Unified Stream Processor for Batch Performance
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Solution Overview
Problem
Existing data pipeline architectures, such as Lambda and Kappa, face challenges in achieving real-time processing while maintaining data integrity and supporting complex operations, leading to inefficiencies and increased hardware and software complexity.
Innovation Solution
A stream processing engine with additional hardware and software is used to process events, featuring a stream processor, queue, spooler, and auditor to ensure event accuracy and reduce processing time, eliminating the need for batch processors and duplicating processing logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If Lambda pipelines use both batch and stream processors concurrently, then real-time processing speed is improved, but hardware requirements and processing complexity increase due to duplication
Solution Approach 1:
The patent merges batch and stream processing into a single unified stream processing pipeline that can handle both real-time events and batch operations sequentially, eliminating the need for separate processing systems and reducing hardware requirements while maintaining real-time processing capabilities
Solution Approach 2:
The stream processor is designed to be multi-functional, capable of performing both real-time stream processing and batch processing operations within a single system, allowing it to replace the need for separate batch and stream processors while maintaining versatility
2Speed
If Kappa pipelines use only stream processing without batch processing, then processing speed is improved, but data integrity and complex operations capability deteriorate
Solution Approach 1:
The stream processor dynamically switches between processing modes, operating in real-time stream processing mode for speed and in batch processing mode for data integrity verification and complex operations, allowing the system to adapt its processing approach based on operational requirements
Solution Approach 2:
The system periodically performs batch processing operations within the stream processing pipeline to verify data integrity and perform complex operations, maintaining reliability while preserving overall processing speed through structured periodic verification
3Reliability
If batch processors are used to ensure data integrity, then reliability is improved, but processing speed and real-time capability deteriorate
Solution Approach 1:
The system performs data integrity verification and complex operations during the stream processing phase itself through periodic batch operations, rather than relying on separate post-processing batch jobs, ensuring reliability is built into the real-time processing flow
4Adaptability or versatility
If Lambda pipelines duplicate processing logic in both stream and batch processors, then processing capability is improved, but maintenance complexity and development time increase
Solution Approach 1:
The patent consolidates processing logic into a single stream processing pipeline that handles both real-time and batch operations, eliminating code duplication between separate stream and batch processors and significantly reducing development and maintenance complexity
Data Source
AI summary
Disclosed are embodiments for providing batch performance using a stream processor. In one embodiment, a system is disclosed comprising a stream processor configured to process events emitted by an event stream and output processed events; a queue configured to receive the processed events from the stream processor; a spooler configured to read the processed events from the queue and write the processed events to a buffer; and an auditor process configured to extract a written event from the buffer and confirm that the written event matches an event present in the event stream.