Multi-Flat-Map Publisher for Microservices Event Stream Flattening
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Solution Overview
Problem
Microservices environments face challenges in efficiently processing streams of data from multiple publishers due to performance issues related to real-time processing and resource management, particularly in traditional message-driven environments where buffering can lead to inefficiencies.
Innovation Solution
A multi-flat-map publisher component is introduced to abstract execution away from thread dependencies, providing rigorous coordination of state transitions by concurrently flattening events from multiple publishers into a single stream for downstream subscribers, utilizing a ticket lock for serialization and inner queues for efficient resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional message-driven environments with buffering are used to process streams from multiple publishers, then data can be accumulated and managed, but performance issues arise due to inefficiencies in real-time processing and resource management
Solution Approach 1:
The patent segments the stream processing system into distinct functional components: multiple publisher components, a flattening map component, and subscriber components. Each publisher independently emits events to the flattening map, which concurrently processes and merges events from multiple sources without traditional buffering. This segmentation enables efficient real-time processing by eliminating bottlenecks associated with centralized buffer management.
Solution Approach 2:
The flattening map component dynamically coordinates state transitions and event merging from multiple publishers concurrently. It adapts to varying event rates from different publishers by dynamically managing the merging process, allowing the system to maintain high productivity under varying load conditions without wasting computational resources on fixed buffering infrastructure.
2Adaptability or versatility
If events from multiple publishers are concurrently merged into a single stream, then downstream subscribers receive comprehensive data, but coordination of state transitions and event ordering becomes complex
Solution Approach 1:
The flattening map component serves as an intermediary between multiple publishers and downstream subscribers. It receives events from multiple publishers, coordinates the merging of these events into a single consolidated stream, and forwards the merged events to subscribers. This intermediary approach simplifies the overall system architecture by centralizing the coordination logic in one component rather than requiring complex peer-to-peer coordination between publishers and subscribers.
Solution Approach 2:
The flattening map component performs multiple functions: it merges events from multiple publishers, maintains event ordering, coordinates state transitions, and handles error propagation. By consolidating these diverse functions into a single universal component, the patent reduces overall system complexity while maintaining comprehensive stream consolidation capability.
3Reliability
If rigorous coordination of state transitions is implemented, then correct event ordering and error handling are maintained, but processing latency may increase
Solution Approach 1:
The flattening map component is designed with pre-established coordination mechanisms for state transitions and event merging. By setting up the coordination framework in advance, the component can efficiently process events without requiring complex runtime negotiations or additional synchronization overhead, thus maintaining reliability while minimizing latency.
4Ease of operation
If buffering is used to manage data streams, then resource management is simplified, but real-time processing performance deteriorates
Solution Approach 1:
The patent extracts the buffering function from the traditional message-driven architecture and replaces it with a direct event streaming approach through the flattening map. By removing the buffer layer, the system achieves simpler resource management (no buffer allocation, memory management, or flush operations) while simultaneously improving real-time processing throughput through direct event forwarding from publishers to subscribers.
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for providing a reactive flattening map for use with a microservices or other computing environment. In a cloud computing environment, reactive programming can be used with publishers and subscribers, to abstract execution away from the thread of execution while providing rigorous coordination of various state transitions. The described approach provides support for processing streams of data involving one or more publishers and subscribers, by use of a multi-flat-map publisher component, to flatten or otherwise combine events emitted by multiple publishers concurrently, into a single stream of events for use by a downstream subscriber.