How to Prevent SQL Injection Attacks With Python

How to Prevent SQL Injection Attacks With Python

Among all injection types, SQL injection is one of the most common attack vectors, and arguably the most dangerous. As Python is one of the most popular programming languages in the world, knowing how to protect against Python SQL injection is critical.

Among all injection types, SQL injection is one of the most common attack vectors, and arguably the most dangerous. As Python is one of the most popular programming languages in the world, knowing how to protect against Python SQL injection is critical.

Every few years, the Open Web Application Security Project (OWASP) ranks the most critical web application security risks. Since the first report, injection risks have always been on top. Among all injection types, SQL injection is one of the most common attack vectors, and arguably the most dangerous. As Python is one of the most popular programming languages in the world, knowing how to protect against Python SQL injection is critical.

In this tutorial, you’re going to learn:

  • What Python SQL injection is and how to prevent it
  • How to compose queries with both literals and identifiers as parameters
  • How to safely execute queries in a database

Table of Contents

This tutorial is suited for users of all database engines. The examples here use PostgreSQL, but the results can be reproduced in other database management systems (such as SQLite, MySQL, Microsoft SQL Server, Oracle, and so on).

Understanding Python SQL Injection

SQL Injection attacks are such a common security vulnerability that the legendary xkcd webcomic devoted a comic to it:

"Exploits of a Mom" (Image: xkcd)

Generating and executing SQL queries is a common task. However, companies around the world often make horrible mistakes when it comes to composing SQL statements. While the ORM layer usually composes SQL queries, sometimes you have to write your own.

When you use Python to execute these queries directly into a database, there’s a chance you could make mistakes that might compromise your system. In this tutorial, you’ll learn how to successfully implement functions that compose dynamic SQL queries without putting your system at risk for Python SQL injection.

Setting Up a Database

To get started, you’re going to set up a fresh PostgreSQL database and populate it with data. Throughout the tutorial, you’ll use this database to witness firsthand how Python SQL injection works.

Creating a Database

First, open your shell and create a new PostgreSQL database owned by the user postgres:

$ createdb -O postgres psycopgtest

Here you used the command line option -O to set the owner of the database to the user postgres. You also specified the name of the database, which is psycopgtest.

Note: postgres is a special user, which you would normally reserve for administrative tasks, but for this tutorial, it’s fine to use postgres. In a real system, however, you should create a separate user to be the owner of the database.

Your new database is ready to go! You can connect to it using psql:

$ psql -U postgres -d psycopgtest
psql (11.2, server 10.5)
Type "help" for help.

You’re now connected to the database psycopgtest as the user postgres. This user is also the database owner, so you’ll have read permissions on every table in the database.

Creating a Table With Data

Next, you need to create a table with some user information and add data to it:

psycopgtest=# CREATE TABLE users (
    username varchar(30),
    admin boolean
);
CREATE TABLE

psycopgtest=# INSERT INTO users
    (username, admin)
VALUES
    ('ran', true),
    ('haki', false);
INSERT 0 2

psycopgtest=# SELECT * FROM users;
 username | admin
----------+-------
 ran      | t
 haki     | f
(2 rows)

The table has two columns: username and admin. The admin column indicates whether or not a user has administrative privileges. Your goal is to target the admin field and try to abuse it.

Setting Up a Python Virtual Environment

Now that you have a database, it’s time to set up your Python environment.

Create your virtual environment in a new directory:

~/src $ mkdir psycopgtest
~/src $ cd psycopgtest
~/src/psycopgtest $ python3 -m venv venv

After you run this command, a new directory called venv will be created. This directory will store all the packages you install inside the virtual environment.

Connecting to the Database

To connect to a database in Python, you need a database adapter. Most database adapters follow version 2.0 of the Python Database API Specification PEP 249. Every major database engine has a leading adapter:

To connect to a PostgreSQL database, you’ll need to install Psycopg, which is the most popular adapter for PostgreSQL in Python. Django ORM uses it by default, and it’s also supported by SQLAlchemy.

