Distributed ETL Job Segmentation for Load Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional ETL jobs face inefficiencies due to increased data processing times and network loads as they handle large volumes of data across distributed systems, often resulting in network timeouts, memory issues, and crashes, especially when data is located in cloud systems.
Innovation Solution
A distributed balanced optimization method that breaks down ETL jobs into segments and maps them to participating ETL servers based on statistics to reduce data movement and balance workloads, utilizing a data flow graph and job execution plan to optimize data processing across multiple servers for parallel execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ETL jobs are executed on a local machine accessing remote source and target databases, then the ETL tool can perform data extraction, transformation, and loading, but the completion time increases and the load on the local machine increases
Solution Approach 1:
The patent segments the ETL job into multiple independent tasks that can be distributed across multiple machines in a cluster. Each task processes a portion of the data locally, eliminating the need for all data to be fetched to a single local machine. This segmentation reduces the completion time by enabling parallel processing and reduces the load on any single machine by distributing the workload across the cluster.
2Productivity
If large volumes of data are fetched from cloud systems to the local machine, then the ETL job can process the data, but network timeouts and memory issues occur
Solution Approach 1:
The patent implements local quality by enabling each machine in the cluster to process data locally rather than centralizing all data processing on one machine. The system intelligently determines which machine should process which data based on data location and machine capabilities. This approach maintains high data processing capability while improving network stability by minimizing data movement across the network and avoiding the memory and network overload that occurs when all data is fetched to a single local machine.
3Productivity
If data is loaded back to cloud systems from the local machine, then the transformed data can be stored, but network congestion occurs
Solution Approach 1:
The patent segments the data loading process into multiple parallel operations distributed across the cluster. Different machines load different portions of the transformed data back to the cloud simultaneously, which maintains high data loading capability while reducing network congestion by distributing the network traffic across multiple connections rather than overwhelming the network with a single large upload operation.
4Productivity
If multiple ETL jobs run in parallel on the same local machine, then processing throughput increases, but the machine crashes due to lack of memory
Solution Approach 1:
The patent segments the ETL jobs across multiple machines in the cluster, allowing multiple jobs to run in parallel without concentrating all memory demands on a single machine. Each machine handles a subset of the jobs, which maintains high processing throughput while preventing any single machine from crashing due to memory exhaustion. This distribution of workload across the cluster provides both the throughput of parallel processing and the stability of distributed memory resources.
Data Source
AI summary
Provided are techniques for distributed balanced optimization for an Extract, Transform, and Load (ETL) job across distributed systems of participating ETL servers using a data flow graph with links and stages for an ETL job to be executed by participating ETL servers is received. A distributed job execution plan is generated that breaks the data flow graph into job segments. The job segments each include a subset of the links and stages and map to one participating ETL server from the distributed systems to meet an optimization criteria across the distributed systems. The job segments are distributed to the participating ETL servers based on the mappings for parallel execution. Also, the distributed job execution plan utilizes statistics to reduce data movement and redundancies and to balance workloads across the distributed systems.