a set of columns from a table to an HCatalog table or loading to a column projection is allowed, subject to table constraints. The dynamic partitioning columns, if any, must be part of the projection when importing data into HCatalog tables.

Dynamic partitioning fields should be mapped to database columns that are defined with the NOT NULL attribute (although this is not validated). A null value during import for a dynamic partitioning column will abort the Sqoop job.

22.8. Support for HCatalog Data Types

All the primitive HCatalog types are supported. Currently all the complex HCatalog types are unsupported.

BLOB/CLOB database types are only supported for imports.

22.9. Providing Hive and HCatalog Libraries for the Sqoop Job

With the support for HCatalog added to Sqoop, any HCatalog job depends on a set of jar files being available both on the Sqoop client host and where the Map/Reduce tasks run. To run HCatalog jobs, the environment variable HADOOP_CLASSPATH must be set up as shown below before launching the Sqoop HCatalog jobs.

HADOOP_CLASSPATH=$(hcat -classpath) export HADOOP_CLASSPATH

The necessary HCatalog dependencies will be copied to the distributed cache automatically by the Sqoop job.

22.10. Examples

Create an HCatalog table, such as:

hcat -e "create table txn(txn_date string, cust_id string, amount float, store_id int) partitioned by (cust_id string) stored as rcfile;"

Then Sqoop import and export of the "txn" HCatalog table can be invoked as follows:

22.11. Import

$SQOOP_HOME/bin/sqoop import --connect <jdbc-url> -table <table-name> --hcatalog-table txn <other sqoop options>

22.12. Export

$SQOOP_HOME/bin/sqoop export --connect <jdbc-url> -table <table-name> --hcatalog-table txn <other sqoop options>

23. Compatibility Notes

Sqoop uses JDBC to connect to databases and adheres to published standards as much as possible. For databases which do not support standards-compliant SQL, Sqoop uses alternate codepaths to provide functionality. In general, Sqoop is believed to be compatible with a large number of databases, but it is tested with only a few.

Nonetheless, several database-specific decisions were made in the implementation of Sqoop, and some databases offer additional settings which are extensions to the standard.

This section describes the databases tested with Sqoop, any exceptions in Sqoop’s handling of each database relative to the norm, and any database-specific settings available in Sqoop.

23.1. Supported Databases

While JDBC is a compatibility layer that allows a program to access many different databases through a common API, slight differences in the SQL language spoken by each database may mean that Sqoop can’t use every database out of the box, or that some databases may be used in an inefficient manner.

When you provide a connect string to Sqoop, it inspects the protocol scheme to determine appropriate vendor-specific logic to use. If Sqoop knows about a given database, it will work automatically. If not, you may need to specify the driver class to load via --driver. This will use a generic code path which will use standard SQL to access the database. Sqoop provides some databases with faster, non-JDBC-based access mechanisms. These can be enabled by specfying the --direct parameter.

Sqoop includes vendor-specific support for the following databases:

Database version --direct support? connect string matches
HSQLDB 1.8.0+ No jdbc:hsqldb:*//
MySQL 5.0+ Yes jdbc:mysql://
Oracle 10.2.0+ No jdbc:oracle:*//
PostgreSQL 8.3+ Yes (import only) jdbc:postgresql://

Sqoop may work with older versions of the databases listed, but we have only tested it with the versions specified above.

Even if Sqoop supports a database internally, you may still need to install the database vendor’s JDBC driver in your $SQOOP_HOME/lib path on your client. Sqoop can load classes from any jars in $SQOOP_HOME/lib on the client and will use them as part of any MapReduce jobs it runs; unlike older versions, you no longer need to install JDBC jars in the Hadoop library path on your servers.

23.2. MySQL

JDBC Driver: MySQL Connector/J

MySQL v5.0 and above offers very thorough coverage by Sqoop. Sqoop has been tested with mysql-connector-java-5.1.13-bin.jar.

23.2.1. zeroDateTimeBehavior

MySQL allows values of '0000-00-00\' for DATE columns, which is a non-standard extension to SQL. When communicated via JDBC, these values are handled in one of three different ways:

  • Convert to NULL.
  • Throw an exception in the client.
  • Round to the nearest legal date ('0001-01-01\').

