Abstract: Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional shared-nothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper reviews the techniques used by such systems, and surveys current commercial and research systems. 1.

In large federated and shared-nothing databases, resources can exhibit widely fluctuating characteristics. Assumptions made at the time a query is submitted will rarely hold throughout the duration of query processing. As a result, traditional static query optimization and execution techniques are ineffective in these environments. In this paper we introduce a query processing mechanism called an eddy, which continuously reorders operators in a query plan as it runs. We characterize the moments of symmetry during which pipelined joins can be easily reordered, and the synchronization barriers that require inputs from different sources to be coordinated. By combining eddies with appropriate join algorithms, we merge the optimization and execution phases of query processing, allowing each tuple to have a flexible ordering of the query operators. This flexibility is controlled by a combination of fluid dynamics and a simple learning algorithm. Our initial implementation demonstrates prom...

In many recent applications, data may take the form of continuous data streams, rather than finite stored data sets. Several aspects of data management need to be reconsidered in the presence of data streams, offering a new research direction for the database community. In this paper we focus primarily on the problem of query processing, specifically on how to define and evaluate continuous queries over data streams. We address semantic issues as well as efficiency concerns. Our main contributions are threefold. First, we specify a general and flexible architecture for query processing in the presence of data streams. Second, we use our basic architecture as a tool to clarify alternative semantics and processing techniques for continuous queries. The architecture also captures most previous work on continuous queries and data streams, as well as related concepts such as triggers and materialized views. Finally, we map out research topics in the area of query processing over data streams, showing where previous work is relevant and describing problems yet to be addressed.

Distributed data processing is fast becoming a reality. Businesses want to have it for many reasons, and they often must have it in order to stay competitive. While much of the infrastructure for distributed data processing is already in place (e.g., modern network technology), there are a number of issues which still make distributed data processing a complex undertaking: (1) distributed systems can become very large involving thousands of heterogeneous sites including PCs and mainframe server machines � (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system� (3) legacy systems need to be integrated|such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the \textbook " architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intra-query parallelism, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses di erent kinds of distributed systems such as client-server, middleware (multi-tier), and heterogeneous database systems and shows how query processing works in these systems. Categories and subject descriptors: E.5 [Data]:Files � H.2.4 [Database Management Systems]: distributed databases, query processing � H.2.5 [Heterogeneous Databases]: data translation General terms: algorithms � performance Additional key words and phrases: query optimization � query execution � client-server databases � middleware � multi-tier architectures � database application systems � wrappers� replication � caching � economic models for query processing � dissemination-based information systems 1

In this paper, the performance and characteristics of the execution of various join-trees on a parallel DBMS are studied. The results of this study, are a step into the direction of the design of a query optimization strategy that is fit for parallel execution of complex queries. Among others, synchronization issues are identified to limit the performance gain from parallelism. A new hashjoin algorithm, called Pipelining hash-join is introduced that has fewer synchronization constraints than the known hash-join algorithms. Also, the behavior of individual join operations in a join-tree is studied in a simulation experiment. The results show that the Pipelining hash-join algorithm yields a better performance for multi-join queries. Also, the format of the optimal join-tree appears to depend on the size of the operands of the join: A multi-join between small operands performs best with a bushy schedule; larger operands are better off with a linear schedule. The results from the simulatio...

Abstract. Recently a new generation of P2P systems, offering distributed hash table (DHT) functionality, have been proposed. These systems greatly improve the scalability and exact-match accuracy of P2P systems, but offer only the exact-match query facility. This paper outlines a research agenda for building complex query facilities on top of these DHT-based P2P systems. We describe the issues involved and outline our research plan and current status. 1

This paper presents a new persistent data management layer designed to simplify cluster-based Internet service construction. This self-managing layer, called a distributed data structure (DDS), presents a conventional single-site data structure interface to service authors, but partitions and replicates the data across a cluster. We have designed and implemented a distributed hash table DDS that has properties necessary for Internet services (incremental scaling of throughput and data capacity, fault tolerance and high availability, high concurrency, consistency, and durability). The hash table uses two-phase commits to present a coherent view of its data across all cluster nodes, allowing any node to service any task. We show that the distributed hash table simplies Internet service construction by decoupling service-specic logic from the complexities of persistent, consistent state management, and by allowing services to inherit the necessary service properties from the DDS rather ...

Overlay networks are used today in a variety of distributed systems ranging from file-sharing and storage systems to communication infrastructures. However, designing, building and adapting these overlays to the intended application and the target environment is a di#cult and time consuming process.

Access to large numbers of data sources introduces new problems for users of heterogeneous distributed databases. End users and application programmers must deal with unavailable data sources. Database administrators must deal with incorporating each new data source into the system. Database implementors must deal with the transformation of queries between query languages and schemas. The Distributed Information Search COmponent (DISCO) addresses these problems. Query processing semantics give meaning to queries that reference unavailable data sources. Data modeling techniques manage connections to data sources. The component interface to data sources flexibly handles different query languages and different interface functionalities. This paper describes in detail (a) the distributed mediator architecture of DISCO, (b) its query processing semantics, (c) the data model and its modeling of data source connections, and (d) the interface to underlying data sources. We describe several advantages of our system and describe the internal architecture of our planned prototype.

We introduce River, a data-flow programming environment and I/O substrate for clusters of computers. River is designed to provide maximum performance in the common case --- even in the face of nonuniformities in hardware, software, and workload. River is based on two simple design features: a high-performance distributed queue, and a storage redundancy mechanism called graduated declustering. We have implemented a number of data-intensive applications on River, which validate our design with near-ideal performance in a variety of non-uniform performance scenarios.