In-database processing: Difference between revisions

Revision as of 15:07, 4 November 2012

Revision as of 17:35, 20 November 2012

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===Loading C or C++ Libraries into the database process space===

With C or C++ UDF libraries that run in process, the functions are typically registered as built-in functions within the database server and called like any other built-in function in a SQL statement. Running in process allows the function to have full access to the database server’s memory, parallelism and processing management capabilities. Because of this, the functions must be well-behaved so as not to negatively impact the database or the engine. This type of UDF gives the highest performance out of any method for OLAP, mathematical, statistical, univariate distributions and data mining algorithms. Vendors such as [http://www.FuzzyL.com Fuzzy Logix] (DBLytix) and RogueWave (IMSL) have pre-built libraries available. IBM Netezza, EMC Greenplum, Sybase and Teradata (AsterData) have the capability to do this type of in-database analytics. Some of these vendors allow customers to write their own custom in-process UDFs.

===Out-of-Process===

Out-of-Process UDFs are typically written in C, C++ or JAVA. By running out of process, they do not run the same risk to the database or the engine as they run in their own process space with their own resources. Here, they wouldn’t be expected to have the same performance as an in-process UDF. They are still typically registered in the database engine and called through standard SQL, usually in a stored procedure. A vendor, [[Zementis Inc|Zementis]], has plug-ins for different database vendors that can be used to take a PMML model and convert it to a JAVA UDF that can be called through the native SQL. Out-of-process UDFs are a safe way to extend the capabilities of a database server and are an ideal way to add custom data mining libraries.

==Uses==