Metaindexing

Metaindexing refers to the practice of building and maintaining an index about other indexes or metadata to assist search and data retrieval operations. It is commonly used in environments that involve multiple data sources, heterogeneous data stores, or several separate indexes, such as federated search systems, data catalogs, and distributed information retrieval architectures. The metaindex sits at a higher level than the underlying indexes and is designed to speed up query planning, index selection, and results merging.

A typical metaindex stores mappings from terms or attributes to the indexes that contain them, along with auxiliary statistics such as term frequencies, document counts, last update times, and possibly lightweight summaries or sketches. By consulting the metaindex, a query planner can quickly identify which underlying indexes are relevant to a given query, avoid unnecessary lookups, and estimate the cost of different execution plans. Metaindexing can also support routing decisions in distributed or parallel search environments, where queries are issued only to a subset of sources likely to contain matching results.

Common uses include federated search, cross-database querying, and large-scale digital libraries, where metaindexing accelerates discovery across many collections or services. It often involves maintaining consistency between the metaindex and its dependent indexes, handling updates efficiently, and balancing the overhead of upkeep against the gains in query performance. Trade-offs may include increased complexity, potential staleness of meta-information, and extra storage requirements for the metaindex itself.