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 Spark Official Website

You can download the Apache Spark distribution(0.8.0-incubating) from official website. 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 official website. 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

What is MQTT?

MQTT (Message Queuing Telemetry Transport) is a publish-subscribe based lightweight messaging protocol for use on top of the TCP/IP protocol. It was designed to be used in resource-constrained environments and for communication between devices with low-bandwidth or unreliable networks. MQTT is often used in Internet of Things (IoT) applications to communicate between devices and a central server, as well as in other types of messaging systems. In an MQTT system, there is a central broker that receives messages from publishers and routes them to subscribers that are subscribed to the relevant topic. MQTT is a lightweight and efficient protocol that makes it well-suited for IoT and messaging use cases.

Here the paint app is modified using with a new database system. The MongoDB is a famous NoSQL database system. The NoSQL database is a simple lightweight mechanism. It provides high scalability and availability. It provides horizontal scaling of data. This system redefined the database concept from the traditional relational database system. MongoDB is an open-source, document-oriented database designed for ease of development and scaling. The main features of MongoDB are flexibility, power, speed, and ease of use. The MongoDB can installed in local machine by following the instructions from official website

Some commands used in the MonoDB operations are given below:

db :- After starting the mongo shell your session will use the test database for context, by default. At any time issue the above operation at the mongo to report the current database. show dbs :- Display the list of databases from the mongo shell. use mydb :- Switch to a new database named mydb. help :- At any point you can access help for the mango shell using this operation. db.things.insert() :- Insert documents into the collection things.When you insert the first document, the mangod will create both the database and the things collection. show collections :- Displays the available collections in the database. db.things.find() :- Finds the documents in the collection. The documents to be found can be specified through arguments of the find function. The cursor of the MongoDB displays only the first 20 output documents. it command is used to display the rest of the documents.

The source code is available in

An application to draw simple drawings using lines, rectangles and circles in different colours.

The application is developed using JavaScript and HTML5. The canvas feature in HTML5 is used for providing a drawable region. The JavaScript is used to handle drawing functions in this region. The select button to select the different tools to draw.

This blog post is an introduction to the world of GPU programming with CUDA. We will cover the fundamental concepts and tools necessary to get started with CUDA, including:

• The steps involved in a typical GPU program, such as allocating storage on the GPU, transferring data between the CPU and GPU, and launching kernels on the GPU to process the data.
• How to use the Nvidia C Compiler (nvcc) to compile CUDA code and follow conventions like naming GPU data with a “d” prefix.
• Key functions like cudaMalloc and cudaMemcpy that are used to allocate GPU memory and transfer data between the host and device.
• The kernel launch operator and how to set the number of blocks and threads in the grid, as well as how to pass arguments to the kernel function.
• The importance of error checking in CUDA code.

To help illustrate these concepts, provided a simple example code that computes the squares of 64 numbers using CUDA. By the end of this post, you will have a basic foundation in GPU programming with CUDA and be ready to write your own programs and experience the performance benefits of using the GPU for parallel processing.

In my previous post I wrote about an introduction to parallel programming with CUDA. In this post explaining a simple example CUDA code to compute squares of 64 numbers. A typical GPU program consists of following steps.

1- CPU allocates storage on GPU
2- CPU copies input data from CPU to GPU
3- CPU launch kernels on GPU to process the data
4- CPU copies result back to CPU from GPU

nvcc -o square square.cu

Here is instead of running the regular C compiler we are running nvcc, the Nvidia C Compiler. The output is going to go an executable called square and our input file is “square.cu”. cu is the convention for how we name.Source code is available on github

We are going to walk through the CPU code first.

This post focuses on parallel computing on the GPU. Parallel computing is a way of solving large problems by breaking them into smaller pieces and run these smaller pieces at the same time.

Main reasons of technical trends in the parallel computing on the GPU

Modern processors are made from transistors. And each year, those transistors get smaller and smaller. The feature size is the minimum size of a transistor on a chip. As the feature size decreases, transistors get smaller, run faster, use less power, and put more of them on a chip. And the consequence is that ,more and more resources for computation every single year. One of the primary features of processors is clock speed . Over many years, the clock speeds continue to go up. However, over the last decade, that have essentially remained constant. Even though transistors are continuing to get smaller and faster and consume less energy per transistor, Running a billion transistors generates a high amount of heat . since we can’t keep all these processors cool, Power has emerged as a primary driving factor.

HTML5 is the new HTML standard. One of the most interesting new features in HTML5 is the canvas element canvas for 2D drawing. A canvas is a rectangular area on an HTML page. All drawing on the canvas must be done using JavaScript. This post goes through the basics of implementing a 2D canvas context, and using the basic canvas functions for developing a simple game. Creating a canvad context, adding the canvas element to your HTML document like so

<canvas id="Canvas" width="800" height="450"></canvas>

To draw inside the canvas need to use Javascript. First find the canvas element using getElementById, then initialize the context.

	<script>
	var canvas = documnet.getElementById("Canvas");
	var context = canvas.getContext("2d")


	</script>

To draw text on a canvas, the most import property and methods are: