MongoDB's Atlas Search allows fine-grained text indexing and querying of data
on your Atlas cluster. It enables advanced search functionality for
your applications without any additional management or separate search
system alongside your database. Atlas Search provides options for several
kinds of text analyzers, a rich query
language that uses Atlas Search aggregation pipeline
conjunction with other MongoDB aggregation pipeline stages, and
score-based results ranking.
Atlas Search is available on Atlas instances running MongoDB 4.2 or higher versions only. For certain features, Atlas Search might require a specific version of MongoDB. The following table lists the Atlas Search features that require specific MongoDB versions.
The Atlas Search
mongot Java web process uses Apache Lucene and runs alongside
mongod on each
node in the Atlas cluster. The
Creates Atlas Search indexes based on the rules in the index definition for the collection.
Monitors change streams for the current state of the documents and index changes for the collections for which you defined Atlas Search indexes.
Processes Atlas Search queries and returns matching documents.
If you define stored source
fields in your Atlas Search index, the
mongot process stores the
specified fields and, for matching documents, returns the stored fields
mongot instead of doing a full document lookup on the
database if you specify the returnStoredSource Option in your query.
Atlas Search index is a data structure that categorizes data in an easily searchable format. It is a mapping between terms and the documents that contain those terms. Atlas Search indexes enable faster retrieval of documents using certain identifiers. You must configure an Atlas Search index to query data in your Atlas cluster using Atlas Search.
You can create an Atlas Search index on a single field or on multiple fields. We recommend that you index the fields that you regularly use to sort or filter your data in order to quickly retrieve the documents that contain the relevant data at query-time.
When you configure one or more Atlas Search indexes, Atlas enables the
mongot process on the nodes in the cluster. Each
talks to the
mongod on the same node. To create and update search
mongot process performs collection scans on the
backend database and opens change streams for each index.
You can specify the fields to index using the following methods:
Static mappings, which allows you to selectively identify the fields to index.
Atlas Search performs inverted indexing and stores the indexed fields on disk.
An inverted index is a mapping between terms and which documents contain
those terms. Atlas Search indexes contain the term, the
_id, and other
relevant metadata about the term, such as the position of the term, in
Although the data stored on Atlas Search isn't an identical copy of data from
the collection on your Atlas cluster, Atlas Search indexes still take some
disk space and memory. If you enable the
store option for fields
that contain string values or if you configure
the stored source fields in your
index, Atlas Search stores an identical copy of the specified fields on disk,
which can take disk space.
Atlas Search provides built-in analyzers for creating indexable terms that correct for differences in punctuation, capitalization, stop words, and more. Analyzers apply parsing and language rules to the query. You can also create a custom analyzer using available built-in character filters, tokenizers, and token filters. To learn more about the built-in and custom analyzers, see Process Data with Analyzers.
For text fields, the
mongot performs the following tasks to create
Analysis of the text
Tokenization, which is breaking up of words in a string to indexable tokens
Normalization, such as transforming the text to lower case, folding diacritics, and removing stop words
Stemming, such as ignoring plural and other word forms to index the word in the most reduced form
To learn more about Atlas Search support for other data types, see
BSON Data Types and Atlas Search Field Types. The
process stores the indexed fields and the
_id field on disk per
index for the collections on the cluster.
If you change an existing index, Atlas Search rebuilds the index without downtime. This allows you to continue using the old index for existing and new queries until the index rebuilding is complete.
If you make changes to the collection for which you defined Atlas Search
indexes, the latest data might not be available immediately for
mongot monitors the change streams, which allows
it to update stored copies of data, and Atlas Search indexes are eventually
After you set up an Atlas Search index for a collection, you can run queries against the indexed fields.
Atlas Search queries take the form of an aggregation pipeline stage. Atlas Search provides
$searchMeta stages, both of which must be the first stage
in the query pipeline. These stages can be used in conjunction with
other aggregation pipeline stages in your
query pipeline. To learn more about these pipeline stages, see
Choose an Aggregation Pipeline Stage.
Atlas Search also provides query operators and collectors that you can use inside the aggregation pipeline stage. The Atlas Search operators allow you to locate and retrieve matching data from the collection on your Atlas cluster. The collector returns a document representing the search metadata results.
You can use Atlas Search operators to query terms, phrases, geographic shapes
and points, numeric values, similar documents, synonymous terms, and more.
You can also search using regex and wildcard expressions. The Atlas Search
compound operator allows you to combine multiple operators
$search stage to perform a complex search and
filter of data based on what must, must not, or should be present
in the documents returned by Atlas Search. You can use the compound
operator to also match or filter documents in the
stage itself. Running
less performant than running
$search with the
To learn more about the syntax, options, and usage of the Atlas Search operators, see Use Operators and Collectors in Atlas Search Queries.
When you run a query, Atlas Search uses the configured read preference to identify the node on which to run the query.
The query first goes to the MongoDB process, which is
mongod for a
replica set cluster or
mongos for a sharded cluster. For sharded
clusters, your cluster data is partitioned across
mongot knows about the data on the
mongod on the same
node only. Therefore, you can't run queries that target a particular
mongos directs the queries to all shards, making these
scatter gather queries.
The MongoDB process routes the query to the
mongot on the same
node. Atlas Search performs the search and scoring and returns the document
IDs and other search metadata for the matching results to
mongod then performs a full document lookup implicitly for the
matching results and returns the results to the client.
Atlas Search associates a relevance-based score with every document in the result set. The relevance-based scoring allows Atlas Search to return documents in the order from the highest score to the lowest. Atlas Search scores documents higher if the query term appears frequently in a document and lower if the query term appears across many documents in the collection. Atlas Search also supports customizing the relevance-based default score by boosting, decaying, or other modifying options. To learn more about customizing the resulting scores, see Customize and Normalize the Score in Results.
For hands-on experience creating Atlas Search indexes and running Atlas Search queries against the sample datasets, try the tutorials in the following pages: