Continuous Query Initialization with Archived Relations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional database systems are ill-equipped to handle continuous data streams and event-driven applications, as they are designed for finite data sets and lack the processing flexibility required for real-time data processing.
Innovation Solution
A system is described that manages archived relations within continuous queries, allowing for the initialization of queries with historical data and evaluation based on both real-time data streams and historical data, using a query graph to traverse and initialize stateful operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional database systems are used to store and query data, then data can be stored in structured tables and queried using SQL, but the system cannot handle continuous unbounded data streams and lacks temporal processing capabilities
Solution Approach 1:
The system segments the data handling architecture into distinct components: traditional database tables for structured data storage, continuous query mechanisms for stream processing, and temporal operators for time-based analysis. This segmentation allows each component to specialize in specific data types and operations, enabling the system to handle both finite and continuous data effectively.
Solution Approach 2:
The patent implements a universal query processing framework that can handle multiple data types (structured tables, continuous streams, archived relations) and multiple query types (snapshot queries, continuous queries, historical queries) through a unified system architecture. This multi-functionality allows the system to adapt to different data processing needs without requiring separate specialized systems.
2Duration of action of moving object
If SQL queries are executed on finite data sets, then queries produce static results, but the system cannot perform long-running queries over continuous unbounded data
Solution Approach 1:
The system performs preliminary actions by pre-processing and archiving historical data into archived relations before continuous query execution. This allows the continuous query mechanism to start with pre-computed historical contexts already in place, enabling long-running queries to immediately process both historical and real-time data without loss of accuracy or completeness.
Solution Approach 2:
The patent implements continuous query processing that maintains uninterrupted evaluation over time, where queries continuously consume data from streams and archived relations, producing evolving results that reflect the current state of the data. This continuous action ensures that long-running queries remain accurate and up-to-date throughout their execution duration.
3Productivity
If data is stored in traditional database tables, then data structure is well-defined and queryable, but the system lacks temporal focus and cannot process event streams with time-based operations
Solution Approach 1:
The system implements dynamic data management where data structures and query mechanisms adapt based on the type of data being processed. Traditional tables remain static for structured data, while continuous streams and archived relations provide dynamic, time-based data structures that automatically update and evolve, enabling real-time processing without manual intervention.
Solution Approach 2:
The patent introduces archived relations as an intermediary layer between traditional database tables and continuous query processing. This intermediary structure stores historical data in a format optimized for temporal operations, bridging the gap between static table storage and dynamic stream processing, thereby enabling real-time analysis with reduced complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Techniques for managing continuous queries with archived relations are provided. In some examples, a query that includes at least a data stream may be identified. Additionally, the query may be initialized with at least a portion of historical data. Further, in some instances, the query may be evaluated based at least in part on the data stream and the portion of the historical data.