Tuple Attribute Locking in Stream Computing
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Solution Overview
Problem
Database systems are not well-suited for real-time processing and analysis of large volumes of streaming data, as they struggle to efficiently store, index, and analyze data in real-time, leading to potential unauthorized modifications of tuple attributes by stream operators.
Innovation Solution
Implementing tuple attribute locking in a stream computing environment, where locks can be set manually or automatically based on trigger conditions, inhibiting access to attributes by stream operators, and stored as metadata or in shared storage, ensuring data integrity and protecting attributes from unauthorized modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If database systems are used to store and process streaming data, then data can be stored and indexed, but real-time processing efficiency deteriorates
Solution Approach 1:
The patent segments the data processing architecture by introducing a separate stream computing engine that operates independently from the database system. This allows streaming data to be processed in real-time through memory-based operations while the database handles persistent storage, resolving the contradiction between data reliability and real-time processing efficiency.
Solution Approach 2:
The patent introduces an intermediary stream computing engine that sits between the data source and the database system. This intermediary processes streaming data in real-time using in-memory data structures, enabling efficient real-time analysis while maintaining data integrity through controlled access to the underlying database storage.
2Adaptability or versatility
If stream operators access tuple attributes freely, then processing flexibility is improved, but data integrity deteriorates due to unauthorized modifications
Solution Approach 1:
The patent applies local quality control by implementing fine-grained locking mechanisms on specific tuple attributes rather than locking entire tuples. This allows stream operators to access and modify only the specific attributes they need while maintaining integrity controls on locked attributes, thus preserving both processing flexibility and data integrity.
Solution Approach 2:
The patent implements feedback control through lock status checking mechanisms. Stream operators continuously check the lock status of tuple attributes before accessing them, and the system provides feedback about lock states to coordinate access across multiple operators. This feedback mechanism ensures data integrity while allowing flexible processing of unlocked attributes.
3Reliability
If locks are implemented on tuple attributes, then data integrity is improved, but processing overhead increases
Solution Approach 1:
The patent applies partial action by implementing locks only on specific tuple attributes that require integrity protection, rather than locking all attributes of all tuples. This selective locking approach reduces the overall processing overhead while maintaining data integrity for critical attributes, resolving the contradiction between reliability and device complexity.
Data Source
AI summary
Tuple attribute locking is implemented in a stream computing environment to selectively inhibit access to one or more attributes of tuples by stream operators.