It is possible to retrieve an array of documents as if it were a separate table. Take the following JSON structure from the restaurants collection for example:

{
  "_id" : ObjectId("568c37b748ddf53c5ed98932"),
  "address" : {
    "building" : "1007",
    "coord" : [-73.856077, 40.848447],
    "street" : "Morris Park Ave",
    "zipcode" : "10462"
  },
  "borough" : "Bronx",
  "cuisine" : "Bakery",
  "grades" : [{
      "date" : ISODate("2014-03-03T00:00:00Z"),
      "grade" : "A",
      "score" : 2
    }, {
      "date" : ISODate("2013-09-11T00:00:00Z"),
      "grade" : "A",
      "score" : 6
    }, {
      "date" : ISODate("2013-01-24T00:00:00Z"),
      "grade" : "A",
      "score" : 10
    }, {
      "date" : ISODate("2011-11-23T00:00:00Z"),
      "grade" : "A",
      "score" : 9
    }, {
      "date" : ISODate("2011-03-10T00:00:00Z"),
      "grade" : "B",
      "score" : 14
    }],
  "name" : "Morris Park Bake Shop",
  "restaurant_id" : "30075445"
}

Vertical flattening will allow you to retrieve the grades array as a separate table:

SELECT * FROM [restaurants.grades]

This query returns the following data set:

date	grade	score	P_id	_index
2014-03-03T00:00:00.000Z	A	2	568c37b748ddf53c5ed98932	1
2013-09-11T00:00:00.000Z	A	6	568c37b748ddf53c5ed98932	2
2013-01-24T00:00:00.000Z	A	10	568c37b748ddf53c5ed98932	3

You may also want to include information from the base restaurants table. You can do this with a join. Flattened arrays can only be joined with the root document. The add-in expects the left part of the join is the array document you want to flatten vertically. Disable SupportEnhancedSQL to join nested MongoDB documents -- this type of query is supported through the MongoDB API.

SELECT [restaurants].[restaurant_id], [restaurants.grades].* FROM [restaurants.grades] JOIN [restaurants] WHERE [restaurants].name = 'Morris Park Bake Shop'

This query returns the following data set:

restaurant_id	date	grade	score	P_id	_index
30075445	2014-03-03T00:00:00.000Z	A	2	568c37b748ddf53c5ed98932	1
30075445	2013-09-11T00:00:00.000Z	A	6	568c37b748ddf53c5ed98932	2
30075445	2013-01-24T00:00:00.000Z	A	10	568c37b748ddf53c5ed98932	3
30075445	2011-11-23T00:00:00.000Z	A	9	568c37b748ddf53c5ed98932	4
30075445	2011-03-10T00:00:00.000Z	B	14	568c37b748ddf53c5ed98932	5

It's also possible to build queries targeting arrays within other arrays.

Consider this sample Inventory collection:

{
	"_id": {
		"$oid": "xxxxxxxxxxxxxxxxxxxxxx"
	},
	"Company Branch": "Main Branch",
	"ItemList": [
		{
			"item": "journal",
			"instock": [
				{
					"warehouse": "A",
					"qty": 15
				},
				{
					"warehouse": "B",
					"qty": 45
				}
			]
		},
		{
			"item": "paper",
			"instock": [
				{
					"warehouse": "A",
					"qty": 50
				},
				{
					"warehouse": "B",
					"qty": 5
				}
			]
		}
	]
}

Insert data into the nested arrays using the syntax of <parent array>.<index>.<child array>, as follows:

INSERT INTO [Inventory.ItemList] (p_id, item, [instock.0.warehouse], [instock.0.qty], [instock.0.price]) VALUES ('xxxxxxxxxxxxxxxxxxxxxx', 'NoteBook', 'B', 20, '5$')

The Inventory collection after executing the INSERT statement:

{
	"_id": {
		"$oid": "xxxxxxxxxxxxxxxxxxxxxx"
	},
	"Company Branch": "Main Branch",
	"ItemList": [
		{
			"item": "journal",
			"instock": [
				{
					"warehouse": "A",
					"qty": 15
				},
				{
					"warehouse": "B",
					"qty": 45
				}
			]
		},
		{
			"item": "paper",
			"instock": [
				{
					"warehouse": "A",
					"qty": 50
				},
				{
					"warehouse": "B",
					"qty": 5
				}
			]
		},
		{
			"item": "NoteBook",
			"instock": [
				{
					"warehouse": "B",
					"qty": 20,
					"price": "5$"
				}
			]
		}
	]
}