Multiple database indexes increase application speed when working with complex data structures.
What are 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
- “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)
eXtremeDB’s diverse index types enable the developer to significantly increase application speed when working with complex data structures, and to make important design tradeoffs involving search performance, memory consumption, and update performance.