You specify the behavior by using the zeroDateTimeBehavior property of the connect string. If a zeroDateTimeBehavior property is not specified, Sqoop uses the convertToNull behavior.

You can override this behavior. For example:

$ sqoop import --table foo \
    --connect jdbc:mysql://db.example.com/someDb?zeroDateTimeBehavior=round

23.2.2. UNSIGNED columns

Columns with type UNSIGNED in MySQL can hold values between 0 and 2^32 (4294967295), but the database will report the data type to Sqoop as INTEGER, which will can hold values between -2147483648 and \+2147483647. Sqoop cannot currently import UNSIGNED values above 2147483647.

23.2.3. BLOB and CLOB columns

Sqoop’s direct mode does not support imports of BLOB, CLOB, or LONGVARBINARY columns. Use JDBC-based imports for these columns; do not supply the --direct argument to the import tool.

23.2.4. Importing views in direct mode

Sqoop is currently not supporting import from view in direct mode. Use JDBC based (non direct) mode in case that you need to import view (simply omit --direct parameter).

23.2.5. Direct-mode Transactions

For performance, each writer will commit the current transaction approximately every 32 MB of exported data. You can control this by specifying the following argument before any tool-specific arguments: -D sqoop.mysql.export.checkpoint.bytes=size, where size is a value in bytes. Set size to 0 to disable intermediate checkpoints, but individual files being exported will continue to be committed independently of one another.

Sometimes you need to export large data with Sqoop to a live MySQL cluster that is under a high load serving random queries from the users of your application. While data consistency issues during the export can be easily solved with a staging table, there is still a problem with the performance impact caused by the heavy export.

First off, the resources of MySQL dedicated to the import process can affect the performance of the live product, both on the master and on the slaves. Second, even if the servers can handle the import with no significant performance impact (mysqlimport should be relatively "cheap"), importing big tables can cause serious replication lag in the cluster risking data inconsistency.

With -D sqoop.mysql.export.sleep.ms=time, where time is a value in milliseconds, you can let the server relax between checkpoints and the replicas catch up by pausing the export process after transferring the number of bytes specified in sqoop.mysql.export.checkpoint.bytes. Experiment with different settings of these two parameters to archieve an export pace that doesn’t endanger the stability of your MySQL cluster.

[Important]Important

Note that any arguments to Sqoop that are of the form -D parameter=value are Hadoop generic arguments and must appear before any tool-specific arguments (for example, --connect, --table, etc). Don’t forget that these parameters are only supported with the --direct flag set.

23.3. PostgreSQL

Sqoop supports JDBC-based connector for PostgreSQL: http://jdbc.postgresql.org/

The connector has been tested using JDBC driver version "9.1-903 JDBC 4" with PostgreSQL server 9.1.

23.3.1. Importing views in direct mode

Sqoop is currently not supporting import from view in direct mode. Use JDBC based (non direct) mode in case that you need to import view (simply omit --direct parameter).

23.4. Oracle

JDBC Driver: Oracle JDBC Thin Driver - Sqoop is compatible with ojdbc6.jar.

Sqoop has been tested with Oracle 10.2.0 Express Edition. Oracle is notable in its different approach to SQL from the ANSI standard, and its non-standard JDBC driver. Therefore, several features work differently.

23.4.1. Dates and Times

Oracle JDBC represents DATE and TIME SQL types as TIMESTAMP values. Any DATE columns in an Oracle database will be imported as a TIMESTAMP in Sqoop, and Sqoop-generated code will store these values in java.sql.Timestamp fields.

When exporting data back to a database, Sqoop parses text fields as TIMESTAMP types (with the form yyyy-mm-dd HH:MM:SS.ffffffff) even if you expect these fields to be formatted with the JDBC date escape format of yyyy-mm-dd. Dates exported to Oracle should be formatted as full timestamps.

Oracle also includes the additional date/time types TIMESTAMP WITH TIMEZONE and TIMESTAMP WITH LOCAL TIMEZONE. To support these types, the user’s session timezone must be specified. By default, Sqoop will specify the timezone "GMT" to Oracle. You can override this setting by specifying a Hadoop property oracle.sessionTimeZone on the command-line when running a Sqoop job. For example:

$ sqoop import -D oracle.sessionTimeZone=America/Los_Angeles \
    --connect jdbc:oracle:thin:@//db.example.com/foo --table bar

Note that Hadoop parameters (-D …) are generic arguments and must appear before the tool-specific arguments (--connect, --table, and so on).

