1樓：楊建國
Relational data is often normalized to retain its integrity and remove redundancy.
Normalization poses problems for MapReduce because it makes reading a record a
nonlocal operation, and one of the central assumptions that MapReduce makes is that
it is possible to perform (high-speed) streaming reads and writes.
A web server log is a good example of a set of records that is not normalized (for example,
the client hostnames are specified in full each time, even though the same client
well-suited to analysis with MapReduce.
MapReduce is a linearly scalable programming model. The programmer writes two
from one set of key-value pairs to another. These functions are oblivious to the size of
the data or the cluster that they are operating on, so they can be used unchanged for a
small dataset and for a massive one. More important, if you double the size of the input
data, a job will run twice as slow. But if you also double the size of the cluster, a job
will run as fast as the original one. This is not generally true of SQL queries.摘自英文原版，第二段已經解釋了上面的論述，可以看看。
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