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摘要
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Abstract
With the rise of the web 2.0 and the Internet of things, it has become feasible to track all kinds of information over
time, in particular fine-grained user activities and sensor data on their environment and even their biometrics. However,
while efficiency remains mandatory for any application trying to cope with huge amounts of data, only part of the
potential of today's Big Data repositories can be exploited using traditional batch-oriented approaches as the value of
data often decays quickly and high latency becomes unacceptable in some applications. In the last couple of years, several
distributed data processing systems have emerged that deviate from the batch-oriented approach and tackle data items as
they arrive, thus acknowledging the growing importance of timeliness and velocity in Big Data analytics.
In this article, we give an overview over the state of the art of stream processors for low-latency Big Data analytics and
conduct a qualitative comparison of the most popular contenders, namely Storm and its abstraction layer Trident, Samza and
Spark Streaming. We describe their respective underlying rationales, the guarantees they provide and discuss the
trade-offs that come with selecting one of them for a particular task.
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