Name
Abstract | ||
|---|---|---|
Real-time enterprise applications and data-intensive analytics often require very low latency access to large volumes of data. In-memory data cache is a promising approach to enable real-time analytics. In this paper we examine the issue of scaling out memory cache over multiple machines while providing a common data query language with rich expressive power, and allowing the data cached in memory to persist with the ACID properties. Designing and building a product quality scaled-out data cache engine from scratch is an option, but it requires large investment in time and engineering efforts. We propose to build such a system by extending existing SQL systems. In our approach, we extend database buffer pool with a Distributed Caching Platform (DCP). We have developed a prototype of the system, which we call MemcacheSQL, engine by integrating PostgreSQL's buffer management with Memcached, a widely used DCP platform. Our contributions include optimized data coherence management along the memory hierarchy, and flexibly configurable page buffering policies for accommodating various workloads. Our preliminary experiments show that the proposed system has the potential to achieve scalable performance gains by dynamically leveraging scale-out memory nodes, while retaining the full SQL's expressive power and DBMS's ACID support. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-33500-6_2 | Lecture Notes in Business Information Processing |
Field | DocType | Volume |
SQL,CPU cache,Cache,Computer science,Page cache,Cache algorithms,Analytics,Smart Cache,SQL injection,Database | Conference | 126 |
ISSN | Citations | PageRank |
1865-1348 | 2 | 0.41 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiming Chen | 1 | 2010 | 233.16 |
| Meichun Hsu | 2 | 3437 | 778.34 |
| Ren Wu | 3 | 92 | 17.28 |