Streaming RDBMS Continuous Query Evaluation
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Solution Overview
Problem
Traditional Relational Database Management Systems (RDBMS) are limited in handling continuous data streams and supporting continuous queries, as they operate on 'store first, query later' paradigms, which do not efficiently manage real-time data streams and concurrent queries.
Innovation Solution
A Streaming Relational Database Management System (SRDBMS) is developed to transform traditional RDBMS into a system capable of managing streams, tables, and archived stream histories, supporting continuous and snapshot queries using Structured Query Language (SQL), with features like shared query evaluation, on-the-fly query addition, and real-time visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional RDBMS operates on 'store first, query later' paradigm, then data consistency is maintained, but real-time data stream processing efficiency deteriorates
Solution Approach 1:
The system dynamically adapts the query processing paradigm from static 'store first, query later' to continuous 'stream first, query ongoing' mode. The SRDBMS enables queries to be added, modified, or removed at runtime without system restart, allowing the database to dynamically respond to changing real-time analytics requirements while maintaining data consistency through transactional guarantees.
Solution Approach 2:
The SRDBMS provides multi-functionality by supporting both traditional snapshot queries and continuous streaming queries within the same system. It can handle batch processing, real-time streaming, ad-hoc analytics, and structured query operations, making it a universal platform that eliminates the need for separate stream processing systems.
2Adaptability or versatility
If the RDBMS is transformed to support continuous queries on streams, then real-time processing capability is improved, but system complexity increases
Solution Approach 1:
The patent merges stream processing capabilities with traditional RDBMS architecture by integrating continuous query evaluation into the existing query optimizer and executor. The shared evaluation framework combines multiple continuous queries that operate on the same data streams, reusing computational resources and reducing overall system complexity despite adding real-time capabilities.
Solution Approach 2:
The system introduces an intermediary layer between data streams and queries in the form of a continuous query evaluation framework. This framework includes components like the continuous query processor, shared evaluation manager, and result delivery mechanism that mediate between incoming data streams and user queries, abstracting complexity while enabling real-time processing.
3Productivity
If shared query evaluation is implemented to handle high concurrent continuous queries, then query processing scalability is improved, but resource management complexity increases
Solution Approach 1:
The shared evaluation framework merges multiple continuous queries that share common data sources or processing logic into a unified execution plan. By identifying common sub-expressions and data flow paths across multiple queries, the system evaluates them simultaneously using shared computational resources, achieving linear scalability with increasing query concurrency while reducing duplicate processing.
Solution Approach 2:
The system dynamically adjusts resource allocation parameters based on query characteristics, data stream throughput, and system load. The resource manager monitors execution metrics and reallocates computational resources (CPU, memory, I/O) to different query evaluation plans, enabling efficient handling of high concurrent queries while adapting to changing workloads without manual intervention.
4Adaptability or versatility
If on-the-fly query addition is supported, then system flexibility is improved, but evaluation consistency challenges arise
Solution Approach 1:
The system implements feedback mechanisms where the continuous query processor monitors data stream changes and automatically triggers re-evaluation of affected queries. When new queries are added or data streams change schema, the system detects these changes and propagates them through the shared evaluation framework, ensuring all queries remain consistent with current data state without requiring manual intervention or system restart.
Data Source
AI summary
Systems, methods, and media are disclosed herein that can be embodied in a traditional Relational Database Management System (RDBMS) in order to transform it into a Streaming Relational Database Management System (SRDBMS). An SRDBMS may provide functionality such as to manage and populate streams, tables, and archived stream histories and support the evaluation of continuous queries on streams and tables. Both continuous and snapshot queries support the full spectrum of the industry standard, widely used, Structured Query Language. The present technology can support a high number of concurrent continuous queries using a scalable and efficient shared query evaluation scheme, support on-the-fly addition of continuous queries into a mechanism that implements the shared evaluation scheme, reuse RDBMS modules such as relational operators and expression evaluators, and visualize results of continuous queries in real time.