Conference Paper

Hybrid-Range Partitioning Strategy: A New Declustering Strategy for Multiprocessor Database Machines.

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Abstract

In shared-nothing multiprocessor database machines, the relational operators that form a query are executed on the processors where the relations they reference are stored. In general, as the number of processors over which a relation is declustered is increased, the execution time for the query is decreased because more processors are used, each of which has to process fewer tuples. However, for some queries increasing the degree of declustering actually increases the query's response time as the result of increased overhead for query startup, com- munication, and termination. In general, the declustering strategy selected for a relation can have a significant impact on the overall performance of the system. This paper presents the hybrid-range partitioning strategy, a new declustering strategy for multiproces- sor database machines. In addition to describing its characteris- tics and operation, its performance is compared to that of the current partitioning strategies provided by the Gamma database machine.

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... We note that the data partitioning problem is very well studied in the database literature in both the parallel and distributed context [5,6,7,8]. We compare and contrast the partitioning problem that results in the context of hybrid clouds with the previously developed state-of-the-art approaches in the related work section. ...
... Our work builds upon a significant body of prior work on data partitioning (e.g., [7,8,5,6]), distributed query processing (e.g., evolution from systems such as SDD-1 [17] to DISCO [18] that operates on heterogeneous data sources, to Internet-scale systems such as Astrolabe [19], and cloud systems [20]), and data privacy [21,4,22]. However, to our knowledge, this is the first paper that takes a risk-based approach to data security in the hybrid model. ...
... We summarize a few of the most relevant previous works below. Data partitioning has been studied fairly extensively in distributed and parallel databases from a variety of perspectives, ranging from load balancing [5], efficient transaction processing [6] to physical database design [7,8]. In [8], the authors consider the problem of workload driven horizontal data partitioning for parallel databases. ...
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This paper explores query processing in a hybrid cloud model where a user's local computing capa-bility is exploited alongside public cloud services to deliver an efficient and secure data management solution. Hybrid clouds offer numerous economic advantages including the ability to better manage data privacy and confidentiality, as well as exerting control on monetary expenses of consuming cloud services by exploiting local resources. Nonetheless, query processing in hybrid clouds introduces nu-merous challenges, the foremost of which is, how to partition data and computation between the public and private components of the cloud. The solution must account for the characteristics of the workload that will be executed, the monetary costs associated with acquiring/operating cloud services as well as the risks affiliated with storing sensitive data on a public cloud. This paper pro-poses a principled framework for distributing data and processing in a hybrid cloud that meets the conflicting goals of performance, disclosure risk and resource allocation cost. The proposed solution is implemented as an add-on tool for a Hadoop and Hive based cloud computing infrastructure.
... The Hybrid-Range Partitioning Strategy [GD90] is an extension to range partitioning which divides a relation into a large number of small logical fragments that do not depend on the number of processors in the system. Each fragment is a small range of the partitioning key domain. ...
... Such a scheme will be called an assignment scheme and should include reassignment rules for self repair and node reintegration. Further on, the assignment scheme should give a fragment allocation resembling the Hybrid-Range Partitioning Strategy [GD90] (described in Section 7.1.5) to ensure scalable performance for a wide range of query types. ...
... The Hybrid-Range Partitioning Strategy (HRPS) [GD90] ensures efficient execution of both exact match, small range, and long range queries on the partitioning key through partitioning the relation into a number of fragments which are independent of the available number of nodes in the system. Small relations will be partitioned into few fragments and large into many. ...
