697,061 research outputs found
Extending a multi-set relational algebra to a parallel environment
Parallel database systems will very probably be the future for high-performance data-intensive applications. In the past decade, many parallel database systems have been developed, together with many languages and approaches to specify operations in these systems. A common background is still missing, however. This paper proposes an extended relational algebra for this purpose, based on the well-known standard relational algebra. The extended algebra provides both complete database manipulation language features, and data distribution and process allocation primitives to describe parallelism. It is defined in terms of multi-sets of tuples to allow handling of duplicates and to obtain a close connection to the world of high-performance data processing. Due to its algebraic nature, the language is well suited for optimization and parallelization through expression rewriting. The proposed language can be used as a database manipulation language on its own, as has been done in the PRISMA parallel database project, or as a formal basis for other languages, like SQL
Database Middleware for High Performance Transaction Processing with Isolation
制度:新 ; 報告番号:甲3289号 ; 学位の種類:博士(工学) ; 授与年月日:2011/2/25 ; 早大学位記番号:新559
Pattern based processing of XPath queries
As the popularity of areas including document storage and
distributed systems continues to grow, the demand for high
performance XML databases is increasingly evident. This
has led to a number of research eorts aimed at exploiting
the maturity of relational database systems in order to in-
crease XML query performance. In our approach, we use an
index structure based on a metamodel for XML databases
combined with relational database technology to facilitate
fast access to XML document elements. The query process
involves transforming XPath expressions to SQL which can
be executed over our optimised query engine. As there are
many dierent types of XPath queries, varying processing
logic may be applied to boost performance not only to indi-
vidual XPath axes, but across multiple axes simultaneously.
This paper describes a pattern based approach to XPath
query processing, which permits the execution of a group of
XPath location steps in parallel
A storage and access architecture for efficient query processing in spatial database systems
Due to the high complexity of objects and queries and also due to extremely
large data volumes, geographic database systems impose stringent requirements on their
storage and access architecture with respect to efficient query processing. Performance
improving concepts such as spatial storage and access structures, approximations, object
decompositions and multi-phase query processing have been suggested and analyzed as
single building blocks. In this paper, we describe a storage and access architecture which
is composed from the above building blocks in a modular fashion. Additionally, we incorporate
into our architecture a new ingredient, the scene organization, for efficiently
supporting set-oriented access of large-area region queries. An experimental performance
comparison demonstrates that the concept of scene organization leads to considerable
performance improvements for large-area region queries by a factor of up to 150
An Application-Specific Instruction Set for Accelerating Set-Oriented Database Primitives
The key task of database systems is to efficiently manage large amounts of data. A high query throughput and a low query latency are essential for the success of a database system. Lately, research focused on exploiting hardware features like superscalar execution units, SIMD, or multiple cores to speed up processing. Apart from these software optimizations for given hardware, even tailor-made processing circuits running on FPGAs are built to run mostly stateless query plans with incredibly high throughput. A similar idea, which was already considered three decades ago, is to build tailor-made hardware like a database processor. Despite their superior performance, such application-specific processors were not considered to be beneficial because general-purpose processors eventually always caught up so that the high development costs did not pay off. In this paper, we show that the development of a database processor is much more feasible nowadays through the availability of customizable processors. We illustrate exemplarily how to create an instruction set extension for set-oriented database rimitives. The resulting application-specific processor provides not only a high performance but it also enables very energy-efficient processing. Our processor requires in various configurations more than 960x less energy than a high-end x86 processor while providing the same performance
Write-limited sorts and joins for persistent memory
To mitigate the impact of the widening gap between the memory needs of CPUs and what standard memory technology can deliver, system architects have introduced a new class of memory technology termed persistent memory. Persistent memory is byteaddressable, but exhibits asymmetric I/O: writes are typically one order of magnitude more expensive than reads. Byte addressability combined with I/O asymmetry render the performance profile of persistent memory unique. Thus, it becomes imperative to find new ways to seamlessly incorporate it into database systems. We do so in the context of query processing. We focus on the fundamental operations of sort and join processing. We introduce the notion of write-limited algorithms that effectively minimize the I/O cost. We give a high-level API that enables the system to dynamically optimize the workflow of the algorithms; or, alternatively, allows the developer to tune the write profile of the algorithms. We present four different techniques to incorporate persistent memory into the database processing stack in light of this API. We have implemented and extensively evaluated all our proposals. Our results show that the algorithms deliver on their promise of I/O-minimality and tunable performance. We showcase the merits and deficiencies of each implementation technique, thus taking a solid first step towards incorporating persistent memory into query processing. 1
Vectorwise: Beyond Column Stores
textabstractThis paper tells the story of Vectorwise, a high-performance analytical database system, from multiple perspectives: its history from academic project to commercial product, the evolution of its technical
architecture, customer reactions to the product and its future research and development roadmap. One take-away from this story is that the novelty in Vectorwise is much more than just column-storage:
it boasts many query processing innovations in its vectorized execution model, and an adaptive mixed
row/column data storage model with indexing support tailored to analytical workloads. Another one is that there is a long road from research prototype to commercial product, though database research continues to achieve a strong innovative influence on product development
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