Creating Maps and accessing their elements You’ll see concrete examples of each of these in this post. Associating each word with its possible parts-of-speech.Associating each word with its count in a given text.
Associating English words with their German translations.(Yes, this has the unfortunate ambiguity with the map function, but their use will be quite clear from context.) Maps allow you to store a collection of key-value pairs and to access the values by the keys associated with them, rather than via an index (as with a List). Another important kind of data structure is the associative array, which you’ll come to know in Scala as a Map. Lists (and other sequence data structures, like Ranges and Arrays) allow you to group collections of objects in an ordered manner: you can access elements of a list by indexing their position in the list, or iterate over the list elements, one by one, using for expressions and sequence functions like map, filter, reduce and fold.
FLATMAP SCALA SERIES
Other posts are on this blog, and you can get links to those and other resources on the links page of the Computational Linguistics course I’m creating these for. Additionally you can find this and other tutorial series on the JCG Java Tutorials page. In this Spark Tutorial, we learned the syntax and examples for RDD.flatMap() method.This is part 7 of tutorials for first-time programmers getting into Scala. 17/11/29 15:15:30 INFO DAGScheduler: ResultStage 0 (collect at /home/tutorialkart/workspace/spark/spark-rdd-flatmap-example.py:18) finished in 1.127 sġ7/11/29 15:15:30 INFO DAGScheduler: Job 0 finished: collect at /home/tutorialkart/workspace/spark/spark-rdd-flatmap-example.py:18, took 1.299076 sġ7/11/29 15:15:30 INFO SparkContext: Invoking stop() from shutdown hook Conclusion Spark will submit this python application for running. $ spark-submit spark-rdd-flatmap-example.py Run the following command in your console, from the location of your python file. Words = lines.flatMap(lambda line: line.split(" ")) Lines = sc.textFile("/home/tutorialkart/workspace/spark/sample.txt") # create Spark context with Spark configurationĬonf = SparkConf().setAppName("Read Text to RDD - Python") We shall implement the same use case as in the previous example, but as a Python application.įrom pyspark import SparkContext, SparkConf Output 17/11/29 12:33:59 INFO DAGScheduler: ResultStage 0 (collect at RDDflatMapExample.java:26) finished in 0.513 sġ7/11/29 12:33:59 INFO DAGScheduler: Job 0 finished: collect at RDDflatMapExample.java:26, took 0.793858 sġ7/11/29 12:33:59 INFO SparkContext: Invoking stop() from shutdown hook Python Example – Spark RDD.flatMap() Run this Spark Java application, and you will get following output in the console. JavaRDD words = lines.flatMap(s -> Arrays.asList(s.split(" ")).iterator()) įollowing is the input file we used in the above Java application. flatMap each line to words in the line String path = "data/rdd/input/sample.txt" JavaSparkContext sc = new JavaSparkContext(sparkConf) SparkConf sparkConf = new SparkConf().setAppName("Read Text to RDD") In this case, flatMap() kind of converts a list of sentences to a list of words. In this example, we will use flatMap() to convert a list of strings into a list of words. Where is the transformation function that could return multiple elements to new RDD for each of the element of source RDD.
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