Konrad Beiske discusses the challenges and strategies involved in implementing an indexing strategy for multi-tenant applications using Elasticsearch, emphasizing the importance of data separation, efficient resource use, and scalability. Initially, the fictive blog hosting provider used a separate index per tenant but faced memory issues, leading to a shared index approach with filtered queries based on tenant IDs. To address cache warming inefficiencies and ensure performance, they adopted custom routing using tenant IDs and later adjusted shard numbers to accommodate growth. As tenants' data and traffic expanded, particularly the largest tenant, the team implemented special cases and increased replicas to manage search loads effectively. Concerns about scalability and uneven shard distribution prompted the development of a configuration scheme allowing dynamic allocation of indices based on tenant needs, using index aliases for smooth data transitions. The article concludes by highlighting the importance of flexibility, especially as tenant sizes and demands evolve unpredictably, and suggests encapsulating complexity within the application's data access layer to maintain performance and scalability.