{"id":6189,"date":"2018-12-21T01:07:09","date_gmt":"2018-12-21T09:07:09","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=6189"},"modified":"2025-06-13T20:28:50","modified_gmt":"2025-06-14T03:28:50","slug":"searching-json-comparing-text-search-in-couchbase-and-mongodb","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/searching-json-comparing-text-search-in-couchbase-and-mongodb\/","title":{"rendered":"Searching JSON: compare text search in Couchbase and MongoDB."},"content":{"rendered":"

“Apps without search is like Google homepage without the search bar.”<\/p><\/blockquote>\n

\"\"<\/p>\n

It’s hard to design an app without a good search.\u00a0 These days, it’s also hard to find a database without a built-in search. MySQL to NoSQL, Sybase to Couchbase, every database has text search support — built-in like Couchbase or via integration to Elastic — as is the case in Cassandra. Unlike SQL, text search functionality isn’t standardized. Every application needs best of the breed search, but not every database provides the same text search functionality. It’s important to understand the available feature, performance of each text search implementation and choose what fits your application need. After motivating text search, you’ll learn about the text search features you’d need for an effective, compare and contrast those features in MongoDB and Couchbase with examples.<\/p>\n

Let’s look at the application level search requirements.<\/h5>\n
    \n
  1. \n
    Exact Search:\u00a0(WHERE item_id = “ABC482”)<\/h5>\n<\/li>\n
  2. \n
    Range Search:\u00a0(WHERE item_type = “shoes” and size = 6 and price between 49.99 and 99.99)<\/h5>\n<\/li>\n
  3. \n
    String search:<\/h5>\n