Cascading Payload Replication for Distributed Compute Load Balancing
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Solution Overview
Problem
Distributed computing systems face challenges in efficiently processing and analyzing massive quantities of machine data from diverse sources, including system logs, network packet data, and sensor data, due to the complexity and variability of the data formats and the need for real-time operational intelligence.
Innovation Solution
A data intake and query system that uses a flexible schema to process and store machine data as events with timestamps, allowing for field-searchable queries and late-binding schema extraction rules, enabling efficient data retrieval and analysis across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored with pre-defined formats and schemas, then data retrieval efficiency is improved, but the system's ability to handle diverse and variable data formats from multiple sources deteriorates
Solution Approach 1:
The patent implements a dynamic schema evolution mechanism where data schemas are not fixed but can be automatically updated and adapted based on incoming data from diverse sources. The system dynamically creates, modifies, and versions schemas to accommodate new data formats while maintaining backward compatibility, allowing the system to simultaneously handle both structured and unstructured data efficiently.
Solution Approach 2:
The system changes the parameter of schema rigidity to schema flexibility by implementing a configurable schema validation mechanism. It allows dynamic adjustment of schema strictness levels, enabling the system to switch between enforcing strict schema compliance for efficient retrieval and accepting flexible, evolving schemas when handling new data sources, thus optimizing both retrieval efficiency and adaptability.
2Stability of the object's composition
If all compute nodes receive all shared information updates, then data consistency across the system is improved, but the network bandwidth consumption and processing overhead deteriorates
Solution Approach 1:
The patent implements a selective update distribution mechanism where different subsets of compute nodes receive different updates based on their specific needs and roles. Instead of broadcasting all updates to all nodes, the system determines which nodes require which updates and delivers them selectively, reducing redundant network traffic while maintaining the necessary data consistency for each node's operations.
Solution Approach 2:
The system segments the compute nodes into different groups or clusters based on their functional roles and data requirements. Updates are then targeted to specific segments rather than distributed universally, allowing the system to maintain data consistency within each segment while minimizing overall network bandwidth consumption across the entire distributed system.
Data Source
AI summary
Cascading payload replication to target compute nodes is disclosed. Cascading payload replication can be accomplished using a two-stage operation for a replication operation. In the first stage, a plan is generated and distributed for the replication operation. The plan includes an assignment of compute nodes to tree nodes in a tree hierarchy. In the second phase, the payload is distributed according to the plan. The plan is different for at least two replication operations. Thus, the cascading payload replication is adaptable to changing target compute nodes and provides for load balancing.