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From: Christopher Douglas <cdouglas@apache.org> git-svn-id: https://svn.apache.org/repos/asf/incubator/sqoop/trunk@1149834 13f79535-47bb-0310-9956-ffa450edef68
93 lines
3.7 KiB
Plaintext
93 lines
3.7 KiB
Plaintext
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////
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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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this work for additional information regarding copyright ownership.
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The ASF licenses this file to You under the Apache License, Version 2.0
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(the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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////
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Automatic Full-database Import
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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If you want to import all the tables in a database, you can use the
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+--all-tables+ command to do so:
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----
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$ sqoop --connect jdbc:mysql://database.example.com/employees --all-tables
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----
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This will query the database for the available tables, generate an ORM
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class for each table, and run a MapReduce job to import each one.
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Hadoop uses the DBInputFormat to read from a database into a Mapper
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instance. To read a table into a MapReduce program requires creating a
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class to hold the fields of one row of the table. One of the benefits
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of Sqoop is that it generates this class definition for you, based on
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the table definition in the database.
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The generated +.java+ files are, by default, placed in the current
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directory. You can supply a different directory with the +--outdir+
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parameter. These are then compiled into +.class+ and +.jar+ files for use
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by the MapReduce job that it launches. These files are created in a
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temporary directory. You can redirect this target with +--bindir+.
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Each table will be imported into a separate directory in HDFS, with
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the same name as the table. For instance, if my Hadoop username is
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aaron, the above command would have generated the following
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directories in HDFS:
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----
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/user/aaron/employee_names
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/user/aaron/payroll_checks
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/user/aaron/job_descriptions
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/user/aaron/office_supplies
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----
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You can change the base directory under which the tables are loaded
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with the +--warehouse-dir+ parameter. For example:
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----
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$ sqoop --connect jdbc:mysql://database.example.com/employees --all-tables \
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--warehouse-dir /common/warehouse
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----
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This would create the following directories instead:
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----
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/common/warehouse/employee_names
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/common/warehouse/payroll_checks
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/common/warehouse/job_descriptions
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/common/warehouse/office_supplies
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----
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By default the data will be read into text files in HDFS. Each of the
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columns will be represented as comma-delimited text. Each row is
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terminated by a newline. See the section on "Controlling the Output
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Format" below for information on how to change these delimiters.
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If you want to leverage compression and binary file formats, the
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+--as-sequencefile+ argument to Sqoop will import the table
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to a set of SequenceFiles instead. This stores each field of each
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database record in a separate object in a SequenceFile.
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This representation is also likely to be higher performance when used
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as an input to subsequent MapReduce programs as it does not require
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parsing. For completeness, Sqoop provides an +--as-textfile+ option, which is
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implied by default. An +--as-textfile+ on the command-line will override
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a previous +--as-sequencefile+ argument.
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The SequenceFile format will embed the records from the database as
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objects using the code generated by Sqoop. It is important that you
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retain the +.java+ file for this class, as you will need to be able to
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instantiate the same type to read the objects back later, in other
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user-defined applications.
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