In your terminal, activate the virtual environment and use pip to install psycopg:

~/src/psycopgtest $ source venv/bin/activate
~/src/psycopgtest $ python -m pip install psycopg2>=2.8.0
Collecting psycopg2
  Using cached https://....
  psycopg2-2.8.2.tar.gz
Installing collected packages: psycopg2
  Running setup.py install for psycopg2 ... done
Successfully installed psycopg2-2.8.2

Now you’re ready to create a connection to your database. Here’s the start of your Python script:

import psycopg2

connection = psycopg2.connect(
    host="localhost",
    database="psycopgtest",
    user="postgres",
    password=None,
)
connection.set_session(autocommit=True)

You used psycopg2.connect() to create the connection. This function accepts the following arguments:

  • host is the IP address or the DNS of the server where your database is located. In this case, the host is your local machine, or localhost.

  • database is the name of the database to connect to. You want to connect to the database you created earlier, psycopgtest.

  • user is a user with permissions for the database. In this case, you want to connect to the database as the owner, so you pass the user postgres.

  • password is the password for whoever you specified in user. In most development environments, users can connect to the local database without a password.

After setting up the connection, you configured the session with autocommit=True. Activating autocommit means you won’t have to manually manage transactions by issuing a commit or rollback. This is the default behavior in most ORMs. You use this behavior here as well so that you can focus on composing SQL queries instead of managing transactions.

Note: Django users can get the instance of the connection used by the ORM from django.db.connection:

from django.db import connection

Executing a Query

Now that you have a connection to the database, you’re ready to execute a query:

>>> with connection.cursor() as cursor:
...     cursor.execute('SELECT COUNT(*) FROM users')
...     result = cursor.fetchone()
... print(result)
(2,)

You used the connection object to create a cursor. Just like a file in Python, cursor is implemented as a context manager. When you create the context, a cursor is opened for you to use to send commands to the database. When the context exits, the cursor closes and you can no longer use it.

While inside the context, you used cursor to execute a query and fetch the results. In this case, you issued a query to count the rows in the users table. To fetch the result from the query, you executed cursor.fetchone() and received a tuple. Since the query can only return one result, you used fetchone(). If the query were to return more than one result, then you’d need to either iterate over cursor or use one of the other fetch* methods.

Using Query Parameters in SQL

In the previous section, you created a database, established a connection to it, and executed a query. The query you used was static. In other words, it had no parameters. Now you’ll start to use parameters in your queries.

First, you’re going to implement a function that checks whether or not a user is an admin. is_admin() accepts a username and returns that user’s admin status:

# BAD EXAMPLE. DON'T DO THIS!
def is_admin(username: str) -> bool:
    with connection.cursor() as cursor:
        cursor.execute("""
            SELECT
                admin
            FROM
                users
            WHERE
                username = '%s'
        """ % username)
        result = cursor.fetchone()
    admin, = result
    return admin

This function executes a query to fetch the value of the admin column for a given username. You used fetchone() to return a tuple with a single result. Then, you unpacked this tuple into the variable admin. To test your function, check some usernames:

>>> is_admin('haki')
False
>>> is_admin('ran')
True

So far so good. The function returned the expected result for both users. But what about non-existing user? Take a look at this Python traceback:

>>> is_admin('foo')
Traceback (most recent call last):
  File "", line 1, in 
  File "", line 12, in is_admin
TypeError: cannot unpack non-iterable NoneType object

When the user does not exist, a TypeError is raised. This is because .fetchone() returns None when no results are found, and unpacking None raises a TypeError. The only place you can unpack a tuple is where you populate admin from result.

To handle non-existing users, create a special case for when result is None:

# BAD EXAMPLE. DON'T DO THIS!
def is_admin(username: str) -> bool:
    with connection.cursor() as cursor:
        cursor.execute("""
            SELECT
                admin
            FROM
                users
            WHERE
                username = '%s'
        """ % username)
        result = cursor.fetchone()

    if result is None:
        # User does not exist
        return False

    admin, = result
    return admin

Here, you’ve added a special case for handling None. If username does not exist, then the function should return False. Once again, test the function on some users:

>>> is_admin('haki')
False
>>> is_admin('ran')
True
>>> is_admin('foo')
False

Great! The function can now handle non-existing usernames as well.

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Exploiting Query Parameters With Python SQL Injection

In the previous example, you used string interpolation to generate a query. Then, you executed the query and sent the resulting string directly to the database. However, there’s something you may have overlooked during this process.

