django REST framework : ================ #urls url dispatcher maps urls to views #views Coordinates the entire process. It talks to the Serializer, fetches data from the Model, and decides what the final Response looks like. #serializers Converts complex Database/Model objects into JSON. It also acts as a Security Guard by validating user input. #models Defines the structure of your data (tables, columns, types) and how it is stored in the Database. #softwaredevelopment #serializers #python #pythondeveloper #django #djangorestframework #models #views #databases #ORM #sqlite #MySQL #request #response

Django ORM vs Raw SQL ================= Django ORM: * Abstracts database operations into clean Python code (models & QuerySets) * A project typically uses ORM for 90-95% of queries (one primary way to interact) * Faster development, more readable, secure by default (prevents SQL injection) Raw SQL: * Direct SQL queries executed via .raw(), extra(), or cursor * Can be used multiple times when needed * Potentially faster for complex joins, CTEs, window functions, or heavy optimizations (but slower to write & maintain) Use ORM first -> back to raw SQL only when performance is measured #Django #Python #ORM #DjangoORM #SQL #Database #BackendDevelopment #WebDevelopment #SoftwareEngineering #PythonDeveloper

🐍 MongoEngine Installation – Python ODM for MongoDB MongoEngine is an Object Document Mapper (ODM) for MongoDB that allows developers to interact with MongoDB using Python classes and objects instead of raw queries. Before installing MongoEngine, MongoDB must already be installed and the MongoDB server should be running. python explanations (29) The easiest way to install MongoEngine is by using the PIP installer: pip3 install mongoengine If your Python installation does not include Setuptools, you can download MongoEngine manually and install it using: python setup.py install MongoEngine relies on a few dependencies, including: pymongo ≥ 3.4 dnspython ≥ 3.6.1 python explanations (29) After installation, you can verify that MongoEngine is correctly installed by checking its version in Python: import mongoengine print(mongoengine.__version__) If the installation is successful, Python will display the installed version (for example 0.29.1). python explanations (29) 💡 MongoEngine simplifies MongoDB development in Python by providing an object-oriented approach for defining schemas, performing queries, and managing database operations. #Python #MongoDB #MongoEngine #Database #NoSQL #PythonProgramming #DataEngineering #BackendDevelopment #AshokIT

FastAPI with SQLAlchemy: Building a Robust API with Databases In the world of web development, building robust and scalable APIs is crucial. Databases are the backbone of most applications, storing and managing the critical data that drives them. FastAPI, a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints, has gained immense popularity for its speed, simplicity, and ease of use. SQLAlchemy, a powerful and versatile SQL toolkit and Object-Relational Mapper (ORM) for Python, provides a high-level abstraction for interacting with databases, allowing developers to work with Python objects instead of writing raw SQL queries....

If your Django API is slow, check this first: Count your database queries. A view that looks clean can still execute: 1 main query N queries for related objects M queries inside serializers Suddenly one API call = 150+ DB hits. Before adding caching: Use select_related Use prefetch_related Add proper DB indexes Analyze query plans Most performance issues aren’t solved by Redis. They’re solved by understanding your ORM. #Django #Python #ORM #DatabaseOptimization #BackendEngineering #PerformanceTuning

## In PySpark architecture, the driver node contains a container-like component called the Application Master. Inside it, there is the Application Driver, which runs on the JVM and has the standard main() function. If we write code in Java or Scala, it directly runs on this JVM driver, so no additional layer is needed. However, when we write code in Python (PySpark), a separate PySpark driver is required. This Python driver communicates with the JVM-based driver, and the interaction between them is handled by Py4J, which enables conversion and communication between Python and JVM objects. On the worker node side, things are simpler. Each worker primarily runs a JVM process that executes tasks sent by the driver. However, when Python-specific logic is involved—such as using UDFs (User Defined Functions)—a Python worker (or wrapper) is also started alongside the JVM to execute the Python code. So in summary: the driver involves both Python and JVM layers connected via Py4J in PySpark, while workers are mostly JVM-based, with Python processes added only when needed (e.g., for UDFs).

In my latest tutorial, I explored the transition from TinyDB to SQLite when building Python web applications. While TinyDB is great for beginners because it stores data in simple JSON files and is easy to set up, it has limitations as applications grow. Querying large datasets, managing structured data, and handling multiple users can become challenging. That’s why I introduced SQLite as a practical alternative. SQLite provides a lightweight relational database with powerful SQL queries, better data integrity, and improved performance—all without requiring a separate database server. For developers building apps with Python and Flask, SQLite is often the next logical step when moving from simple prototypes to more structured applications. Here is the tutorial link: https://lnkd.in/g8UYe2pz #Python #Flask #SQLite #WebDevelopment #Programming #Database

Today I learned about Models in Django and finally understood the concept of ORM. ORM stands for Object Relational Mapper. It means we can create and manage database tables using Python instead of writing SQL directly. In Django, we define tables inside the models.py file. Each model class represents a table, and each attribute inside the class represents a column. What I understood: We write everything in Python. Django automatically converts it into database queries. No need to manually create tables in SQL. 🔹 Some Common Fields I Learned: CharField() – used to store text (like name, title, etc.) TextField() – used for long text IntegerField() – used to store numbers FloatField() – used for decimal values BooleanField() – used for True/False DateField() – used for dates EmailField() – used to store email addresses After creating models, we run migrations so Django can create the tables in the database. Today’s learning helped me understand how Django connects Python code with the database smoothly. Slowly everything is connecting — Views, Templates, and now Models. #Django #Python #WebDevelopment #BackendDevelopment #LearningJourney

The Django ORM feels like magic. But how does it work internally? It's actually three layers doing very specific jobs. Layer 1: Query -> When a QuerySet is built, Django creates a Query object internally. -> This object is a structured, database-agnostic representation of the intended operation. -> All this is stored as Python data structures. No SQL yet. This is why QuerySets are chainable. Layer 2: SQL Compiler -> At evaluation time, Django takes the Query object and hands it to a SQLCompiler. -> Compiler translates Python representation into actual SQL. -> This is where database-specific syntax is handled. The same Query object produces different SQL depending on which backend is active. Layer 3: Connection -> The compiled SQL string is handed to the database Connection. -> This manages the actual connection to the database, sends the query, and returns raw results. -> Django then maps those raw results back into model instances. The ORM is not a black box. It's a pipeline and knowing it means knowing exactly where to look when something goes wrong. I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

SQLite is too complex for small Python projects, a common misconception. In reality, SQLite is lightweight, easy to set up, and requires minimal configuration. It provides a fully functional SQL database engine, allowing for complex queries and data modeling. It is also highly portable, making it ideal for development and testing environments. Full breakdown in [8 mins] on YouTube: #SQLite #PythonDevelopment #DatabaseManagement