We considered several approaches for implementing this functionality into the product, ultimately landing on one that would use the Lucene facet API-a decision influenced by an email exchange on the Lucene mailing list between Greg Miller of Amazon and Alexander Lukyanchikov of MongoDB. Different search use cases, common search goalsĮarlier this year, MongoDB product and engineering teams were in discussions about how to build a highly requested feature for Atlas Search: facets. But with Amazon’s help, we were able to go farther. So far, these changes only helped MongoDB and our customers. This design also allows our engineering teams to implement new features and capabilities in Lucene and pass them onto our customers in the future. A Lucene node is embedded with every Atlas cluster and handles search indexing and querying so users don’t have to worry about provisioning and running that infrastructure themselves.įollowing is an overview the Atlas Search architecture: Our goal is to make it easy for developers building on MongoDB to add full-text search functionality to their applications, so we designed it to be fully managed and integrated. But more than merely using the project, we also determined that it made sense to contribute actively to Lucene to improve it for our specific use case, in a way that would generally improve Lucene for all users. Ultimately, building with Apache Lucene made sense, given that it provides the foundation for both Elasticsearch and Solr. When the MongoDB team first considered building a search product, we evaluated many options, such as starting from scratch, or building on Elasticsearch or with Apache Solr. Lucene is also what enables MongoDB customers to design rich search experiences in their applications on top of data stored in their Atlas databases. At Amazon, the product catalog search millions of users have come to know is powered by Lucene, but that’s a relatively recent phenomenon, one explained in the “ What Amazon gets by giving back to Apache Lucene” blog post. How MongoDB uses Lucene to power Atlas SearchĪmazon and MongoDB both use Lucene every day, and the most important use case is no doubt application search, in which the engine is primarily used by humans. In this blog post, we discuss some of the ways MongoDB and Amazon’s search teams have collaborated on Lucene to tailor it for our own needs while simultaneously improving the project for all. Lucene, however, isn’t merely great code. Similarly, when MongoDB customers asked that full-text search be integrated into our distributed database, we also turned to Lucene. Hence, when Amazon wanted to upgrade its product search capabilities on, it turned to Lucene. For enterprises, there are a number of search alternatives to choose from, but the open source project Apache Lucene has become integral to a wide variety of applications since its introduction in 1999. Search is essential to delivering exceptional customer experiences, whether those customers are individuals scouring for a new webcam, or enterprises building search into their own applications. This post was written by Marcus Eagan, Senior Product Manager MongoDB Atlas Search, MongoDB and Matt Asay, Head of Evangelism, MongoDB.
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