PostgreSQL – PRIVILEGES
Whenever an object is created in a database, an owner is assigned to it. The owner is usually the one who executed the creation statement. For most kinds of objects, the initial state is that only the owner (or a superuser) can modify or delete the object. To allow other roles or users to use it, privileges or permission must be granted.
Different kinds of privileges in PostgreSQL are −
- SELECT,
- INSERT,
- UPDATE,
- DELETE,
- TRUNCATE,
- REFERENCES,
- TRIGGER,
- CREATE,
- CONNECT,
- TEMPORARY,
- EXECUTE, and
- USAGE
Depending on the type of the object (table, function, etc.,), privileges are applied to the object. To assign privileges to the users, the GRANT command is used.
Syntax for GRANT
Basic syntax for GRANT command is as follows −
GRANT privilege [, ...] ON object [, ...] TO { PUBLIC | GROUP group | username }
- privilege − values could be: SELECT, INSERT, UPDATE, DELETE, RULE, ALL.
- object − The name of an object to which to grant access. The possible objects are: table, view, sequence
- PUBLIC − A short form representing all users.
- GROUP group − A group to whom to grant privileges.
- username − The name of a user to whom to grant privileges. PUBLIC is a short form representing all users.
The privileges can be revoked using the REVOKE command.
Syntax for REVOKE
Basic syntax for REVOKE command is as follows −
REVOKE privilege [, ...] ON object [, ...] FROM { PUBLIC | GROUP groupname | username }
- privilege − values could be: SELECT, INSERT, UPDATE, DELETE, RULE, ALL.
- object − The name of an object to which to grant access. The possible objects are: table, view, sequence
- PUBLIC − A short form representing all users.
- GROUP group − A group to whom to grant privileges.
- username − The name of a user to whom to grant privileges. PUBLIC is a short form representing all users.
Example
To understand the privileges, let us first create a USER as follows −
testdb=# CREATE USER manisha WITH PASSWORD 'password'; CREATE ROLE
The message CREATE ROLE indicates that the USER “manisha” is created.
Consider the table COMPANY having records as follows −
testdb# select * from COMPANY; id | name | age | address | salary ----+-------+-----+-----------+-------- 1 | Paul | 32 | California| 20000 2 | Allen | 25 | Texas | 15000 3 | Teddy | 23 | Norway | 20000 4 | Mark | 25 | Rich-Mond | 65000 5 | David | 27 | Texas | 85000 6 | Kim | 22 | South-Hall| 45000 7 | James | 24 | Houston | 10000 (7 rows)
Next, let us grant all privileges on a table COMPANY to the user “manisha” as follows −
testdb=# GRANT ALL ON COMPANY TO manisha; GRANT
The message GRANT indicates that all privileges are assigned to the USER.
Next, let us revoke the privileges from the USER “manisha” as follows −
testdb=# REVOKE ALL ON COMPANY FROM manisha; REVOKE
The message REVOKE indicates that all privileges are revoked from the USER.
You can even delete the user as follows −
testdb=# DROP USER manisha; DROP ROLE
The message DROP ROLE indicates USER ‘Manisha’ is deleted from the database.
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
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
Latest end-to-end Learn by Coding Recipes in Project-Based Learning:
Applied Statistics with R for Beginners and Business Professionals
Data Science and Machine Learning Projects in Python: Tabular Data Analytics
Data Science and Machine Learning Projects in R: Tabular Data Analytics
Python Machine Learning & Data Science Recipes: Learn by Coding
R Machine Learning & Data Science Recipes: Learn by Coding
Comparing Different Machine Learning Algorithms in Python for Classification (FREE)
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.