流数据处理的博文

Figure 7: Computing approximations on unbounded data. Data are run through a complex algorithm,yielding output data that look more or less like the desired result on the other side. Image: Tyler Akidau.

The second major category of approaches is approximation algorithms,such as approximate Top-N,streaming K-means,etc. They take an unbounded source of input and provide output data that,if you squint at them,look more or less like what you were hoping to get. The upside of approximation algorithms is that,by design,they are low overhead and designed for unbounded data. The downsides are that a limited set of them exist,the algorithms themselves are often complicated (which makes it difficult to conjure up new ones),and their approximate nature limits their utility.

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