SQL to MongoDB Mapping Chart
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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/Concepts | MongoDB Terms/Concepts |
---|---|
database | database |
table | collection |
row | document or BSON document |
column | field |
index | index |
table joins | $lookup , embedded documents |
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. |
SELECT INTO NEW_TABLE | $out See the SQL to Aggregation Mapping Chart. |
MERGE INTO TABLE | $merge (Available starting in MongoDB 4.2)See the SQL to Aggregation Mapping Chart. |
UNION ALL | $unionWith (Available starting in MongoDB 4.4) |
transactions | transactions
TipFor many scenarios, the denormalized data model (embedded documents and arrays) will continue to be optimal for your data and use cases instead of multi-document transactions. That is, for many scenarios, modeling your data appropriately will minimize the need for multi-document transactions.
|
Executables
The following table presents some database executables and the corresponding MongoDB executables. This table is not meant to be exhaustive.
MongoDB | MySQL | Oracle | Informix | DB2 | |
---|---|---|---|---|---|
Database Server | mongod | mysqld | oracle | IDS | DB2 Server |
Database Client | mongosh | 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
people
. -
The MongoDB examples assume a collection named
people
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 Statements | MongoDB Schema Statements |
---|---|
CREATE TABLE people ( id MEDIUMINT NOT NULL AUTO_INCREMENT, user_id Varchar(30), age Number, status char(1), PRIMARY KEY (id) ) | Implicitly created on first insertOne() or insertMany() operation. The primary key _id is automatically added if _id field is not specified.
db.people.insertOne( { user_id: "abc123", age: 55, status: "A" } ) However, you can also explicitly create a collection: db.createCollection("people") |
ALTER TABLE people 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, updateMany() operations can add fields to existing documents using the $set operator.
db.people.updateMany( { }, { $set: { join_date: new Date() } } ) |
ALTER TABLE people 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, updateMany() operations can remove fields from documents using the $unset operator.
db.people.updateMany( { }, { $unset: { "join_date": "" } } ) |
CREATE INDEX idx_user_id_asc ON people(user_id) | db.people.createIndex( { user_id: 1 } ) |
CREATE INDEX idx_user_id_asc_age_desc ON people(user_id, age DESC) | db.people.createIndex( { user_id: 1, age: -1 } ) |
DROP TABLE people | db.people.drop() |
For more information on the methods and operators used, see:
Tip
Insert
The following table presents the various SQL statements related to inserting records into tables and the corresponding MongoDB statements.
SQL INSERT Statements | MongoDB insertOne() Statements |
---|---|
INSERT INTO people(user_id, age, status) VALUES ("bcd001", 45, "A") | db.people.insertOne( { user_id: "bcd001", age: 45, status: "A" } ) |
For more information, see db.collection.insertOne()
.
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 Statements | MongoDB find() Statements |
---|---|
SELECT * FROM people | db.people.find() |
SELECT id, user_id, status FROM people | db.people.find( { }, { user_id: 1, status: 1 } ) |
SELECT user_id, status FROM people | db.people.find( { }, { user_id: 1, status: 1, _id: 0 } ) |
SELECT * FROM people WHERE status = "A" | db.people.find( { status: "A" } ) |
SELECT user_id, status FROM people WHERE status = "A" | db.people.find( { status: "A" }, { user_id: 1, status: 1, _id: 0 } ) |
SELECT * FROM people WHERE status != "A" | db.people.find( { status: { $ne: "A" } } ) |
SELECT * FROM people WHERE status = "A" AND age = 50 | db.people.find( { status: "A", age: 50 } ) |
SELECT * FROM people WHERE status = "A" OR age = 50 | db.people.find( { $or: [ { status: "A" } , { age: 50 } ] } ) |
SELECT * FROM people WHERE age > 25 | db.people.find( { age: { $gt: 25 } } ) |
SELECT * FROM people WHERE age < 25 | db.people.find( { age: { $lt: 25 } } ) |
SELECT * FROM people WHERE age > 25 AND age <= 50 | db.people.find( { age: { $gt: 25, $lte: 50 } } ) |
SELECT * FROM people WHERE user_id like "%bc%" | db.people.find( { user_id: /bc/ } ) -or- db.people.find( { user_id: { $regex: /bc/ } } ) |
SELECT * FROM people WHERE user_id like "bc%" | db.people.find( { user_id: /^bc/ } ) -or- db.people.find( { user_id: { $regex: /^bc/ } } ) |
SELECT * FROM people WHERE status = "A" ORDER BY user_id ASC | db.people.find( { status: "A" } ).sort( { user_id: 1 } ) |
SELECT * FROM people WHERE status = "A" ORDER BY user_id DESC | db.people.find( { status: "A" } ).sort( { user_id: -1 } ) |
SELECT COUNT(*) FROM people | db.people.count() or db.people.find().count() |
SELECT COUNT(user_id) FROM people | db.people.count( { user_id: { $exists: true } } ) or db.people.find( { user_id: { $exists: true } } ).count() |
SELECT COUNT(*) FROM people WHERE age > 30 | db.people.count( { age: { $gt: 30 } } ) or db.people.find( { age: { $gt: 30 } } ).count() |
SELECT DISTINCT(status) FROM people | db.people.aggregate( [ { $group : { _id : "$status" } } ] ) or, for distinct value sets that do not exceed the BSON size limit db.people.distinct( "status" ) |
SELECT * FROM people LIMIT 1 | db.people.findOne() or db.people.find().limit(1) |
SELECT * FROM people LIMIT 5 SKIP 10 | db.people.find().limit(5).skip(10) |
EXPLAIN SELECT * FROM people WHERE status = "A" | db.people.find( { status: "A" } ).explain() |
For more information on the methods and operators used, see
Tip
Update Records
The following table presents the various SQL statements related to updating existing records in tables and the corresponding MongoDB statements.
SQL Update Statements | MongoDB updateMany() Statements |
---|---|
UPDATE people SET status = "C" WHERE age > 25 | db.people.updateMany( { age: { $gt: 25 } }, { $set: { status: "C" } } ) |
UPDATE people SET age = age + 3 WHERE status = "A" | db.people.updateMany( { status: "A" } , { $inc: { age: 3 } } ) |
For more information on the method and operators used in the examples, see:
Delete Records
The following table presents the various SQL statements related to deleting records from tables and the corresponding MongoDB statements.
SQL Delete Statements | MongoDB deleteMany() Statements |
---|---|
DELETE FROM people WHERE status = "D" | db.people.deleteMany( { status: "D" } ) |
DELETE FROM people | db.people.deleteMany({}) |
For more information, see db.collection.deleteMany()
.
Tip
See also:
Further Reading
If you are considering migrating your SQL application to MongoDB, download the MongoDB Application Modernization Guide.
The download includes the following resources:
-
Presentation on the methodology of data modeling with MongoDB
-
White paper covering best practices and considerations for migrating to MongoDB from an RDBMS data model
-
Reference MongoDB schema with its RDBMS equivalent
-
Application Modernization scorecard