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Inside RavenDB Book–Chapter 8 is done
time to read 1 min | 33 words
Chapter 8 talks about indexing, this time from the point of view of what you can do with them, and how to best utilize them.
You can read it here: https://github.com/ayende/book/releases/
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Ayende about index base on MapReduce is obsolete? Spark Is the Next Top (Compute) Model ?
http://www.infoq.com/presentations/spark-scala-mapreduce-java?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global
Tonio, I'm afraid that I don't understand your question
Ayende,
"Dean Wampler argues that Spark/Scala is a better data processing engine than MapReduce/Java because tools inspired by mathematics, such as FP, are ideal tools for working with data"
see at
http://www.infoq.com/presentations/spark-scala-mapreduce-java?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global
Tonio, That is pretty much irrelevant, because we are talking about very different things here.
that mean RavenDb is not a BigData solution or Map/Reduce is not obsolete techinque?
Tonio,
Map-Reduce is just one of the ways to deal with BigData scenarios. Spark is oriented toward streaming data analysis and extraction which is relevant in realtime and from the stream itself. Map-Reduce in contrast is aimed at processing a very high amount of "static" (in the sense that it is already stored) through batching. Spark on the other hand will work (usually) with sliding window/slices kind of data. When the data is already "old" in the sense like extracting relevant information from yesterday, Map-Reduce is typically the way to go.
So what I mean, is that if you are using Map-Reduce for everything you are in a bad spot, because the model is prepared to deal with a very specific and narrow set of conditions. Spark on the other hand, has a very different, specific and narrow set of conditions.
Map-Reduce & Spark are complementary but are not the entire landscape either.
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