Name
Abstract | ||
|---|---|---|
Mobile applications, such as those on WebOS, increasingly depend on continuous analytics results of real-time events, for monitoring oil & gas production, watching traffic status and detecting accident, etc, which has given rise to the need of providing Continuous analytics as a Service (CaaaS). While representing a paradigm shift in cloud computing, CaaaS poses several challenges in scalability, latency, time-window semantics, transaction control and result-set staging. A data stream is infinite thus can only be analyzed in granules. We propose a continuous query model over both static relations and dynamic streaming data, which allows a long-standing SQL query instance to run cycle by cycle, each cycle for a chunk of data from the data stream, using a cut-and-rewind mechanism. We further support the cycle-based transaction model with cycle-based isolation and visibility, for delivering analytics results to the clients continuously while the query is running. To have the continuously generated analytics results staged efficiently, we developed the table-ring and label switching mechanism characterized by staging data through metadata manipulation without physical data moving and copying. To scale-out analytics computation, we support both parallel database based and network distributed Map-Reduce based infrastructure with multiple cooperating engines. We have built the proposed infrastructure by extending the PostgreSQL engine. We tested the throughput and latency of this service based on a well-known stream processing benchmark; the results show that the proposed approach is highly competitive. Our experiments indicate that the database technology can be extended and applied to real-time continuous analytics service provisioning. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1951365.1951426 | EDBT |
Keywords | Field | DocType |
continuous analytics result,well-known stream processing benchmark,analytics computation,continuous analytics,physical data,continuous query model,real-time continuous analytics service,long-standing sql query instance,data stream,analytics result,stream processing,real time,paradigm shift,cloud computing,base isolation,cloud service | SQL,Metadata,Data stream,Computer science,Parallel database,Real-time computing,Analytics,Stream processing,Database,Cloud computing,Scalability | Conference |
Citations | PageRank | References |
24 | 1.19 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiming Chen | 1 | 2010 | 233.16 |
| Meichun Hsu | 2 | 3437 | 778.34 |
| Hans Zeller | 3 | 24 | 1.19 |