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210
Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks
- In EuroSys
, 2007
"... Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad applica-tion combines computational “vertices ” with communica-tion “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of availa ..."
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Cited by 762 (27 self)
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Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad applica-tion combines computational “vertices ” with communica-tion “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of available computers, communicating as appropriate through files, TCP pipes, and shared-memory FIFOs. The vertices provided by the application developer are quite simple and are usually written as sequential programs with no thread creation or locking. Concurrency arises from Dryad scheduling vertices to run simultaneously on multi-ple computers, or on multiple CPU cores within a computer. The application can discover the size and placement of data at run time, and modify the graph as the computation pro-gresses to make efficient use of the available resources. Dryad is designed to scale from powerful multi-core sin-gle computers, through small clusters of computers, to data centers with thousands of computers. The Dryad execution engine handles all the difficult problems of creating a large distributed, concurrent application: scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between ver-tices.
Parallel database systems: the future of high performance database systems
- COMMUNICATIONS OF THE ACM
, 1992
"... 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 t ..."
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Cited by 641 (13 self)
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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.
Eddies: Continuously Adaptive Query Processing
- In SIGMOD
, 2000
"... 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 i ..."
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Cited by 411 (21 self)
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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...
The state of the art in distributed query processing
- ACM Computing Surveys
, 2000
"... 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 ..."
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Cited by 320 (3 self)
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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
Continuous Queries over Data Streams
, 2001
"... 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 re-considered in the presence of data streams, offering a new research direction for the database community. In this pa-per we focus prim ..."
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Cited by 308 (10 self)
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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 re-considered in the presence of data streams, offering a new research direction for the database community. In this pa-per 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
DryadLINQ: A System for General-Purpose Distributed Data-Parallel Computing Using a High-Level Language
"... DryadLINQ is a system and a set of language extensions that enable a new programming model for large scale distributed computing. It generalizes previous execution environments such as SQL, MapReduce, and Dryad in two ways: by adopting an expressive data model of strongly typed.NET objects; and by s ..."
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Cited by 273 (27 self)
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DryadLINQ is a system and a set of language extensions that enable a new programming model for large scale distributed computing. It generalizes previous execution environments such as SQL, MapReduce, and Dryad in two ways: by adopting an expressive data model of strongly typed.NET objects; and by supporting general-purpose imperative and declarative operations on datasets within a traditional high-level programming language. A DryadLINQ program is a sequential program composed of LINQ expressions performing arbitrary sideeffect-free transformations on datasets, and can be written and debugged using standard.NET development tools. The DryadLINQ system automatically and transparently translates the data-parallel portions of the program into a distributed execution plan which is passed to the Dryad execution platform. Dryad, which has been in continuous operation for several years on production clusters made up of thousands of computers, ensures efficient, reliable execution of this plan. We describe the implementation of the DryadLINQ compiler and runtime. We evaluate DryadLINQ on a varied set of programs drawn from domains such as web-graph analysis, large-scale log mining, and machine learning. We show that excellent absolute performance can be attained—a general-purpose sort of 1012 Bytes of data executes in 319 seconds on a 240-computer, 960disk cluster—as well as demonstrating near-linear scaling of execution time on representative applications as we vary the number of computers used for a job. 1
Implementing Declarative Overlays
, 2005
"... 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 difficult and time consuming proces ..."
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Cited by 211 (58 self)
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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 difficult and time consuming process. To ease the development and the deployment of such overlay networks we have implemented P2, a system that uses a declarative logic language to express overlay networks in a highly compact and reusable form. P2 can express a Naradastyle mesh network in 16 rules, and the Chord structured overlay in only 47 rules. P2 directly parses and executes such specifications using a dataflow architecture to construct and maintain overlay networks. We describe the P2 approach, how our implementation works, and show by experiment its promising trade-off point between specification complexity and performance.
Dataflow query execution in a parallel main-memory environment
- In Proc. of the International Conference on Parallel and Distributed Information Systems (PDIS
, 1991
"... Abstract. 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 othe ..."
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Cited by 210 (5 self)
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Abstract. 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 hash-join algorithm is introduced that has fewer synchronization constraints han the known hash-join algorithms. Also, the behavior of individual join operations in a join-tree is studied in a simulation experiment. The results how that the introduced Pipelining hash-join algorithm yields a better performance for multi-join queries. 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 simulation study are confirmed with an analytic model for dataflow query execution. Ke~,ords: parallel query processing, multi-join queries, simulation, analytical modeling 1.
Complex queries in dht-based peer-to-peer networks
- in Proceedings of IPTPS’02
, 2002
"... 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 ..."
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Cited by 198 (4 self)
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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
Mapreduce online
, 2009
"... MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, many implementations of MapReduce materialize the entire output of each map and reduce task before it can be consumed. In this paper, we propose a modified MapReduce architecture tha ..."
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Cited by 181 (3 self)
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MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, many implementations of MapReduce materialize the entire output of each map and reduce task before it can be consumed. In this paper, we propose a modified MapReduce architecture that allows data to be pipelined between operators. This extends the MapReduce programming model beyond batch processing, and can reduce completion times and improve system utilization for batch jobs as well. We present a modified version of the Hadoop MapReduce framework that supports online aggregation, which allows users to see “early returns” from a job as it is being computed. Our Hadoop Online Prototype (HOP) also supports continuous queries, which enable MapReduce programs to be written for applications such as event monitoring and stream processing. HOP retains the fault tolerance properties of Hadoop and can run unmodified user-defined MapReduce programs. 1
