Aggregation Engine for Distributed Constraint Enforcement
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Solution Overview
Problem
Existing systems face challenges in scalably enforcing aggregation constraints within transactions, particularly in hybrid transactional and analytical processing environments, where constraints spanning multiple nodes require complex locking mechanisms and often lead to concurrency issues and transaction rollbacks.
Innovation Solution
A system with an aggregation engine that includes a receiver, assessment manager, and director, which leverages a last committed aggregate value as a prefix sum for interval aggregations, asynchronously receives preliminary values from compute nodes, adjusts the prefix sum with constraint requirements, and selectively applies rollback protocols to violating transactions, allowing for local evaluation and mitigation of constraint violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If global locking mechanisms are used to enforce aggregation constraints across multiple nodes, then constraint enforcement reliability is improved, but system concurrency deteriorates and transaction rollbacks increase
Solution Approach 1:
The system segments the aggregation constraint enforcement into distributed components at each compute node. Each node maintains its own aggregate state and independently evaluates local transactions against aggregation constraints, eliminating the need for global locking while maintaining constraint enforcement reliability through distributed coordination.
Solution Approach 2:
The aggregation engine acts as an intermediary between compute nodes and the constraint enforcement system. It collects aggregate values from multiple nodes, evaluates aggregation constraints, and coordinates transaction rollbacks without requiring global locks, thereby maintaining both reliability and concurrency.
2Reliability
If complex locking mechanisms are implemented to enforce aggregation constraints, then constraint enforcement reliability is improved, but device complexity increases
Solution Approach 1:
The patent extracts the constraint evaluation logic from the transaction processing logic and places it in a dedicated aggregation engine. This separation allows complex constraint enforcement to be handled independently without increasing the complexity of individual compute nodes or transaction processors.
Solution Approach 2:
Each compute node independently maintains its own aggregate state and evaluates local transactions against aggregation constraints without requiring coordination with other nodes. This self-service approach simplifies the overall system architecture by eliminating complex inter-node locking mechanisms while maintaining reliable constraint enforcement.
3Reliability
If transactions are rolled back to enforce aggregation constraints, then constraint reliability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary evaluation of aggregation constraints before transaction commit by collecting aggregate values from all compute nodes in advance. This allows the aggregation engine to identify constraint violations early and roll back transactions more efficiently, reducing the time loss associated with constraint enforcement.
Solution Approach 2:
The aggregation engine provides feedback to compute nodes about aggregate state changes and constraint violations. This feedback mechanism enables rapid identification and rollback of violating transactions without requiring extensive re-evaluation, thereby reducing the time penalty for maintaining constraint reliability.
Data Source
AI summary
Embodiments relate to aggregation operations. More specifically, embodiments relate to enforcing an aggregation constraint across a distributed network of compute nodes responding asynchronously. Each compute node individually processes the local partial aggregate value in parallel and asynchronously communicates with a conflict resolution node (CRN).