DAG (directed acyclic graph) 作为大数据执行引擎的优点

TL;DR - Conceptually DAG model is a strict generalization of MapReduce model. DAG-based systems like Spark and Tez that are aware of the whole DAG of operations can do better global optimizations than systems like Hadoop MapReduce which are unaware of the DAG to be executed.

Long version:
Conceptually speaking, the MapReduce model simply states that distributed computation on a large dataset can be boiled down to two kinds of computation steps - a map step and a reduce step. One pair of map and reduce does one level of aggregation over the data. Complex computations typically require multiple such steps. When you have multiple such steps, it essentially forms a DAG of operations. So a DAG execution model is essentially a generalization of the MapReduce model.

While this is the theory, different systems implement this theory in different ways, and that is where the "advantages" and "disadvantages" come from. Computations expressed