Data Access
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Using Sqoop

Sqoop is a tool that enables you to bulk import and export data from a database. You can use Sqoop to import data into HDFS or directly into Hive. However, Sqoop can only import data into Hive as a text file or as a SequenceFile. To use the ORC file format, you must use a two-phase approach: first use Sqoop to move the data into HDFS, and then use Hive to convert the data into the ORC file format as described in the above Steps 3 and 4 of "Moving Data from HDFS to Hive Using an External Table."

For more information on using Sqoop, refer to Using Apache Sqoop to Transfer Bulk Data.

A detailed Sqoop user guide is also available on the Apache web site here.

The process for using Sqoop to move data into Hive is shown in the following diagram:

Figure 2.2. Using Sqoop to Move Data into Hive


Moving Data into Hive Using Sqoop

  1. Specify the source connection information.

    First, you must specify the:

    • database URI (db.foo.com in the following example)

    • database name (bar)

    • connection protocol (jdbc:mysql:)

    For this example, use the following command:

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

    If the source database requires credentials, such as a username and password, you can enter the password on the command line or specify a file where the password is stored.

    For example:

    • Enter the password on the command line:

      sqoop import --connect jdbc:mysql://db.foo.com/bar --table EMPLOYEES --username <username> -P
      Enter password: (hidden)
    • Specify a file where the password is stored:

      sqoop import --connect jdbc:mysql://db.foo.com/bar --table EMPLOYEES --username <username> --password-file ${user.home}/.password

    More connection options are described in the Sqoop User Guide on the Apache web site.

  2. Specify the data and the parallelism for import:

    1. Specify the data simply.

      Sqoop provides flexibility to specify exactly the data you want to import from the source system:

      • Import an entire table:

        sqoop import --connect jdbc:mysql://db.foo.com/bar --table EMPLOYEES
      • Import a subset of the columns from a table:

        sqoop import --connect jdbc:mysql://db.foo.com/bar --table EMPLOYEES --columns "employee_id,first_name,last_name,job_title"
      • Import only the latest records by specifying them with a WHERE clause and then that they be appended to an existing table:

        sqoop import --connect jdbc:mysql://db.foo.com/bar --table EMPLOYEES  --where "start_date > '2010-01-01'"
        
        sqoop import --connect jdbc:mysql://db.foo.com/bar --table EMPLOYEES --where "id > 100000" --target-dir /incremental_dataset --append

      You can also use a free-form SQL statement.

    2. Specify parallelism.

      There are three options for specifying write parallelism (number of map tasks):

      • Explicitly set the number of mappers using --num-mappers. Sqoop evenly splits the primary key range of the source table:

        sqoop import --connect jdbc:mysql://db.foo.com/bar --table EMPLOYEES --num-mappers 8

        In this scenario, the source table must have a primary key.

      • Provide an alternate split key using --split-by. This evenly splits the data using the alternate split key instead of a primary key:

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

        This method is useful if primary keys are not evenly distributed.

      • When there is not split key or primary key, the data import must be sequential. Specify a single mapper by using --num-mappers 1 or --autoreset-to-one-mapper.

    3. Specify the data using a query.

      Instead of specifying a particular table or columns, you can specify the date with a query. You can use one of the following options:

      • Explicitly specify a split-by column using --split-by and put $ CONDITIONS that Sqoop replaces with range conditions based on the split-by key. This method requires a target directory:

        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
      • Use sequential import if you cannot specify a split-by column:

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

      To try a sample query without importing data, use the eval option to print the results to the command prompt:

      sqoop eval --connect jdbc:mysql://db.foo.com/bar --query "SELECT * FROM employees LIMIT 10"
  3. Specify the destination for the data: HDFS or Hive.

    Here is an example of specifying the HDFS target directory:

    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

    If you can add text data into your Hive table, you can specify that the data be directly added to Hive. Using --hive-import is the primary method to add text data directly to Hive:

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

    This method creates a metastore schema after storing the text data in HDFS.

    If you have already moved data into HDFS and want to add a schema, use the create-hive-table Sqoop command:

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

    Additional options for importing data into Hive with Sqoop:

    Table 2.12. Sqoop Command Options for Importing Data into Hive

    Sqoop Command Option

    Description

    --hive-home <directory>

    Overrides $HIVE_HOME.

    --hive-import

    Imports tables into Hive using Hive's default delimiters if none are explicitly set.

    --hive-overwrite

    Overwrites existing data in the Hive table.

    --create-hive-table

    Creates a hive table during the operation. If this option is set and the Hive table already exists, the job will fail. Set to false by default.

    --hive-table <table_name>

    Specifies the table name to use when importing data into Hive.

    --hive-drop-import-delims

    Drops the delimiters \n, \r, and \01 from string fields when importing data into Hive.

    --hive-delims-replacement

    Replaces the delimiters \n, \r, and \01 from strings fields with a user-defined string when importing data into Hive.

    --hive-partition-key

    Specifies the name of the Hive field on which a sharded database is partitioned.

    --hive-partition-value <value>

    A string value that specifies the partition key for data imported into Hive.

    --map-column-hive <map>

    Overrides the default mapping from SQL type to Hive type for configured columns.