Workflow Scheduler for Distributed Analytics
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Solution Overview
Problem
Current distributed data processing systems face inefficiencies in executing complex analytics workflows, as they fail to effectively leverage the strengths of in-memory data processing and Map Reduce paradigms, leading to suboptimal performance and increased latency.
Innovation Solution
A server-based system with a processor and memory that utilizes scheduling tools, including an entity workload profile and data profile store, to analyze workflow task dependencies and prioritize tasks based on processed data, dynamically altering schedules and selecting appropriate execution frameworks like Apache Spark and Hadoop to optimize job execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional Map Reduce paradigm is used for distributed data processing, then fault tolerance and redundancy are provided, but processing speed and performance are reduced
Solution Approach 1:
The system dynamically selects between Map Reduce and in-memory processing frameworks based on workflow task characteristics and data properties. The workflow scheduler analyzes task dependencies and data profiles to determine the optimal execution framework for each task, allowing the system to adapt its processing approach rather than statically committing to one paradigm.
Solution Approach 2:
The system changes the processing parameters by switching between different execution frameworks (Map Reduce vs. in-memory) based on the specific requirements of each workflow task. This parameter change allows optimization of both reliability and speed by matching the appropriate framework to the task characteristics.
2Speed
If in-memory data processing is used, then processing speed is improved, but the system cannot effectively leverage Map Reduce strengths and increases memory resource requirements
Solution Approach 1:
The system applies different processing qualities to different parts of the workflow based on local requirements. Tasks that benefit from in-memory processing are executed using Spark, while tasks requiring distributed file system operations use Hadoop. This local quality approach optimizes speed for appropriate tasks without unnecessarily consuming memory resources across the entire system.
3Ease of operation
If complex analytics workflows are executed without optimized scheduling, then task execution is simplified, but performance is suboptimal and latency increases
Solution Approach 1:
The system performs preliminary analysis of workflow task dependencies and data profiles before execution. The workflow scheduler examines the directed acyclic graph structure, identifies critical paths, and pre-determines the optimal execution framework for each task. This preliminary action enables optimized scheduling that improves productivity without complicating the actual task execution.
Solution Approach 2:
The system uses feedback from data profiling and task dependency analysis to dynamically adjust the execution schedule. By monitoring task characteristics and interdependencies, the workflow scheduler continuously optimizes the execution plan, selecting appropriate frameworks and ordering tasks to minimize latency while maintaining ease of operation.
Data Source
AI summary
A server has a processor and a memory storing instructions executed by the processor to access scheduling tools including an entity workload profile with a work flow tasks schedule and work flow task dependencies. Processed data associated with a work flow task within the entity workload profile is identified. The work flow task dependencies are analyzed to alter the work flow tasks schedule to prioritize work flow tasks that rely upon the processed data.