Relational database systems often face a trade-off between supporting multi-table queries and frequent updates. Materialized join views can drastically speed up queries, but they slow down updates and may consume significant storage. Conversely, query-time joins over tables and their indexes optimizes update performance at the cost of query latency. Our recent study of two-table joins introduced “merged indexes” (a form of multi-table index) to break this trade-off, approaching the query performance of materialized views without sacrificing the update efficiency of traditional single-table indexes. This study generalizes this technique to “order-sharing pipelines”—multi-table joins and grouping operations on shared keys. By incorporating interesting orderings into the physical database design, merged indexes partially pre-compute these order-sharing pipelines, closely approaching the query performance of materialized views. Meanwhile, they match traditional indexes on update performance.