Cut-rewind Query Processing for Unbounded Data Streams
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Solution Overview
Problem
Current database management systems are inefficient in processing unbounded data streams, as they are designed for bounded data and lack the capability to handle continuous query processing without terminating or shutting down, leading to inefficiencies in handling large-scale data analytics and decision-making processes.
Innovation Solution
The implementation of a cut-rewind procedure in query processing systems allows for continuous query execution by halting and rewinding query processing steps based on data items, enabling the preservation of application context and history-sensitive data across processing cycles, and integrating stream source functions and user-defined buffer functions to handle unbounded data streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional database management systems process bounded data in fixed sets, then data processing is efficient and manageable, but the system cannot handle unbounded continuous data streams effectively
Solution Approach 1:
The patent segments the continuous data stream into discrete portions or batches that can be processed sequentially. The system divides unbounded stream data into manageable units, allowing traditional bounded data processing mechanisms to handle each segment efficiently while maintaining overall continuous processing capability.
Solution Approach 2:
The patent implements dynamic query execution where query processing steps can be halted, rewound, and restarted based on data items encountered in the stream. This dynamic approach allows the system to adapt to unbounded data while maintaining processing efficiency through controlled reexecution of query steps rather than complete query termination.
2Duration of action of stationary object
If query processing steps are halted and rewound based on data items, then continuous query execution is enabled, but system complexity increases
Solution Approach 1:
The patent prepares query processing steps in advance and maintains them in a state ready for reexecution. By pre-configuring the query processing pipeline and maintaining application context, the system enables rapid halting and rewinding without requiring complete query redefinition, thus reducing the operational complexity of continuous execution.
Solution Approach 2:
The patent creates and maintains copies of query processing steps and application context that can be reused across multiple execution cycles. Instead of recreating query logic each time a rewind occurs, the system preserves processed query steps and context, allowing efficient restoration and continuation with reduced complexity.
3Measurement precision
If application context and history-sensitive data are preserved across processing cycles, then real-time analytics accuracy improves, but memory requirements increase
Solution Approach 1:
The patent extracts and preserves only the essential application context and history-sensitive data elements required for accurate real-time analytics. By identifying and retaining only the critical state information needed for continuous processing accuracy, the system minimizes memory consumption while maintaining analytics precision.
Solution Approach 2:
The patent applies different retention strategies to different parts of the application context. Rather than uniformly preserving all data, the system maintains history-sensitive information selectively based on its importance for continuous query accuracy, optimizing the balance between memory usage and analytics precision for each specific context element.
Data Source
AI summary
A method and system for processing a data stream are described. The method executes, until the occurrence of a cut condition, a map function from a set of query processing steps to generate map results for a first portion of the data stream, executes a reduce function from the set of query processing steps to generate history-sensitive data from the map results, and rewinds the set of query processing steps, without termination of processing. The history-sensitive data is maintained for a second execution of the map function and reduce function on a second portion of the data stream.