Local Key-Value Database Synchronization Emulation
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Solution Overview
Problem
Distributed data stores exhibit different behaviors compared to local data stores due to techniques like horizontal partitioning and replication, making it challenging to test applications and synchronize data effectively between them.
Innovation Solution
The implementation of a system that emulates the behavior of a distributed data store on a local data store using key-value APIs, error injection, provisioned throughput emulation, latency simulation, and fine-grained access control to enable seamless synchronization and testing across both environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a distributed data store uses horizontal partitioning and replication to scale the system, then the system can meet customer demand and improve productivity, but the data store behaves differently than a local data store, causing inconsistencies and making synchronization difficult
Solution Approach 1:
The patent creates a local copy of the distributed data store on the customer's premises. This local data store replicates the key-value data model and behavior of the remote distributed data store, allowing applications to run locally with consistent data access patterns while the actual distributed store scales to meet demand
Solution Approach 2:
The local data store acts as an intermediary between the application and the remote distributed data store. It emulates the distributed store's behavior locally, allowing applications to interact with a consistent interface regardless of whether data is accessed locally or remotely, thereby resolving behavior inconsistencies
2Productivity
If a local data store is used for testing applications, then testing procedures are simplified and productivity improves, but the local data store behaves differently than the remote distributed data store, reducing measurement precision and testing accuracy
Solution Approach 1:
The local data store is configured to copy the key-value data model, API interface, and operational behavior of the remote distributed data store. This allows applications to be tested locally against an accurate representation of the production environment, ensuring testing precision while maintaining high testing efficiency
Solution Approach 2:
The local data store can be configured with adjustable parameters to match or simulate the remote distributed store's characteristics. This includes configuring data models, access patterns, and operational parameters to reflect the actual distributed environment, enabling accurate local testing
3Adaptability or versatility
If vendor lock-in is avoided by using a common code base for both local and remote data stores, then adaptability improves, but the different behaviors of local and distributed data stores create synchronization challenges and increase device complexity
Solution Approach 1:
The local data store is designed to serve multiple functions: it acts as a true local data store for fast access, emulates the remote distributed store for testing and development, and can synchronize with the remote store when needed. This universal design allows a single code base to work across both local and distributed environments without requiring complex synchronization logic
Solution Approach 2:
Instead of making the remote distributed data store adapt to local requirements, the patent inverts the approach by making the local data store adapt to emulate the remote store's behavior. This allows the remote store to maintain its optimized distributed architecture while the local store handles adaptation, reducing overall system complexity
Data Source
AI summary
A remote distributed data store may be configured to process data updates received through invocation of a common API with reference to a common schema. A local data store may also be configured to process updates through the common API with reference to the common schema. Updates to the local data store may be mapped from the local data store schema to the common schema, and applied to the distributed data store. Updates to the distributed data store may be mapped from the common schema to the local data store schema. User identity may be verified to limit data synchronization to authorized users.