Thesis
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The thesis introduces the concept of multi-site declustering strategies with self repair for databases demanding very high service availability. Existing work on declustering strategies are centered around providing high performance and reliability inside a small geographical area (site). Applications demanding robustness against site failures like fire and power outages, can not use these methods. Such applications will often both replicate information inside one site and then replicate the site on another site and thus resulting in unnecessary high redundancy cost. Multi-site declustering provides robustness against site failures with only two replicas of data without compromising the performance and reliability. Self repair is proposed for reducing the probability of double-failures causing data loss and reducing the need for rapid replacement of failed hardware. A prerequisite for multi-site declustering with self repair is fast, long-distance, communication networks like ATM. The thesis shows how existing declustering strategies like Mirrored, Interleaved, Chained, and HypRa declustering can be used as multi-site declustering strategies. In addition a new strategy called Q-rot declustering is proposed. Compared with the others it gives larger flexibility with respect to repair strategy, number of sites, and usage pattern. To evaluate availability of systems using the methods a general evaluation model has been developed. Multi-site Chained declustering provides the best availability of the methods evaluated. Q-rot declustering has comparable availability but is significantly more flexible. The evaluation model provides insight and can be used to understand the declustering problem better and to develop new and improved multi-site declustering strategies. The model can also be used as a configuration tool by organizations wanting to deploy one of the declustering strategies.
... Some tasks need to get the whole parameter, while others need a portion of the parameter [18]. To achieve a trade-off between query balance and fast range query, we adopt a hybrid strategy, called range-hash partition [19]. We first partition the parameter to several ranges based on the indexes, and then use hash partition to assign the partitions to distributed nodes [28]. ...
... Range partition accelerates range query as it minimizes the number of multi-sited transactions, while hash partition obtains better load balance for random queries [15]. Some other works propose hybrid partition strategies [19] to combine different partition methods according to specific scenarios. ...
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... Hash partition achieves a more balanced query distribution, while range partition facilitates range queries [9]. To achieve a trade-off between query balance and fast range query, we adopt a hybrid range-hash strategy [16]. We first partition a vector to several ranges based on feature indexes, then use hash partition to put each partition onto one node. ...
... Our system, however, is able to customize both push and pull functions. Therefore, we move the split finding operation of Algorithm 1 (line [10][11][12][13][14][15][16][17] to the pull function to implement our two-phase split algorithm. With this method, we reduce the transferred size of a partition to one integer and two floating-point numbers. ...
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... Instead of using hash partitioning, which directs a skewed key to a single processing node, we use rangebased partitioning which distributes the skewed keys among a number of processing nodes. Range-based partitioning exploits the characteristics of data for load balancing; two of such strategies are simple range partitioning and virtual processor range partitioning [7]. ...
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... Finally, we note that partitioning algorithms for relational databases have been studied extensively in the past, but the focus has been on data declustering and physical design tuning in order to speed up query evaluation by taking advantage of parallelization. Partitioning strategies include range partitioning, hash partitioning, and partitioning based on query cost models [5,23,21,19,12,18,20]. Furthermore, modern distributed storage systems, such as BigTable [7], are not optimized for relational workloads on multiple tables. ...
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With the widespread use of shared-nothing clusters of servers, there has been a proliferation of distributed object stores that offer high availability, reliability and enhanced performance for MapReduce-style workloads. However, relational workloads cannot always be evaluated efficiently using MapReduce without extensive data migrations, which cause network congestion and reduced query throughput. We study the problem of computing data placement strategies that minimize the data communication costs incurred by typical relational query workloads in a distributed setting. Our main contribution is a reduction of the data placement problem to the well-studied problem of {\sc Graph Partitioning}, which is NP-Hard but for which efficient approximation algorithms exist. The novelty and significance of this result lie in representing the communication cost exactly and using standard graphs instead of hypergraphs, which were used in prior work on data placement that optimized for different objectives (not communication cost). We study several practical extensions of the problem: with load balancing, with replication, with materialized views, and with complex query plans consisting of sequences of intermediate operations that may be computed on different servers. We provide integer linear programs (IPs) that may be used with any IP solver to find an optimal data placement. For the no-replication case, we use publicly available graph partitioning libraries (e.g., METIS) to efficiently compute nearly-optimal solutions. For the versions with replication, we introduce two heuristics that utilize the {\sc Graph Partitioning} solution of the no-replication case. Using the TPC-DS workload, it may take an IP solver weeks to compute an optimal data placement, whereas our reduction produces nearly-optimal solutions in seconds.
