Tuesday, June 21, 2016

SQL to MongoDB Mapping Chart

In addition to the charts that follow, you might want to consider the Frequently Asked Questions section for a selection of common questions about MongoDB.

Terminology and Concepts

The following table presents the various SQL terminology and concepts and the corresponding MongoDB terminology and concepts.
SQL Terms/ConceptsMongoDB Terms/Concepts
databasedatabase
tablecollection
rowdocument or BSON document
columnfield
indexindex
table joinsembedded documents and linking
primary key
Specify any unique column or column combination as primary key.
primary key
In MongoDB, the primary key is automatically set to the _id field.
aggregation (e.g. group by)
aggregation pipeline
See the SQL to Aggregation Mapping Chart below.

Executables

The following table presents some database executables and the corresponding MongoDB executables. This table is not meant to be exhaustive.
MongoDBMySQLOracleInformixDB2
Database ServermongodmysqldoracleIDSDB2 Server
Database ClientmongomysqlsqlplusDB-AccessDB2 Client

Examples

The following table presents the various SQL statements and the corresponding MongoDB statements. The examples in the table assume the following conditions:
  • The SQL examples assume a table named users.
  • The MongoDB examples assume a collection named users that contain documents of the following prototype:
    
{
    

    
_id: ObjectId("509a8fb2f3f4948bd2f983a0"),
    

    
user_id: "abc123",
    

    
age: 55,
    

    
status: 'A'
    

    
}

Create and Alter

The following table presents the various SQL statements related to table-level actions and the corresponding MongoDB statements.
SQL Schema StatementsMongoDB Schema Statements
 
CREATE TABLE users (
    id MEDIUMINT NOT NULL
        AUTO_INCREMENT,
    user_id Varchar(30),
    age Number,
    status char(1),
    PRIMARY KEY (id)
)
Implicitly created on first insert() operation. The primary key_id is automatically added if _id field is not specified.
db.users.insert( {
    user_id: "abc123",
    age: 55,
    status: "A"
 } )
However, you can also explicitly create a collection:
db.createCollection("users")

ALTER TABLE users
ADD join_date DATETIME
Collections do not describe or enforce the structure of its documents; i.e. there is no structural alteration at the collection level.
However, at the document level, update() operations can add fields to existing documents using the $set operator.
db.users.update(
    { },
    { $set: { join_date: new Date() } },
    { multi: true }
)

ALTER TABLE users
DROP COLUMN join_date
Collections do not describe or enforce the structure of its documents; i.e. there is no structural alteration at the collection level.
However, at the document level, update() operations can remove fields from documents using the $unset operator.
db.users.update(
    { },
    { $unset: { join_date: "" } },
    { multi: true }
)

CREATE INDEX idx_user_id_asc
ON users(user_id)

db.users.createIndex( { user_id: 1 } )

CREATE INDEX
       idx_user_id_asc_age_desc
ON users(user_id, age DESC)

db.users.createIndex( { user_id: 1, age: -1 } )

DROP TABLE users

db.users.drop()

Insert

The following table presents the various SQL statements related to inserting records into tables and the corresponding MongoDB statements.
SQL INSERT StatementsMongoDB insert() Statements
 
INSERT INTO users(user_id,
                  age,
                  status)
VALUES ("bcd001",
        45,
        "A")

db.users.insert(
   { user_id: "bcd001", age: 45, status: "A" }
)

Select

The following table presents the various SQL statements related to reading records from tables and the corresponding MongoDB statements.
NOTE
The find() method always includes the _id field in the returned documents unless specifically excluded through projection. Some of the SQL queries below may include an _id field to reflect this, even if the field is not included in the corresponding find() query.
SQL SELECT StatementsMongoDB find() Statements
 
SELECT *
FROM users

db.users.find()

SELECT id,
       user_id,
       status
FROM users

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

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 user_id, status
FROM users
WHERE status = "A"

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

SELECT *
FROM users
WHERE status != "A"

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

SELECT *
FROM users
WHERE status = "A"
AND age = 50

db.users.find(
    { status: "A",
      age: 50 }
)

SELECT *
FROM users
WHERE status = "A"
OR age = 50

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

SELECT *
FROM users
WHERE age > 25

db.users.find(
    { age: { $gt: 25 } }
)

SELECT *
FROM users
WHERE age < 25

db.users.find(
   { age: { $lt: 25 } }
)

SELECT *
FROM users
WHERE age > 25
AND   age <= 50

db.users.find(
   { age: { $gt: 25, $lte: 50 } }
)

SELECT *
FROM users
WHERE user_id like "%bc%"

db.users.find( { user_id: /bc/ } )

SELECT *
FROM users
WHERE user_id like "bc%"

db.users.find( { user_id: /^bc/ } )

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 } )

SELECT COUNT(*)
FROM users

db.users.count()
or
db.users.find().count()

SELECT COUNT(user_id)
FROM users

db.users.count( { user_id: { $exists: true } } )
or
db.users.find( { user_id: { $exists: true } } ).count()

SELECT COUNT(*)
FROM users
WHERE age > 30

db.users.count( { age: { $gt: 30 } } )
or
db.users.find( { age: { $gt: 30 } } ).count()

SELECT DISTINCT(status)
FROM users

db.users.distinct( "status" )

SELECT *
FROM users
LIMIT 1

db.users.findOne()
or
db.users.find().limit(1)

