Footage.net has adopted Solr search technology to power its online stock footage search and screening platform. The newly deployed search engine allows Footage.net to better manage huge datasets, organize diverse metadata fields, perform a vast number of simultaneous searches and filter search results dynamically. It's also extremely fast, significantly expediting the footage search and discovery process for Footage.net's global user base.
An open source enterprise search platform from the
Apache Lucene project, Solr powers the search and navigation features of many of the world's largest internet sites including Netflix, eBay, eHarmony, WhiteHouse.gov and AOL. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document handling, and geospatial search.
"The Solr integration significantly improves the search experience for our users," said David Seevers, Footage.net Chief Marketing Officer. "With nearly 10 million records now in the Footage.net database, we needed a search engine that could scour a huge volume of diverse records and deliver results to our users quickly and efficiently."
In addition to delivering fast search results, Solr enables users to filter search results by keywords, categories, dates and content partners. Known as "faceted search," this is a critical feature for most footage users, who are typically looking for very specific shots and need a way to plow through a huge volume of search results to get to the shot they need.
"The ability to narrow a search down is critical," said Seevers. "Solr allows for the application of multiple filters, and for applying and removing them dynamically. This lets users slice and dice search results in a wide variety of useful ways."
A vibrant and growing developer community supports Solr's open-source platform. As such, Solr is highly configurable, scalable and extensible. Going forward, Solr will allow Footage.net to scale and incorporate future capabilities such as related searches ('more like this') and auto-suggest ('did you mean?').