Apache Sqoop - Overview Apache Sqoop 概述

使用Hadoop来分析和处理数据需要将数据加载到集群中并且将它和企业生产数据库中的其他数据进行结合处理。从生产系统加载大块数据到Hadoop中或者从大型集群的map reduce应用中获得数据是个挑战。用户必须意识到确保数据一致性，消耗生产系统资源，供应下游管道的数据预处理这些细节。用脚本来转化数据是低效和耗时的方式。使用map reduce应用直接去获取外部系统的数据使得应用变得复杂和增加了生产系统来自集群节点过度负载的风险。

这就是Apache Sqoop能够做到的。Aapche Sqoop 目前是Apache软件会的孵化项目。更多关于这个项目的信息可以在http://incubator.apache.org/sqoop查看

Sqoop能够使得像关系型数据库、企业数据仓库和NoSQL系统那样简单地从结构化数据仓库中导入导出数据。你可以使用Sqoop将数据从外部系统加载到HDFS，存储在Hive和HBase表格中。Sqoop配合Ooozie能够帮助你调度和自动运行导入导出任务。Sqoop使用基于支持插件来提供新的外部链接的连接器。

当你运行Sqoop的时候看起来是非常简单的，但是表象底层下面发生了什么呢？数据集将被切片分到不同的partitions和运行一个只有map的作业来负责数据集的某个切片。因为Sqoop使用数据库的元数据来推断数据类型所以每条数据都以一种类型安全的方式来处理。

在这篇文章其余部分中我们将通过一个例子来展示Sqoop的各种使用方式。这篇文章的目标是提供Sqoop操作的一个概述而不是深入高级功能的细节。

导入数据

下面的命令用于将一个MySQL数据库中名为ORDERS的表中所有数据导入到集群中
---
$ sqoop import --connect jdbc:mysql://localhost/acmedb \
  --table ORDERS --username test --password ****
---

在这条命令中的各种选项解释如下：

• • import: 指示Sqoop开始导入
  • --connect <connect string>, --username <user name>, --password <password>: 这些都是连接数据库时需要的参数。这跟你通过JDBC连接数据库时所使用的参数没有区别
  • --table <table name>: 指定要导入哪个表

导入操作通过下面Figure1所描绘的那两步来完成。第一步，Sqoop从数据库中获取要导入的数据的元数据。第二步，Sqoop提交map-only作业到Hadoop集群中。第二步通过在前一步中获取的元数据做实际的数据传输工作。

Figure 1: Sqoop Import Overview

导入的数据存储在HDFS目录下。正如Sqoop大多数操作一样，用户可以指定任何替换路径来存储导入的数据。

默认情况下这些文档包含用逗号分隔的字段，用新行来分隔不同的记录。你可以明确地指定字段分隔符和记录结束符容易地实现文件复制过程中的格式覆盖。

Sqoop也支持不同数据格式的数据导入。例如，你可以通过指定 --as-avrodatafile 选项的命令行来简单地实现导入Avro 格式的数据。

There are many other options that Sqoop provides which can be used to further tune the import operation to suit your specific requirements.

Sqoop提供许多选项可以用来满足指定需求的导入操作。

导入数据到 Hive

在许多情况下，导入数据到Hive就跟运行一个导入任务然后使用Hive创建和加载一个确定的表和partition。手动执行这个操作需要你要知道正确的数据类型映射和其他细节像序列化格式和分隔符。Sqoop负责将合适的表格元数据填充到Hive 元数据仓库和调用必要的指令来加载table和partition。这些操作都可以通过简单地在命令行中指定--hive-import 来实现。
----
$ sqoop import --connect jdbc:mysql://localhost/acmedb \
  --table ORDERS --username test --password **** --hive-import
----

当你运行一个Hive import时，Sqoop将会将数据的类型从外部数据仓库的原生数据类型转换成Hive中对应的类型，Sqoop自动地选择Hive使用的本地分隔符。如果被导入的数据中有新行或者有其他Hive分隔符，Sqoop允许你移除这些字符并且获取导入到Hive的正确数据。

一旦导入操作完成，你就像Hive其他表格一样去查看和操作。

导入数据到 HBase

你可以使用Sqoop将数据插入到HBase表格中特定列族。跟Hive导入操作很像，可以通过指定一个额外的选项来指定要插入的HBase表格和列族。所有导入到HBase的数据将转换成字符串并以UTF-8字节数组的格式插入到HBase中

----
$ sqoop import --connect jdbc:mysql://localhost/acmedb \
 --table ORDERS --username test --password **** \
--hbase-create-table --hbase-table ORDERS --column-family mysql
----

下面是命令行中各种选项的解释：

• • --hbase-create-table: 这个选项指示Sqoop创建HBase表.
  • --hbase-table: 这个选项指定HBase表格的名字.
  • --column-family: T这个选项指定列族的名字.

