Table of Contents
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distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.If you are a developer or an application programmer who intends to modify Sqoop or build an extension using one of Sqoop’s internal APIs, you should read this document. The following sections describe the purpose of each API, where internal APIs are used, and which APIs are necessary for implementing support for additional databases.
Apache Sqoop is an open source software product of The Apache Software Foundation. Development for Sqoop occurs at http://sqoop.apache.org. At that site, you can obtain:
The following prerequisite knowledge is required for Sqoop:
Software development in Java
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.
You can obtain the source code for Sqoop using following command: git clone https://git-wip-us.apache.org/repos/asf/sqoop.git
Sqoop source code is held in a git repository. Instructions for
retrieving source from the repository are provided at:
TODO provide a page in the web site.
Compilation instructions are provided in the COMPILING.txt file in
the root of the source repository.
This section specifies the APIs available to application writers who want to integrate with Sqoop, and those who want to modify Sqoop.
The next three subsections are written for the following use cases:
Each section describes the system in successively greater depth.
Sqoop automatically generates classes that represent the tables imported into the Hadoop Distributed File System (HDFS). The class contains member fields for each column of the imported table; an instance of the class holds one row of the table. The generated classes implement the serialization APIs used in Hadoop, namely the Writable and DBWritable interfaces. They also contain these other convenience methods:
The full set of methods guaranteed to exist in an auto-generated class
is specified in the abstract class
com.cloudera.sqoop.lib.SqoopRecord.
Instances of SqoopRecord may depend on Sqoop’s public API. This is all classes
in the com.cloudera.sqoop.lib package. These are briefly described below.
Clients of Sqoop should not need to directly interact with any of these classes,
although classes generated by Sqoop will depend on them. Therefore, these APIs
are considered public and care will be taken when forward-evolving them.
RecordParser class will parse a line of text into a list of fields,
using controllable delimiters and quote characters.
FieldFormatter class provides a method which handles quoting and
escaping of characters in a field which will be used in
SqoopRecord.toString() implementations.
JdbcWritableBridge.
BigDecimalSerializer contains a pair of methods that facilitate
serialization of BigDecimal objects over the Writable interface.
The full specification of the public API is available on the Sqoop Development Wiki as SIP-4.
This section covers the API and primary classes used by extensions for Sqoop which allow Sqoop to interface with more database vendors.
While Sqoop uses JDBC and DataDrivenDBInputFormat to
read from databases, differences in the SQL supported by different vendors as
well as JDBC metadata necessitates vendor-specific codepaths for most databases.
Sqoop’s solution to this problem is by introducing the ConnManager API
(com.cloudera.sqoop.manager.ConnMananger).
ConnManager is an abstract class defining all methods that interact with the
database itself. Most implementations of ConnManager will extend the
com.cloudera.sqoop.manager.SqlManager abstract class, which uses standard
SQL to perform most actions. Subclasses are required to implement the
getConnection() method which returns the actual JDBC connection to the
database. Subclasses are free to override all other methods as well. The
SqlManager class itself exposes a protected API that allows developers to
selectively override behavior. For example, the getColNamesQuery() method
allows the SQL query used by getColNames() to be modified without needing to
rewrite the majority of getColNames().
ConnManager implementations receive a lot of their configuration
data from a Sqoop-specific class, SqoopOptions. SqoopOptions are
mutable. SqoopOptions does not directly store specific per-manager
options. Instead, it contains a reference to the Configuration
returned by Tool.getConf() after parsing command-line arguments with
the GenericOptionsParser. This allows extension arguments via "-D
any.specific.param=any.value" without requiring any layering of
options parsing or modification of SqoopOptions. This
Configuration forms the basis of the Configuration passed to any
MapReduce Job invoked in the workflow, so that users can set on the
command-line any necessary custom Hadoop state.
All existing ConnManager implementations are stateless. Thus, the
system which instantiates ConnManagers may implement multiple
instances of the same ConnMananger class over Sqoop’s lifetime. It
is currently assumed that instantiating a ConnManager is a
lightweight operation, and is done reasonably infrequently. Therefore,
ConnManagers are not cached between operations, etc.
