Spark中的narrow/wide dependency如何理解，有什么作用?

我在RDD的论文中看到他们的定义是这样的”narrow dependencies, where eachpartition of the parent RDD is used by at most one partitionof the child RDD, wide dependencies, where multiplechild partitions may depend on it”我的理解是narrow就是独生子女，一个父亲只有一个孩子；wide是一个父亲多个孩子。但是我感觉这个定义与论文中区分这两种dependency的作用是矛盾的：“First, narrowdependencies allow for pipelined execution on one clusternode, which can compute all the parent partitions. Forexample, one can apply a map followed by a filter on anelement-by-element basis. In contrast, wide dependenciesrequire data from all parent partitions to be availableand to be shuffled across the nodes using a MapReducelikeoperation. Second, recovery after a node failure ismore efficient with a narrow dependency, as only the lostparent partitions need to be recomputed, and they can berecomputed in parallel on different nodes. In contrast, ina lineage graph with wide dependencies, a single failednode might cause the loss of some partition from all theancestors of an RDD, requiring a complete re-execution.”narrow的第一个作用,我的理解是可以不用等上一次操作全部做完，每完成一条记录就可以进行下次操作，如map产生一条新纪录马上就做filter，但是对于wide，为什么不能这样做呢？另外，union是narrow操作，对两个分区做合并不需要shuffle吗，为什么只有wide操作才需要shuffle？对于narrow的第二个作用，两种dependency丢失单个分区会分别导致哪些其他分区也丢失呢？