Difference Between DDL and DML
In a Relational Database Management System (RDBMS), the huge amount of data gets stored in tables. These tables are the collection of related data where the data gets stored across rows and columns. This manner of storage of data makes it efficient to be used when the requirement arises. It is very important to access the data from these tables to use for the business requirements and also when the need is there to modify the existing data contained in the Database. To retrieve the data or manipulate the data, we need the Structured Query Language (SQL). SQL comes with standard commands to interact with the RDBMS. The Data Definition Language (DDL) is used to define a database schema and the Data Manipulation Language (DML) is used to manipulate the data which already exists in the database. In this topic, we are going to learn about DDL vs DM. In this topic, we are going to learn about DDL vs DML.
Head to Head Comparison Between DDL and DML (Infographics)
Below are the top differences between DDL vs DML
Key Differences Between DDL and DML
The key differences are mentioned below:
- One of the important differences between DDL and DML is that Data Definition Language (DDL) defines the schema of the Database whereas the Data Manipulation Language (DML) is used to modify the schema of the Database.
- DDL commands are CREATE, ALTER, DROP, TRUNCATE, etc. whereas DML commands are INSERT, UPDATE, DELETE, SELECT, etc.
- DDL statements operate on the entire table whereas the DML statements operate on rows.
- The DDL statements do not have a WHERE clause to filter the data whereas the DML statements use WHERE clause to filter the data.
- DDL statements get executed in their transaction and so get committed instantly as the changes made by each of these statements are permanent. But as the DML statements operate by modifying the data of the database objects, so these statements get executed according to the rules of the transaction.
- While using the DDL statements, the changes made by them cannot be rolled back. So we do not need to run COMMIT or ROLLBACK command whereas, in DML statements, the COMMIT and ROLLBACK commands should be run to confirm the changes.
DDL vs DML Comparison Table
Let’s discuss the top 6 difference :
|DDL(Data Definition Language)||DML(Data Manipulation Language)|
|Data Definition Language is used to define the schema of a database. It deals with how the data gets stored in the database.||Data Manipulation Language is used to manipulate i.e. retrieve, update and delete the data in a database.|
|The DDL commands that are used in SQL are CREATE, DROP, ALTER, TRUNCATE, etc.||The DML commands used in SQL are INSERT, UPDATE, DELETE, SELECT, etc.|
|The CREATE command is used to create a table or view of a table. It can also be used to create other objects of the database like index, stored procedure, triggers, etc.
The syntax to create a table is as below:
CREATE TABLE table_name (
COLUMN_1 datatype PRIMARY KEY,
|The INSERT command is used to insert the data into the table.
The syntax to insert data into a table is as below:
INSERT INTO table_name (column_1, column_2,…column_N) VALUES (value1, value2…valueN );
|The ALTER command is used to modify the existing table structure or the database objects.
The syntax for using the ALTER command is as below:
ALTER TABLE table_name RENAME TO table_name_new;
|The UPDATE command is used to update the existing data in the table.
The syntax for using the UPDATE command is as below:
UPDATE table_name SET column1 = value1, column2 = value2,…columnN = valueN WHERE [condition];
|The DROP command is used to delete a table or the view of the table or other database objects. The DROP command will remove the data as well as the table definition. So this command should be used carefully.
The syntax for dropping a database is as below:
DROP DATABASE database_name;
The syntax to drop a table is as below:
DROP TABLE table_name;
|The DELETE command is used to delete the records from the table.
The syntax to use the DELETE command is as below:
DELETE FROM table_name;
In the above syntax, all the rows of the table will be deleted but the structure of the table will remain. But if we use the DELETE command along with a WHERE clause, only the specific records according to the WHERE clause will be deleted. The syntax of the DELETE command along with a WHERE clause is as below:
DELETE FROM table_name WHERE [condition];
|The TRUNCATE command is used to remove the data from a table but the structure of the table remains intact. So with this command, the data gets deleted only, not the table.
The syntax of the TRUNCATE command is as below:
TRUNCATE TABLE table_name;
|The SELECT command is used to retrieve data from the tables in the database.
The syntax to use the SELECT command is as below:
SELECT column1, column2…columnN FROM table_name;
The above statement selects the columns specified in the select statement. But when we want to select all the columns of a table, we need to use “*” in the select statement.
The syntax to select all the columns of a table is as below:
SELECT * FROM table_name;
SQL provides the flexibility of defining the schema and then modifying it according to the requirement in a database by using the Data Definition Language and Data Manipulation Language. With the usage of simple DDL statements, it becomes easier for the developer to define the database schema, table structure for large amounts of data. Also with usage of the DML statements, we can manipulate the data i.e. retrieve the data, modify the existing data, etc. whenever the need arises. There are certain important points to keep in view while working with the various DDL and DML commands. The software developer or designer needs to get a thorough understanding of the working of various DDL and DML operations as they play a vital role in building an efficient database as required by the business.
This is a guide to DDL Vs DML. Here we discuss the DDL vs DML with respective key differences, infographics, and comparison table in detail. You may also have a look at the following articles to learn more –
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