Distributed Data Storage Layer for Web Services
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Solution Overview
Problem
Current technologies face challenges in providing a scalable, reliable, and fast data storage solution for large-scale web data services that can efficiently store and retrieve information from various data sources, including merchant domains, web-wide data, and user behavior data, while ensuring data consistency and accuracy.
Innovation Solution
A distributed and fast data storage layer is implemented, featuring a copy-on-demand dynamic cluster with a data store and search index, a merge product information engine, a product attribute computation engine, a resource-aware adaptive capacity/throttling engine, and a resource manager, which enables real-time data processing, storage, and retrieval across multiple geographic locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a distributed data storage layer is implemented to handle large-scale web data services, then data availability and accessibility across multiple geographic locations are improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The system divides data storage across multiple distributed data centers located in different geographic regions. Each data center contains multiple clusters that store portions of the overall data set, allowing data to be accessed from the nearest location and improving availability while managing complexity through modular organization.
Solution Approach 2:
The system creates and maintains copies of data across multiple geographic locations and clusters. When data is updated in one location, copies are propagated to other locations, ensuring data availability and consistency across the distributed system without requiring centralized control.
2Reliability
If real-time data processing and updates are implemented across distributed clusters, then data consistency and accuracy are improved, but processing time and computational resources increase
Solution Approach 1:
The system continuously propagates data updates across all clusters in real-time as changes occur. Rather than periodic synchronization, the update mechanism operates continuously, ensuring that all geographic locations maintain consistent data without manual intervention or batch processing delays.
Solution Approach 2:
The system implements a feedback mechanism where data updates are automatically detected and propagated to all other clusters. The update propagation process monitors and confirms successful replication, ensuring data consistency across the distributed system while minimizing processing time through automated real-time synchronization.
3Ease of operation
If multiple clusters store copies of data for redundancy and availability, then data accessibility is improved, but storage space requirements and data synchronization complexity increase
Solution Approach 1:
The system segments data into portions that are distributed across multiple clusters. Each cluster stores a subset of the overall data rather than complete copies, reducing total storage requirements while maintaining accessibility. Data can be reconstructed from distributed portions across clusters when needed.
Data Source
AI summary
Techniques for providing a distributed and fast data storage layer for large scale web data services are disclosed. In some embodiments, a distributed data storage layer for web data services includes storing web related data for a plurality of merchants in the distributed data storage layer; and copying on demand a data store and a search index for web related data for a first merchant to perform a job for updating the web related data for the first merchant.