Apache Spark Scala Tutorial With Examples

This tutorial provides a quick introduction to using Spark. As per the above diagram, we will create an object of SparkSession, which provides SparkContext, SqlContext and HiveContext together in Spark 2.x. Thus, there is no need to create the SparkContext and SQLContext separately as we would do in Spark 1.x.

The full dataset with file is available here at Additionally I created a smaller version of this file with only 10 itemsposts in it. This file contains a small size of original dataset. Now that we have the code, we have to add the Spark library to the Scala project.

In summary, if you are interested in using Apache Spark to analyze log files - Apache access log files in particular - I hope this article has been helpful. Main objective is to jump-start your first Scala code on Spark platform with a very shot and simple code, i.e., the real Hello World”.

Spark has 100 times faster execution speed than Hadoop MapReduce, that is beneficial for large-scale data processing. The only downside to using DataFrames is that you've lost compile-time type safety when you work with DataFrames, which makes your code more prone to errors.

Now-a-days, whenever we talk about Big Data, only one word strike us - the next-gen Big Data tool - Apache Spark”. Moreover, it also overcomes the limitations of Hadoop since it can only build applications in Java. Spark-submit script has several flags that help control the resources used by your Apache Spark application.

When a client submits spark application code to the Spark Driver, Spark Driver implicitly converts the transformations and actions to (DAG)Directed Acyclic Graph and submits it to a DAG Scheduler (During this conversion to DAG, it also performs optimization such as pipelining transformations).

To compile and run the Scala code on Spark platform. Their main goal is to make Spark easier to use and run and all of their work is donated back to the Apache Spark project. In the Spark shell, a special interpreter-aware SparkContext is already created for us, in the variable called sc. So, making our own SparkContext will not work.

Apache Spark is a powerful, fast open source framework for big data processing. On remote worker machines, PythonRDD objects launch Python subprocesses and communicate with them using pipes, sending the user's code and the data to be processed. The SparkContext represents the connection to a Spark cluster and can be used to create RDD's and DataFrames.

We will walk through a simple application in Scala (with sbt), Java (with Maven), and Python (pip). If you are planning to start a career in Apache Hadoop , Spark or Big Data then you are on the right path to pave an established career with JanBask Training right away.

Existing RDDs - By applying transformation operation on existing RDDs we can create new RDD. Apache Spark is a fast and general engine for large-scale data processing”. Execute the process using jar file created in "target" directory. It Apache Spark Tutorial creates a new Spark RDD from the existing one.

The csv file comes with all HDInsight Spark clusters. In the DataFrame SQL query, we showed how to issue an SQL order by query on a dataframe We can re-write the dataframe group by, count and order by tag query using Spark SQL as shown below. Basically, for further processing, Streaming divides continuous flowing input data into discrete units.

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