Designing a multi-tenant search architecture involves creating a system that can efficiently handle search queries from multiple users or organizations (tenants) while ensuring data isolation, performance, and scalability. The architecture typically includes a shared indexing structure, tenant-specific configurations, and a robust access control mechanism. This allows different tenants to share resources without compromising data security or performance.
In a multi-tenant search system, one effective approach is to use a shared index for common data types while incorporating tenant IDs to segment the data. For example, each document indexed can contain metadata, including a tenant ID, which helps in filtering relevant search results for each tenant. This strategy not only reduces storage costs but also simplifies indexing and querying processes. Additionally, using dedicated shards for high-traffic tenants can improve performance, making sure popular tenants can process their queries swiftly without being affected by less active ones.
Access controls play a critical role in ensuring that each tenant only sees their own data during searches. Implementing fine-grained security measures, such as row-level security in databases or document-level security in search engines like Elasticsearch, is essential. For instance, when a tenant performs a search, the system needs to verify their identity and make sure only the documents associated with their tenant ID are returned. This multi-layered approach enables secure, efficient, and scalable search capabilities, ensuring that each tenant has a personalized and isolated experience.