How soccer players would do stream joins

Teubner, Jens; Mueller, Rene

doi:10.1145/1989323.1989389

Cited by 107 publications

(126 citation statements)

References 23 publications

Supporting

Mentioning

126

Contrasting

Order By: Relevance

“…Alternatively, another work [9] concentrates on the execution of SPJ (Select-Project-Join) queries with multi-query optimization. Other works [6], [16]- [18] focus on the acceleration of window join operators. Mueller et al propose Glacier [4], [5], a query-to-hardware compiler for continuous queries.…”

Section: Continuous Query Processing On Fpgasmentioning

confidence: 99%

Design and Evaluation of a Configurable Query Processing Hardware for Data Streams

Oge

Yoshimi

Miyoshi³

et al. 2015

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Yasin OGE†a) , Student Member, Masato YOSHIMI †b) , Takefumi MIYOSHI † †c) , Hideyuki KAWASHIMA † † †d) , Hidetsugu IRIE † † † †e) , Members, and Tsutomu YOSHINAGA †f) , Senior Member SUMMARYIn this paper, we propose Configurable Query Processing Hardware (CQPH), an FPGA-based accelerator for continuous query processing over data streams. CQPH is a highly optimized and minimaloverhead execution engine designed to deliver real-time response for highvolume data streams. Unlike most of the other FPGA-based approaches, CQPH provides on-the-fly configurability for multiple queries with its own dynamic configuration mechanism. With a dedicated query compiler, SQLlike queries can be easily configured into CQPH at run time. CQPH supports continuous queries including selection, group-by operation and sliding-window aggregation with a large number of overlapping sliding windows. As a proof of concept, a prototype of CQPH is implemented on an FPGA platform for a case study. Evaluation results indicate that a given query can be configured within just a few microseconds, and the prototype implementation of CQPH can process over 150 million tuples per second with a latency of less than a microsecond. Results also indicate that CQPH provides linear scalability to increase its flexibility (i.e., on-the-fly configurability) without sacrificing performance (i.e., maximum allowable clock speed). key words: FPGA, query processing, data stream, sliding-window aggregation, configurable hardware architecture IntroductionAn important and growing class of applications requires the ability to process online data streams on the fly in order to identify emerging trends in a timely manner. Data Stream Management Systems (DSMSs) [1] deal with potentially infinite streams of data that should be processed for real-time applications, executing SQL-like continuous queries [2] over data streams. It is essential for DSMSs that incoming data be processed in real time, or at least near realtime, depending on the applications' requirements. In particular, low-latency and high-throughput processing are key given the dynamic environment of data streams, queries can join and leave a streaming system at any time. It is therefore imperative for a query processing accelerator to support on-the-fly configurability for easy adaptation to the dynamic environment. To address the problem, Najafi et al. propose Flexible Query Processor (FQP) [11] for sliding-window join queries. To the best of our knowledge, however, slidingwindow aggregate queries-an important class of slidingwindow queries-are not discussed in their work [11]. Our Contribution. This paper presents Configurable Query Processing Hardware (CQPH), a highly optimized and minimal-overhead query processing engine, especially designed for sliding-window aggregate queries. CQPH is an FPGA-based query processor that contains a collection of configurable hardware modules, each of which supports (i) filtering, (ii) grouping, and (iii) aggregation. CQPH is highly optimized for performance with a fully pi...

show abstract

Section: Continuous Query Processing On Fpgasmentioning

confidence: 99%

Design and Evaluation of a Configurable Query Processing Hardware for Data Streams

Oge

Yoshimi

Miyoshi³

et al. 2015

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…While their work shares the similar objective of achieving flexible query processing, their focuses was limited to XML projections, while we focus on SQL-like query processing. Furthermore, Teubner et al [7] proposed a design for implementing a stream join on FPGAs. Their design aimed to efficiently evaluate a single join operation over two event streams; however, their design is static and lacks the flexibility for online changes to queries.…”

Section: Background and Related Workmentioning

confidence: 99%

“…There is a growing interest in accelerating data management and event stream processing with FPGAs [6,7,8]. These approaches are characterized by a processing pipeline that synthesizes static (sets of) queries into circuits operating on an FPGA (i.e., a fieldprogrammable gate array).…”

Section: Introductionmentioning

confidence: 99%

Flexible query processor on FPGAs

Najafi

Sadoghi²,

Jacobsen

2013

Proc. VLDB Endow.

View full text Add to dashboard Cite

In this work, we demonstrate Flexible Query Processor (FQP), an online reconfigurable event stream query processor. FQP is an FPGA-based query processor that supports select, project and join queries over event streams at line rate. While processing incoming events, FQP can accept new query expressions, a key distinguishing characteristic from related approaches employing FPGAs for acceleration. Our solution aims to address performance limitations experienced with general purpose processors needing to operate at line rate and lack of on the fly reconfigurability with custom designed hardware solutions on FPGAs.

show abstract

“…However, our work concentrates on supporting a more general multi-query stream processing (stateful matching) specifically designed to accelerate the execution of SPJ queries. Alternatively, [4] presented an efficient FPGA implementation of a single query (without join) while [8] focused on the data flow for streaming join over a large window size that spans many processing cores. Our approach also differs from [4], [8] as we are primarily concerned with multi-query optimization (with joins computed over a moderate size window) using Rete-like processing networks, supporting a rich relational algebra over event data streams, and offering an unprecedented degree of inter-and intraoperator parallelism that is only available through low-level logic design.…”

Section: Background and Related Workmentioning

confidence: 99%