Legal values for the session timezone string are enumerated at http://download-west.oracle.com/docs/cd/B19306_01/server.102/b14225/applocaledata.htm#i637736.

23.5. Schema Definition in Hive

Hive users will note that there is not a one-to-one mapping between SQL types and Hive types. In general, SQL types that do not have a direct mapping (for example, DATE, TIME, and TIMESTAMP) will be coerced to STRING in Hive. The NUMERIC and DECIMAL SQL types will be coerced to DOUBLE. In these cases, Sqoop will emit a warning in its log messages informing you of the loss of precision.

24. Notes for specific connectors

24.1. MySQL JDBC Connector

This section contains information specific to MySQL JDBC Connector.

24.1.1. Upsert functionality

MySQL JDBC Connector is supporting upsert functionality using argument --update-mode allowinsert. To achieve that Sqoop is using MySQL clause INSERT INTO … ON DUPLICATE KEY UPDATE. This clause do not allow user to specify which columns should be used to distinct whether we should update existing row or add new row. Instead this clause relies on table’s unique keys (primary key belongs to this set). MySQL will try to insert new row and if the insertion fails with duplicate unique key error it will update appropriate row instead. As a result, Sqoop is ignoring values specified in parameter --update-key, however user needs to specify at least one valid column to turn on update mode itself.

24.2. Microsoft SQL Connector

24.2.1. Extra arguments

List of all extra arguments supported by Microsoft SQL Connector is shown below:

Table 42. Supported Microsoft SQL Connector extra arguments:

Argument Description
--schema <name> Scheme name that sqoop should use. Default is "dbo".
--table-hints <hints> Table hints that Sqoop should use for data movement.

24.2.2. Schema support

If you need to work with tables that are located in non-default schemas, you can specify schema names via the --schema argument. Custom schemas are supported for both import and export jobs. For example:

$ sqoop import ... --table custom_table -- --schema custom_schema

24.2.3. Table hints

Sqoop supports table hints in both import and export jobs. Table hints are used only for queries that move data from/to Microsoft SQL Server, but they cannot be used for meta data queries. You can specify a comma-separated list of table hints in the --table-hints argument. For example:

$ sqoop import ... --table custom_table -- --table-hints NOLOCK

24.3. PostgreSQL Connector

24.3.1. Extra arguments

List of all extra arguments supported by PostgreSQL Connector is shown below:

Table 43. Supported PostgreSQL extra arguments:

Argument Description
--schema <name> Scheme name that sqoop should use. Default is "public".

The direct connector (used when specified --direct parameter), offers also additional extra arguments:

Table 44. Additional supported PostgreSQL extra arguments in direct mode:

Argument Description
--boolean-true-string <str> String that will be used to encode true value of boolean columns.
Default is "TRUE".
--boolean-false-string <str> String that will be used to encode false value of boolean columns.
Default is "FALSE".

24.3.2. Schema support

If you need to work with table that is located in schema other than default one, you need to specify extra argument --schema. Custom schemas are supported for both import and export job (optional staging table however must be present in the same schema as target table). Example invocation:

$ sqoop import ... --table custom_table -- --schema custom_schema

24.4. pg_bulkload connector

24.4.1. Purpose

pg_bulkload connector is a direct connector for exporting data into PostgreSQL. This connector uses pg_bulkload. Users benefit from functionality of pg_bulkload such as fast exports bypassing shared bufferes and WAL, flexible error records handling, and ETL feature with filter functions.

24.4.2. Requirements

pg_bulkload connector requires following conditions for export job execution:

  • The pg_bulkload must be installed on DB server and all slave nodes. RPM for RedHat or CentOS is available in then download page.
  • The PostgreSQL JDBC is required on client node.
  • Superuser role of PostgreSQL database is required for execution of pg_bulkload.

24.4.3. Syntax

Use --connection-manager option to specify connection manager classname.

