内容简介：Continuous data streams arise in many applications like the following:Sometimes these pipelines are very simple, with a linear sequence of processing steps:And sometimes these pipelines are more complex, involving branching, look-back periods, feedback int
Continuous data streams arise in many applications like the following:
- Log processing from web servers
- Scientific instrument data like telemetry or image processing pipelines
- Financial time series
- Machine learning pipelines for real-time and on-line learning
Sometimes these pipelines are very simple, with a linear sequence of processing steps:
And sometimes these pipelines are more complex, involving branching, look-back periods, feedback into earlier stages, and more.
Streamz endeavors to be simple in simple cases, while also being powerful enough to let you define custom and powerful pipelines for your application.
Why not Python generator expressions?
Python users often manage continuous sequences of data with iterators or generator expressions.
def fib():
a, b = 0, 1
while True:
yield a
a, b = b, a + b
sequence = (f(n) for n in fib())
However iterators become challenging when you want to fork them or control the flow of data. Typically people rely on tools like itertools.tee , and zip .
x1, x2 = itertools.tee(x, 2) y1 = map(f, x1) y2 = map(g, x2)
However this quickly become cumbersome, especially when building complex pipelines.
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数据资本时代
Viktor Mayer-Schnberger / 李晓霞、周涛 / 中信出版集团股份有限公司 / 2018-11-1 / CNY 58.00
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