Leaderless Distributed Database Consensus Using HashDAG Event Ordering
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Solution Overview
Problem
Existing distributed database systems require a trusted leader or are inefficient in achieving consensus without one, limiting transaction capacity and scalability.
Innovation Solution
A distributed database system that operates without a leader, using a protocol to identify an order of events based on a hashDAG (Directed Acyclic Graph) and cryptographic hashes, allowing compute devices to collectively converge on a value for parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a leader server is used to achieve consensus in a distributed database system, then consensus can be achieved efficiently, but the system requires a trusted third party and has single points of failure
Solution Approach 1:
The patent extracts the leader role from the distributed database system entirely. Instead of having a dedicated leader server that coordinates consensus, the system allows any compute device to propose events and have them ordered through a decentralized protocol based on cryptographic hashes and directed acyclic graphs, eliminating the trusted third party requirement while maintaining consensus capability
Solution Approach 2:
The system implements self-service consensus where compute devices autonomously determine event ordering through the hashDAG protocol without external coordination. Each device independently validates events using cryptographic proofs and reaches consensus through mathematical verification rather than relying on a trusted leader to make decisions
2Reliability
If a block chain data structure is used to achieve consensus without a leader, then no trusted third party is needed, but the system is limited to a small number of transactions per second
Solution Approach 1:
The patent segments the event ordering process into independent, parallelizable operations. Instead of sequentially appending blocks, the hashDAG structure allows multiple events to be proposed and validated simultaneously across different compute devices, with each device working on its own subset of event validation and ordering independently
Solution Approach 2:
The system dynamically adapts the event ordering structure based on network conditions and device capabilities. The directed acyclic graph allows flexible reconfiguration of event relationships without requiring rigid block structures, enabling the system to optimize transaction throughput by adjusting the complexity and branching of the event graph in real-time
3Reliability
If a decentralized consensus protocol is implemented, then single points of failure are eliminated, but data exchange and computational overhead increase
Solution Approach 1:
The system performs preliminary cryptographic hashing and event validation before events need to be fully processed by all compute devices. By pre-computing hash values and validation proofs, the system reduces the amount of data that needs to be exchanged during consensus reaching, as devices can quickly verify pre-prepared cryptographic evidence rather than exchanging and analyzing raw event data
Data Source
AI summary
In some embodiments, an apparatus includes an instance of a distributed database at a first compute device configured to be included within a set of compute devices that implement the distributed database. The apparatus also includes a processor configured to define a first event linked to a first set of events. The processor is configured to receive, from a second compute device from the set of compute devices, a signal representing a second event (1) defined by the second compute device and (2) linked to a second set of events. The processor is configured to identify an order associated with a third set of events based at least one a result of a protocol. The processor is configured to store in the instance of the distributed database the order associated with the third set of events.