Think back to the username argument you passed to is_admin(). What exactly does this variable represent? You might assume that username is just a string that represents an actual user’s name. As you’re about to see, though, an intruder can easily exploit this kind of oversight and cause major harm by performing Python SQL injection.

Try to check if the following user is an admin or not:

>>> is_admin("'; select true; --")
True

Wait… What just happened?

Let’s take another look at the implementation. Print out the actual query being executed in the database:

>>> print("select admin from users where username = '%s'" % "'; select true; --")
select admin from users where username = ''; select true; --'

The resulting text contains three statements. To understand exactly how Python SQL injection works, you need to inspect each part individually. The first statement is as follows:

select admin from users where username = '';

This is your intended query. The semicolon (;) terminates the query, so the result of this query does not matter. Next up is the second statement:

select true;

This statement was constructed by the intruder. It’s designed to always return True.

Lastly, you see this short bit of code:

--'

This snippet defuses anything that comes after it. The intruder added the comment symbol (--) to turn everything you might have put after the last placeholder into a comment.

When you execute the function with this argument, it will always return True. If, for example, you use this function in your login page, an intruder could log in with the username '; select true; --, and they’ll be granted access.

If you think this is bad, it could get worse! Intruders with knowledge of your table structure can use Python SQL injection to cause permanent damage. For example, the intruder can inject an update statement to alter the information in the database:

>>> is_admin('haki')
False
>>> is_admin("'; update users set admin = 'true' where username = 'haki'; select true; --")
True
>>> is_admin('haki')
True

Let’s break it down again:

';

This snippet terminates the query, just like in the previous injection. The next statement is as follows:

update users set admin = 'true' where username = 'haki';

This section updates admin to true for user haki.

Finally, there’s this code snippet:

select true; --

As in the previous example, this piece returns true and comments out everything that follows it.

Why is this worse? Well, if the intruder manages to execute the function with this input, then user haki will become an admin:

psycopgtest=# select * from users;
 username | admin
----------+-------
 ran      | t
 haki     | t
(2 rows)

The intruder no longer has to use the hack. They can just log in with the username haki. (If the intruder really wanted to cause harm, then they could even issue a DROP DATABASE command.)

Before you forget, restore haki back to its original state:

psycopgtest=# update users set admin = false where username = 'haki';
UPDATE 1

So, why is this happening? Well, what do you know about the username argument? You know it should be a string representing the username, but you don’t actually check or enforce this assertion. This can be dangerous! It’s exactly what attackers are looking for when they try to hack your system.

Crafting Safe Query Parameters

In the previous section, you saw how an intruder can exploit your system and gain admin permissions by using a carefully crafted string. The issue was that you allowed the value passed from the client to be executed directly to the database, without performing any sort of check or validation. SQL injections rely on this type of vulnerability.

Any time user input is used in a database query, there’s a possible vulnerability for SQL injection. The key to preventing Python SQL injection is to make sure the value is being used as the developer intended. In the previous example, you intended for username to be used as a string. In reality, it was used as a raw SQL statement.

To make sure values are used as they’re intended, you need to escape the value. For example, to prevent intruders from injecting raw SQL in the place of a string argument, you can escape quotation marks:

>>> # BAD EXAMPLE. DON'T DO THIS!
>>> username = username.replace("'", "''")

This is just one example. There are a lot of special characters and scenarios to think about when trying to prevent Python SQL injection. Lucky for you, modern database adapters, come with built-in tools for preventing Python SQL injection by using query parameters. These are used instead of plain string interpolation to compose a query with parameters.

Note: Different adapters, databases, and programming languages refer to query parameters by different names. Common names include bind variables, replacement variables, and substitution variables.

Now that you have a better understanding of the vulnerability, you’re ready to rewrite the function using query parameters instead of string interpolation:

def is_admin(username: str) -> bool:

    with connection.cursor() as cursor:

        cursor.execute("""

            SELECT

                admin

            FROM

                users

            WHERE

                username = %(username)s

        """, {

            'username': username

        })

        result = cursor.fetchone()


    if result is None:

        # User does not exist

        return False


    admin, = result

    return admin

Here’s what’s different in this example:

  • In line 9, you used a named parameter username to indicate where the username should go. Notice how the parameter username is no longer surrounded by single quotation marks.

  • In line 11, you passed the value of username as the second argument to cursor.execute(). The connection will use the type and value of username when executing the query in the database.