剩下的选项跟普通的导入操作一样。

导出数据

在一些情况中，通过Hadoop pipelines来处理数据可能需要在生产系统中运行额外的关键业务函数来提供帮助。Sqoop可以在必要的时候用来导出这些的数据到外部数据仓库。还是使用上面的例子，如果Hadoop pieplines产生的数据对应数据库OREDERS表格中的某些地方，你可以使用下面的命令行：

----
$ sqoop export --connect jdbc:mysql://localhost/acmedb \
 --table ORDERS --username test --password **** \
--export-dir /user/arvind/ORDERS
----

下面是各种选项的解释：

• • export: 指示Sqoop开始导出
  • --connect <connect string>, --username <user name>, --password <password>:这些都是连接数据库时需要的参数。这跟你通过JDBC连接数据库时所使用的参数没有区别
  • --table <table name>: 指定要被填充的表格
  • --export-dir <directory path>: 导出路径.

导入操作通过下面Figure2所描绘的那两步来完成。第一步，从数据库中获取要导入的数据的元数据，第二步则是数据的传输。Sqoop将输入数据集分割成片然后用map任务将片插入到数据库中。为了确保最佳的吞吐量和最小的资源使用率，每个map任务通过多个事务来执行这个数据传输。

Figure 2: Sqoop Export Overview

一些连接器支持临时表格来帮助隔离那些任何原因导致的作业失败而产生的生产表格。一旦所有的数据都传输完成，临时表格中的数据首先被填充到map任务和合并到目标表格。

Sqoop 连接器

使用专门连接器，Sqoop可以连接那些拥有优化导入导出基础设施的外部系统，或者不支持本地JDBC。连接器是插件化组件基于Sqoop的可扩展框架和可以添加到任何当前存在的Sqoop。一旦连接器安装好，Sqoop可以使用它在Hadoop和连接器支持的外部仓库之间进行高效的传输数据。

默认情况下，Sqoop包含支持各种常用数据库例如MySQL，PostgreSQL,Oracle，SQLServer和DB2的连接器。它也包含支持MySQL和PostgreSQL数据库的快速路径连接器。快速路径连接器是专门的连接器用来实现批次传输数据的高吞吐量。Sqoop也包含一般的JDBC连接器用于连接通过JDBC连接的数据库

跟内置的连接不同的是，许多公司会开发他们自己的连接器插入到Sqoop中，从专门的企业仓库连接器到NoSQL数据库。

总结

在这篇文档中可以看到大数据集在Hadoop和外部数据仓库例如关系型数据库的传输是多么的简单。除此之外，Sqoop提供许多高级提醒如不同数据格式、压缩、处理查询等等。我们建议你多尝试Sqoop并给我们提供反馈。

更多关于Sqoop的信息可以在下面路径找到：

Project Website: http://incubator.apache.org/sqoop

Wiki: https://cwiki.apache.org/confluence/display/SQOOP

Project Status:  http://incubator.apache.org/projects/sqoop.html

Mailing Lists: https://cwiki.apache.org/confluence/display/SQOOP/Mailing+Lists

下面是原文

---

Apache Sqoop - Overview

Using Hadoop for analytics and data processing requires loading data into clusters and processing it in conjunction with other data that often resides in production databases across the enterprise. Loading bulk data into Hadoop from production systems or accessing it from map reduce applications running on large clusters can be a challenging task. Users must consider details like ensuring consistency of data, the consumption of production system resources, data preparation for provisioning downstream pipeline. Transferring data using scripts is inefficient and time consuming. Directly accessing data residing on external systems from within the map reduce applications complicates applications and exposes the production system to the risk of excessive load originating from cluster nodes.

This is where Apache Sqoop fits in. Apache Sqoop is currently undergoing incubation at Apache Software Foundation. More information on this project can be found at http://incubator.apache.org/sqoop.

Sqoop allows easy import and export of data from structured data stores such as relational databases, enterprise data warehouses, and NoSQL systems. Using Sqoop, you can provision the data from external system on to HDFS, and populate tables in Hive and HBase. Sqoop integrates with Oozie, allowing you to schedule and automate import and export tasks. Sqoop uses a connector based architecture which supports plugins that provide connectivity to new external systems.

What happens underneath the covers when you run Sqoop is very straightforward. The dataset being transferred is sliced up into different partitions and a map-only job is launched with individual mappers responsible for transferring a slice of this dataset. Each record of the data is handled in a type safe manner since Sqoop uses the database metadata to infer the data types.

In the rest of this post we will walk through an example that shows the various ways you can use Sqoop. The goal of this post is to give an overview of Sqoop operation without going into much detail or advanced functionality.

Importing Data

The following command is used to import all data from a table called ORDERS from a MySQL database:

---
$ sqoop import --connect jdbc:mysql://localhost/acmedb \
  --table ORDERS --username test --password ****
---

In this command the various options specified are as follows:

• • import: This is the sub-command that instructs Sqoop to initiate an import.
  • --connect <connect string>, --username <user name>, --password <password>: These are connection parameters that are used to connect with the database. This is no different from the connection parameters that you use when connecting to the database via a JDBC connection.
  • --table <table name>: This parameter specifies the table which will be imported.

