流数据处理的博文

When windowing by processing time,the system essentially buffers up incoming data into windows until some amount of processing time has passed. For example,in the case of five-minute fixed windows,the system would buffer up data for five minutes of processing time,after which it would treat all the data it had observed in those five minutes as a window and send them downstream for processing.

There are a few nice properties of processing time windowing:

• It’s simple. The implementation is extremely straightforward since you never worry about shuffling data within time. You just buffer things up as they arrive and send them downstream when the window closes.
• Judging window completeness is straightforward. Since the system has perfect knowledge of whether all inputs for a window have been seen or not,it can make perfect decisions about whether a given window is complete or not. This means there is no need to be able to deal with “late” data in any way when windowing by processing time.
• If you’re wanting to infer information about the source as it is observed,processing time windowing is exactly what you want. Many monitoring scenarios fall into this category. Imagine tracking the number of requests per second sent to a global-scale Web service. Calculating a rate of these requests for the purpose of detecting outages is a perfect use of processing time windowing.

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