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 in Hadoop MapReduce boil down to multiple iterations of (i) read data from HDFS, (ii) apply map and reduce, (iii) write back to HDFS. Each map-reduce round is completely independent of each other, and Hadoop does not have any global knowledge of what MR steps are going to come after each MR. For many iterative algorithms this is inefficient as the data between each map-reduce pair gets written and read from filesystem. Newer systems like Spark and Tez improves performance over Hadoop by considering the whole DAG of map-reduce steps and optimizing it globally (e.g., pipelining consecutive map steps into one, not write intermediate data to HDFS). This prevents writing data back and forth after every reduce.
Storm, being a streaming system, is slightly different from the batch processing systems referred earlier. It also sets up a DAG of nodes lets the records stream between the nodes. Its best to compare Storm with Spark Streaming (streaming system built over Spark) than Hadoop MapReduce. Both accepts a DAG of operations representing the streaming computation, but then process the DAG in slightly different ways. Storm sets up a DAG of node and allocates each operation in the DAG of ops to different nodes. Spark Streaming does not pre-allocate, rather uses the underlying Spark's mechanisms to dynamically allocate tasks to available resources. This gives different kinds of performance characteristics.
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 in Hadoop MapReduce boil down to multiple iterations of (i) read data from HDFS, (ii) apply map and reduce, (iii) write back to HDFS. Each map-reduce round is completely independent of each other, and Hadoop does not have any global knowledge of what MR steps are going to come after each MR. For many iterative algorithms this is inefficient as the data between each map-reduce pair gets written and read from filesystem. Newer systems like Spark and Tez improves performance over Hadoop by considering the whole DAG of map-reduce steps and optimizing it globally (e.g., pipelining consecutive map steps into one, not write intermediate data to HDFS). This prevents writing data back and forth after every reduce.
Storm, being a streaming system, is slightly different from the batch processing systems referred earlier. It also sets up a DAG of nodes lets the records stream between the nodes. Its best to compare Storm with Spark Streaming (streaming system built over Spark) than Hadoop MapReduce. Both accepts a DAG of operations representing the streaming computation, but then process the DAG in slightly different ways. Storm sets up a DAG of node and allocates each operation in the DAG of ops to different nodes. Spark Streaming does not pre-allocate, rather uses the underlying Spark's mechanisms to dynamically allocate tasks to available resources. This gives different kinds of performance characteristics.
