Spark

kafka with spark

Tuning a Spark Application

Having trouble optimizing your Spark application? If yes, then this blog will surely guide you on how you can optimize it and what parameters should be tuned so that our spark application gives the best performance. Spark applications can cause a bottleneck due to resources such as CPU, memory, network etc. We need to tune our memory usage, data structures tuning, how RDDs need to Continue Reading

HDFS: A Conceptual View

There has been a significant boom in distributed computing over the past few years. Various components communicate with each other over network inspite of being deployed on different physical machines. A distributed file system (DFS) is a file system with data stored on a server. The data is accessed and processed as if it was stored on the local client machine. The DFS makes it convenient to share information Continue Reading

Spark: Why should we use SparkSession ?

Spark 2.0 is the next major release of Apache Spark. This brings major change for the level of abstraction for the spark API and libraries. The release has the major change for the ones who want to make use of all the advancement in this release, So in this blog post, I’ll be discussing Spark-Session. Need Of Spark-Session

CuriosityX: RDDs – The backbone of Apache Spark

In our last blog, we tried to understand about using the spark streaming to transform and transport data between Kafka topics. After reading that many of the readers asked us to give a brief description of RDDs in Spark which we used. So, this blog is totally dedicated to the RDDs in Spark. So let’s start with the very basic question that comes to our mind Continue Reading

Spark Stream-Stream Join

Tuning spark on yarn

In this blog we will learn how to tuning yarn with spark in both mode yarn-client and yarn-cluster,the only requirement to get started is that you must have a hadoop based yarn-spark cluster with you. In case you want to create a cluster you can follow this blog here. 1. yarn-client mode:  In client mode, the driver runs in the client process, and the application master is only used Continue Reading

Structured Streaming: Philosophy behind it

In our previous blogs: Structured Streaming: What is it? & Structured Streaming: How it works? We got to know 2 major points about Structured Streaming – It is a fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing API that helps users in building streaming applications. It treats the live data stream as a table that is being continuously appended/updated which allows us to express our streaming computation as Continue Reading

Structured Streaming: How it works?

In our previous blog post – Structured Streaming: What is it? we got to know that Structured Streaming is a fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing API that helps users in building streaming applications. Now it’s time to learn  – How it works? So, in this blog post, we will look at the working of a structured stream via an example. So, let’s take a Continue Reading

Running Spark on DC/OS

Devops engineers for long needed an open source tool to make it easy to deploy the code developed through all the ups and downs to reach this far and is considerably more capable of evolving (pun intended). As we all know in this world of agile we need to shift our requirements after a short duration of time. Be it addition of a feature or tweaking Continue Reading

Are you missing on the Digital wave?

It has been an interesting week for Knoldus. At this time, almost half of the organization is awake worldwide for us in Toronto, Singapore, Berlin, Chicago, Miami, Mumbai and Noida participating in our CodeCombat 2018 (24 hours long Hackathon). This week Knoldus also spoke at the ScalaDays 2018, Berlin. The other wonderful part is onboarding of a huge healthcare organization who would like to transform Continue Reading

Structured Streaming: What is it?

With the advent of streaming frameworks like Spark Streaming, Flink, Storm etc. developers stopped worrying about issues related to a streaming application, like – Fault Tolerance, i.e., zero data loss, Real-time processing of data, etc. and started focussing only on solving business challenges. The reason is, the frameworks (the ones mentioned above) provided inbuilt support for all of them. For example: In Spark Streaming, by just adding Continue Reading

How Spark Internally Executes A Program

Hello everyone! In my previous blog, I explained the difference between RDD, DF, and DS you can find this blog Here In this blog, I will try to explain How spark internally works and what are the Components of Execution: Jobs, Tasks, and Stages. As we all know spark gives us two operations for solving any problem. Transformation  Action When we do the transformation on any Continue Reading

HDFS Erasure Coding in Hadoop 3.0

HDFS Erasure Coding(EC) in Hadoop 3.0 is the solution of the problem that we have in the earlier version of Hadoop, that is nothing but its 3x replication factor which is the simplest way to protect our data even in the failure of Datanode but needs too much extra storage. Now,  in EC storage overhead magically reduced to 50% which is earlier 200% because of Continue Reading

Kafka And Spark Streams: The happily ever after !!

Hi everyone, Today we are going to understand a bit about using the spark streaming to transform and transport data between Kafka topics. The demand for stream processing is increasing every day. The reason is that often, processing big volumes of data is not enough. We need real-time processing of data especially when we need to handle continuously increasing volumes of data and also need Continue Reading

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