$ sqoop export (generic-args) --connection-manager org.apache.sqoop.manager.PGBulkloadManager (export-args)
$ sqoop-export (generic-args) --connection-manager org.apache.sqoop.manager.PGBulkloadManager (export-args)

This connector supports export arguments shown below.

Table 45. Supported export control arguments:

Argument Description
--export-dir <dir> HDFS source path for the export
-m,--num-mappers <n> Use n map tasks to export in parallel
--table <table-name> Table to populate
--input-null-string <null-string> The string to be interpreted as null for string columns
--clear-staging-table Indicates that any data present in the staging table can be deleted.

There are additional configuration for pg_bulkload execution specified via Hadoop Configuration properties which can be given with -D <property=value> option. Because Hadoop Configuration properties are generic arguments of the sqoop, it must preceed any export control arguments.

Table 46. Supported export control properties:

Property Description
mapred.reduce.tasks Number of reduce tasks for staging. The defalt value is 1. Each tasks do staging in a single transaction.
pgbulkload.bin Path of the pg_bulkoad binary installed on each slave nodes.
pgbulkload.check.constraints Specify whether CHECK constraints are checked during the loading. The default value is YES.
pgbulkload.parse.errors The maximum mumber of ingored records that cause errors during parsing, encoding, filtering, constraints checking, and data type conversion. Error records are recorded in the PARSE BADFILE. The default value is INFINITE.
pgbulkload.duplicate.errors Number of ingored records that violate unique constraints. Duplicated records are recorded in the DUPLICATE BADFILE on DB server. The default value is INFINITE.
pgbulkload.filter Specify the filter function to convert each row in the input file. See the pg_bulkload documentation to know how to write FILTER functions.

Here is a example of complete command line.

$ sqoop export \
    -Dmapred.reduce.tasks=2
    -Dpgbulkload.bin="/usr/local/bin/pg_bulkload" \
    -Dpgbulkload.input.field.delim=$'\t' \
    -Dpgbulkload.check.constraints="YES" \
    -Dpgbulkload.parse.errors="INFINITE" \
    -Dpgbulkload.duplicate.errors="INFINITE" \
    --connect jdbc:postgresql://pgsql.example.net:5432/sqooptest \
    --connection-manager org.apache.sqoop.manager.PGBulkloadManager \
    --table test --username sqooptest --export-dir=/test -m 2

24.4.4. Data Staging

Each map tasks of pg_bulkload connector’s export job create their own staging table on the fly. The Name of staging tables is decided based on the destination table and the task attempt ids. For example, the name of staging table for the "test" table is like test_attempt_1345021837431_0001_m_000000_0 .

Staging tables are automatically dropped if tasks successfully complete or map tasks fail. When reduce task fails, staging table for the task are left for manual retry and users must take care of it.

24.5. Netezza Connector

24.5.1. Extra arguments

List of all extra arguments supported by Netezza Connector is shown below:

Table 47. Supported Netezza extra arguments:

Argument Description
--partitioned-access Whether each mapper acts on a subset of data slices of a table or all Default is "false" for standard mode and "true" for direct mode.
--max-errors Applicable only in direct mode. This option specifies the error threshold per mapper while transferring data. If the number of errors encountered exceed this threshold then the job will fail.
Default value is 1.
--log-dir Applicable only in direct mode. Specifies the directory where Netezza external table operation logs are stored. Default value is /tmp.

24.5.2. Direct Mode

Netezza connector supports an optimized data transfer facility using the Netezza external tables feature. Each map tasks of Netezza connector’s import job will work on a subset of the Netezza partitions and transparently create and use an external table to transport data. Similarly, export jobs will use the external table to push data fast onto the NZ system. Direct mode does not support staging tables, upsert options etc.

Here is an example of complete command line for import using the Netezza external table feature.

$ sqoop import \
    --direct \
    --connect jdbc:netezza://nzhost:5480/sqoop \
    --table nztable \
    --username nzuser \
    --password nzpass \
    --target-dir hdfsdir

Here is an example of complete command line for export with tab as the field terminator character.

