Distributed Event Processing Query Optimization
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Solution Overview
Problem
Traditional continuous query systems face scalability and performance issues due to the need for collocation of large event data streams, which can lead to increased latency and costs, especially in applications requiring real-time processing of vast data volumes like financial trading systems.
Innovation Solution
Implementing distributed event processing techniques that allow event streams to be processed wherever the data originates, enabling only the required subset of data to be collocated and processed, rather than the entire stream, through optimized querying and remote data sourcing/mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all event data streams are collocated in a single continuous query system, then complete event processing capability is achieved, but system scalability and performance deteriorate due to overwhelming data volumes
Solution Approach 1:
The system divides the monolithic continuous query system into multiple distributed event processors, each handling a portion of event streams. Event streams are segmented and distributed across different processors based on event types, sources, or other criteria, allowing parallel processing while maintaining complete event processing capability across the distributed system.
Solution Approach 2:
The system transitions from a single-dimension collocation model to a multi-dimensional distributed model. Event processors are distributed across multiple dimensions (physical locations, logical groups, event types) while maintaining logical connectivity through the event stream processing framework, enabling scalability without sacrificing processing capability.
2Reliability
If all event data streams are collocated for processing, then complete event analysis is achieved, but latency increases due to data propagation and merging requirements
Solution Approach 1:
Each event processor is assigned specific event streams or event types that it processes locally with specialized logic optimized for that particular event category. This local quality approach allows faster processing of specific event types without requiring all processors to handle all event types, reducing overall latency while maintaining complete event analysis capability.
Solution Approach 2:
Event processors perform preliminary filtering, aggregation, and processing of event streams before results need to be combined. By doing preliminary actions locally at each processor, the system reduces the volume and complexity of data that needs to be propagated and merged, thereby reducing latency while ensuring complete analysis through subsequent combination of results.
3Reliability
If all event data streams are collocated in a single system, then unified event processing is achieved, but system costs increase due to required hardware and infrastructure
Solution Approach 1:
Each distributed event processor is designed as a universal component capable of processing multiple types of event streams using a common event processing framework. This multi-functionality allows the system to achieve unified event processing across diverse event types without requiring separate specialized systems, reducing overall resource requirements while maintaining processing unity.
Solution Approach 2:
Instead of replicating entire event streams across all processors (which would require excessive resources), the system creates logical copies or references to event streams that can be efficiently shared across multiple processors. This approach enables unified processing of the same event data by multiple processors without duplicating the actual data storage, reducing resource consumption.
Data Source
AI summary
In one embodiment, a system for distributed event processing includes a first event processor and a second event processor. The first event processor receives event data associated with an event stream. The second event processor receives a query associated with the event stream. A request to the first event processor is generated by the second event processor based on the query for a first portion of event data associated with the event stream. The second event processor receives the first portion of event data from the first event processor. The second event processor then generates an event based on the first portion of event data.