By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this sensible ebook, 4 Cloudera information scientists current a collection of self-contained styles for appearing large-scale info research with Spark. The authors deliver Spark, statistical tools, and real-world info units jointly to educate you ways to method analytics difficulties through example.
You’ll begin 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, safeguard, and finance. in case you have an entry-level knowing of laptop studying and information, and also you software in Java, Python, or Scala, you’ll locate those styles important for engaged on your individual facts applications.
• Recommending tune and the Audioscrobbler information set
• Predicting woodland disguise with selection trees
• Anomaly detection in community site visitors with K-means clustering
• realizing Wikipedia with Latent Semantic Analysis
• interpreting co-occurrence networks with GraphX
• Geospatial and temporal info research at the long island urban Taxi journeys data
• Estimating monetary probability via Monte Carlo simulation
• studying genomics information and the BDG project
• interpreting neuroimaging information with PySpark and Thunder
Read or Download Advanced Analytics with Spark: Patterns for Learning from Data at Scale PDF
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Additional resources for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
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. Deciding when to cache data can be an art. The decision typically involves trade-offs between space and speed, with the specter of garbage collecting looming overhead to occasionally confound things further.
For our missing value analysis, our first task is to write an analogue of Spark’s Stat Counter class that correctly handles missing values. scala, and copy the following class defi‐ nitions into the file. add(x) } Our NAStatCounter class has two member variables: an immutable StatCounter instance named stats, and a mutable Long variable named missing. Note that we’re marking this class as Serializable because we will be using instances of this class inside Spark RDDs, and our job will fail if Spark cannot serialize the data contained inside an RDD.
Scala... StatCounter defined class NAStatCounter defined module NAStatCounter warning: previously defined class NAStatCounter is not a companion to object NAStatCounter. Companions must be defined together; you may wish to use :paste mode for this. merge(nas2) Creating Reusable Code for Computing Summary Statistics | 33 Let’s use our new NAStatCounter class to process the scores in the MatchData records within the parsed RDD. Each MatchData instance contains an array of scores of type Array[Double].