NoSQL and Search Engines
It may seem strange to mention search engines and NoSQL together, but many of today’s search engines use an architecture very similar to NoSQL databases. Their indexes and query processing are highly distributed. Many search engines are even capable of acting as a key‐value or document store in their own right.
NoSQL databases are often used to store unstructured data, documents, or data that may be stored in a variety of structures, such as social media posts or web pages. The structures of this indexed data vary greatly.
Also, document databases are appropriate in cases where system administrators or developers frequently don’t have control of the structures. This situation is common in publishing, where one storefront receives feeds of new books and their metadata from many publishers.
Although publishers use similar standards such as PDF and ePub for documents and ONIX XML files for metadata, they all produce documents in slightly different ways. As a result, consistent handling of data is difficult, and publishing is a great use case for a Document database.
Similar problems occur in the defense and intelligence realms. An agency may receive data from an ally or a terrorist’s hard disk in a variety of formats. Waiting six months to develop a revised relational database schema to handle a new type of target is not viable! This is where document NoSQL databases can be used.
Storing many structures in a single database necessitates a way to provide a standard query mechanism over all content. Search engines are great for that purpose. Consider search as a key requirement to unstructured data management with NoSQL Document databases.
Search technology is different from traditional query database interface technology. SQL is not a search technology; it’s a query language. Search deals with imperfect matches and relevancy scoring, whereas query deals with Boolean exact matching logic (that is, all results of a query are equally relevant).