Hi,
Any {fan-out -> process in parallel -> fan-in -> aggregate} pattern of data
flow can be conceptually Map-Reduce(MR, as it is done in Hadoop).
Apart from the bigger list of map, reduce, sort, filter, pipe, join,
combine,... functions, that are many times more efficient and productive for
developers, it is how Spark does it that is different.
Ex: RDDs enable high availability of Data with only a single copy. HDFS needs
multiple copies causing a lot of I/O resource usage.
Regards,
Prajod
From: Ashic Mahtab [mailto:as...@live.com]
Sent: 28 June 2015 22:21
To: YaoPau; Apache Spark
Subject: RE: What does "Spark is not just MapReduce" mean? Isn't every Spark
job a form of MapReduce?
Spark comes with quite a few components. At it's core is..surprise....spark
core. This provides the core things required to run spark jobs. Spark provides
a lot of operators out of the box...take a look at
https://spark.apache.org/docs/latest/programming-guide.html#transformations
https://spark.apache.org/docs/latest/programming-guide.html#actions
While all of them can be implemented with variations of rd.map().reduce(),
there are optimisations to be gained in terms of data locality, etc., and the
additional operators simply make life simpler.
In addition to the core stuff, spark also brings things like Spark Streaming,
Spark Sql and data frames, MLLib, GraphX, etc. Spark Streaming gives you
microbatches of rdds at periodic intervals.Think "give me the last 15 seconds
of events every 5 seconds". You can then program towards the small collection,
and the job will run in a fault tolerant manner on your cluster. Spark Sql
provides hive like functionality that works nicely with various data sources,
and RDDs. MLLib provide a lot of oob machine learning algorithms, and the new
Spark ML project provides a nice elegant pipeline api to take care of a lot of
common machine learning tasks. GraphX allows you to represent data in graphs,
and run graph algorithms on it. e.g. you can represent your data as RDDs of
vertexes and edges, and run pagerank on a distributed cluster.
And there's more....so, yeah...Spark is definitely "not just" MapReduce. :)
> Date: Sun, 28 Jun 2015 09:13:18 -0700
> From: jonrgr...@gmail.com<mailto:jonrgr...@gmail.com>
> To: user@spark.apache.org<mailto:user@spark.apache.org>
> Subject: What does "Spark is not just MapReduce" mean? Isn't every Spark job
> a form of MapReduce?
>
> I've heard "Spark is not just MapReduce" mentioned during Spark talks, but it
> seems like every method that Spark has is really doing something like (Map
> -> Reduce) or (Map -> Map -> Map -> Reduce) etc behind the scenes, with the
> performance benefit of keeping RDDs in memory between stages.
>
> Am I wrong about that? Is Spark doing anything more efficiently than a
> series of Maps followed by a Reduce in memory? What methods does Spark have
> that can't easily be mapped (with somewhat similar efficiency) to Map and
> Reduce in memory?
>
>
>
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> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>
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