Big data processing has been at the forefront of technological innovation in recent years. Big data refers to the ever-growing volume of structured and unstructured data that inundates organizations on a daily basis. This data is so large and complex that traditional data processing techniques are unable to handle it. So how did yarn revolutionize big data processing? In this deep dive, we will explore the impact of yarn on big data processing.
Yarn is a distributed computing framework that was introduced as part of Apache Hadoop 2.0 in 2013. It stands for “Yet Another Resource Negotiator.” Yarn was designed to address the shortcomings of the previous MapReduce framework, which was limited in its ability to handle real-time and interactive workloads.
Yarn has revolutionized big data processing in three significant ways. First, it has improved resource allocation and utilization. Yarn enables organizations to allocate resources dynamically, based on workload demands. This means that computational resources can be directed to where they are needed most, resulting in better resource utilization and reduced processing times.
Second, Yarn has made it possible to run multiple workloads concurrently. In the traditional MapReduce model, only one job could run at a time. Yarn, on the other hand, can run multiple types of processing all at once, including MapReduce, HBase, and Spark. This allows organizations to process large volumes of data more quickly and efficiently.
Lastly, Yarn has made big data processing more modular. In the past, each processing framework had to be integrated into the Hadoop ecosystem, making it difficult to add new frameworks or upgrade existing ones. With Yarn, each framework can operate independently, making it easier to incorporate new technologies and keep existing ones up-to-date.
To get a sense of the impact of Yarn on big data processing, take the example of Yahoo! Prior to the adoption of Yarn, Yahoo! was limited to running one job at a time, which meant that data processing was slow and inefficient. After adopting Yarn, Yahoo! was able to run multiple processing frameworks concurrently, including HBase and Storm. As a result, Yahoo! was able to process data more quickly and efficiently than ever before.
In conclusion, Yarn has revolutionized big data processing in a number of ways. By improving resource allocation and utilization, enabling multiple concurrent workloads, and making big data processing more modular, Yarn has enabled organizations to process data more quickly and efficiently than ever before. As big data continues to grow, it’s clear that Yarn will play an increasingly important role in helping organizations stay competitive and make better decisions based on their data.
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