Designing a multi-tenant search architecture requires balancing data isolation, scalability, and performance. The core challenge is ensuring each tenant’s data remains segregated while efficiently sharing resources. A common approach is to use a combination of index partitioning, tenant-aware query routing, and access controls. For example, Elasticsearch or OpenSearch can be configured to store tenant data in separate indices or use a shared index with a tenant identifier field. The choice depends on factors like the number of tenants, data size per tenant, and compliance requirements.
For data isolation, using a shared index with a tenant_id field is efficient for large-scale systems with many small tenants. Queries automatically filter results by tenant_id, reducing overhead. However, if tenants require strict data separation (e.g., for regulatory compliance), dedicated indices or even separate search clusters may be necessary. Access control must enforce tenant boundaries at the API layer—for instance, using middleware to validate permissions and inject tenant_id filters into search requests. Tools like Elasticsearch’s document-level security or custom application logic can prevent accidental data leaks between tenants.
Performance and scalability depend on resource allocation and indexing strategies. For shared indices, routing documents to specific shards based on tenant_id improves query efficiency by limiting the shards scanned. Rate limiting and priority queues can prevent noisy neighbors from degrading performance. Monitoring tools like Kibana or Prometheus help track per-tenant metrics (e.g., query latency, index size) to identify bottlenecks. If a tenant’s data grows disproportionately, reindexing their data into a dedicated index or partitioning their data across shards can maintain system-wide performance. Testing with realistic tenant workloads is critical to validate design choices.
