Search and Query
MarkLogic’s search and query capability makes it easier to find better answers in today’s complex heterogeneous data. As the only Enterprise NoSQL database, MarkLogic gives organizations the ability to accelerate virtually any query over all of your data, thanks to sophisticated, best-in-class indexes. These same indexes also power full-text search, and MarkLogic is consistently chosen to power enterprise search applications over other offerings from the world’s largest search engine companies.
Better Answers in Today’s Data
Enterprise search that’s built-in, not bolt-on
MarkLogic has enterprise search built-in, enabling organizations to turn “big data” – petabytes of information stored across multiple systems – into useful results, without the need to shred the data. MarkLogic indexes data on load and makes it immediately searchable.
Powerful, complex query capability
Build Advanced Search Applications
MarkLogic’s full-text search engine makes it an ideal platform to power advanced search applications. MarkLogic’s full-text search includes faceting, real-time alerting, type-ahead suggestions, snippeting, language support, and much more. Search applications are in production that have hundreds of billions of documents and hundreds of Terabytes of data—and provide relevant, filtered search results that are returned in microseconds. For a longer list of MarkLogic’s specific search features, download the Built-in Search Datasheet.
Run Complex Queries Across All of Your Data
The Underlying Search Technology
The underlying technology beneath MarkLogic’s search and query capability is the Universal Index. The Universal Index helps MarkLogic function like a search engine. When new documents are loaded, the database immediately compiles a list of words or numbers that appear in each document. As more documents are added, each word is associated with a list of documents. These are called term lists because they list all documents associated with a particular term. An index is composed of these term lists, and the Universal Index is a compilation of the key indexes in MarkLogic.
The Universal Index keeps track of words, phrases, and values in documents. It also indexes the structure of documents—thus providing context for search. By indexing like a search engine, queries become really fast. But, the Universal Index does more than just speed up queries. It makes it possible to determine schema later, reducing application time-to-market and facilitating agile development.
The Universal Index is what gives MarkLogic a clear speed and cost advantage over what is offered by traditional relational databases and even newer NoSQL databases that limit what can be indexed and the number of indexes that can be queried. For a deeper understanding of MarkLogic’s indexing capabilities, read the whitepaper, Inside MarkLogic.
The range index is useful for searching values like dates quickly and returning the results or extracting information from the documents in the result set. It is also good for sorting information, and is the index that enables facets—one of the key MarkLogic search features. Range indexes are also used for bitemporal queries across valid and system time axes, a new feature in MarkLogic 8 that allows you to track information “as it actually was” in combination with “as it was recorded.”
The geospatial index is similar to a range index, with built-in support for point, box, circle, linestrings, and complex polygons. MarkLogic also supports multiple geospatial data types such as GML, KML, and GeoRSS. MarkLogic integrates with a variety of geographic-aware products such as Esri ArcGIS, Google Earth, Google Maps, Yahoo Maps, and Microsoft Bing Maps to help visualize the data. Download the Geospatial Search datasheet to learn more.
The triple index is what powers the semantics capabilities for storing and managing RDF triples. Triples are facts–a subject, predicate, and object–that are stored natively in MarkLogic and can be queried with SPARQL. Both RDF and SPARQL are W3C standards for linked data. Download the Semantics datasheet to learn more.
Tuning the Database
Dig into the details by reading through the documentation on search, learning how to use the Search API and more
Do a quick run-through of the Search API so you can start doing flexible, Google-style searches today
Hear from a search expert on how to build a search application and the tools you can use to do it