Pages: 2
Rating : ⭐⭐⭐⭐⭐
Price: $10.99
Page 1 Preview
such those that handle key pairs and doubles

Such those that handle key pairs and doubles

Iterative Computation with Spark

It is not possible to override the default SparkContext class, nor is it possible to create a new one within a running Spark shell. It is however possible to specify which master the context connects to using the MASTER environment variable.

There are a few key differences between the Java and Scala APIs:, Function2, and other classes. As of Spark version 1.0 the API has been refactored to support Java 8 lambda expressions. With Java 8, Function classes can be replaced with inline expressions that act as a shorthand for anonymous functions.

The RDD methods return Java collections

First of all, we create a context using the JavaSparkContext class:

JavaSparkContext sc = new JavaSparkContext(master, "JavaWordCount", System.getenv("SPARK_HOME"), JavaSparkContext.

public Iterable<String> call(String s) {
return Arrays.asList(s.split(" "));

JavaPairRDD<String, Integer> ones = PairFunction<String, String, Integer>() {
public Tuple2<String, Integer> call(String s) { return new Tuple2<String, Integer>(s, 1); }

tweets = sc.textFile("/tmp/sample.txt")
counts = tweets.flatMap(lambda tweet: tweet.split(' ')) \ .map(lambda word: (word, 1)) \
.reduceByKey(lambda m,n:m+n)

[ 139 ]

You are viewing 1/3rd of the document.Purchase the document to get full access instantly

Immediately available after payment
Both online and downloadable
No strings attached
How It Works
Login account
Login Your Account
Place in cart
Add to Cart
send in the money
Make payment
Document download
Download File

Uploaded by : Matthew Moore

PageId: ELI6751883