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docs/20_Intro_to_Atlas_Search/1_system.md

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## Dedicated search nodes
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![dedicated search nodes](/img/search_nodes.png)
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## Aggregation pipeline search stages
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### $search
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where the magic happens
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### $searchMeta
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The `$searchMeta` stage performs the same search that `$search` does, but only returns the results metadata, not actual matching documents. Results metadata includes the count of matching results and facets. This same metadata is available when using `$search` too, accessible in the $$SEARCH_META context variable.

docs/20_Intro_to_Atlas_Search/2_aggregation_stages.mdx

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# Aggregation pipeline search stages
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# 👐 Aggregation pipeline search stages
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## $search
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docs/30_Index_configuration/2_index_config.md

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## Dynamic mapping
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## String-focused workshop
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Most data types are queried straightforwardly, and match as one would expect. We'll survey non-string types in the next section, however we primarily focus on `string` fields, as that's where the power of Atlas Search shines.
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## Configuring a real Atlas Search index
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* Atlas Search Visual Index Builder or JSON Editor
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# 👐 Field types
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# 👐 Basic field types
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Document fields are mapped to a search index based on their BSON data type.
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**TODO**: Turn each of these playgrounds into a challenge
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## Basic field types
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### boolean
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## boolean
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* `equals`: https://search-playground.mongodb.com/tools/code-playground/snapshots/669e6ab1d49ef6fad98118b4
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* `in`: ...
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### date
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## date
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* `equals`: ...
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* `in`: ...
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* `near`: ...
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* `range`: https://search-playground.mongodb.com/tools/code-playground/snapshots/669e6b18d49ef6fad98118b6
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### number
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## number
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* `equals`:
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* `in`: ...
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* `near`: ...
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* `range`: https://search-playground.mongodb.com/tools/code-playground/snapshots/669e6b4ad49ef6fad98118b8
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### ObjectId
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## ObjectId
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* `equals`: https://search-playground.mongodb.com/tools/code-sandbox/snapshots/678506c2b6487c1cfd0bb540
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* `in`:
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## Other field types
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* Geo-spatial
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* Nested documents
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* Strings

docs/30_Index_configuration/4_geo.mdx

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# 📘 Geo-spatial
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# 📘 Geo-spatial fields
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GeoJSON and Points
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* https://www.mongodb.com/docs/atlas/atlas-search/geoWithin/
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* https://www.mongodb.com/docs/atlas/atlas-search/geoShape/
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### geo
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Dynamic mapping does not support geo-spatial fields, and require a manual/static mapping configuration.
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# 📘 Nested documents
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A document can contain a nested document, or an array of them.
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There are two distinct ways to map and index the contents of nested documents.
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## `document`
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Default dynamic mapping behavior, flattens nested documents into “arrays” of values on main document
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## `embeddedDocuments`
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Attribute pattern `elemMatch`-like matching, more nuanced and expensive than document type flattening
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WARNING: Each nested document mapped as as `embeddedDocuments` counts as an additional "index object".

docs/30_Index_configuration/6_string.mdx

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# 📘 String fields
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## String-focused workshop
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Most data types are queried straightforwardly, and match as one would expect.
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We'll survey non-string types in the next section, however we primarily focus on `string` fields,
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as that's where the power of Atlas Search shines.
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### autocomplete
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blah, don’t do it! ;)
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See also: Relevant As-You-Type Suggestions Search Solution

docs/90_Summary.mdx

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* Faceting
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* Highlighting
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*
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* Pagination

docs/99_TODO.md

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* rename .md's to .mdx's
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* rename Playground section to something else - it's the workshop practicals
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* should completed/solved playgrounds be saved and linked in here as well?
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* Add coverage of "index objects" counting
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* Add transition verbiage at the end of each page/section to lead into the next one
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Cool Tricks
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Which clause matches?

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