PostgreSQL – Schema
A schema is a named collection of tables. A schema can also contain views, indexes, sequences, data types, operators, and functions. Schemas are analogous to directories at the operating system level, except that schemas cannot be nested. PostgreSQL statement CREATE SCHEMA creates a schema.
The basic syntax of CREATE SCHEMA is as follows −
CREATE SCHEMA name;
Where name is the name of the schema.
Syntax to Create Table in Schema
The basic syntax to create table in schema is as follows −
CREATE TABLE myschema.mytable ( ... );
Let us see an example for creating a schema. Connect to the database testdb and create a schema myschema as follows −
testdb=# create schema myschema; CREATE SCHEMA
The message “CREATE SCHEMA” signifies that the schema is created successfully.
Now, let us create a table in the above schema as follows −
testdb=# create table myschema.company( ID INT NOT NULL, NAME VARCHAR (20) NOT NULL, AGE INT NOT NULL, ADDRESS CHAR (25), SALARY DECIMAL (18, 2), PRIMARY KEY (ID) );
This will create an empty table. You can verify the table created with the command given below −
testdb=# select * from myschema.company;
This would produce the following result −
id | name | age | address | salary ----+------+-----+---------+-------- (0 rows)
Syntax to Drop Schema
To drop a schema if it is empty (all objects in it have been dropped), use the command −
DROP SCHEMA myschema;
To drop a schema including all contained objects, use the command −
DROP SCHEMA myschema CASCADE;
Advantages of using a Schema
- It allows many users to use one database without interfering with each other.
- It organizes database objects into logical groups to make them more manageable.
- Third-party applications can be put into separate schemas so they do not collide with the names of other objects.
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
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