$ sqoop export \
    --direct \
    --connect jdbc:netezza://nzhost:5480/sqoop \
    --table nztable \
    --username nzuser \
    --password nzpass \
    --export-dir hdfsdir \
    --input-fields-terminated-by "\t"

24.5.3. Null string handling

Netezza direct connector supports the null-string features of Sqoop. The null string values are converted to appropriate external table options during export and import operations.

Table 48. Supported export control arguments:

Argument Description
--input-null-string <null-string> The string to be interpreted as null for string columns.
--input-null-non-string <null-string> The string to be interpreted as null for non string columns.

In the case of Netezza direct mode connector, both the arguments must be left to the default values or explicitly set to the same value. Furthermore the null string value is restricted to 0-4 utf8 characters.

On export, for non-string columns, if the chosen null value is a valid representation in the column domain, then the column might not be loaded as null. For example, if the null string value is specified as "1", then on export, any occurrence of "1" in the input file will be loaded as value 1 instead of NULL for int columns.

It is suggested that the null value be specified as empty string for performance and consistency.

Table 49. Supported import control arguments:

Argument Description
--null-string <null-string> The string to be interpreted as null for string columns.
--null-non-string <null-string> The string to be interpreted as null for non string columns.

In the case of Netezza direct mode connector, both the arguments must be left to the default values or explicitly set to the same value. Furthermore the null string value is restricted to 0-4 utf8 characters.

On import, for non-string columns, the chosen null value in current implementations the null value representation is ignored for non character columns. For example, if the null string value is specified as "\N", then on import, any occurrence of NULL for non-char columns in the table will be imported as an empty string instead of \N, the chosen null string representation.

It is suggested that the null value be specified as empty string for performance and consistency.

25. Getting Support

Some general information is available at the http://sqoop.apache.org/

Report bugs in Sqoop to the issue tracker at https://issues.apache.org/jira/browse/SQOOP.

Questions and discussion regarding the usage of Sqoop should be directed to the sqoop-user mailing list.

Before contacting either forum, run your Sqoop job with the --verbose flag to acquire as much debugging information as possible. Also report the string returned by sqoop version as well as the version of Hadoop you are running (hadoop version).

26. Troubleshooting

26.1. General Troubleshooting Process

The following steps should be followed to troubleshoot any failure that you encounter while running Sqoop.

  • Turn on verbose output by executing the same command again and specifying the --verbose option. This produces more debug output on the console which can be inspected to identify any obvious errors.
  • Look at the task logs from Hadoop to see if there are any specific failures recorded there. It is possible that the failure that occurs while task execution is not relayed correctly to the console.
  • Make sure that the necessary input files or input/output tables are present and can be accessed by the user that Sqoop is executing as or connecting to the database as. It is possible that the necessary files or tables are present but the specific user that Sqoop connects as does not have the necessary permissions to access these files.
  • If you are doing a compound action such as populating a Hive table or partition, try breaking the job into two separate actions to see where the problem really occurs. For example if an import that creates and populates a Hive table is failing, you can break it down into two steps - first for doing the import alone, and the second to create a Hive table without the import using the create-hive-table tool. While this does not address the original use-case of populating the Hive table, it does help narrow down the problem to either regular import or during the creation and population of Hive table.
  • Search the mailing lists archives and JIRA for keywords relating to the problem. It is possible that you may find a solution discussed there that will help you solve or work-around your problem.

26.2. Specific Troubleshooting Tips

26.2.1. Oracle: Connection Reset Errors

Problem: When using the default Sqoop connector for Oracle, some data does get transferred, but during the map-reduce job a lot of errors are reported as below:

