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