To test this function, try some valid and invalid values, including the dangerous string from before:

>>> is_admin('haki')
False
>>> is_admin('ran')
True
>>> is_admin('foo')
False
>>> is_admin("'; select true; --")
False

Amazing! The function returned the expected result for all values. What’s more, the dangerous string no longer works. To understand why, you can inspect the query generated by execute():

>>> with connection.cursor() as cursor:
...    cursor.execute("""
...        SELECT
...            admin
...        FROM
...            users
...        WHERE
...            username = %(username)s
...    """, {
...        'username': "'; select true; --"
...    })
...    print(cursor.query.decode('utf-8'))
SELECT
    admin
FROM
    users
WHERE
    username = '''; select true; --'

The connection treated the value of username as a string and escaped any characters that might terminate the string and introduce Python SQL injection.

Passing Safe Query Parameters

Database adapters usually offer several ways to pass query parameters. Named placeholders are usually the best for readability, but some implementations might benefit from using other options.

Let’s take a quick look at some of the right and wrong ways to use query parameters. The following code block shows the types of queries you’ll want to avoid:

# BAD EXAMPLES. DON'T DO THIS!
cursor.execute("SELECT admin FROM users WHERE username = '" + username + '");
cursor.execute("SELECT admin FROM users WHERE username = '%s' % username);
cursor.execute("SELECT admin FROM users WHERE username = '{}'".format(username));
cursor.execute(f"SELECT admin FROM users WHERE username = '{username}'");

Each of these statements passes username from the client directly to the database, without performing any sort of check or validation. This sort of code is ripe for inviting Python SQL injection.

In contrast, these types of queries should be safe for you to execute:

# SAFE EXAMPLES. DO THIS!
cursor.execute("SELECT admin FROM users WHERE username = %s'", (username, ));
cursor.execute("SELECT admin FROM users WHERE username = %(username)s", {'username': username});

In these statements, username is passed as a named parameter. Now, the database will use the specified type and value of username when executing the query, offering protection from Python SQL injection.

Using SQL Composition

So far you’ve used parameters for literals. Literals are values such as numbers, strings, and dates. But what if you have a use case that requires composing a different query—one where the parameter is something else, like a table or column name?

Inspired by the previous example, let’s implement a function that accepts the name of a table and returns the number of rows in that table:

# BAD EXAMPLE. DON'T DO THIS!
def count_rows(table_name: str) -> int:
    with connection.cursor() as cursor:
        cursor.execute("""
            SELECT
                count(*)
            FROM
                %(table_name)s
        """, {
            'table_name': table_name,
        })
        result = cursor.fetchone()

    rowcount, = result
    return rowcount

Try to execute the function on your users table:

Traceback (most recent call last):
  File "", line 1, in 
  File "", line 9, in count_rows
psycopg2.errors.SyntaxError: syntax error at or near "'users'"
LINE 5:                 'users'
                        ^

The command failed to generate the SQL. As you’ve seen already, the database adapter treats the variable as a string or a literal. A table name, however, is not a plain string. This is where SQL composition comes in.

You already know it’s not safe to use string interpolation to compose SQL. Luckily, Psycopg provides a module called psycopg.sql to help you safely compose SQL queries. Let’s rewrite the function using psycopg.sql.SQL():

from psycopg2 import sql

def count_rows(table_name: str) -> int:
    with connection.cursor() as cursor:
        stmt = sql.SQL("""
            SELECT
                count(*)
            FROM
                {table_name}
        """).format(
            table_name = sql.Identifier(table_name),
        )
        cursor.execute(stmt)
        result = cursor.fetchone()

    rowcount, = result
    return rowcount

There are two differences in this implementation. First, you used sql.SQL() to compose the query. Then, you used sql.Identifier() to annotate the argument value table_name. (An identifier is a column or table name.)

Note: Users of the popular package django-debug-toolbar might get an error in the SQL panel for queries composed with psycopg.sql.SQL(). A fix is expected for release in version 2.0.

Now, try executing the function on the users table:

>>> count_rows('users')
2

Great! Next, let’s see what happens when the table does not exist:

>>> count_rows('foo')
Traceback (most recent call last):
  File "", line 1, in 
  File "", line 11, in count_rows
psycopg2.errors.UndefinedTable: relation "foo" does not exist
LINE 5:                 "foo"
                        ^

The function throws the UndefinedTable exception. In the following steps, you’ll use this exception as an indication that your function is safe from a Python SQL injection attack.

