Multi-Adapter Cloud Indexing for Scalable Data Search
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Solution Overview
Problem
Existing systems for indexing and searching structured data in cloud environments face challenges in scalability and load balancing, as different client applications have varying resource requirements, leading to potential overload and impact on other applications.
Innovation Solution
An elastic scalable architecture with independent scaling of client applications, utilizing indexing adapters, a distributed database, and sharding manager to manage data streams and index shards, allowing for dynamic capacity allocation and minimal impact between client applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in a dedicated database with direct search access, then searching efficiency is improved, but scalability and resource isolation are worsened when moving to cloud environments with multiple client applications
Solution Approach 1:
The patent segments the database into multiple independent shards distributed across different servers. Each shard is managed by a dedicated indexing adapter, allowing independent scaling and resource allocation. This segmentation enables the system to handle multiple client applications with varying resource requirements while maintaining search efficiency within each shard.
Solution Approach 2:
The patent introduces indexing adapters as intermediary components between client applications and the distributed database shards. These adapters manage the complexity of data distribution, indexing, and search operations, allowing client applications to access data efficiently without directly managing the underlying distributed storage infrastructure.
2Productivity
If multiple client applications share the same database resources, then resource utilization is improved, but system stability and performance are worsened due to potential overload from any single application
Solution Approach 1:
The patent divides the database into separate shards that can be independently allocated to different client applications. This segmentation allows resource isolation where one application's overload cannot impact others, while the collective system maintains high resource utilization through efficient packing of workloads onto available shards.
Solution Approach 2:
The patent implements dynamic shard allocation and load balancing mechanisms that can adapt resource distribution in real-time based on application performance and resource availability. The sharding manager can dynamically reassign shards between applications or create new shards to maintain optimal resource utilization while preventing any single application from overloading the system.
3Device complexity
If a single database structure is used for all client applications, then device complexity is reduced, but adaptability to different application requirements is worsened
Solution Approach 1:
The patent creates a universal sharding architecture that can serve multiple client applications with different requirements through a common infrastructure. The sharding manager and indexing adapter framework provide standardized interfaces and mechanisms that adapt to various application needs without requiring separate database systems, thus maintaining manageable complexity while achieving high adaptability.
Solution Approach 2:
The patent allows each shard to be optimized for specific application requirements while maintaining a uniform overall architecture. Different shards can have customized indexing strategies, data formats, or access patterns tailored to their assigned applications' needs, while the global sharding management system maintains consistency and coordination across all shards.
Data Source
AI summary
In an example embodiment, zone information is received from a distributed database, the zone information including a mapping between each of a plurality of client applications and a corresponding zone. Then a first adapter is assigned to a first client application based on the mapping. A second adapter is then assigned to a second client application based on the mapping. When data is received from the first client application, the data from the first client application is parsed using the first adapter, producing output to a search core and an indexing core associated with a zone corresponding to the first client application.