Diverse index types increase application speed when working with complex data structures.

eXtremeDB offers diverse index types for specific workloads

eXtremeDB’s multiple database index types enable the developer to solve longest prefix match, geospatial, fuzzy search and other look up problem domains.

Multiple embedded database indexes

B-Trees indexes are offered universally in embedded database systems, and are highly efficient for basic database search operations like exact match, prefix and range searches.

But B-Trees indexes are not ideal for certain data and access patterns. For purposes as varied as IP routing, geospatial searching and soundex algorithm development, less common indexes can be much more efficient.

For this reason, the eXtremeDB database offers multiple indexes, including the following:

B-Trees for common sorting and searches, insertions, and deletions.
R-Trees for geospatial indexing (common in GPS/navigation systems)
Hash tables for quickly locating a single unique index entry
Patricia trie index which speeds searches in networking and telephony applications
Trigram indexes are ideal for text searches when the exact spelling of the target object is not precisely known. It finds objects which match the maximum number of three-character strings in the entered search terms, i.e., near matches.
Custom indexes or b-trees that allow the application to define the collating sequence of entries; this is useful in implementing soundex algorithm searches, for example
KD-Trees or k-dimensional trees, for spatial and pattern-matching tasks and in applications where query predicates contain various combinations of object fields (for example, to construct Query-By-Example, or QBE features)
OID An eXtremeDB oid is a unique object identifier which is implemented internally as a unique hash index. Generally, an oid is a user-defined structure which has fixed-length fields corresponding to identifying characteristics of a principle database class.
Autoid is similar to oid in that it is a unique object identifier implemented internally as a unique hash index. However it differs in that its value is determined by the eXtremeDB runtime. Autoids are 8-byte signed integer values which means that the value can be incremented every microsecond for nearly 300,000 years without overflowing.
Approximate Nearest Neighbor (ANN). The native ANN indexes enable high speed similarity searches on high dimensional data directly inside the database engine.
- - - The HNSW index is a multi-layered graph-based data structure specifically designed for efficient approximate nearest neighbor search in high-dimensional spaces
    - Vamana index shares conceptual similarities with HNSW, as both utilize graph-based structures for navigation in vector space. However, unlike the multi-layered hierarchy of HNSW, Vamana constructs a single-layer graph (typically based on a directed neighborhood graph, such as NGT or variations of k-NN graphs).