> For the complete documentation index, see [llms.txt](https://docs.chartsql.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.chartsql.com/chartsql-studio/datasources/mongodb.md).

# MongoDB

## Overview

MongoDB is a document database and does not have a direct SQL interface. ChartSQL uses a local SQLite database to convert documents into SQL tables that ChartSQL can use for charting.

{% hint style="info" %}
See [SQLite datasource documentation](/chartsql-studio/datasources/sqlite.md) for working with SQLite's flavor of SQL
{% endhint %}

## Installing MongoDB

Install a MongoDB datasource in ChartSQL studio following the typical [datasource instructions](/chartsql-studio/settings-and-customization.md#datasources)

## Understanding MongoDB Charts

ChartSQL visualizations for MongoDB have a two step process: 

1. Write a MongoDB collection or aggregation pipeline query
2. Shape the final data with SQL

The ChartSQL Editor has an additional tab, "MongoDB Query" for writing the Mongo collection query

<div align="left"><figure><img src="/files/UoctYSPUqzM2bzcDdfaM" alt=""><figcaption><p>Screenshot of the MongoDB collection query editor</p></figcaption></figure></div>

### Write a MongoDB Collection Query

To query a MongoDB colleciton, you will specify a `collection`, and a `find` structure, which maps to a [mongodb.collection.find](https://www.mongodb.com/docs/v3.0/reference/method/db.collection.find/) method call

When find() is executed against the collection, the projected result set will be stored in a local SQLite database for further querying. The name of the table is the same as the collection.

<details>

<summary>Example MongoDB Collection Data</summary>

![](/files/NiTKucNymwqCjUthxHeY)

</details>

{% hint style="warning" %}
You should not find\* an entire collection, because it will necessarily be copied to your local database. Match and limit the items of your collection to just those you need to visualize&#x20;
{% endhint %}

```json
{
	"collection":"sales",
	"find":{
		"match":{
			"Status":"Lost" 
		},
		"projection":{
			"Amount":1,
			"DateClosed":1,
			"Name":1
		},
		"sort":{
			
		},
		"limit":10,
		"skip":0
	}
}
```

### Shape the Final Data with SQL

The result of find() is transferred to a local SQLite database. You can then query the result with SQL.&#x20;

Select all records returned from the mongodb find:

```sql
-- @chart: column
-- @mongodb-query: {"collection":"sales","find":{"match":{"Status":"Lost" },"projection":{"Amount":1,"DateClosed":1,"Name":1},"sort":{},"limit":0,"skip":0}}
SELECT *
FROM sales
```

<figure><img src="/files/vz3m1Heu0ugVtjrawez4" alt=""><figcaption></figcaption></figure>

If necessary you can further shape the data with SQL:

```sql
-- @chart: column
-- @mongodb-query: {"collection":"sales","find":{"match":{"Status":"Lost" },"projection":{"Amount":1,"DateClosed":1,"Name":1},"sort":{},"limit":0,"skip":0}}
SELECT
TRUNC(DateClosed, 'MONTH') AS DateClosed,
sum(Amount) as Amount
FROM sales
GROUP BY TRUNC(DateClosed, 'MONTH')
ORDER BY TRUNC(DateClosed, 'MONTH') asc;
```

<figure><img src="/files/K62qNOrf71rrgr7759sP" alt=""><figcaption></figcaption></figure>

## ChartSQL SQL Dialect

ChartSQL uses SQLite as the local database to query collections. See the [SQLite datasource](/chartsql-studio/datasources/sqlite.md) for syntax to query with.&#x20;

## MongoDB Atlas SQL

MongoDB has a new [SQL interface](https://www.mongodb.com/docs/atlas/data-federation/query/query-with-sql/). However, as of 1/14/2024 we could not get it to work reliably, and it is not very documented. In the future, we may be able to provide direct support for MongoDB Atlas SQL.