Map Reduce Execution Environment Configuration Adjustment
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Solution Overview
Problem
ETL tools struggle to optimize the execution of data processing operations across diverse infrastructure environments, as existing tools often bind operations to default configurations, neglecting performance objectives such as speed and fault tolerance.
Innovation Solution
A system and method that determine whether operations should be implemented in a map reduce execution environment and adjust its configuration to meet predefined performance objectives, such as balancing speed and fault tolerance by selecting appropriate execution environments based on resource consumption and performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If default configuration is used for map reduce operations, then ease of operation is improved, but performance objective (speed and fault tolerance) deteriorates
Solution Approach 1:
The system dynamically adjusts map reduce configuration parameters based on the specific performance objectives of each operation sequence. Instead of using static default configurations, the system modifies parameters such as parallelism degree, data storage in backup, and task distribution according to whether speed or fault tolerance is the primary objective, thereby resolving the contradiction between ease of operation and performance optimization
Solution Approach 2:
The system changes configuration parameters of the map reduce environment based on performance objectives. For speed-oriented operations, parameters are adjusted to maximize processing throughput; for fault-tolerance-oriented operations, parameters are adjusted to increase data redundancy and backup storage. This parameter adaptation allows the system to maintain ease of operation while achieving specific performance goals
2Reliability
If map reduce configuration is customized for each operation, then performance objective is improved, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically determining and adjusting map reduce configuration parameters based on the performance objectives of each operation sequence. The system analyzes the operation characteristics, selects appropriate performance objectives (speed or fault tolerance), and configures parameters autonomously without requiring manual intervention, thus improving performance while managing complexity through automation
Solution Approach 2:
The system uses feedback mechanisms to adjust map reduce configuration. By monitoring operation characteristics and performance requirements, the system dynamically modifies configuration parameters to achieve optimal performance. This feedback-driven approach allows customized configuration for each operation while managing overall system complexity through systematic control
3Reliability
If data storage in backup is increased for fault tolerance, then reliability is improved, but loss of energy increases
Solution Approach 1:
The system changes the parameter of data storage in backup based on the performance objective. For fault-tolerance-oriented operations, the system increases backup storage to improve reliability; for speed-oriented operations, it reduces backup storage to minimize energy consumption. This dynamic parameter adjustment resolves the contradiction between reliability and energy efficiency by aligning storage levels with operational priorities
Data Source
AI summary
Disclosed herein are techniques for adjusting a map reduce execution environment. It is determined whether some operations in a sequence of operations should be implemented in a map reduce execution environment. If it is determined that some operations in a sequence of operations should be implemented in a map reduce execution environment, the map reduce execution environment is adjusted to achieve a predefined performance objective.