6,830 research outputs found
Exploring run-time reduction in programming codes via query optimization and caching
Object oriented programming languages raised the level of abstraction by supporting the explicit first class query constructs in the programming codes. These query constructs allow programmers to express operations on collections more abstractly than relying on their realization in loops or through provided libraries. Join optimization techniques from the field of database technology support efficient realizations of such language constructs. However, the problem associated with the existing techniques such as query optimization in Java Query Language (JQL) incurs run time overhead. Besides the programming languages supporting first-class query constructs, the usage of annotations has also increased in the software engineering community recently. Annotations are a common means of providing metadata information to the source code. The object oriented programming languages such as C# provides attributes constraints and Java has its own annotation constructs that allow the developers to include the metadata information in the program codes. This work introduces a series of query optimization approaches to reduce the run time of the programs involving explicit queries over collections. The proposed approaches rely on histograms to estimate the selectivity of the predicates and the joins in order to construct the query plans. The annotations in the source code are also utilized to gather the metadata required for the selectivity estimation of the numerical as well as the string valued predicates and joins in the queries. Several cache heuristics are proposed that effectively cache the results of repeated queries in the program codes. The cached query results are incrementally maintained up-to-date after the update operations to the collections --Abstract, page iv
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
Efficient Multi-way Theta-Join Processing Using MapReduce
Multi-way Theta-join queries are powerful in describing complex relations and
therefore widely employed in real practices. However, existing solutions from
traditional distributed and parallel databases for multi-way Theta-join queries
cannot be easily extended to fit a shared-nothing distributed computing
paradigm, which is proven to be able to support OLAP applications over immense
data volumes. In this work, we study the problem of efficient processing of
multi-way Theta-join queries using MapReduce from a cost-effective perspective.
Although there have been some works using the (key,value) pair-based
programming model to support join operations, efficient processing of multi-way
Theta-join queries has never been fully explored. The substantial challenge
lies in, given a number of processing units (that can run Map or Reduce tasks),
mapping a multi-way Theta-join query to a number of MapReduce jobs and having
them executed in a well scheduled sequence, such that the total processing time
span is minimized. Our solution mainly includes two parts: 1) cost metrics for
both single MapReduce job and a number of MapReduce jobs executed in a certain
order; 2) the efficient execution of a chain-typed Theta-join with only one
MapReduce job. Comparing with the query evaluation strategy proposed in [23]
and the widely adopted Pig Latin and Hive SQL solutions, our method achieves
significant improvement of the join processing efficiency.Comment: VLDB201
Query Optimization to Improve Performance of the Code Execution
Object-Oriented Programming (OOP) is one of the most successful techniques for abstraction. Bundling together objects into collections of objects, and then operating on these collections, is a fundamental part of main stream object-oriented programming languages. Object querying is an abstraction of operations over collections, whereas manual implementations are performed at low level which forces the developers to specify how a task must be done. Some object-oriented languages allow the programmers to express queries explicitly in the code, which are optimized using the query optimization techniques from the database domain. In this regard, we have developed a technique that performs query optimization at compile-time to reduce the burden of optimization at run-time to improve the performance of the code execution. Keywords- Querying; joins; compile time; run-time; histograms; query optimizatio
Implementation of Query Optimization for Reducing Run Time
Query optimization is the process of selecting the most efficient query-evaluation plan from many strategies so, In this paper we have developed a technique that performs query optimization at compile-time to reduce the burden of optimization at run-time to improve the performance of the code execution. using histograms that are computed from the data and these histograms are used to get the estimate of selectivity for query joins and predicates in a query at compile-time. With these estimates, a query plan is constructed at compile-time and executed it at run-time Keywords: runtime, query optimization ,compile time histogra
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