Atlas Search

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100x Faster Facets and Counts with MongoDB Atlas Search: Public Preview

Today we’ve released one of the most powerful features of Atlas Search in public preview, and ready for your evaluation: lightning fast facets and counts over large data sets. Faceted search allows users to filter and quickly navigate search results by categories and see the total number of results per category for at-a-glance statistics. With the new facet operator , facet and count operations are pushed down into Atlas Search’s embedded Lucene index and processed locally – taking advantage of 20+ years of Lucene optimizations – before returning the faceted result set back to the application. What this means is that now facet-heavy workloads such as ecommerce product catalogs, content libraries, and counts run up to 100x faster . The power of facets and counts in full-text search Faceting is a popular search and analytics capability that allows an application to group information into related categories by applying filters to query results. Users can narrow their search results by simply selecting a facet value as a filter criteria. They can intuitively explore complex data sets, providing fast and convenient navigation to quickly drill into the data that is of most interest. A common use of faceting is navigating product catalogs. With travel starting to reopen, let's take a travel site as an example. By using faceted search, the site can present vacation options by destination region, trip type (i.e. hotel, self-catering, beach, ski, city break), price band, season, and more, enabling users to quickly navigate to the category that is most relevant to them. Facets also enable fast results counting. Extending our travel site example, business analysts can use facets to quickly compare sales statistics by counting the number of trips sold by region and season. Prior to the new facet operator, the only way Atlas Search could facet and count data was to retrieve the entire result set back to MongoDB’s internal $facet aggregation pipeline stage . While that was OK for smaller data sets, it became slow when the result set exceeded tens of thousands of documents. This all changes as now operations are pushed down to Atlas Search’s embedded and optimized Lucene library in a single $search pipeline stage. From our internal testing of a collection with one million documents, the new Atlas Search faceting improves performance by 100x. How to use faceting in Atlas Search Our new Atlas Search facets tutorial will help you get started. It describes how to: Create an index with a facet definition on string, date, and numeric fields in the sample_mflix.movies collection. Then run an Atlas Search query against those fields for results grouped by values for the string field and by ranges for the date and numeric fields, including the count for each of those groups. To use Atlas Search facets, you must be running your Atlas cluster on MongoDB 4.4.11 and above or MongoDB 5.0.4 and above. These clusters must be running on the M10 tier or higher. Facets and counts currently work on non-sharded collections. Support for sharded collections is scheduled for next year. The power of Atlas Search in a unified application data platform in the cloud MongoDB Atlas Search makes it easy to build fast, relevant full-text search on top of your data in the cloud. A couple of API calls or clicks in the Atlas UI, and you instantly expose your data to sophisticated search experiences that boost engagement and improve satisfaction with your applications. Your data is immediately more discoverable, usable, and valuable. By embedding the Apache Lucene library directly alongside your database, data is automatically synchronized with the search index; developers get to work with a single API; there is no separate system to run and pay for; and everything is fully-managed for you on any cloud you choose. Figure 1: Rather than bolting-on a separate search engine to your database, Atlas Search provides a fully integrated platform. Atlas Search provides the power you get with Lucene — including faceted navigation, autocomplete, fuzzy search, built-in analyzers, highlighting, custom scoring, and synonyms — combining it with the productivity you get fromMongoDB. As a result, developers can ship search applications and new features 30%+ faster. Next steps You can try out Atlas Search with the public preview of lightning-fast facets and counts today: If you are new to Atlas Search, simply spin up a cluster (M10 tier or above) and get started with our Atlas Search facets tutorial . If you are already using Atlas Search on M10 tiers and above then update your indexes to use the facet field mapping , and then start querying ! Your data remains searchable while it is being re-indexed. If you want to dig into the use cases you can serve with Atlas Search — along with users who are already taking advantage of it today — download our new Atlas Search whitepaper . Safe Harbor The development, release, and timing of any features or functionality described for our products remains at our sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality.

November 9, 2021

Fine-Tune Relevance in MongoDB Atlas Search with Function Scoring and Synonyms

MongoDB Atlas Search is an embedded full-text search solution in MongoDB Atlas that gives developers a seamless and scalable experience for building fast, relevance-based application features. We announced its general availability last year at MongoDB.live 2020 and over the past year we’ve introduced many new features, including a visual index builder, search query tester, custom analyzers , and wildcard path queries . This year at MongoDB.live 2021 , we’re excited to highlight two new capabilities that help developers tune the relevance of search results. See how easy it is to get started with MongoDB Atlas Search in this demo video by Marcus Eagan, Senior Product Manager for Atlas Search. Building relevance into search results Understanding the behavior of your users is essential when thinking about search result relevance. People don’t always tell you what they want, and they sometimes use words or phrases that don’t match your content exactly. To cover these scenarios, you can use full-text search features like function scoring and synonyms. Influence search rankings with function scoring There are often multiple factors that influence how search results should be ranked. For example, let’s say you have a restaurant finder application. The explicit inputs are things like the user’s location and what they’re searching for, but what’s implied is that they likely want to see highly rated restaurants or ones with more reviews. What’s Cooking: a sample restaurant finder application using MongoDB Atlas Search Function scoring allows you to influence the order of results returned by manipulating the score of each result. In Atlas Search, that means you can use a numeric field in a document and apply a mathematical expression to it. For example, you might want to increase the score of restaurants that are sponsored or have higher star ratings. This can easily be accomplished within the same search query by simply adding the function option to the score parameter of your query. Learn more about how to use function scores in our developer tutorial . Show results for more search queries with synonyms Synonyms are often used to define terms that are semantically similar to each other to improve search results. For example, someone searching for “noodles” might want to find results for “spaghetti”, “chow mein”, or “pad thai”. Synonyms can also help with typos, especially on mobile and small keyboards. In Atlas Search, you can define collections of synonyms for a search index via the API. Synonyms can be explicit (one-way) or equivalent (two-way). Explicit synonyms are good for defining relationships between terms that are subsets of each other, like the noodle example above: “spaghetti”, “chow mein”, and “pad thai” are all explicit synonyms for “noodles”, but not each other (you don’t want results for “chow mein” in a search for “spaghetti”). Equivalent synonyms are often used for terms that have regional variations or are otherwise interchangeable both ways, like soda and pop, or Kleenex and tissues. What's next for Atlas Search Developers are increasingly turning to full-text search to make content more discoverable and relevant for application end users. With Atlas Search, we hope to not only make building full-text search easier, but also more powerful and expressive. Join our community to ask questions and find out what other developers are building with Atlas Search and let us know what you think we should build next in our feedback forums .

July 13, 2021

MongoDB Atlas Search is now GA

MongoDB Atlas Search, the easiest way to build fast, relevant, full-text search on top of your data in the cloud, is now generally available.

June 9, 2020