Distributed Graph Result Set Rebalancing With Order Preservation
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Solution Overview
Problem
Distributed graph query engines face challenges with imbalanced result sets due to data skew, leading to inefficient resource utilization and performance issues, especially in asynchronous traversals, where data remains on original machines, and rebalancing must maintain result set performance and ordering consistency.
Innovation Solution
Implementing a result set rebalancing mechanism that segments data into fixed-sized chunks, allowing for both unordered and ordered rebalancing, ensuring efficient data movement and maintaining performance characteristics by leveraging the internal structure of distributed graph processing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data remains on original machines during asynchronous traversals, then communication overhead is minimized, but result set distribution becomes imbalanced
Solution Approach 1:
The result set is segmented into multiple partitions that can be independently distributed across different machines. Each partition represents a portion of the overall result set that can be moved to balance the distribution without requiring movement of the entire result set, thus resolving the contradiction between minimizing communication overhead and achieving balanced distribution.
Solution Approach 2:
The system dynamically adjusts result set distribution by moving partitions between machines based on current cluster state and workload requirements. This dynamic rebalancing allows the system to adapt to changing conditions and achieve balanced distribution while maintaining the benefit of localized data storage during query execution.
2Productivity
If rebalancing operations are performed, then data distribution balance is improved, but result set ordering consistency may be compromised
Solution Approach 1:
The result set is divided into ordered partitions where each partition maintains its internal ordering. By operating at the partition level rather than individual row level, the system can rebalance data distribution while preserving ordering consistency within each partition, thus resolving the contradiction between achieving balance and maintaining order.
Solution Approach 2:
The system introduces an intermediary coordination mechanism that manages the rebalancing process and ensures ordering consistency is maintained. This intermediary layer coordinates the movement of partitions while enforcing ordering constraints, allowing rebalancing to occur without compromising the stability of result set composition.
3Speed
If virtual rebalancing is used, then rebalancing speed is improved, but performance characteristics of result set operations deteriorate
Solution Approach 1:
The system segments the result set into physical partitions that can be independently managed and moved. This physical segmentation enables faster rebalancing operations compared to virtual approaches, while maintaining performance characteristics by allowing direct access to data on its physical location without requiring complex virtual remapping operations.
Data Source
AI summary
Two modes of result set rebalancing are provided to cover the needs of distributed graph engines: unordered and ordered rebalancing. Unordered rebalancing allows moving any combination of chunks from one machine to another with the only goal being to reach a target balance. Ordered rebalancing maintains the order of a result set, dividing and rebuilding new chunks as they are moved between consecutive machines in the order of the result set to reach the target balance. The rebalancing solutions capitalize on the internal structure of result sets commonly present distributed graph processing systems.