Indexdoubling

Indexdoubling is a term used in data indexing and search systems to describe techniques that extend or replicate index structures by effectively doubling their capacity or coverage. In practice, it refers to constructing an additional index layer that mirrors the primary index or partitioning data in a way that doubles the granularity of index entries. The aim is to improve query performance, especially for range queries or large-scale lookups, by providing more navigable paths to the underlying data.

Implementation varies. A common pattern is to create a second level of indexing that covers the same keys but at a different granularity. Data is partitioned into blocks; for each block an index entry is created for both the original and the doubled partitions. Queries are executed by consulting both index levels and combining results, which can reduce leaf-level scans and improve cache locality. Updates require maintaining both levels consistently.

Advantages include faster lookups, better parallelism, and improved scalability in read-heavy workloads. Drawbacks include increased storage, greater maintenance cost, and potential write amplification. Because the term is not standardized, different systems may implement indexdoubling in distinct ways, and it is often related to, but separate from, two-tier indexing, B-tree variants with auxiliary indexes, and log-structured merge-tree designs used to optimize reads.

See also: inverted index, B-tree, two-tier indexing, LSM-tree, range query optimization.