Is caching the only advantage of spark over map-reduce?

Question

I have started to learn about Apache Spark and am very impressed by the framework. Although one thing which keeps bothering me is that in all Spark presentations they talk a

Answer 1

Caching + in memory computation is definitely a big thing for spark, However there are other things.

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RDD(Resilient Distributed Data set): an RDD is the main abstraction of spark. It allows recovery of failed nodes by re-computation of the DAG while also supporting a more similar recovery style to Hadoop by way of checkpointing, to reduce the dependencies of an RDD. Storing a spark job in a DAG allows for lazy computation of RDD's and can also allow spark's optimization engine to schedule the flow in ways that make a big difference in performance.

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Spark API: Hadoop MapReduce has a very strict API that doesn't allow for as much versatility. Since spark abstracts away many of the low level details it allows for more productivity. Also things like broadcast variables and accumulators are much more versatile than DistributedCache and counters IMO.

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Spark Streaming: spark streaming is based on a paper Discretized Streams, which proposes a new model for doing windowed computations on streams using micro batches. Hadoop doesn't support anything like this.

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As a product of in memory computation spark sort of acts as it's own flow scheduler. Whereas with standard MR you need an external job scheduler like Azkaban or Oozie to schedule complex flows

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The hadoop project is made up of MapReduce, YARN, commons and HDFS; spark however is attempting to create one unified big data platform with libraries (in the same repo) for machine learning, graph processing, streaming, multiple sql type libraries and I believe a deep learning library is in the beginning stages. While none of this is strictly a feature of spark it is a product of spark's computing model. Tachyon and BlinkDB are two other technologies that are built around spark.