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Coroutine-Based Distributed Data Processing System

Student: Kaznacheev Dmitrii

Supervisor: Ivan Bliznets

Faculty: St. Petersburg School of Physics, Mathematics, and Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

Distributed data processing is highly important in various disciplines, such as big data analytics, high scale machine learning and scientific computations. Modern solutions to distributed data processing, such as Hadoop MapReduce, rely on a synchronous model of computations in their worker node implementation, meaning that transformations on datasets happen in stages, one stage after another and each stage starting upon the end of previous stage. This model, however, makes it hard to run non-blocking tasks in dataset transformations. For example, sending an HTTP request for each URL in a dataset would prove problematic because a lot of time would be wasted on waiting. In this work, we make use of Kotlin coroutines – a structured concurrency model in Kotlin programming language to handle running and managing asynchronous non-blocking operations at a scale while allowing the end user to use asynchronous non-blocking operations within the MapReduce paradigm.

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