MarkLogic is a database with a search engine built-in to its core, providing a single platform to load data from silos and search and query across all of that data. MarkLogic requires less time and effort to build and configure indexes for standard queries, and does not require a bolt-on search engine for full-text search like other databases.
MarkLogic uses an “Ask Anything” Universal Index that indexes data as soon as it is loaded so you can immediately begin asking questions of your data.
MarkLogic was designed from the ground up with search in mind. The power of MarkLogic is its sophisticated indexing and exceptional performance when querying off the indexes. All data is immediately indexed at ingest using MarkLogic’s “Ask Anything” Universal Index and has additional indexes that can be toggled on and off based on the user’s needs.
The Universal Index indexes the content and structure of documents immediately at ingest, including words, phrases, relationships, and values
Toggle range indexes, geospatial indexes, the triple index, and reverse indexes on or off based on your data, the kinds of queries that you will run, and your desired performance
Load content in over 200 languages and get advanced support including tokenization, collation, and stemming for core languages
Built-in search is extremely useful for data integration. It enables you to immediately search and discover any new data loaded into MarkLogic, and also keep track of your data as you harmonize it.
Built-in search is also very useful for application development. You can leverage it when building both transactional and analytical applications that require powerful queries to be run efficiently, and when you want to build Google-like search features into an app.
When it comes to search, MarkLogic has all the key features expected of an enterprise-grade search engine. These key features are listed below, and a more complete list can be found in the Search Guide.
Word or phrase search, Boolean logic, stemming, wildcards, case sensitivity, punctuation sensitivity, diacritic sensitivity, and search term weighting
See results as you type, including relevant suggestions for words and phrases
Search for pairs or N-tuples of specified elements across disparate data sources
Not just content, structure too. Search for specific XML tags in the hierarchy of documents
Show a sidebar of aggregate counts based on categories
Return snippets of documents to show context
Highlight search terms or phrases in results
Push results higher based on whether terms are found in closer proximity
Tunable, document-level “page ranking” based on term frequency and other variables
Order by relevance or metadata (string, numerical, or date and time formats)
Search across data that has geospatial coordinates using complex queries and get precise results
Expand search based on relationships (e.g., a search for “cardiac catheter” can be expanded to include anything related to “implantable devices” by using a semantic ontology)