In the last decade, Stream Processing Engines (SPEs) have emerged as a new processing paradigm that can process huge amounts of data while retaining low latency and high-throughputs. Yet, it is often necessary to join streaming data with traditiol databases to provide more contextual information for the end-users and applications. The major problem that we confront is to join the fast arriving stream tuples with the static relation tuples that are on a slow database. This is what we call the Stream-Relation Join (SRJ) problem. Currently, SPEs use a ive tuple-by-tuple approach for SRJ processing where the SPE accesses the database for every incoming tuple. Some SPEs use cache to avoid accessing the database for every incoming tuple, while others do not because of the stochastic ture of streaming data. In this paper, we propose a new SRJ operator to facilitate SRJ processing regardless of the cache performance using two techniques: batching and out-of-order processing. The proposed operator provides an effective generic solution to the SRJ problem and the cost of incorporating our operator into different SPEs is minimal. Our experiments use a variety of synthetic and real data sets demonstrating that our operator outperforms the state-of-the-art tuple-by-tuple approach in terms of maximizing the throughput under ordering and memory constraints.
CIKM 2013 : ACM International Conference on Information and Knowledge Management (2013)
Burlingame, CA, United States
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