Query Mapping

The adapter maps SQL queries into the corresponding MongoDB queries. A detailed description of all the transformations is out of scope, but we will describe some of the common elements that are used. The adapter takes advantage of MongoDB features such as the aggregation framework to compute the desired results.

SELECT Queries

The SELECT statement is mapped to the find() function as shown below:

SQL QueryMongoDB Query

SELECT * FROM Users

db.users.find()

SELECT user_id, status 
FROM Users

db.users.find(
  {}, 
  { user_id: 1, status: 1, _id: 0 }
)

SELECT * 
FROM Users 
WHERE status = 'A'

db.users.find( 
  { status: "A" }
)

SELECT * 
FROM Users 
WHERE status = 'A' OR age=50

db.users.find(
  { $or: [ { status: "A" }, 
           { age: 50 } ] }
)

SELECT * 
FROM Users 
WHERE name LIKE 'A%'

db.users.find(
  {name: /^a/}
)

SELECT * FROM Users 
WHERE status = 'A'
ORDER BY user_id ASC

db.users.find( { status: "A" }.sort( { user_id: 1 } )

SELECT * 
FROM Users 
WHERE status = 'A' 
ORDER BY user_id DESC

db.users.find( {status: "A" }.sort( {user_id: -1} )

Aggregate Queries

The MongoDB aggregation framework was added in MongoDB version 2.2. The adapter makes extensive use of this for various aggregate queries. See some examples below:

SQL QueryMongoDB Query

SELECT Count(*) As Count 
FROM Orders

db.orders.aggregate( [ 
  { 
    $group: { 
      _id: null, 
      count: { $sum: 1 } 
    } 
  } 
] )

SELECT Sum(price) As Total 
FROM Orders

db.orders.aggregate( [ 
  { 
    $group: { 
      _id: null, 
      total: { $sum: "$price" } 
    }
  } 
] )

SELECT cust_id, Sum(price) As total 
FROM Orders 
GROUP BY cust_id 
ORDER BY total

db.orders.aggregate( [ 
  { 
    $group: { 
      _id: "$cust_id", 
      total: { $sum: "$price" } 
    } 
  } ,
  { $sort: {total: 1 } }
] )

SELECT cust_id, ord_date, Sum(price) As total 
FROM Orders 
GROUP BY cust_id, ord_date 
HAVING total > 250

db.orders.aggregate( [ 
  { 
    $group: { 
      _id: { 
        cust_id: "$cust_id", 
        ord_date: { 
          month: { $month: "$ord_date" }, 
          day: { $dayOfMonth: "$ord_date" }, 
          year: { $year: "$ord_date"} 
        } 
      }, 
      total: { $sum: "$price" } 
    }
  }, 
  { $match: { total: { $gt: 250 } } } 
] )

Insert Statements

The INSERT statement is mapped to the insert function as shown below:

SQL QueryMongoDB Query

INSERT INTO users(user_id, age, status, [address.city], [address.postalcode]) 
VALUES ('bcd001', 45, 'A', 'Chapel Hill', 27517)

db.users.insert( 
  { user_id: "bcd001", age: 45, status: "A", address:{ city:"Chapel Hill", postalCode:27514} }
) 

INSERT INTO t1 ("c1") VALUES(('a1', 'a2', 'a3'))

db.users.insert({"c1": ['a1', 'a2', 'a3']})

INSERT INTO t1 ("c1") VALUES(())

db.users.insert({"c1": []})

INSERT INTO t1 ("a.b.c.c1") VALUES(('a1', 'a2', 'a3'))

db.users.insert("a":{"b":{"c":{"c1":['a1','a2', 'a3']}}})

Update Statements

The UPDATE statement is mapped to the update function as shown below:

SQL QueryMongoDB Query

UPDATE users 
SET status = 'C', [address.postalcode] = 90210
WHERE age > 25

db.users.update( 
  { age: { $gt: 25 } }, 
  { $set: { status: "C", address.postalCode: 90210 }, 
  { multi: true }
)

Delete Statements

The DELETE statement is mapped to the delete function as shown below:

SQL QueryMongoDB Query

DELETE FROM users WHERE status = 'D'

db.users.remove( { status: "D" } )