Note: The exception UndefinedTable was added in psycopg2 version 2.8. If you’re working with an earlier version of Psycopg, then you’ll get a different exception.

To put it all together, add an option to count rows in the table up to a certain limit. This feature might be useful for very large tables. To implement this, add a LIMIT clause to the query, along with query parameters for the limit’s value:

from psycopg2 import sql

def count_rows(table_name: str, limit: int) -> int:
    with connection.cursor() as cursor:
        stmt = sql.SQL("""
            SELECT
                COUNT(*)
            FROM (
                SELECT
                    1
                FROM
                    {table_name}
                LIMIT
                    {limit}
            ) AS limit_query
        """).format(
            table_name = sql.Identifier(table_name),
            limit = sql.Literal(limit),
        )
        cursor.execute(stmt)
        result = cursor.fetchone()

    rowcount, = result
    return rowcount

In this code block, you annotated limit using sql.Literal(). As in the previous example, psycopg will bind all query parameters as literals when using the simple approach. However, when using sql.SQL(), you need to explicitly annotate each parameter using either sql.Identifier() or sql.Literal().

Note: Unfortunately, the Python API specification does not address the binding of identifiers, only literals. Psycopg is the only popular adapter that added the ability to safely compose SQL with both literals and identifiers. This fact makes it even more important to pay close attention when binding identifiers.

Execute the function to make sure that it works:

>>> count_rows('users', 1)
1
>>> count_rows('users', 10)
2

Now that you see the function is working, make sure it’s also safe:

>>> count_rows("(select 1) as foo; update users set admin = true where name = 'haki'; --", 1)
Traceback (most recent call last):
  File "", line 1, in 
  File "", line 18, in count_rows
psycopg2.errors.UndefinedTable: relation "(select 1) as foo; update users set admin = true where name = '" does not exist
LINE 8:                     "(select 1) as foo; update users set adm...
                            ^

This traceback shows that psycopg escaped the value, and the database treated it as a table name. Since a table with this name doesn’t exist, an UndefinedTable exception was raised and you were not hacked!

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Conclusion

You’ve successfully implemented a function that composes dynamic SQL without putting your system at risk for Python SQL injection! You’ve used both literals and identifiers in your query without compromising security.

You’ve learned:

  • What Python SQL injection is and how it can be exploited
  • How to prevent Python SQL injection using query parameters
  • How to safely compose SQL statements that use literals and identifiers as parameters

You’re now able to create programs that can withstand attacks from the outside. Go forth and thwart the hackers!

Connect to Microsoft SQL Server database on MacOS using Python

Connect to Microsoft SQL Server database on MacOS using Python

Connect to your MS SQL using python. The first thing you need is to install Homebrew. You need the copy the content in the square brackets which in my case is “ODBC Driver 13 for SQL Server”. Replace “ODBC Driver 13 for SQL Server” with the content you copied in the square brackets.

There are always situations where I want to automate small tasks. I like using Python for these kind of things, you can quickly get something working without much hassle. I needed to perform some database changes in a Microsoft SQL Server database and wanted to connect to it using Python. On Windows this is usually pretty straight forward. But I use macOS as my main operating system and I had some hurdles along the way so here is a quick how to.

Preparing

If Homebrew isn't installed yet let's do that first. It's an excellent package manager for macOS. Paste the following command in the terminal to install it:

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Once finished run the following commands to install the Microsoft ODBC Driver 13.1 for SQL Server. This driver will be used to connect to the MS SQL database.

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
brew tap microsoft/mssql-release https://github.com/Microsoft/homebrew-mssql-release
brew update
brew install [email protected] [email protected]
Python package: pyodbc or pypyodbc

With the driver installed, we'll be using a Python package that uses the driver to connect to the database. There are two frequently used packages to connect in Python: pyodbc and pypyodbcpypyodbc is a pure Python re-implementation of pyodbc. The main thing I took a way was that pypyodbcis easier to set up on different platforms. I could not get pyodbc working, it simply wouldn't connect to the database.

