MongoDB is often chosen for online transaction processing (OLTP) due to its flexible document model, which can align with domain-specific data structures and access patterns. Beyond basic transactional workloads, MongoDB also supports search capabilities through Atlas Search, built on Apache Lucene. When combined with the aggregation pipeline, this enables limited online analytical processing (OLAP) functionality suitable for near-real-time analytics. Because MongoDB uses a unified document model, these analytical queries can run without restructuring the data, allowing for certain hybrid transactional and analytical (HTAP) workloads. This article explores such a use case in the context of healthcare.
Traditional relational databases employ a complex query optimization method known as “star transformation” and rely on multiple single-column indexes, along with bitmap operations, to support efficient ad-hoc queries. This typically requires a dimensional schema, or star schema, which is distinct from the normalized operational schema used for transactional updates. MongoDB can support a similar querying approach using its document schema, which is often designed for operational use. By adding an Atlas Search index to the collection storing transactional data, certain analytical queries can be supported without restructuring the schema.