... Prior literature review mainly examines placement schemes, without referring to the problem of declustering [48,41,69,67]. To the best of our knowledge, heuristic approaches are mainly adopted for the creation of the decluster objects, such as in the Virtual Microscope [151,65], where the application developer is responsible for generating the declustered image data and the studies presented by Faloutsos and Bhagwat [64], Chen and Shinha [51] and Ghandeharizadeh and DeWitt [158], where objects are assumed to be declustered across all their coordinate axes. ...
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This copy of the thesis has been supplied on the condition that anyone who consults it is understood to recognise that the copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without the prior written consent of the author or the university (as may be appropriate).
... The range partitioning has long been studied for assigning tuples to partitions on the basis of ranges rather than hash values of a join key. [23] sorts input datasets according to join keys. Depending on the processing capability of the system, the number of tuples T to be allocated to each partition is determined. ...
... Range partitioning splits a set of keys based on a given set of range splitters [GD90], which can be derived, e.g., from quantiles [DNS91] or samples [MRL98]. It is used to, e.g., horizontally partition (shard) the data in distributed database systems [HCZZ21]. ...
... Atte et al. (2011) adapted the range-based partitioning method (DeWitt et al., 1992) for MapReduce, which primarily divides the join attribute into subranges that approximately have the same number of records in order to balance the workload in subsequent processing. The introduced skew handling algorithm (SAND Join) utilizes two partitioning methods: the simple range partitioner and the virtual processor range partitioner (DeWitt & Ghandeharizadeh, 1990). The simple range partitioner collects samples from each input split and merges them into table T. ...
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... This strategy has been termed as declustering. Early studies on declustering for parallel range searching date back to the beginning of the 90's [56, 64, 49]. However, newer declustering techniques have been found and there has been plenty of recent attention to this topic. ...
... We describe two of the main works: the Multi-Attribute GrId deClustering (MAGIC) [GD94] and Bubba's Extended Range Declustering (BERD) [BAC + 90]. MAGIC is an extension of the Hybrid-Range partitioning strategy published in [GD90] that strikes a compromise between the sequential execution paradigm of range declustering and the intra-query parallelism achieved with hash and round-robin. ...
Thesis
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... Unlike the other declustering strategies, the hybrid-range partitioning strategy utilizes the characteristics of the queries that access a relation to obtain the appropriate degree of intra-query parallelism. In particular, it strikes a compromise between the sequential execution paradigm of the range declustering strategy and the load balancing/intra-query parallelism characteristics of the hash and round-robin declustering strategies [10]. In our system, we choose range portioning method to divide our data. ...
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Voraussetzung für die parallele Verarbeitung von Anfragen und die Nutzung von Datenparallelität in Parallelen Datenbanksystemen ist eine geeignete Datenverteilung, sodass mehrere Prozesse auf disjunkten Datenbereichen parallel arbeiten können. Während in Shared-Everything- und in Shared-Disk-Systemen lediglich eine Verteilung der Daten über mehrere Platten bzw. Externspeicher zu finden ist, erfordert Shared Nothing zugleich eine Verteilung der Daten unter den Verarbeitungsrechnern. Die Datenverteilung hat in dieser Architektur daher auch direkten Einfluss auf den Kommunikations-Overhead und ist daher von besonderer Bedeutung für die Leistungsfähigkeit. Wir konzentrieren uns daher weitgehend auf die Bestimmung der Datenverteilung für Shared Nothing, die ähnlich wie für Verteilte DBS die Schritte der Fragmentierung und Allokation erfordert. Um jedoch eine effektive Parallelisierung erreichen zu können, sind diese Aufgaben enger aufeinander abzustimmen. Insbesondere empfiehlt sich vor der Fragmentierung bereits die Festlegung des Verteilgrades einer Relation. Bei der Vorstellung der Teilschritte gehen wir besonders ausführlich auf Varianten einer horizontalen Fragmentierung ein, wobei auch mehrdimensionale bzw. mehrstufige Ansätze behandelt werden. Danach stellen wir noch drei für Shared Nothing einsetzbare Verfahren zur Datenallokation mit replizierter Zuordnung der Daten vor. Im Anschluss besprechen wir die Datenverteilung für Shared Everything und Shared Disk. Am Ende des Kapitels gehen wir auf die Datenverteilung in NoSQL-Systemen ein.
