• Mongodb 和 普通数据库 各种属性 和语句 的对应

    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
    database database
    table collection
    row document or BSON document
    column field
    index index
    table joins embedded 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.

    Executables

    The following table presents some database executables and the corresponding MongoDB executables. This table is not meant to be exhaustive.

     MongoDBMySQLOracleInformixDB2
    Database Server mongod mysqld oracle IDS DB2 Server
    Database Client mongo mysql sqlplus DB-Access DB2 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()

    For more information, see db.collection.insert(), db.createCollection(),db.collection.update(), $set, $unset, db.collection.createIndex(), indexes,db.collection.drop(), and Data Modeling Concepts.

    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" }
)

    For more information, see db.collection.insert().

    Select

    The following table presents the various SQL statements related to reading records from tables and the corresponding MongoDB statements.

    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()

    For more information, see db.collection.find(), db.collection.distinct(),db.collection.findOne(), $ne $and, $or, $gt, $lt, $exists, $lte, $regex, limit(),skip(), explain(), sort(), and count().

    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 }
)

    For more information, see db.collection.update(), $set, $inc, and $gt.

    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({})

    For more information, see db.collection.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() $sum
    join No direct corresponding operator; however, the$unwind operator allows for somewhat similar functionality, but with fields embedded within the document.

    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 theprice 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 uniquecust_id, sum theprice 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 uniquecust_id, sum theprice 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 uniquecust_id,ord_dategrouping, sum the pricefield. 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 uniquecust_id,ord_dategrouping, 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_id with status A, sum the pricefield. 
    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_id with status A, sum the pricefield 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 }
     }
   }
] )
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  • 原文地址：https://www.cnblogs.com/haoliansheng/p/4390272.html
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