Database caching uses full-fledged DBMSs as caches to adaptively maintain sets of records from a remote DB and to evaluate queries on them, whereas Web caching keeps single Web objects ready somewhere in caches in the user-to-server path. Using DB caching, we are able to perform declarative and setoriented query processing nearby the application, although data storage and consistency maintenance is remote. We explore which query types can be supported by DBMS-controlled caches whose contents are constructed using parameterized cache constraints. Schemes on single cache tables or on cache groups correctly perform local evaluation of query predicates. In practical applications, only safe schemes guaranteeing recursion-free load operations are acceptable. Finally, we comment on future application scenarios and research problems including empirical performance evaluation of DB caching schemes.

SQL extensions that allow queries to explicitly specify data quality requirements in terms of currency and consistency were proposed in an earlier paper. This paper develops a data quality-aware, finer grained cache model and studies cache design in terms of four fundamental properties: presence, consistency, completeness and currency. Such a model provides an abstract view of the cache to the query processing layer, and opens the door for adaptive cache management. We describe an implementation approach that builds on the MTCache framework for partially materialized views. The optimizer checks most consistency constraints and generates a dynamic plan that includes currency checks and inexpensive checks for dynamic consistency constraints that cannot be validated during plan compilation. Our solution not only supports transparent caching but also provides transactional fine grained data currency and consistency guarantees. 1.

Abstract — Performance of proxy caches for database federations that serve a large number of users is crucially dependent on its physical design. Current techniques, automated or otherwise, for physical design depend on the identification of a representative workload. In proxy caches, however, such techniques are inadequate since workload characteristics change rapidly. This is remarkably shown at the proxy cache of SkyQuery, an Astronomy federation, which receives a continuously evolving workload. We present novel techniques for automated physical design that adapt with the workload and balance the performance benefits of physical design decisions with the cost of implementing these decisions. These include both competitive and incremental algorithms that optimize the combined cost of query evaluation and making physical design changes. Our techniques are general in that they do not make assumptions about the underlying schema nor the incoming workload. Preliminary experiments on the TPC-D benchmark demonstrate significant improvement in response time when the physical design continually adapts to the workload using our online algorithm compared with offline techniques. I.

Abstract. A Web client request traverses four types of Web caches, before the Web server as the origin of the requested document is reached. This client-to-server path is continued to the backend DB server if timely and transaction-consistent data is needed to generate the document. Web caching typically supports access to single Web objects kept ready somewhere in caches up to the server, whereas database caching, applied in the remaining path to the DB data, allows declarative query processing in the cache. Optimization issues in Web caches concern management of documents decomposed into templates and fragments to support dynamic Web documents with reduced network bandwidth usage and server interaction. When fragment-enabled caching of fine-grained objects can be performed in proxy caches close to the client, user-perceived delays may become minimal. On the other hand, database caching uses a full-fledged DBMS as cache manager to adaptively maintain sets of records from a remote database and to evaluate queries on them. Using so-called cache groups, we introduce the new concept of constraint-based database caching. These cache groups are constructed from parameterized cache constraints, and their use is based on the key concepts of value completeness and predicate completeness. We show how cache constraints affect the correctness of query evaluations in the cache and which optimizations they allow. Cache groups supporting practical applications must exhibit controllable load behavior for which we identify necessary conditions. Finally, we comment on future research problems. 1

Caching is a proven means to improve scalability and availability of software systems as well as to reduce latency of user requests. In contrast to Web caching where single Web objects are kept ready somewhere in caches in the userto-server path, database caching uses a full-fledged DBMS as a cache to adaptively maintain sets of records from a remote DB and to evaluate queries on them. We give an introduction to the new class of constraint-based DB caching, by the example of cache groups. These cache groups are constructed from parameterized cache constraints, and their use is based on the key concepts of value and domain completeness. We show how cache constraints affect the correctness of query evaluations in the cache and which optimizations they allow. Finally, once unsafe cache configurations, whose performance is uncontrollable, are identified, the costs of safe ones can be analyzed quantitatively.

Caching is a proven means to improve scalability and availability of software systems as well as to reduce latency of user requests. In contrast to Web caching where single Web objects are kept ready somewhere in caches in the userto-server path, database caching uses a full-fledged DBMS as a cache to adaptively maintain sets of records from a remote DB and to evaluate queries on them. We give an introduction to the new class of constraint-based DB caching, by the example of cache groups. These cache groups are constructed from parameterized cache constraints, and their use is based on the key concepts of value and domain completeness. We show how cache constraints affect the correctness of query evaluations in the cache and which optimizations they allow. Finally, once unsafe cache configurations, whose performance is uncontrollable, are identified, the costs of safe ones can be analyzed quantitatively. 1