Conference Paper
In this paper, we discuss a self-managed distributed columnstore system which would adapt its physical design to changing workloads. Architectural novelties of column-stores hold a great promise for construction of an efficient self-managed database. At first, we present a short survey of an existing self-managed systems. Then, we provide some views on the organization of a self-managed distributed column-store system. We discuss its three core components: alerter, reorganization controller and the set of physical design options (actions) available to such a system. We present possible approaches to each of these components and evaluate them. This study is the first step towards a creation of an adaptive distributed column-store system.
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This thesis defines an approach for exploring parallelism in object-oriented database systems outside of the context of SQL queries. We propose a technique for parallelazing transactions in a flat classical transaction model where a transaction is sequence of operations. Intra-transaction parallelism is accomplished by transforming transaction code definition in order to execute operations in parallel. Our approach for exploring parallelism inside applications first extends the intra-transaction parallelization model so that a transaction is considered as an unit of parallelization. We have then considered a nested transaction model for exploring parallelism inside applications. We developed a parallelization model for applications where we merge capabilities for parallel execution already given in nested transactions with our approach for transaction parallelization by transformation. We implemented a prototype for the intra-transaction parallelization model, using the O2 object-oriented database system. The prototype introduces parallel execution inside O2 transactions through creation and synchronization of threads inside an O2 client running an application. Our prototype runs in a monoprocessor Unix-like workstation and supports virtual parallelism. We also applied the transaction parallelization model to the NAOS Rule System. Our approach considers a set of rules of an execution cycle for parallelization. We build an execution plan for the rules of a cycle which defines sequential or parallel execution for the rules.
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With the widespread use of shared-nothing clusters of servers, there has been a proliferation of distributed object stores that offer high availability, reliability and enhanced performance for MapReduce-style workloads. However, data-intensive scientific workflows and join-intensive queries cannot always be evaluated efficiently using MapReduce-style processing without extensive data migrations, which cause network congestion and reduced query throughput. In this paper, we study the problem of computing data placement strategies that minimize the data communication costs incurred by such workloads in a distributed setting. Our main contribution is a reduction of the data placement problem to the well-studied problem of Graph Partitioning, which is NP-Hard but for which efficient approximation algorithms exist. The novelty and significance of this result lie in representing the communication cost exactly and using standard graphs instead of hypergraphs, which were used in prior work on data placement that optimized for different objectives. We study several practical extensions of the problem: with load balancing, with replication, and with complex workflows consisting of multiple steps that may be computed on different servers. We provide integer linear programs (IPs) that may be used with any IP solver to find an optimal data placement. For the no-replication case, we use publicly available graph partitioning libraries (e.g., METIS) to efficiently compute nearly-optimal solutions. For the versions with replication, we introduce two heuristics that utilize the Graph Partitioning solution of the no-replication case. Using a workload based on TPC-DS, it may take an IP solver weeks to compute an optimal data placement, whereas our reduction produces nearly-optimal solutions in seconds.
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Scaling complex transactional workloads in parallel and distributed systems is a challenging problem. When transactions span data partitions that reside in different nodes, significant overheads emerge that limit the throughput of these systems. In this paper, we present a low-overhead data partitioning approach, termed JECB, that can reduce the number of distributed transactions in complex database workloads such as TPC-E. The proposed approach analyzes the transaction source code of the given workload and the database schema to find a good partitioning solution. JECB leverages partitioning by key-foreign key relationships to automatically identify the best way to partition tables using attributes from tables. We experimentally compare our approach with the state of the art data-partitioning techniques and show that over the benchmarks considered, JECB provides better partitioning solutions with significantly less overhead.
