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
	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
	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.

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

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.

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
	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
	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.

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.

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
	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.

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).

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.

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:

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.

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.

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.

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:

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

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.

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.

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
	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.

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
	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.

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.

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 ...

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.

The following options are ignored with HCatalog jobs.

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:

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

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.

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.

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).

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
	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.

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).

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.

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.

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

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

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

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

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

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

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.

pg_bulkload connector requires following conditions for export job execution:

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.

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.

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

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.

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

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"

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.

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.

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.

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.

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)

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.

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.

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.