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Since associative access to storage can rapidly retrieve all relevant data from multiple les and bring it into memory, and associative processing in memory can rapidly link related information from those les, the combination of the two technologies could provide order of magnitude gains in data processing As an example where matching records from multiple les have to be brought together for data analysis, we can consider two les, Sales by region and Population centers by region An associative system permits matching records on
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Combining Associative Memory and Storage
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the attribute region to be brought together for analysis without expensive intermediate sorting by region A speedup yielding linear performance, O(n), versus the best sort performance, O(n log n), is conceivable Analyses of machines combining associative memory and storage have been made, and experiments have been conducted and published However, the risk and complexity of combining two radical technologies has prevented any commercial demonstrations
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2-5-6 Status of Specialized Database Architectures A number of specialized database machines are now available, and they provide some part of the advantages described in this section It remains di cult for hardware solutions to keep up with advanced algorithms rapidly implemented in software, so that it is di cult to beat the performance of well designed software systems For instance, associative access is also provided, at only somewhat higher cost, by index and hash mechanisms The software, while complex, tends to deal better with problems of varying elds, variable le sizes, and long records We hence nd associative technology used in only highly specialized systems We note also that associative access is logically equivalent to the key-based software methods to access records described throughout this book The use of specialized hardware typically increases performance but reduces exibility and capacity To obtain high productivity from systems which include specialized hardware, the loads on the various components must be balanced carefully so that no unit is consistently a bottleneck for all others Di erent transactions will, of course, have di erent patterns of component usage, so that excessive optimization by hardware is apt to be futile for the breadth of requirements posed by practical applications
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We have concentrated up to this point on approaches where the storage system is connected, directly or indirectly, to one central processor There are a number of situations where the use of a central processor is not the best solution Reasons for distributed processing include: Functional A system carries out a number of distinct functions, and it is desirable to develop and manage these functions separately If the activity within a function is greater than the interaction between functions, a system where functions are distributed to specialized processors will be more manageable Geographical If groups of users are located in distinct sites, it may be e cient if substantial local processing can e carried out on a local processor Performance Retrieval from local nodes avoids communication delays Highly specialized nodes can provide high performance for certain functions, without having to alter other aspects of a data system Back-end database processors or front-end communication systems are examples of such specialized nodes Autonomy When, in an organization, responsibility over business or scienti c areas has been delegated, a corresponding delegation of control over the related information is implied This may be achieved by distribution of the data, so that access privileges, quality-control responsibilities, reliable communications, and access convenience correspond to the pattern of responsibility Reliability If parts of a data system can be isolated, much of the system can continue to function while one processor or its storage devices have failed Growth If the demands on a data system grow, adding a new node can provide additional capacity without replacement of the entire previous system
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