Conference Paper
With the development of Internet technology and Cloud Computing, more and more applications have to be confronted with the challenges of big data. NoSQL Database is fit to the management of big data because of the characteristics of high scalability, high availability and high fault-tolerance. The data partitioning strategy plays an important role in the NoSQL database. The existing data partitioning strategies will cause some problems such as low scalability, hot spot and low performance and so on. In this paper we proposed a new data partitioning strategy---HRCH, which can partitioning the data in a reasonable way. At last we use some experiments to verify the effectiveness of HRCH. It shows that the HRCH can improve the scalability of the system. It also can avoid the hot spot problem as far as possible. And it also can improve the parallel degree of processing to improve the system's performance in some processing.
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In heterogeneous database cluster, the performance of load balancing is closely related to the computing capabilities of heterogeneous nodes and the different types of workloads. Thus, a method is introduced to evaluate the load status of nodes by the weighted load values with consideration of both the utilization of different resources and the workload types in a load balancer and an efficient and dynamic load balancing scheme is proposed for OLTP(online transaction processing) workloads to maximize the utilization of distributed resources and achieve better performance, which need not collect the feedback of load information from the lower nodes and effectively keeps from the data skew. The simulation results for OLTP services gained by TPC-C tool show that the dynamic weighted balancing policy leads to sub-linear throughput speedup and keeps the heterogeneous cluster well balanced.
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Increasing performance of CPUs and memories will be squandered if not matched by a similar performance increase in I/O. While the capacity of Single Large Expensive Disks (SLED) has grown rapidly, the performance improvement of SLED has been modest. Redundant Arrays of Inexpensive Disks (RAID), based on the magnetic disk technology developed for personal computers, offers an attractive alternative to SLED, promising improvements of an order of magnitude in performance, reliability, power consumption, and scalability. This paper introduces five levels of RAIDs, giving their relative cost/performance, and compares RAID to an IBM 3380 and a Fujitsu Super Eagle.
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In this paper we present data distribution methods for parallel processing environment. The primary objective is to process partial match retrieval type queries for parallel devices. The main contribution of this paper is the development of a new approach called FX (Fieldwise eXclusive) distribution for maximizing data access concurrency. An algebraic property of exclusive-or operation, and field transformation techniques are fundamental to this data distribution techniques. We have shown through theorems and corollaries that this FX distribution approach performs better than other methods proposed earlier. We have also shown, by computing probability of optimal distribution and query response time, that FX distribution gives better performance than others over a large class of partial match queries. This approach presents a new basis in which optimal data distribution for more general type of queries can be formulated.
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In dataflow architectures, each dataflow operation is typically executed on a single physical node. We are concerned with distributed data-intensive systems, in which each base (i.e., persistent) set of data has been declustered over many physical nodes to achieve load balancing. Because of large base set size, each operation is executed where the base set resides, and intermediate results are transferred between physical nodes. In such systems, each dataflow operation is typically executed on many physical nodes. Furthermore, because computations are data-dependent, we cannot know until run time which subset of the physical nodes containing a particular base set will be involved in a given dataflow operation. This uncertainty creates several problems. We examine the problems of efficient program loading, dataflow—operation activation and termination, control of data transfer among dataflow operations, and transaction commit and abort in a distributed data-intensive system. We show how these problems are interrelated, and we present a unified set of mechanisms for efficiently solving them. For some of the problems, we present several solutions and compare them quantitatively.
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This paper presents the results of an initial performance evaluation of the Gamma database machine. In our experiments we measured the effect of relation size and indices on response time for selection, join, and aggregation queries, and single-tuple updates. A Teradata DBC/1012 database machine of similar size is used as a basis for interpreting the results obtained. We also analyze the performance of Gemma relative to the number of processors employed and study the impact of varying the memory size and disk page size on the execution time of a variety of selection and join queries. We analyze and interpret the results of these experiments based on our understanding of the system hardware and software, and conclude with an assessment of the strengths and weaknesses of Gamma.
Article
The optimization problem discussed in this paper is the translation of an SQL query into an efficient parallel execution plan for a multiprocessor database machine under the performance goal of reduced response times as well as increased throughput in a multiuser environment. We describe and justify the most important research problems which have to be solved to achieve this task, and we explain our approach to solve these problems.
