By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this functional ebook, 4 Cloudera information scientists current a suite of self-contained styles for acting large-scale info research with Spark. The authors carry Spark, statistical tools, and real-world information units jointly to coach you the way to procedure analytics difficulties by means of example.
You’ll commence with an advent to Spark and its environment, after which dive into styles that follow universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields resembling genomics, safety, and finance. when you've got an entry-level figuring out of computing device studying and facts, and also you application in Java, Python, or Scala, you’ll locate those styles worthy for engaged on your personal information applications.
• Recommending tune and the Audioscrobbler information set
• Predicting wooded area hide with selection trees
• Anomaly detection in community site visitors with K-means clustering
• figuring out Wikipedia with Latent Semantic Analysis
• examining co-occurrence networks with GraphX
• Geospatial and temporal information research at the big apple urban Taxi journeys data
• Estimating monetary possibility via Monte Carlo simulation
• reading genomics facts and the BDG project
• reading neuroimaging information with PySpark and Thunder
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Additional resources for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
When we use the right format (more on this in a bit), serialized data usually takes up two to five times less space than its raw equivalent. Spark can use disk for caching RDDs as well. The MEMORY_AND_DISK and MEM ORY_AND_DISK_SER are similar to the MEMORY and MEMORY_SER storage levels, respec‐ tively. For the latter two, if a partition will not fit in memory, it is simply not stored, meaning that it must be recomputed from its dependencies the next time an action uses it. For the former, Spark spills partitions that will not fit in memory to disk.
This is where the “alternating” part comes from. There’s just one small problem: Y was made up, and random! X was computed optimally, yes, but given a bogus solution for Y. Fortunately, if this process is repeated, X and Y do even‐ tually converge to decent solutions. When used to factor a matrix representing implicit data, there is a little more com‐ plexity to the ALS factorization. It is not factoring the input matrix A directly, but a matrix P of 0s and 1s, containing 1 where A contains a positive value and 0 elsewhere.
There are a few things happening on this line that are worth going over. First, we’re declaring a new variable called rawblocks. As we can see from the shell, the raw blocks variable has a type of RDD[String], even though we never specified that type information in our variable declaration. This is a feature of the Scala programming language called type inference, and it saves us a lot of typing when we’re working with the language. Whenever possible, Scala figures out what type a variable has based on its context.
Advanced Analytics with Spark: Patterns for Learning from Data at Scale by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills