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IBM looks to make middleware SMILE

Streams-based technology is in development

By Paul Krill
November 21, 2005
 

Researchers at IBM are developing stream-based middleware intended to broaden query capabilities in distributed application environments.

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SMILE (Smart Middleware Light Ends) technology enables stream-based queuing of information sets. For example, a query can be made that asks for the 20 most actively traded stocks in the last 10 minutes, with SMILE memorizing the state of events from the previous 10 minutes, according to Chitra Dorai, manager of Distributed Messaging at IBM Research.

"The SMILE system is a distributed stream-processing engine that processes messages in memory without requiring databases," Dorai said. SMILE also features a declarative programming model for distributed application programmers that relies on SQL.

IBM sees SMILE having applicability in SOA and Web services environments. SMILE could tie together messages sent by service providers to requesters, Dorai said.

SMILE provides differentiation in SOA, said Jason Bloomberg, senior analyst at ZapThink.

"SMILE takes SOA to a new level for two reasons," Bloomberg said in an e-mail. "First, it shifts the focus of service interaction away from request/response, and second, it illustrates that SOA and EDA [event-driven architecture] shouldn't be different approaches, but rather SOA should fundamentally be event-driven."

IBM has no timetable for including SMILE in any products. Possible destinations include IBM's WebSphere Message Broker, WebSphere ESB (enterprise service bus) and the DB2 database.

SMILE supports an optimal placement algorithm and systems management services that are hidden from the developer, Dorai said.

IBM acknowledged similarities between SMILE and software offered by StreamBase Systems, which also offers a stream-processing engine. But IBM said it believes SMILE's quality of service functionality, called an eventual correctness guarantee, is a differentiator.





 


 
Paul Krill is an InfoWorld editor at large.
 

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