11/05/26 16:23:47 INFO mapred.JobClient: Task Id : attempt_201105261333_0002_m_000002_0, Status : FAILED
java.lang.RuntimeException: java.lang.RuntimeException: java.sql.SQLRecoverableException: IO Error: Connection reset
at com.cloudera.sqoop.mapreduce.db.DBInputFormat.setConf(DBInputFormat.java:164)
at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62)
at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:605)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:322)
at org.apache.hadoop.mapred.Child$4.run(Child.java:268)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:396)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1115)
at org.apache.hadoop.mapred.Child.main(Child.java:262)
Caused by: java.lang.RuntimeException: java.sql.SQLRecoverableException: IO Error: Connection reset
at com.cloudera.sqoop.mapreduce.db.DBInputFormat.getConnection(DBInputFormat.java:190)
at com.cloudera.sqoop.mapreduce.db.DBInputFormat.setConf(DBInputFormat.java:159)
... 9 more
Caused by: java.sql.SQLRecoverableException: IO Error: Connection reset
at oracle.jdbc.driver.T4CConnection.logon(T4CConnection.java:428)
at oracle.jdbc.driver.PhysicalConnection.<init>(PhysicalConnection.java:536)
at oracle.jdbc.driver.T4CConnection.<init>(T4CConnection.java:228)
at oracle.jdbc.driver.T4CDriverExtension.getConnection(T4CDriverExtension.java:32)
at oracle.jdbc.driver.OracleDriver.connect(OracleDriver.java:521)
at java.sql.DriverManager.getConnection(DriverManager.java:582)
at java.sql.DriverManager.getConnection(DriverManager.java:185)
at com.cloudera.sqoop.mapreduce.db.DBConfiguration.getConnection(DBConfiguration.java:152)
at com.cloudera.sqoop.mapreduce.db.DBInputFormat.getConnection(DBInputFormat.java:184)
... 10 more
Caused by: java.net.SocketException: Connection reset
at java.net.SocketOutputStream.socketWrite(SocketOutputStream.java:96)
at java.net.SocketOutputStream.write(SocketOutputStream.java:136)
at oracle.net.ns.DataPacket.send(DataPacket.java:199)
at oracle.net.ns.NetOutputStream.flush(NetOutputStream.java:211)
at oracle.net.ns.NetInputStream.getNextPacket(NetInputStream.java:227)
at oracle.net.ns.NetInputStream.read(NetInputStream.java:175)
at oracle.net.ns.NetInputStream.read(NetInputStream.java:100)
at oracle.net.ns.NetInputStream.read(NetInputStream.java:85)
at oracle.jdbc.driver.T4CSocketInputStreamWrapper.readNextPacket(T4CSocketInputStreamWrapper.java:123)
at oracle.jdbc.driver.T4CSocketInputStreamWrapper.read(T4CSocketInputStreamWrapper.java:79)
at oracle.jdbc.driver.T4CMAREngine.unmarshalUB1(T4CMAREngine.java:1122)
at oracle.jdbc.driver.T4CMAREngine.unmarshalSB1(T4CMAREngine.java:1099)
at oracle.jdbc.driver.T4CTTIfun.receive(T4CTTIfun.java:288)
at oracle.jdbc.driver.T4CTTIfun.doRPC(T4CTTIfun.java:191)
at oracle.jdbc.driver.T4CTTIoauthenticate.doOAUTH(T4CTTIoauthenticate.java:366)
at oracle.jdbc.driver.T4CTTIoauthenticate.doOAUTH(T4CTTIoauthenticate.java:752)
at oracle.jdbc.driver.T4CConnection.logon(T4CConnection.java:366)
... 18 more

Solution: This problem occurs primarily due to the lack of a fast random number generation device on the host where the map tasks execute. On typical Linux systems this can be addressed by setting the following property in the java.security file:

securerandom.source=file:/dev/../dev/urandom

The java.security file can be found under $JAVA_HOME/jre/lib/security directory. Alternatively, this property can also be specified on the command line via:

-D mapred.child.java.opts="-Djava.security.egd=file:/dev/../dev/urandom"

Please note that it’s very important to specify this weird path /dev/../dev/urandom as it is due to a Java bug 6202721, or /dev/urandom will be ignored and substituted by /dev/random.

26.2.2. Oracle: Case-Sensitive Catalog Query Errors

Problem: While working with Oracle you may encounter problems when Sqoop can not figure out column names. This happens because the catalog queries that Sqoop uses for Oracle expect the correct case to be specified for the user name and table name.

