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.
以上所述就是小编给大家介绍的《Streamz: Python pipelines to manage continuous streams of data》，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持！
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Spring技术内幕
计文柯 / 机械工业出版社 / 2010-1-1 / 55.00元
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