> For the complete documentation index, see [llms.txt](https://openai.gitbook.io/code-cheatsheets/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://openai.gitbook.io/code-cheatsheets/all/databases/mongo.md).

# General Mongo

## Queries

`.find({query})` or in MongoDb Compass find

#### Example Queries

```javascript
{"loc" : {$regex : ".*Public Affairs Building 1015A.*"}}
{ tag: "red" }
{ tags: "red" } //tags is an array i.e ["red", "blue"]
{ dim_cm: { $gt: 25 } } //dim_cm > 25
{ dim_cm: { $gt: 15, $lt: 20 } //15 < dim_cm < 20 
{ "size.uom": "in" } //nested field
{ "size.h": { $lt: 15 }, "size.uom": "in"} //AND
{ 'instock.qty': { $lte: 20 } } //instock is an array, selects docs where some object.qty in instock array is <=20
{ item: { $not: /^p.*/ } } //find something not starting with p
```

## Structuring Schema

Embedding documents

* Embedded documents are documents with schemas of their own that are part of other documents
* Lots of unneccessary data could be sent
* Many to many get messy, great for one to one
* Memory problem

Referenced documents

* when you populate it requires an individual query for each
* saves are only atomic at the document level, so all referenced saves not atomic
* Efficiency problems

### Advanced

**Storing and Querying Ranges**

Store start and end

Query by checking if its greater than start and less then end