ConnManagers are currently created by instances of the abstract
class ManagerFactory (See
http://issues.apache.org/jira/browse/MAPREDUCE-750). One
ManagerFactory implementation currently serves all of Sqoop:
com.cloudera.sqoop.manager.DefaultManagerFactory. Extensions
should not modify DefaultManagerFactory. Instead, an
extension-specific ManagerFactory implementation should be provided
with the new ConnManager. ManagerFactory has a single method of
note, named accept(). This method will determine whether it can
instantiate a ConnManager for the user’s SqoopOptions. If so, it
returns the ConnManager instance. Otherwise, it returns null.
The ManagerFactory implementations used are governed by the
sqoop.connection.factories setting in sqoop-site.xml. Users of extension
libraries can install the 3rd-party library containing a new ManagerFactory
and ConnManager(s), and configure sqoop-site.xml to use the new
ManagerFactory. The DefaultManagerFactory principly discriminates between
databases by parsing the connect string stored in SqoopOptions.
Extension authors may make use of classes in the com.cloudera.sqoop.io,
mapreduce, and util packages to facilitate their implementations.
These packages and classes are described in more detail in the following
section.
Sqoop supports imports from databases to HBase. When copying data into HBase, it must be transformed into a format HBase can accept. Specifically:
All of this is done via Put statements in the HBase client API.
Sqoop’s interaction with HBase is performed in the com.cloudera.sqoop.hbase
package. Records are deserialzed from the database and emitted from the mapper.
The OutputFormat is responsible for inserting the results into HBase. This is
done through an interface called PutTransformer. The PutTransformer
has a method called getPutCommand() that
takes as input a Map<String, Object> representing the fields of the dataset.
It returns a List<Put> describing how to insert the cells into HBase.
The default PutTransformer implementation is the ToStringPutTransformer
that uses the string-based representation of each field to serialize the
fields to HBase.
You can override this implementation by implementing your own PutTransformer
and adding it to the classpath for the map tasks (e.g., with the -libjars
option). To tell Sqoop to use your implementation, set the
sqoop.hbase.insert.put.transformer.class property to identify your class
with -D.
Within your PutTransformer implementation, the specified row key
column and column family are
available via the getRowKeyColumn() and getColumnFamily() methods.
You are free to make additional Put operations outside these constraints;
for example, to inject additional rows representing a secondary index.
However, Sqoop will execute all Put operations against the table
specified with --hbase-table.
This section describes the internal architecture of Sqoop.
The Sqoop program is driven by the com.cloudera.sqoop.Sqoop main class.
A limited number of additional classes are in the same package; SqoopOptions
(described earlier) and ConnFactory (which manipulates ManagerFactory
instances).
The general program flow is as follows:
com.cloudera.sqoop.Sqoop is the main class and implements Tool. A new
instance is launched with ToolRunner. The first argument to Sqoop is
a string identifying the name of a SqoopTool to run. The SqoopTool
itself drives the execution of the user’s requested operation (e.g.,
import, export, codegen, etc).
The SqoopTool API is specified fully in
SIP-1.
The chosen SqoopTool will parse the remainder of the arguments,
setting the appropriate fields in the SqoopOptions class. It will
then run its body.
Then in the SqoopTool’s run() method, the import or export or other
action proper is executed. Typically, a ConnManager is then
instantiated based on the data in the SqoopOptions. The
ConnFactory is used to get a ConnManager from a ManagerFactory;
the mechanics of this were described in an earlier section. Imports
and exports and other large data motion tasks typically run a
MapReduce job to operate on a table in a parallel, reliable fashion.
An import does not specifically need to be run via a MapReduce job;
the ConnManager.importTable() method is left to determine how best
to run the import. Each main action is actually controlled by the
ConnMananger, except for the generating of code, which is done by
the CompilationManager and ClassWriter. (Both in the
com.cloudera.sqoop.orm package.) Importing into Hive is also
taken care of via the com.cloudera.sqoop.hive.HiveImport class
after the importTable() has completed. This is done without concern
for the ConnManager implementation used.
A ConnManager’s importTable() method receives a single argument of
type ImportJobContext which contains parameters to the method. This
class may be extended with additional parameters in the future, which
optionally further direct the import operation. Similarly, the
exportTable() method receives an argument of type
ExportJobContext. These classes contain the name of the table to
import/export, a reference to the SqoopOptions object, and other
related data.
