Building Blocks Of Apache Spark
In this section, we will discuss on Apache Spark architecture and its core components. Apache spark is built upon 3 main components as Data Storage, API and Resource Management. In this section, we will discuss about these 3 building blocks of the framework.
As per Data Storage, Spark is built upon an HDFS file system and capable of handling data from HBase or Cassandra systems as well. Spark API consists of interfaces to develop applications based on it in Java, Python and Scala languages. Using Spark, resource management can be done either in a single server instance or using a framework such as Mesos or YARN in a distributed manner.
Resilient Distributed Data Set
Apache Spark is built upon RDD (Resilient Distributed Data Set) concept. Similar to a table in a database, an RDD can hold data in various formats and is an immutable distributed collection of records. Spark can alternatively execute multiple programs between RDDs and RDD can recover faults efficiently through recomputing lost partitions upon a failure.
If you call any transformation upon an RDD, new RDD will be returned, while the original remains unmodified, since RDDs are immutable objects.
An RDD has 2 sets of parallel operations as Transformation and Action. A Transformation operation will always return an RDD, not a value and no evaluation happens in that case. Example Transformation operations are map, filter, flatMap, groupByKey, reduceByKey, sample, union, etc.
An Action operation, evaluates called functions on RDDs they are called upon,executing the queries and returns a value as the result. Example Action operations are count, reduce, collect, take, etc.
Getting Hands Dirty with Spark
Let us now install Apache spark and run a simple word count application. You can either use Spark setups available from vendors like Cloudera, MapR or HortonWorks or use it in the cloud. Spark needs Java installed on the system for it to run on your local machine. Hence we will first set up Java Development Kit. The steps described below are for a machine running Windows operating system. Note that,steps to set up Apache Spark on Linux or Mac OS will be similar but, the manner of setting up the environment variables may differ.
Installing JDK is quite straight forward. Download the JDK (Version 1.7 recommended) from the official vendor (Oracle) website and run the installer. Once installation is completed, verify successful completion by running below in the command line. Upon successful installation, it will show the Java version.
Now let us install Spark.
To install Spark on the system, navigate to the official Apache Spark website, download the latest version,unzip the file if necessary and move it to a convenient location. Move to that folder and launch the Spark Shell. An example is shown below, assuming it has been extracted to the location: C:spark_setup.
cd C:spark_setup spark-1.4.0-bin-hadoop2.1
If you see the below prompt, Whola!! You have installed Spark successfully.
15/07/21 13:15:32 INFO Http-Server:- Starting HTTP-Server
15/07/21 13:15:32 INFO Utils:- service is successfully in progress ‘HTTP-class_
server’ on the port of 58132.
Making Use of Scala (version is 2.10.4) (Java HotSpot)
Enter the expressions to evaluate them.
Type:- Taking help to find further info.
15/07/21 13:15:41INFO BlockManagerMaster: Registered BlockManager
15/07/21 13:15:41 INFO Spark-I Loop: Spark-context is created..
Spark-context can be made available – as sc.
To verify whether Spark shell executes properly, try below
To quit the shell, use below command.
Windows does not come with Python interpreter and hence, to run the Spark Python shell, we need to setup Pyhon in our environment. Python official website provides an installer for Windws or we can use a package like Anaconda, which comes with an added collection of computational tools written in Python.
Once python has been installed, you can launch the Spark Python Shell by executing pyspark in Spark installation directory. An example is given below:
cd C:spark_setup spark-1.4.0-bin-hadoop2.1
That is all we need to run Apache Spark interactive shell in Scala or Python. It also comes with a web console. Let us see how to use Apache Spark web console.
Using Apache Spark web Console
While working with Spark, to view analysis results and other information, navigate to below URL.
MasterURL for different modes:
Connection to Spark engine can be done in different modes. When running Spark locally or on cloud, this is done configuring the ‘MasterUrl’ parameter as per below.
Setting MasterURL parameter to:
Two types of shared variables can be used in Apache Spark to speed up the applications running on a cluster.
// Initializing broadcast variables
valbroadCastElement = sc.broadcast(Array(‘Nirman’, ‘Shan’, ‘Srini’))
// using broadcast variables
Below, is an example way of using an accumulator in Scalaprompt.
//Usage of accumulator variables
ValaccumuatorVar = sc.accumulator(0, “Examle Accumulator Variable”)
sc.parallelize(Array(‘Nirman’, ‘Shan’, ‘Srini’)).foreach(i =>accumuatorVar += i)
With the tools in hand, let us now collect the pieces together and build our simple word count application.
Word Count Application with Apache Spark
Using Spark API, data can be easily read from text files and processed. With the below example in Scalashell and we’ll see how they can be used.
To run the conventional word count application,in a Scala shell, run below commands.
valtextFileRead = “sample_data.md”
valtextFileData = sc.textFile(textFileRead)
Calling cache(),stores the RDD in cache and it can be easily read in further queries.cache() will be lazy-evaluated, meaning that it will be storing data not immediately, but whenever an action is called upon the RDD.
To read number of lines in the text file, run below command.
Now, can print out the word count next to each word in the file, as below.
valwordCountData= textFileData.flatMap(list =>list.split(” “)).map(word => (word, 1)).reduceByKey(_ + _)
Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.