Task Management Library for Map-Reduce Frameworks
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Solution Overview
Problem
Developers face a steep learning curve when using map-reduce frameworks like Hadoop, requiring expertise in multiple technologies, making it difficult to focus on programming data analysis applications without needing to understand the technical details of the framework.
Innovation Solution
A task management library that includes a wrapper API to parse data input values, a configurator to extract parameters from a configuration file, a scheduler to determine execution plans, and a launcher to manage the execution of mappers and reducers, allowing for easy configuration and parallelization of map-reduce tasks without requiring deep knowledge of the underlying technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If developers use a map-reduce framework like Hadoop to process large quantities of data, then data processing capability is improved, but the learning curve and technical expertise required increase
Solution Approach 1:
The patent introduces a task management library as an intermediary layer between the developer and the map-reduce framework. This library provides high-level abstractions and simplified APIs that hide the complexity of the underlying framework, allowing developers to focus on data processing logic without needing to master framework-specific technologies like data serialization systems, distributed file systems, or programming models.
Solution Approach 2:
The patent segments the complex map-reduce framework into manageable components through the task management library. It separates the concerns of task definition, execution management, and framework interaction, allowing developers to work with simplified task definitions while the library handles the complex orchestration of mappers, reducers, and framework-specific operations.
2Ease of operation
If developers need to become familiar with multiple technologies within the Hadoop framework, then execution control capability is improved, but ease of operation deteriorates
Solution Approach 1:
The task management library implements self-service by automatically handling framework-specific configurations, task scheduling, and execution details. Developers simply define their data processing tasks at a high level, and the library automatically manages the complexity of interacting with the map-reduce framework, including configuring mappers, reducers, and handling data flow without requiring developers to manually set up framework infrastructure.
3Adaptability or versatility
If a flexible environment is provided for experimenting with different configurations, then adaptability is improved, but system complexity increases
Solution Approach 1:
The task management library provides dynamic configuration capabilities that allow developers to easily experiment with different task configurations, parameters, and execution strategies. The system dynamically adjusts task definitions, scheduling parameters, and execution plans based on user input, enabling flexible experimentation without requiring changes to the underlying framework structure or creating complex custom configurations.
Data Source
AI summary
An improved system and method for a task management library to execute map-reduce applications is provided. A map-reduce application may be operably coupled to a task manager library and a map-reduce library on a client device. The task manager library may include a wrapper application programming interface that provides application programming interfaces invoked by a wrapper to parse data input values of the map-reduce application. The task manager library may also include a configurator that extracts data and parameters of the map-reduce application from a configuration file to configure the map-reduce application for execution, a scheduler that determines an execution plan based on input and output data dependencies of mappers and reducers, a launcher that iteratively launches the mappers and reducers according to the execution plan, and a task executor that requests the map-reduce library to invoke execution of mappers on mapper servers and reducers on reducer servers.