Installing pypyodbc can be done using pip, the python package installer.

pip install pypyodbc
Code

Now that the necessary components have been installed, let's open our favorite code editor and write code. The first thing that needs to be done is importing pypyodbc. The script starts with this:

import pypyodbc

Then we need a connection to the database:

sqlConnection = pypyodbc.connect(
                "Driver={ODBC Driver 13 for SQL Server};"
        "Server=<server IP address>;"
        "Database=<database>;"
        "uid=<username>;pwd=<password>");

Note that you have to replace four values in this code: server IP addressdatabase , username and password. The value for the driver is a hard coded value which indicates what driver to use to connect to the database, this value points to the driver that was installed earlier.

Now all what rests is use the connection and run a query.

cursor = sqlConnection.cursor()
cursor.execute("select * from Values")

The cursor now contains the result of our query, the property cursor.rowcount returns the number of rows the query returned. It's now possible to loop through the rows and access the different columns:

for row in cursor:
    print(cursor)
    # first column
    firstColumn = row[0]
    # ...

When we're done, we need to clean up the cursor and database connection by closing it.

cursor.close()
sqlConnection.close()

And that's it, save the file and use the python <filename>.py or python3 <filename.py> command, this depends on your configuration, to run. Here is the entire script:

import pypyodbc

sqlConnection = pypyodbc.connect(
"Driver={ODBC Driver 13 for SQL Server};"
"Server=<server IP address>;"
"Database=<database>;"
"uid=<username>;pwd=<password>");

cursor = sqlConnection.cursor()
cursor.execute("select * from Values")

for row in cursor:
print(cursor)
# first column
firstColumn = row[0]
# ...

cursor.close()
sqlConnection.close()

The with syntax can also be used to automatically close the cursor and the connection, this is another way of writing the same script:

import pypyodbc

with pypyodbc.connect(
"Driver={ODBC Driver 13 for SQL Server};"
"Server=<server IP address>;"
"Database=<database>;"
"uid=<username>;pwd=<password>") as sqlConnection:

with sqlConnection.cursor() as cursor:

    cursor.execute("select * from Values")

    for row in cursor:
        print(cursor)
        # first column
        firstColumn = row[0]
        # ...

If you're looking for some more reading on the topic:

Thanks for reading. If you liked this post, share it with all of your programming buddies!

Further reading

☞ Python for Time Series Data Analysis

☞ Python Programming For Beginners From Scratch

☞ Python Network Programming | Network Apps & Hacking Tools

☞ Intro To SQLite Databases for Python Programming

☞ Ethical Hacking With Python, JavaScript and Kali Linux

☞ Beginner’s guide on Python: Learn python from scratch! (New)

☞ Python for Beginners: Complete Python Programming

Learn Database Administration - PostgreSQL Database Administration (DBA) for Beginners

In this video, we will go over the basics of the PostgreSQL. We will cover topics ranging from installations, to writing basic queries and retrieving data from tables. We will also explore the logic of joining tables to retrieve data and much more.

The course also covers the basics of creating tables, storing data with data types, and working with expressions, operators, and strings.

Topics also includes:

Installing PostgreSQL

Loading sample database

Creating database and tables

Performing CRUD operations

Joining Tables

Using aggregate and analytic functions

Creating views and triggers

What you’ll learn

Install PostgreSQL Server

Load sample database

Create a database

Create a table

Insert data into tables

Update existing records inside a table

Delete Records in a table

Remove duplicate records

Query data from a table

Create a subquery

Get data from multiple tables

Create and manage roles

Create a view

Create tablespace

Backup and restore database

Filter and sort data

Use various operators

Use aggregate and analytic functions

Create triggers

Thanks for reading

If you liked this post, share it with all of your programming buddies!

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The Complete SQL Bootcamp

The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert

An Introduction to Queries in PostgreSQL

Build a Basic App with Spring Boot and JPA using PostgreSQL

Why We Moved From NoSQL MongoDB to PostgreSQL?

Using PostgreSQL Database with Python

Using PostgreSQL Database with Python

In this article we will see how to connect to PostgreSQL from Python Script and perform queries.

In this article we will see how to connect to PostgreSQL from Python Script and perform queries.

PostgreSQL is an open source object-relational database management system. PostgreSQL is ACID-compliant and is transactional. It has triggers, foreign keys and supports functions and stored procedures.

PostgreSQL is used by giants like Uber, Apple, Netflix and Instagram.

