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DOCSP-19054 Query API Page (#729)
* DOCSP-19054 inital scaffolding * update to messaging * moving query-api to root * CR 1 * toc update * tweak TOC again * adjust query api position in TOC * CR updates to query-api bullet list and more * geoWithin update * Additional feedback updates * bolding bullet points * removing atlas search * update data transformations bullet * TR1 Add materialized view bullet * add document join support bullet * remove the * remove last blank
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source/introduction.txt

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The advantages of using documents are:
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- Documents (i.e. objects) correspond to native data types in
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many programming languages.
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- Documents correspond to native data types in many programming
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languages.
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- Embedded documents and arrays reduce need for expensive joins.
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Create an Atlas Free Tier Cluster <https://docs.atlas.mongodb.com/getting-started/>
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/core/databases-and-collections
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/core/document
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/query-api
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/reference/bson-types
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source/query-api.txt

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.. |query-api| replace:: Query API
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===================
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MongoDB |query-api|
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===================
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.. default-domain:: mongodb
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.. contents:: On this page
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:local:
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:backlinks: none
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:depth: 1
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:class: singlecol
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The MongoDB |query-api| is the mechanism that you use to interact with
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your data.
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The |query-api| comprises two ways to query data in MongoDB:
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- :doc:`CRUD Operations </crud>`
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- :doc:`Aggregation pipelines </core/aggregation-pipeline>`
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You can use the |query-api| to perform:
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- **Adhoc queries**. Explore your MongoDB data with :binary:`~bin.mongosh`,
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`Compass <https://docs.mongodb.com/compass/current/>`_ ,
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`VSCode <https://code.visualstudio.com/docs/azure/mongodb>`_
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or a MongoDB :driver:`driver </>`.
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- **Data transformations**. Use
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:doc:`aggregation pipelines </core/aggregation-pipeline>` to
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reshape your data and perform calculations.
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- **Document join support** Use :pipeline:`$lookup` and
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:pipeline:`$unionWith` to combine data from different collections.
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- **Graph and geospatial queries**. Use operators such as
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:query:`$geoWithin` and :pipeline:`$geoNear` to analyze geospatial
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data and :pipeline:`$graphLookup` for graph data.
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- **Full-text search**. Use the :pipeline:`$search` stage to
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perform efficient text search on your data.
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- **Indexing**. Improve your MongoDB query performance by using the correct
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:ref:`index type <indexes>` for your data architecture.
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- **On-demand materialized views**. Use :pipeline:`$out` and
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:pipeline:`$merge` to :doc:`create materialized views
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</core/materialized-views>`
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on common queries.
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- **Time series analysis**. Query and aggregate your time-stamped data
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with :ref:`time series collections <manual-timeseries-collection-create>`.
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Document Model
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--------------
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A document in MongoDB is a data structure composed of field and value
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pairs. Documents are stored as BSON which is the binary representation of
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:term:`JSON`. This low level of abstraction helps you develop quicker
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and reduces the efforts around querying and data modeling. The document
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model provides several advantages, including:
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- Documents correspond to native data types in many programming
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languages.
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- Embedded documents and arrays reduce need for expensive joins.
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- Flexible schema. Documents do not need to have the same set of fields
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and the data type for a field can differ across documents within a
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collection.
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Data as Code
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------------
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The MongoDB |query-api| supports :driver:`drivers</>` for major
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programming languages. These drivers allow you to make calls to the
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database and generate queries using the syntax native to your
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application.
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Getting Started
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---------------
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To get started, visit the MongoDB
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:doc:`Getting Started Guide </tutorial/getting-started>`. Here you can
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find resources, code examples and tutorials that demonstrate the MongoDB
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|query-api|.

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