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System prevalence is a simple software architectural pattern that combines system images (snapshots) and transaction journaling to provide speed, performance scalability, transparent persistence and transparent live mirroring of computer system state.
Simply keeping system state in RAM in its normal, natural, language-specific format is orders of magnitude faster and more programmer-friendly than the multiple conversions that are needed when it is stored and retrieved from a DBMS. As an example, Martin Fowler describes "The LMAX Architecture"  with a transaction-journal and system-image (snapshot) based business system at its core, which can process 6 million transactions per second on a single thread.
A prevalent system needs enough memory to hold its entire state in RAM (the "prevalent hypothesis"). Prevalence advocates claim this is continuously alleviated by decreasing RAM prices, and the fact that many business databases are small enough already to fit in memory.
Programmers need skill in working with business state natively in RAM, rather than using explicit API calls for storage and queries for retrieval.
The system's events must be capturable for journaling.
- "An Introduction to Object Prevalence", by Carlos Villela for IBM Developerworks. 
- "Prevalence: Transparent, Fault-Tolerant Object Persistence", by Jim Paterson for O'Reilly's OnJava.com 
- "Object Prevalence": Original Article by Klaus Wuestefeld published in 2001 on Advogato. 
- Madeleine: a Ruby implementation 
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