SELECT *
FROM users
LIMIT 5
SKIP 10

db.users.find().limit(5).skip(10)

EXPLAIN SELECT *
FROM users
WHERE status = "A"

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

Update Records

The following table presents the various SQL statements related to updating existing records in tables and the corresponding MongoDB statements.
SQL Update StatementsMongoDB update() Statements
 
UPDATE users
SET status = "C"
WHERE age > 25

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

UPDATE users
SET age = age + 3
WHERE status = "A"

db.users.update(
   { status: "A" } ,
   { $inc: { age: 3 } },
   { multi: true }
)

Delete Records

The following table presents the various SQL statements related to deleting records from tables and the corresponding MongoDB statements.
SQL Delete StatementsMongoDB remove() Statements
 
DELETE FROM users
WHERE status = "D"

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

DELETE FROM users

db.users.remove({})

SQL to Aggregation Mapping Chart

The aggregation pipeline allows MongoDB to provide native aggregation capabilities that corresponds to many common data aggregation operations in SQL.
The following table provides an overview of common SQL aggregation terms, functions, and concepts and the corresponding MongoDB aggregation operators:
SQL Terms, Functions, and ConceptsMongoDB Aggregation Operators
WHERE$match
GROUP BY$group
HAVING$match
SELECT$project
ORDER BY$sort
LIMIT$limit
SUM()$sum
COUNT()$sum
join
$lookup
New in version 3.2.

Examples

The following table presents a quick reference of SQL aggregation statements and the corresponding MongoDB statements. The examples in the table assume the following conditions:
  • The SQL examples assume two tables, orders and order_lineitem that join by theorder_lineitem.order_id and the orders.id columns.
  • The MongoDB examples assume one collection orders that contain documents of the following prototype:
    {
  cust_id: "abc123",
  ord_date: ISODate("2012-11-02T17:04:11.102Z"),
  status: 'A',
  price: 50,
  items: [ { sku: "xxx", qty: 25, price: 1 },
           { sku: "yyy", qty: 25, price: 1 } ]
}
SQL ExampleMongoDB ExampleDescription
 
SELECT COUNT(*) AS count
FROM orders

db.orders.aggregate( [
   {
     $group: {
        _id: null,
        count: { $sum: 1 }
     }
   }
] )
Count all records fromorders
SELECT SUM(price) AS total
FROM orders

db.orders.aggregate( [
   {
     $group: {
        _id: null,
        total: { $sum: "$price" }
     }
   }
] )
Sum the price field from orders
SELECT cust_id,
       SUM(price) AS total
FROM orders
GROUP BY cust_id

db.orders.aggregate( [
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   }
] )
For each unique cust_id, sum the price field.
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 } }
] )
For each unique cust_id, sum the price field, results sorted by sum.
SELECT cust_id,
       ord_date,
       SUM(price) AS total
FROM orders
GROUP BY cust_id,
         ord_date

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" }
     }
   }
] )
For each unique cust_id, ord_date grouping, sum the price field. Excludes the time portion of the date.
SELECT cust_id,
       count(*)
FROM orders
GROUP BY cust_id
HAVING count(*) > 1

db.orders.aggregate( [
   {
     $group: {
        _id: "$cust_id",
        count: { $sum: 1 }
     }
   },
   { $match: { count: { $gt: 1 } } }
] )
For cust_idwith multiple records, return thecust_id and the corresponding record count.
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 } } }
] )
For each unique cust_id, ord_date grouping, sum the pricefield and return only where the sum is greater than 250. Excludes the time portion of the date.
SELECT cust_id,
       SUM(price) as total
FROM orders
WHERE status = 'A'
GROUP BY cust_id

db.orders.aggregate( [
   { $match: { status: 'A' } },
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   }
] )
For each uniquecust_idwith status A, sum theprice field.
SELECT cust_id,
       SUM(price) as total
FROM orders
WHERE status = 'A'
GROUP BY cust_id
HAVING total > 250

db.orders.aggregate( [
   { $match: { status: 'A' } },
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   },
   { $match: { total: { $gt: 250 } } }
] )
For each uniquecust_idwith status A, sum theprice field and return only where the sum is greater than 250.
SELECT cust_id,
       SUM(li.qty) as qty
FROM orders o,
     order_lineitem li
WHERE li.order_id = o.id
GROUP BY cust_id

db.orders.aggregate( [
   { $unwind: "$items" },
   {
     $group: {
        _id: "$cust_id",
        qty: { $sum: "$items.qty" }
     }
   }
] )
For each uniquecust_id, sum the corresponding line item qtyfields associated with the orders.
SELECT COUNT(*)
FROM (SELECT cust_id,
             ord_date
      FROM orders
      GROUP BY cust_id,
               ord_date)
      as DerivedTable

db.orders.aggregate( [
   {
     $group: {
        _id: {
           cust_id: "$cust_id",
           ord_date: {
               month: { $month: "$ord_date" },
               day: { $dayOfMonth: "$ord_date" },
               year: { $year: "$ord_date"}
           }
        }
     }
   },
   {
     $group: {
        _id: null,
        count: { $sum: 1 }
     }
   }
] )



at
Labels:

No comments:

Post a Comment

Subscribe to: Post Comments (Atom)

Mongodb explain() Query Analyzer and it's Verbosity

First creating 1 million documents: > for(i=0; i<100; i++) { for(j=0; j<100; j++) {x = []; for(k=0; k<100; k++) { x.push({a:...