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Cartesian product files have recently been shown to exhibit attractive properties for partial match queries. This paper considers the file allocation problem for Cartesian product files, which can be stated as follows: Given a k-attribute Cartesian product file and an m-disk system, allocate buckets among the m disks in such a way that, for all possible partial match queries, the concurrency of disk accesses is maximized. The Disk Modulo (DM) allocation method is described first, and it is shown to be strict optimal under many conditions commonly occurring in practice, including all possible partial match queries when the number of disks is 2 or 3. It is also shown that although it has good performance, the DM allocation method is not strict optimal for all possible partial match queries when the number of disks is greater than 3. The General Disk Modulo (GDM) allocation method is then described, and a sufficient but not necessary condition for strict optimality of the GDM method for all partial match queries and any number of disks is then derived. Simulation studies comparing the DM and random allocation methods in terms of the average number of disk accesses, in response to various classes of partial match queries, show the former to be significantly more effective even when the number of disks is greater than 3, that is, even in cases where the DM method is not strict optimal. The results that have been derived formally and shown by simulation can be used for more effective design of optimal file systems for partial match queries. When considering multiple-disk systems with independent access paths, it is important to ensure that similar records are clustered into the same or similar buckets, while similar buckets should be dispersed uniformly among the disks.
Conference Paper
Scalable parallel computers have been the Holy Grail of much of Computer Sciences research in the past two decades. There are now several products on the market, ranging from dozen processor bus-based systems to the multiple thousand processing element Connection Machine. These products, including data management systems, use parallelism to satisfy a range of system goals including performance, availability and cost. In this paper, I discuss parallelism issues in the context of data management. My focus is the Shared Nothing class of parallel system, examples of which include products from Tandem and Teradata and experimental systems such as the University of Wisconsin's Gamma and MCC's Bubba. I outline a number of key research areas emphasizing the inherent problems and current state of the art. Next, I summarize recent performance results. The basic message of the paper is that the Holy Grail of scalable parallelism, even in the limited application domain of data management is still elusive; but we've made several significant steps towards attaining it.
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Five well-known scheduling policies for movable head disks are compared using the performance criteria of expected seek time (system oriented) and expected waiting time (individual I/O request oriented). Both analytical and simulation results are obtained. The variance of waiting time is introduced as another meaningful measure of performance, showing possible discrimination against individual requests. Then the choice of a utility function to measure total performance including system oriented and individual request oriented measures is described. Such a function allows one to differentiate among the scheduling policies over a wide range of input loading conditions. The selection and implementation of a maximum performance two-policy algorithm are discussed.
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We investigate two schemes for placing data on multiple disks. We show that declustering (spreading each file across several disks) is inherently better than clustering (placing each file on a single disk) due to a number of reasons including parallelism and uniform load on all disks.
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We describe the implementation of a flexible data storage system for the UNIX environment that has been designed as an experimental vehicle for building database management systems. The storage component forms a foundation upon which a variety of database systems can be constructed including support for unconventional types of data. We describe the system architecture, the design decisions incorporated within its implementation, our experiences in developing this large piece of software, and the applications that have been built on top of it.
Article
The design of the Gamma database machine and the techniques employed in its implementation are described. Gamma is a relational database machine currently operating on an Intel iPSC/2 hypercube with 32 processors and 32 disk drives. Gamma employs three key technical ideas which enable the architecture to be scaled to hundreds of processors. First, all relations are horizontally partitioned across multiple disk drives, enabling relations to be scanned in parallel. Second, parallel algorithms based on hashing are used to implement the complex relational operators, such as join and aggregate functions. Third, dataflow scheduling techniques are used to coordinate multioperator queries. By using these techniques, it is possible to control the execution of very complex queries with minimal coordination. The design of the Gamma software is described and a thorough performance evaluation of the iPSC/s hypercube version of Gamma is presented
GAMMA -A High Performance Dataflow Database MachineA Per-formance Analysis of the Gamma Database Machine
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DBC/lOlZ Data Base Computer System Manual, Rel. 2.0
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