How to use create enginge function in sql alchemy

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by vigneshchennai74 dot icon Updated: Nov 9, 2023

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SQLAlchemy empowers developers to work with relational databases. It abstracts away the complexities of SQL, making database interaction more accessible.   

Understanding SQLAlchemy:  

SQLAlchemy is a Python library for Object-Relational Mapping (ORM) and database interaction. It provides a high-level, Pythonic approach to working with relational databases. This allows developers to use Python classes and objects to manipulate data. You can create, query, and change databases without extensive SQL coding.  

Database Variety:  

SQL Alchemy supports various databases, from simple tables to intricate data models. It enables the creation of database tables, views, and functions. It will support SQLite, Microsoft SQL Server, PostgreSQL, MySQL, and Oracle.   

  • SQLite: A self-contained, serverless, and file-based relational database system.  
  • Microsoft SQL Server: An enterprise-level relational database management system developed by Microsoft.  
  • PostgreSQL: An open-source object-relational database system known for its extensibility and standards compliance.  
  • MySQL: It is a relational database management system. It helps in web applications.  
  • Oracle: A powerful, enterprise-level database system developed by Oracle Corporation.   

This offers flexibility in choosing the right database for your project's needs.   

Crafting Database Objects:  

You can create various database objects, including tables, views, and functions. Tables represent structured data, and functions allow for the execution of database operations. SQL Alchemy's ORM enables you to work with Python classes that map to database tables. It makes the interaction between Python and databases seamless.   

Mastering SQL Alchemy: 

When writing about SQL Alchemy, it's essential to cover the foundational concepts of   

  • database connectivity  
  • table creation  
  • data querying,  
  • power of SQL Alchemy's ORM.   

Advanced topics include configuring connection pools for improved performance. It also involves navigating many database dialects and ensuring transaction integrity. Mastering SQL query construction and execution is crucial for complex tasks.  


SQL Alchemy stands out as a versatile Python library. It helps in interacting with relational databases. Its unique strengths lie in its Object-Relational Mapping (ORM) capabilities. SQL Alchemy also boasts extensive support for various database dialects and robust documentation.

SQL Alchemy simplifies database interaction. It's an indispensable tool for developers working on data-driven Python applications. Its flexibility and performance-enhancing features set it apart. It offers a Pythonic way to master the art of database interaction.   

In your journey with SQL Alchemy, you'll discover the power of Python in  

  • managing data  
  • abstracting SQL intricacies  
  • creating dynamic  
  • data-driven applications.  

So, SQL Alchemy is a must-have library in your toolkit. It's ready to simplify the complex world of relational databases.   

Preview of the output that you will get on running this code from your IDE


In this solution, we have use SQL alchemy library

from sqlalchemy import Column, String, create_engine, event, inspect, select, table, union
from sqlalchemy.ext import compiler
from sqlalchemy.orm import Session, declarative_base
from sqlalchemy.schema import DDLElement

# boilerplate from
class CreateView(DDLElement):
    def __init__(self, name, selectable): = name
        self.selectable = selectable

class DropView(DDLElement):
    def __init__(self, name): = name

def _create_view(element, compiler, **kw):
    return "CREATE VIEW %s AS %s" % (,
        compiler.sql_compiler.process(element.selectable, literal_binds=True),

def _drop_view(element, compiler, **kw):
    return "DROP VIEW %s" % (

def view_exists(ddl, target, connection, **kw):
    return in inspect(connection).get_view_names()

def view_doesnt_exist(ddl, target, connection, **kw):
    return not view_exists(ddl, target, connection, **kw)

def view(name, metadata, selectable):
    t = table(name)

        col._make_proxy(t) for col in selectable.selected_columns

        CreateView(name, selectable).execute_if(callable_=view_doesnt_exist),
    return t

# demo
Base = declarative_base()

class Equity(Base):
    __tablename__ = "equities"

    id = Column(String, primary_key=True)
    currency = Column(String, nullable=False)

class Bond(Base):
    __tablename__ = "bonds"

    id = Column(String, primary_key=True)
    currency = Column(String, nullable=False)

common_view = view(
        select("id"), Equity.currency.label("currency")),
        select("id"), Bond.currency.label("currency")),

engine = create_engine("sqlite://", echo=True, future=True)


with Session(engine) as session:
            Equity(id="AAA", currency="EUR"),
            Equity(id="AAB", currency="USD"),
            Bond(id="AAA", currency="EUR"),
            Equity(id="EEF", currency="GBP"),

with Session(engine) as session:
    results = session.execute(select(common_view)).all()

print(results)  # [('AAA', 'EUR'), ('AAB', 'USD'), ('EEF', 'GBP')]
  1. Download and install VS Code on your desktop.
  2. Open VS Code and create a new file in the editor.
  3. Copy the code snippet that you want to run, using the "Copy" button or by selecting the text and using the copy command (Ctrl+C on Windows/Linux or Cmd+C on Mac).,
  4. Paste the code into your file in VS Code, and save the file with a meaningful name and the appropriate file extension for Python use (.py).file extension.
  5. To run the code, open the file in VS Code and click the "Run" button in the top menu, or use the keyboard shortcut Ctrl+Alt+N (on Windows and Linux) or Cmd+Alt+N (on Mac). The output of your code will appear in the VS Code output console.
  6. Paste the code into your file in VS Code.
  7. Save the file with a meaningful name and the appropriate file extension for Python use (.py)
  8. Save and run the Code

I hope this is useful to you. I have added the version information in the following section. I found this code snippet by searching " How to create a Union table inside the SQL database using SQLAlchemy?

" in Kandi. you can try any use case.

Environment Tested

I tested this solution in the following versions. Please be aware of any changes when working with other versions.

  1. The solution is created and tested using Vscode 1.77.2 version
  2. The solution is created in SQLAlchemy 2.02.22 version
  3. The solution is created in Python 3.7.15ersion

This code explains how to create the engine function in sql alchemy. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help. how to use create enginge function in sql alchemy in Python.


1. How do I import the sqlalchemy Column module?   

To import the sqlalchemy Column module, you can use the following code:   

from sqlalchemy import Column  

2. What is the logging module, and how does it apply to create_engine in SQLAlchemy?   

The logging module of SQL Alchemy refers to Python's standard logging module. It allows you to control and configure the logging output generated by SQL Alchemy. When using create_engine, you can configure and format SQL Alchemy's log messages. We can do it with configuration by adjusting the logging module setting.   

3. Is create_engine a single command, or can we use it with other commands?   

create_engine is a standalone command used to create a database engine. It helps in other commands to 

  • establish database connections,  
  • create tables,  
  • perform various database operations.  

4. How do I make new database connections using create_engine in SQL Alchemy?   

To create new database connections using create_engine, you can do the following:   

from sqlalchemy import create_engine  

# Replace 'your_database_url' with the actual database URL.  

engine = create_engine('your_database_url')  

# You can then establish a connection using the engine object.  

Connection = engine.connect()  


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