## Sqoop Developer’s Guide v1.4.2

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## 1. Introduction

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.

## 2. Supported Releases

This documentation applies to Sqoop v1.4.2.

## 3. Sqoop Releases

Apache Sqoop is an open source software product of The Apache Software Foundation. Development for Sqoop occurs at http://svn.apache.org/repos/asf/sqoop/trunk. 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 Sqoop:

- Software development in Java

  - Familiarity with JDBC
  - Familiarity with Hadoop’s APIs (including the "new" MapReduce API of 0.20+)
- Relational database management systems and SQL

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. Compiling Sqoop from Source

You can obtain the source code for Sqoop at: http://svn.apache.org/repos/asf/sqoop/trunk

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.

## 6. Developer API Reference

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:

- Using classes generated by Sqoop and its public library
- Writing Sqoop extensions (that is, additional ConnManager implementations that interact with more databases)
- Modifying Sqoop’s internals

Each section describes the system in successively greater depth.

### 6.1. The External API

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:

- A parse() method that interprets delimited text fields
- A toString() method that preserves the user’s chosen delimiters

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.

- The `RecordParser` class will parse a line of text into a list of fields, using controllable delimiters and quote characters.
- The static `FieldFormatter` class provides a method which handles quoting and escaping of characters in a field which will be used in `SqoopRecord.toString()` implementations.
- Marshaling data between ResultSet and PreparedStatement objects and SqoopRecords is done via `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.

### 6.2. The Extension API

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.

#### 6.2.1. HBase Serialization Extensions

Sqoop supports imports from databases to HBase. When copying data into HBase, it must be transformed into a format HBase can accept. Specifically:

- Data must be placed into one (or more) tables in HBase.
- Columns of input data must be placed into a column family.
- Values must be serialized to byte arrays to put into cells.

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`.

### 6.3. Sqoop Internals

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).

#### 6.3.1. General program flow

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.

#### 6.3.2. Subpackages

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.

#### 6.3.3. Interfacing with MapReduce

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.