How to use create enginge function in sql alchemy

by vigneshchennai74 Updated: Nov 9, 2023

Guide Kit

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.

Conclusion:

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.

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Code

In this solution, we have use SQL alchemy library

How to create a Union table inside the SQL database using SQLAlchemy?

PythonLines of Code : 107License : Strong Copyleft (CC BY-SA 4.0)

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 https://github.com/sqlalchemy/sqlalchemy/wiki/Views
class CreateView(DDLElement):
    def __init__(self, name, selectable):
        self.name = name
        self.selectable = selectable


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


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


@compiler.compiles(DropView)
def _drop_view(element, compiler, **kw):
    return "DROP VIEW %s" % (element.name)


def view_exists(ddl, target, connection, **kw):
    return ddl.name 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)

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

    event.listen(
        metadata,
        "after_create",
        CreateView(name, selectable).execute_if(callable_=view_doesnt_exist),
    )
    event.listen(
        metadata,
        "before_drop",
        DropView(name).execute_if(callable_=view_exists),
    )
    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(
    "bonds_equities_union_view",
    Base.metadata,
    union(
        select(Equity.id.label("id"), Equity.currency.label("currency")),
        select(Bond.id.label("id"), Bond.currency.label("currency")),
    ),
)


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

Base.metadata.create_all(engine)

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

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

print(results)  # [('AAA', 'EUR'), ('AAB', 'USD'), ('EEF', 'GBP')]

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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.

The solution is created and tested using Vscode 1.77.2 version
The solution is created in SQLAlchemy 2.02.22 version
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.

FAQ

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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