The Family of MapReduce and Large Scale Data Processing Systems (1302.2966v1)

Abstract: In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.

• The paper surveys MapReduce and its related large-scale data processing systems, categorizing approaches and exploring enhancements for scalability and fault tolerance.
• MapReduce simplifies distributed programming complexities, while subsequent systems address limitations like iterative processing or provide higher-level SQL interfaces.
• Advancements in these systems enhance capabilities for managing petabyte-scale data and pose future research challenges in optimization and efficiency.

Overview of "The Family of MapReduce and Large Scale Data Processing Systems"

The paper "The Family of MapReduce and Large Scale Data Processing Systems" provides a comprehensive survey of the various approaches and mechanisms derived from the original MapReduce framework, introduced by Google, for processing large-scale data. The paper meticulously categorizes these approaches, investigates various enhancements, and explores the future scope of MapReduce-like systems.

The authors illuminate the essential traits of the MapReduce framework, emphasizing its utility in simplifying the development of scalable parallel applications on clusters of commodity machines. The primary advantage of MapReduce lies in its abstraction of the complexities of distributed programming, such as data distribution, scheduling, and fault tolerance. Moreover, the inherent capability of the framework to scale across thousands of nodes, coupled with fault-tolerance mechanisms and its simplistic programming model, makes it an appealing choice for data-intensive tasks.

The authors proceed to dissect the limitations of the original MapReduce framework related to iterative processing, real-time analytics, and support for complex data models. Several efforts have been initiated to address these limitations, such as enhancements in iterative processing through systems like HaLoop and iMapReduce. The paper also highlights efforts to improve the architecture for better fault tolerance and monitoring mechanisms.

Additionally, the authors explore the emergence of SQL-like interfaces for MapReduce. This aspect caters to the need for higher-level abstraction, making it accessible for those familiar with SQL without exploring the complexity of MapReduce programming paradigms. Systems like Pig Latin, Hive, Tenzing, and Jaql serve as noteworthy examples, providing SQL-like queries while executing jobs in the underlying MapReduce framework. These developments exemplify the trend of integrating declarative programming paradigms with the procedural nature of MapReduce.

The implications of this survey extend into both practical and theoretical domains. Practically, the advancements in MapReduce-like systems enhance capabilities in managing petabyte-scale data, supporting a myriad of applications from scientific data processing to enterprise-scale analytics. Theoretically, the paper prompts further exploration of optimization in execution frameworks, tackling challenges in system configuration for differing workloads, and improving the efficiency and latency of data processing tasks.

In reflecting on the trajectory of MapReduce-like systems, the paper speculates about future innovations that could potentially involve improving energy efficiency, enabling real-time streaming data processing, and advancing the expressiveness of programming models. The continuous evolution in big data technologies suggests an ongoing trajectory toward systems that accommodate complex data models more directly, thus broadening the applicability of such frameworks across diverse domains.

In conclusion, this survey serves as both a detailed retrospective of the MapReduce framework and a forward-looking exploration of emerging data processing paradigms. It lays the groundwork for future inquiries into enhancing the scalability, efficiency, and usability of large-scale data processing mechanisms.