Challenges in implementing apache spark

Challenges in Monitoring Apache Spark

The new age of sophistication has seen the onset of a new age of large scale data processing. The evidence of this change is visible in the high demand for real time data processing. This sudden change in trend has been induced by a number of factors. But the main factor can be analysed in an easy manner by looking into the traditional Time Value of Data belief. You also need to look into the peak behavior of data from the very first moment of its creation. This will guarantee help you to assess the overall change in trend.

The ability to process data in real time has gained tremendous popularity recently. To keep up with this increasing demand and popularity, more and more real time data processors have opened up shop. These processors provide a variety of frameworks. Some of the best ones available are Apex, Kafka Streams, Flink, Heron, Storm, and last but not the least, Apache Spark. Each of these data processing frameworks presents many possibilities to the users. But there are also several operational challenges and difficulties associated with these frameworks. Every user will have to face and tackle these while using these frameworks.

This article is all about how any user can use the Apache Spark framework to process data in real time. We will tell you in detail about the process involved with monitoring the Spark framework, and the associated challenges. So, read on to find out more.

What is Apache Spark

Apache Spark is a powerful framework used for quick and real time data processing. The entire data processing community has welcomed this powerful processing system with open arms. It is vital to monitor the performance of Spark when running it. But the user needs to know the execution model of Spark to do so.

The Spark cluster resource is managed by the Spark Manager. The following are list of modes for running Spark:

  • Standalone mode - One single Spark cluster manager to allow for easy setup of the framework

  • Mesos mode - System resources are abstracted and made available in the form of an elastic distributed system

  • Yarn mode - Hadoop V 2.0 is started by the default Spark resource manager

  • Spark Worker mode - This standalone mode is perfect for the workers to function as separate processes running on their very own nodes

The importance of monitoring Apache Spark and why it is a challenge

The concept might seem to be easy to understand and simple, but internally, it is an extremely complex scenario. Monitoring Apache Spark can be tough, even for the experts. The user interface on Apache Spark comes with a basic utility dashboard. But this simple dashboard is simply not enough to run a production ready setup for the purpose of monitoring the data processing system. No one can monitor any process on Apache Spark without proper knowledge of the internal workings.

You need to break down the entire monitoring process into three different levels. Each of these levels is independent. So it becomes extremely easy for you to monitor each and every level in a careful manner. You will be able to keep an eye on almost all incidents that tend to harm the data processing system. These could include disk failure, server crash, virus corruption, and many others. But if you fail to break down the entire process into levels, then monitoring Apache Spark could be the biggest challenge of your life. Real time data processing is quick, and Spark does it quicker than that. So, one phase would be over before you can blink your eye. Hence, breaking it down is extremely important.

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