The import is done in two steps as depicted in Figure 1 below. In the first Step Sqoop introspects the database to gather the necessary metadata for the data being imported. The second step is a map-only Hadoop job that Sqoop submits to the cluster. It is this job that does the actual data transfer using the metadata captured in the previous step.

Figure 1: Sqoop Import Overview

The imported data is saved in a directory on HDFS based on the table being imported. As is the case with most aspects of Sqoop operation, the user can specify any alternative directory where the files should be populated.

By default these files contain comma delimited fields, with new lines separating different records. You can easily override the format in which data is copied over by explicitly specifying the field separator and record terminator characters.

Sqoop also supports different data formats for importing data. For example, you can easily import data in Avro data format by simply specifying the option --as-avrodatafile with the import command.

There are many other options that Sqoop provides which can be used to further tune the import operation to suit your specific requirements.

Importing Data into Hive

In most cases, importing data into Hive is the same as running the import task and then using Hive to create and load a certain table or partition. Doing this manually requires that you know the correct type mapping between the data and other details like the serialization format and delimiters. Sqoop takes care of populating the Hive metastore with the appropriate metadata for the table and also invokes the necessary commands to load the table or partition as the case may be. All of this is done by simply specifying the option --hive-import with the import command.

----
$ sqoop import --connect jdbc:mysql://localhost/acmedb \
  --table ORDERS --username test --password **** --hive-import
----

When you run a Hive import, Sqoop converts the data from the native datatypes within the external datastore into the corresponding types within Hive. Sqoop automatically chooses the native delimiter set used by Hive. If the data being imported has new line or other Hive delimiter characters in it, Sqoop allows you to remove such characters and get the data correctly populated for consumption in Hive.

Once the import is complete, you can see and operate on the table just like any other table in Hive.

Importing Data into HBase

You can use Sqoop to populate data in a particular column family within the HBase table. Much like the Hive import, this can be done by specifying the additional options that relate to the HBase table and column family being populated. All data imported into HBase is converted to their string representation and inserted as UTF-8 bytes.

----
$ sqoop import --connect jdbc:mysql://localhost/acmedb \
 --table ORDERS --username test --password **** \
--hbase-create-table --hbase-table ORDERS --column-family mysql
----

In this command the various options specified are as follows:

• • --hbase-create-table: This option instructs Sqoop to create the HBase table.
  • --hbase-table: This option specifies the table name to use.
  • --column-family: This option specifies the column family name to use.

The rest of the options are the same as that for regular import operation.

Exporting Data

In some cases data processed by Hadoop pipelines may be needed in production systems to help run additional critical business functions. Sqoop can be used to export such data into external datastores as necessary. Continuing our example from above - if data generated by the pipeline on Hadoop corresponded to the ORDERS table in a database somewhere, you could populate it using the following command:

----
$ sqoop export --connect jdbc:mysql://localhost/acmedb \
 --table ORDERS --username test --password **** \
--export-dir /user/arvind/ORDERS
----

In this command the various options specified are as follows:

• • export: This is the sub-command that instructs Sqoop to initiate an export.
  • --connect <connect string>, --username <user name>, --password <password>: These are connection parameters that are used to connect with the database. This is no different from the connection parameters that you use when connecting to the database via a JDBC connection.
  • --table <table name>: This parameter specifies the table which will be populated.
  • --export-dir <directory path>: This is the directory from which data will be exported.

Export is done in two steps as depicted in Figure 2. The first step is to introspect the database for metadata, followed by the second step of transferring the data. Sqoop divides the input dataset into splits and then uses individual map tasks to push the splits to the database. Each map task performs this transfer over many transactions in order to ensure optimal throughput and minimal resource utilization.

Figure 2: Sqoop Export Overview

Some connectors support staging tables that help isolate production tables from possible corruption in case of job failures due to any reason. Staging tables are first populated by the map tasks and then merged into the target table once all of the data has been delivered it.

Sqoop Connectors

Using specialized connectors, Sqoop can connect with external systems that have optimized import and export facilities, or do not support native JDBC. Connectors are plugin components based on Sqoop’s extension framework and can be added to any existing Sqoop installation. Once a connector is installed, Sqoop can use it to efficiently transfer data between Hadoop and the external store supported by the connector.

By default Sqoop includes connectors for various popular databases such as MySQL, PostgreSQL, Oracle, SQL Server and DB2. It also includes fast-path connectors for MySQL and PostgreSQL databases. Fast-path connectors are specialized connectors that use database specific batch tools to transfer data with high throughput. Sqoop also includes a generic JDBC connector that can be used to connect to any database that is accessible via JDBC.

Apart from the built-in connectors, many companies have developed their own connectors that can be plugged into Sqoop. These range from specialized connectors for enterprise data warehouse systems to NoSQL datastores.

Wrapping Up

In this post you saw how easy it is to transfer large datasets between Hadoop and external datastores such as relational databases. Beyond this, Sqoop offers many advance features such as different data formats, compression, working with queries instead of tables etc. We encourage you to try out Sqoop and give us your feedback.