The following subpackages under com.cloudera.sqoop exist:
hive - Facilitates importing data to Hive.
io - Implementations of java.io.* interfaces (namely, OutputStream and
Writer).
lib - The external public API (described earlier).
manager - The ConnManager and ManagerFactory interface and their
implementations.
mapreduce - Classes interfacing with the new (0.20+) MapReduce API.
orm - Code auto-generation.
tool - Implementations of SqoopTool.
util - Miscellaneous utility classes.
The io package contains OutputStream and BufferedWriter implementations
used by direct writers to HDFS. The SplittableBufferedWriter allows a single
BufferedWriter to be opened to a client which will, under the hood, write to
multiple files in series as they reach a target threshold size. This allows
unsplittable compression libraries (e.g., gzip) to be used in conjunction with
Sqoop import while still allowing subsequent MapReduce jobs to use multiple
input splits per dataset. The large object file storage (see
SIP-3) system’s code
lies in the io package as well.
The mapreduce package contains code that interfaces directly with
Hadoop MapReduce. This package’s contents are described in more detail
in the next section.
The orm package contains code used for class generation. It depends on the
JDK’s tools.jar which provides the com.sun.tools.javac package.
The util package contains various utilities used throughout Sqoop:
ClassLoaderStack manages a stack of ClassLoader instances used by the
current thread. This is principly used to load auto-generated code into the
current thread when running MapReduce in local (standalone) mode.
DirectImportUtils contains convenience methods used by direct HDFS
importers.
Executor launches external processes and connects these to stream handlers
generated by an AsyncSink (see more detail below).
ExportException is thrown by ConnManagers when exports fail.
ImportException is thrown by ConnManagers when imports fail.
JdbcUrl handles parsing of connect strings, which are URL-like but not
specification-conforming. (In particular, JDBC connect strings may have
multi:part:scheme:// components.)
PerfCounters are used to estimate transfer rates for display to the user.
ResultSetPrinter will pretty-print a ResultSet.
In several places, Sqoop reads the stdout from external processes. The most
straightforward cases are direct-mode imports as performed by the
LocalMySQLManager and DirectPostgresqlManager. After a process is spawned by
Runtime.exec(), its stdout (Process.getInputStream()) and potentially stderr
(Process.getErrorStream()) must be handled. Failure to read enough data from
both of these streams will cause the external process to block before writing
more. Consequently, these must both be handled, and preferably asynchronously.
In Sqoop parlance, an "async sink" is a thread that takes an InputStream and
reads it to completion. These are realized by AsyncSink implementations. The
com.cloudera.sqoop.util.AsyncSink abstract class defines the operations
this factory must perform. processStream() will spawn another thread to
immediately begin handling the data read from the InputStream argument; it
must read this stream to completion. The join() method allows external threads
to wait until this processing is complete.
Some "stock" AsyncSink implementations are provided: the LoggingAsyncSink will
repeat everything on the InputStream as log4j INFO statements. The
NullAsyncSink consumes all its input and does nothing.
The various ConnManagers that make use of external processes have their own
AsyncSink implementations as inner classes, which read from the database tools
and forward the data along to HDFS, possibly performing formatting conversions
in the meantime.
Sqoop schedules MapReduce jobs to effect imports and exports.
Configuration and execution of MapReduce jobs follows a few common
steps (configuring the InputFormat; configuring the OutputFormat;
setting the Mapper implementation; etc…). These steps are
formalized in the com.cloudera.sqoop.mapreduce.JobBase class.
The JobBase allows a user to specify the InputFormat,
OutputFormat, and Mapper to use.
JobBase itself is subclassed by ImportJobBase and ExportJobBase
which offer better support for the particular configuration steps
common to import or export-related jobs, respectively.
ImportJobBase.runImport() will call the configuration steps and run
a job to import a table to HDFS.
Subclasses of these base classes exist as well. For example,
DataDrivenImportJob uses the DataDrivenDBInputFormat to run an
import. This is the most common type of import used by the various
ConnManager implementations available. MySQL uses a different class
(MySQLDumpImportJob) to run a direct-mode import. Its custom
Mapper and InputFormat implementations reside in this package as
well.