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Table of contents:

Dynamic Containers™: The Key To Superior Performance

• Introduction
• Objectivity/DB Database Architecture
• Typical Data Access and Usage
• Basic Approaches to Utilizing Containers
• Data Model Abstraction
• Data Acquisition Pattern
• Leveraging Containers
  

Deployment Examples

• The COM21 Solution
• The Coherent Networks Solution
• Space Telescope Institute
• Conclusion

Data Acquisition Pattern: Large scale data acquisition systems may be characterized as having three distinct phases: an acquisition phase; a processing phase; and a presentation phase. In the acquisition phase data arrives in continuous streams or in batches. The data may arrive much too fast for a single processor or disk to handle, e.g. one Objectivity customer is projecting 20 Terabytes per day. Spreading a container pool across multiple machines and files makes it possible to store the arriving data safely. The application can use a near-infinite random access “virtual tape”. The containers are in a federation so other applications see a single address space and need not concern themselves with the physical placement of the data. The second phase is a number crunching phase and it may build indices, hash tables (ooMaps) or associations across the objects. It may even recluster or replicate some of the objects into existing or new containers to make subsequent access more efficient. The third phase benefits from the computing done during the second phase. If programmed correctly, the containerization is generally transparent at this stage.