Requirement:

Create a virtual environment using python 3 and activate it. Install below packages.

psycopg2==2.7.3.2

Installation:

Install the PostgreSQL database and utilities using below commands.

sudo apt-get update
sudo apt-get install postgresql postgresql-contrib

By default, PostgreSQL sets up the user and database “postgres” upon a new installation. We need to switch to this user to use postgres database.

sudo -su postgres

Now go to the Postgres prompt by typing psql on terminal.

We are using version 10.3.

If you get any error in connecting to database, make sure PostgreSQL is running. Check the status using below command.

$ systemctl status postgresql

You can check for errors in logs using below command.

$ tail -f /var/log/postgresql

Creating database:

Before creating a new database, lets list all the databases. Use \l or \list for the same.

To create database, exit the psql terminal by typing \q and use command createdb testdb.

[email protected]:~$ createdb testdb
[email protected]:~$ psql
psql (10.3 (Ubuntu 10.3-1.pgdg16.04+1))
Type "help" for help.

postgres=# \l
                               List of databases
     Name      |  Owner   | Encoding | Collate | Ctype |   Access privileges   
---------------+----------+----------+---------+-------+-----------------------
 postgres      | postgres | UTF8     | en_IN   | en_IN | 
 rana_test     | postgres | UTF8     | en_IN   | en_IN | 
 template0     | postgres | UTF8     | en_IN   | en_IN | =c/postgres          +
               |          |          |         |       | postgres=CTc/postgres
 template1     | postgres | UTF8     | en_IN   | en_IN | =c/postgres          +
               |          |          |         |       | postgres=CTc/postgres
 testdb        | postgres | UTF8     | en_IN   | en_IN | 
(5 rows)

postgres=# \c testdb
You are now connected to database "testdb" as user "postgres".
testdb=# 

To connect to another database, use command \c or \connect and database name. \c testdb in this case.

Creating Table:

Most the query sytanx in PostgreSQL are same as MySQL.

create table users (
    id serial PRIMARY KEY,
    username varchar (20) NOT NULL,
    age smallint NOT NULL,
    location varchar (50) NOT NULL
);

Copy paste the above sytax in terminal and new table will be created. You can list the tables by typing \d.

testdb=# create table users (
testdb(#     username varchar (20) NOT NULL,
testdb(#     age smallint NOT NULL,
testdb(#     location varchar (50) NOT NULL
testdb(# );
CREATE TABLE
testdb=# \d
         List of relations
 Schema | Name  | Type  |  Owner   
--------+-------+-------+----------
 public | users | table | postgres
(1 row)

testdb=# 

You can learn more about querying from psql terminal by visitng official site. Lets go to Python code.

Connecting from Python Script:

We installed the psycopg package in virtual environment. Use below code in Python Script to connect to database.

import psycopg2


# this function will return the connection object
def connect():
    conn = None
    try:
        conn = psycopg2.connect(host="localhost", user="postgres", password="root", database="testdb")
    except Exception as e:
        print(repr(e))

    return conn

Inserting Data into Table:

First get the connection and cursor and then create query. Once query is executed, commit using connection and close the cursor and connection.

conn = connect()
cur = conn.cursor()

last_insert_id = None

# inserting data in users table
sql_query = "insert into users (username, age, location) values (%s, %s, %s) returning id;"

sql_data = (
    "Ajay",
    "25",
    "New York"
)


cur.execute(sql_query, sql_data)
last_insert_id = cur.fetchone()[0]
print("Last Insert ID " + str(last_insert_id))

conn.commit()
cur.close()
conn.close()

return last_insert_id

We are Inserting data in table and returning the primary key id which is the serial key.

Fetching Data from Table:

Select query for PostgreSQL is same as MySQL.

conn = connect()
cur = conn.cursor()

sql_query = "select username, age, location from users where location = %s;"
sql_data = ("Delhi")
cur.execute(sql_query, sql_data)

results = cur.fetchall()
return results

Updating a row:

conn = connect()
cursor = conn.cursor()

sql_query = "update users set location = %s where username = %s;"
sql_data = ("Mumbai", "Ajay")

cursor.execute(sql_query, sql_data)

cursor.close()
conn.close()

return True

To exit the terminal use \q command.

If you are facing any issue, feel free to comment.