Oracle rigs MySQL for NoSQL-like access by Joab Jackson at CIO.
Joab writes:
In an interview in May with the IDG News Service, Tomas Ulin, Oracle vice president of MySQL engineering, described a project to bring the NoSQL-like speed of access to SQL-based MySQL.
“We feel very strongly we can combine SQL and NoSQL,” he said. “If you have really high-scalability performance requirements for certain parts of your application, you can share the dataset” across both NoSQL and SQL interfaces.
The key to Oracle’s effort is the use of Memecached, which Internet-based service providers, Facebook being the largest, have long used to quickly serve MySQL data to their users. Memcached creates a hash table of commonly accessed database items that is stored in a server’s working memory for quick access, by way of an API (application programming interface).
Memcached would provide a natural non-SQL interface for MySQL, Ulin said. Memcached “is heavily used in the Web world. It is something [webmasters] already have installed on their systems, and they know how to use [it]. So we felt that would be a good way to provide NoSQL access,” Ulin said.
Oracle’s thinking is that the Memecached interface can serve as an alternative access point for MySQL itself. Much of the putative sluggishness of SQL-based systems actually stems from the overhead of supporting a fully ACID-based query infrastructure needed to execute complex queries, industry observers said. By providing a NoSQL alternative access method, Oracle could offer customers the best of both worlds–a database that is fully ACID-compliant and has the speed of a NoSQL database.
With Memcached you are not accessing the data through SQL, but by a simple key-value lookup. “You can do a simple key-value-type lookup and get very optimal performance,” Ulin said.
The technology would not require any changes to MySQL itself. “We can just plug it in,” Ulin said. He added that Oracle was considering including this technology in the next version of MySQL, version 5.6.
While you are thinking about what that will mean for using MySQL engines, remember Stratified B-Tree and Versioned Dictionaries.
Suddenly, being able map the structures of data stores as subjects (ne topics) and to merge them, reliably, with structures of other data stores doesn’t seem all that far fetched does it? The thing to remember is that all that “big data” was stored in some “big structure,” a structure that topic maps can view as subjects to be represented by topics.
Not to mention knowing when you are accessing content (addressing) or authoring information about the content (identification).