Advanced Analytics with Spark: Patterns for Learning from by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills

By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills

During this useful e-book, 4 Cloudera information scientists current a suite of self-contained styles for appearing large-scale info research with Spark. The authors deliver Spark, statistical tools, and real-world facts units jointly to coach you the way to method analytics difficulties through example.

You’ll begin with an creation to Spark and its surroundings, after which dive into styles that observe universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields corresponding to genomics, protection, and finance. when you have an entry-level realizing of laptop studying and statistics, and also you application in Java, Python, or Scala, you’ll locate those styles beneficial for engaged on your personal facts applications.

Patterns include:

• Recommending tune and the Audioscrobbler facts set
• Predicting woodland conceal with determination trees
• Anomaly detection in community site visitors with K-means clustering
• figuring out Wikipedia with Latent Semantic Analysis
• reading co-occurrence networks with GraphX
• Geospatial and temporal info research at the manhattan urban Taxi journeys data
• Estimating monetary possibility via Monte Carlo simulation
• studying genomics info and the BDG project
• reading neuroimaging facts with PySpark and Thunder

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Additional resources for Advanced Analytics with Spark: Patterns for Learning from Data at Scale

Sample text

For instances of RDD[Double], the Spark APIs provide an additional set of actions via implicit type conversion, in the same way we saw that the toInt method is provided for the String class. These implicit actions allow us to extend the functionality of an RDD in useful ways when we have additional information about how to process the values it contains. Pair RDDs In addition to the RDD[Double] implicit actions, Spark supports implicit type conver‐ sion for the RDD[Tuple2[K, V]] type that provides methods for performing per-key aggregations like groupByKey and reduceByKey, as well as methods that enable join‐ ing multiple RDDs that have keys of the same type.

Foreach(println) ... foreach(println) ... (true,20931) (false,5728201) Creating Histograms | 29 By default, the sortBy function sorts numeric values in ascending order, but it’s often more useful to look at the values in a histogram in descending order. foreach(println) ... 4% of the input pairs actually match. , many pairs of records will look like matches even though they actually are not). Summary Statistics for Continuous Variables Spark’s countByValue action is a great way to create histograms for relatively low car‐ dinality categorical variables in our data.

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.

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