April 2017
Intermediate to advanced
532 pages
12h 39m
English
book
Machine Learning with Spark - Second Edition
by Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet Singh Ghotra, Nick Pentreath
Content preview from Machine Learning with Spark - Second Edition
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Window operators
As Spark Streaming operates on time-ordered batched streams of data, it introduces a new concept, which is that of windowing. A window function computes a transformation over a sliding window applied to the stream.
A window is defined by the length of the window and the sliding interval. For example, with a 10-second window and a 5-second sliding interval, we will compute results every 5 seconds, based on the latest 10 seconds of data in the DStream. For example, we might wish to calculate the top websites by page view numbers over the last 10 seconds and recompute this metric every 5 seconds using a sliding window.
The following figure illustrates a windowed DStream:
A windowed DStream
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