One example, using --hive-import and resulting in a NullPointerException:

1/09/21 17:18:49 INFO manager.OracleManager: Time zone has been set to
GMT
11/09/21 17:18:49 DEBUG manager.SqlManager: Using fetchSize for next
query: 1000
11/09/21 17:18:49 INFO manager.SqlManager: Executing SQL statement:
SELECT t.* FROM addlabel_pris t WHERE 1=0
11/09/21 17:18:49 DEBUG manager.OracleManager$ConnCache: Caching
released connection for jdbc:oracle:thin:
11/09/21 17:18:49 ERROR sqoop.Sqoop: Got exception running Sqoop:
java.lang.NullPointerException
java.lang.NullPointerException
at com.cloudera.sqoop.hive.TableDefWriter.getCreateTableStmt(TableDefWriter.java:148)
at com.cloudera.sqoop.hive.HiveImport.importTable(HiveImport.java:187)
at com.cloudera.sqoop.tool.ImportTool.importTable(ImportTool.java:362)
at com.cloudera.sqoop.tool.ImportTool.run(ImportTool.java:423)
at com.cloudera.sqoop.Sqoop.run(Sqoop.java:144)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:65)
at com.cloudera.sqoop.Sqoop.runSqoop(Sqoop.java:180)
at com.cloudera.sqoop.Sqoop.runTool(Sqoop.java:219)
at com.cloudera.sqoop.Sqoop.runTool(Sqoop.java:228)
at com.cloudera.sqoop.Sqoop.main(Sqoop.java:237)

Solution:

  1. Specify the user name, which Sqoop is connecting as, in upper case (unless it was created with mixed/lower case within quotes).
  2. Specify the table name, which you are working with, in upper case (unless it was created with mixed/lower case within quotes).

26.2.3. MySQL: Connection Failure

Problem: While importing a MySQL table into Sqoop, if you do not have the necessary permissions to access your MySQL database over the network, you may get the below connection failure.

Caused by: com.mysql.jdbc.exceptions.jdbc4.CommunicationsException: Communications link failure

Solution: First, verify that you can connect to the database from the node where you are running Sqoop:

$ mysql --host=<IP Address> --database=test --user=<username> --password=<password>

If this works, it rules out any problem with the client network configuration or security/authentication configuration.

Add the network port for the server to your my.cnf file /etc/my.cnf:

[mysqld]
port = xxxx

Set up a user account to connect via Sqoop. Grant permissions to the user to access the database over the network: (1.) Log into MySQL as root mysql -u root -p<ThisIsMyPassword>. (2.) Issue the following command:

mysql> grant all privileges on test.* to 'testuser'@'%' identified by 'testpassword'

Note that doing this will enable the testuser to connect to the MySQL server from any IP address. While this will work, it is not advisable for a production environment. We advise consulting with your DBA to grant the necessary privileges based on the setup topology.

If the database server’s IP address changes, unless it is bound to a static hostname in your server, the connect string passed into Sqoop will also need to be changed.

26.2.4. Oracle: ORA-00933 error (SQL command not properly ended)

Problem: While working with Oracle you may encounter the below problem when the Sqoop command explicitly specifies the --driver <driver name> option. When the driver option is included in the Sqoop command, the built-in connection manager selection defaults to the generic connection manager, which causes this issue with Oracle. If the driver option is not specified, the built-in connection manager selection mechanism selects the Oracle specific connection manager which generates valid SQL for Oracle and uses the driver "oracle.jdbc.OracleDriver".

ERROR manager.SqlManager: Error executing statement:
java.sql.SQLSyntaxErrorException: ORA-00933: SQL command not properly ended

Solution: Omit the option --driver oracle.jdbc.driver.OracleDriver and then re-run the Sqoop command.

26.2.5. MySQL: Import of TINYINT(1) from MySQL behaves strangely

Problem: Sqoop is treating TINYINT(1) columns as booleans, which is for example causing issues with HIVE import. This is because by default the MySQL JDBC connector maps the TINYINT(1) to java.sql.Types.BIT, which Sqoop by default maps to Boolean.

Solution: A more clean solution is to force MySQL JDBC Connector to stop converting TINYINT(1) to java.sql.Types.BIT by adding tinyInt1isBit=false into your JDBC path (to create something like jdbc:mysql://localhost/test?tinyInt1isBit=false). Another solution would be to explicitly override the column mapping for the datatype TINYINT(1) column. For example, if the column name is foo, then pass the following option to Sqoop during import: --map-column-hive foo=tinyint. In the case of non-Hive imports to HDFS, use --map-column-java foo=integer.