Performance tuning is an important aspect of working with Apache Spark, as it can help ensure that your data processing tasks are efficient and run smoothly. In this blog post, we will delve into the common issues that can be considered when tuning the performance of Apache Spark. These issues include spill, skew, shuffle, storage, and serialization.

Spill

One problem that can occur is spill, which is the writing of temp files to disk due to a lack of memory. This can happen when the data being processed is too large to fit into memory, and it can significantly impact the performance of your tasks.

Two of the earlier posts are discussing installing Apache Spark-0.8.0 and Apache Spark-1.1.0 on Ubuntu-12.04 and Ubuntu-14.04 respectively. In this post you can discover necessary actions to set up Apache Spark-2.0.2 on Ubuntu 16.04 and Mac OS X Sierra. For enhanced guidance refer above mentioned posts.

Apache Spark is a powerful big data processing engine that is well-suited for use in a distributed environment. One way to interact with Spark is through the use of an IPython Notebook, which allows you to run and debug your Spark code in an interactive manner. This tutorial will guide you through the process of setting up and running a PySpark Notebook using Docker.

Installing Docker

Docker is a containerization platform that allows you to package and deploy your applications in a predictable and isolated environment.

In my previous post, I wrote about installation of Spark and Scala interactive shell. Here in this post, we’ll see how to do the same in Python.

Similar to Scala interactive shell, there is an interactive shell available for Python. You can run it with the below command from spark root folder:

./bin/pyspark

Now you can enjoy Spark using Python interactive shell.

This shell might be sufficient for experimentations and developments. However, for production level, we should use a standalone application.

Update: For Apache Spark 2 refer latest post

One of the previous post mentioning about install Apache Spark-0.8.0 on Ubuntu-12.04. In this post explain about detailed steps to set up Apache Spark-1.1.0 on Ubuntu. For running Spark in Ubuntu machine should install Java. Using following commands easily install Java in Ubuntu machine.

	$ sudo apt-add-repository ppa:webupd8team/java
	$ sudo apt-get update
	$ sudo apt-get install oracle-java7-installer

To check the Java installation is successful

	$ java -version

It shows installed java version

java version "1.7.0_72"_ Java(TM) SE Runtime Environment (build 1.7.0_72-b14)_ Java HotSpot(TM) 64-Bit Server VM (build 24.72-b04, mixed mode)

In next step is install Scala, follow the following instructions to set up Scala.

In this post, we will discuss how to create an assembled JAR for a standalone Spark application using the sbt-assembly plugin. One of my previous posts, we discussed how to build a stand alone Spark Application using SBT eclipse plugin. Now, we will take it one step further and show you how to create a fat JAR for your Spark project using the sbt-assembly plugin.

Adding the sbt-assembly Plugin

The first step in creating an assembled JAR for your Spark application is to add the sbt-assembly plugin. To do this, you will need to add the following line to the project/plugin.sbt file:

addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.9.1")

Configuring Assembly Settings

Next, you will need to specify sbt-assembly.git as a dependency in the project/project/build.scala file:

import sbt._

object Plugins extends Build {
  lazy val root = Project("root", file(".")) dependsOn(
    uri("git://github.com/sbt/sbt-assembly.git#0.9.1")
  )
}

In the build.sbt file, add the following contents:

This blog post will guide you through the process of building a Spark application in Scala that calculates popular hashtags from a Twitter stream. You will also learn how to use the sbt eclipse plugin to run the application in the Eclipse Integrated Development Environment (IDE). Whether you are new to big data processing or looking to improve your skills in data enginering and analytics, this tutorial has something to offer. Follow along with our step-by-step guide to develop your own stand alone Spark application and enhance your abilities in this exciting field.

Sharing some ideas about how to create a Spark-streaming stand-alone application and how to run the Spark applications in scala-SDK (Eclipse IDE).

Building Spark Application using SBT

A Standalone application in Scala using Apache Spark API. The application is build using Simple Build Tool(SBT).

For creating a stand alone app take the twitter popular tag example

This program calculates popular hashtags (popular topics) over sliding 10 and 60 second windows from a Twitter stream. The stream is instantiated with credentials and optionally filters supplied by the command line arguments.

But here modified the code for talking twitter authentication credentials through command line argument. So it needs to give the arguments as

Update: To install Apache Spark-1.0 follow this post

Apache Spark is an open source in memory cluster computing framework. Initially developed in UC Berkely AMPLab and now an Apache Incubator Project. Apache Spark is a cluster computing framework designed for low-latency iterative jobs and interactive use from an interpreter. It provides clean, language-integrated APIs in Scala, Java, and Python, with a rich array of parallel operators. You may read more about it here

You can download the Apache Spark distribution(0.8.0-incubating) from here. After that untar the downloaded file.

$ tar xvf spark-0.8.0-incubating.tgz

You need to have Scala installed, or the SCALA_HOME environment variable pointing to a Scala installation.

Building

SBT(Simple Build Tool) is used for building Spark, which is bundled with it. To compile the code

You will need the following packages to run Mesos.

$ sudo apt-get install python2.7-dev g++ libcppunit-dev libunwind7-dev git libcurl4-nss-dev

You need to have Java installed, or the JAVA_HOME environment variable pointing to a Java installation.

You can download the Mesos distribution from here. After that untar the downloaded file

$ tar xvf mesos-0.13.0.tar.gz

Building and Installing

$ cd mesos-0.13.0
$ mkdir build
$ cd build
$ sudo  ../configure --prefix=/home/user/mesos
$ sudo make
$ sudo make check
$ sudo make install

You can pass the –prefix option while configuring to tell where to install. For example