可以取得关系数据库的数据,比如Oracle,MySQL,ODBC.
还可以取得NOSQL数据库的数据。比如CouchDB,Redis
还可以直接访问text文本文件,csv文件。
还可以访问twitter的数据。等等。
可以说利用好这些功能后,我们就会减少很多关联数据的编码。
具体信息参考了wiki。
In 2003, a new extension called SQL/MED ("SQL Management of External Data") was added to the SQL standard. It is a standardized way of handling access to remote objects in SQL databases. In 2011, PostgreSQL 9.1 was released with a great support of this standard.
In a nutshell, you can now use various Foreign Data Wrappers (FDW) to connect a PostgreSQL Server to remote data stores. This page is an incomplete list of the Wrappers available right now. Another fdw list can be found at the PGXN website.
Please keep in mind that most of these wrappers are not officially supported by the PostgreSQL Global Development Group (PGDG) and that some of these projects are still in Beta version. Use carefully!
This extension implements a Foreign Data Wrapper for MySQL. It is supported on PostgreSQL 9.1 and above.
A wrapper for accessing Sybase and Microsoft SQL Server databases.
A wrapper for databases with an ODBC driver, including Oracle, DB2, Microsoft SQL Server, Sybase, Pervasive SQL, IBM Lotus Domino, MySQL, PostgreSQL and desktop database products such as FileMaker and Microsoft Access:
A wrapper for CouchDB
A wrapper for Redis
A CSV files wrapper. Delivered as an official extension of PostgreSQL 9.1
Another CSV wrapper
Fixed-width flat file wrapper
A wrapper fetching text messages from Twitter over the Internet and returning a table
Allows PostgreSQL to query an LDAP server and retrieve data from some pre-configured Organizational Unit
uses GPU devices to accelarate sequential scan on massive amount of records with complex qualifiers.
Reads files located in Amazon S3
Allows to query different web services:
Multicorn is an extension that allows FDWs to be written in Python
This fdw can be used to access data stored in any database supported by the sqlalchemy python toolkit.
This fdw can be used to access data stored in various files, in a filesystem. The files are looked up based on a pattern, and parts of the file's path are mapped to various columns, as well as the file's content itself.
This fdw can be used to access data stored in CSV files. Each column defined in the table will be mapped, in order, against columns in the CSV file.
This fdw can be used to access items from an rss feed.