fp-datatables 0.1.1

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FastAPI DataTables

A simple integration of DataTables.js (or any standard DataTables-like frontend library) with FastAPI and SQLAlchemy.

This package makes it easy to implement:

• Server-side pagination
• Sorting
• Searching (deep search supported)
• Filtering
• Transforming query results

Perfect for handling large datasets efficiently while keeping your API clean and async-friendly.

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🚀 Installation

pip install fp-datatables

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📖 Quick Start Example

1. Setup database and models

from fastapi import FastAPI, Depends
from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
from sqlalchemy.orm import sessionmaker, declarative_base
from sqlalchemy import Column, Integer, String, select
from datatables import DataTables, DataTablesRequest, DataTablesResponse
from pydantic import BaseModel

DATABASE_URL = "sqlite+aiosqlite:///./students.db"

engine = create_async_engine(DATABASE_URL, echo=False, future=True)
async_session = sessionmaker(bind=engine, class_=AsyncSession, expire_on_commit=False)
Base = declarative_base()

# Dependency for DB session
async def get_db() -> AsyncSession:
    async with async_session() as session:
        yield session

# Model
class Student(Base):
    __tablename__ = "students"
    id = Column(Integer, primary_key=True, index=True)
    name = Column(String, nullable=False)
    age = Column(Integer, nullable=False)
    email = Column(String, nullable=False, unique=True)

# Schema
class StudentSchema(BaseModel):
    id: int
    name: str
    age: int

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2. Setup FastAPI app

app = FastAPI()

@app.on_event("startup")
async def startup():
    async with engine.begin() as conn:
        await conn.run_sync(Base.metadata.create_all)

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3. Insert test data

import random
from faker import Faker

faker = Faker()

@app.get("/insert_students")
async def insert_students():
    async with async_session() as session:
        students = [
            Student(
                name=faker.name(),
                age=random.randint(18, 25),
                email=faker.unique.email(),
            )
            for _ in range(1000)
        ]
        session.add_all(students)
        await session.commit()
    return {"message": "1000 random students inserted successfully!"}

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4. Datatables API endpoint

@app.post("/students", response_model=DataTablesResponse[list[StudentSchema]])
async def get_students(
    request: DataTablesRequest, db: AsyncSession = Depends(get_db)
):
    stmt = select(Student)
    datatable = DataTables(db, Student, stmt)
    return await datatable.process(request)

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⚡ Frontend Usage

You can use DataTables.js (or any DataTables-compatible frontend) to consume this API. Example DataTables setup (jQuery):

$('#studentsTable').DataTable({
    serverSide: true,
    processing: true,
    ajax: {
        url: "/students",
        type: "POST",
        contentType: "application/json",
        data: function (d) {
            return JSON.stringify(d);
        }
    },
    columns: [
        { data: "id" },
        { data: "name" },
        { data: "age" }
    ]
});

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✨ Features

• Async-first (built on FastAPI + SQLAlchemy 2.0 async)
• Pagination, sorting, searching out of the box
• Works with any frontend datatable library (DataTables.js, PrimeVue DataTable, etc.)
• Supports deep field search (relation.field)
• Easy integration with Pydantic models

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🛠️ Roadmap

• Support for joins and relationships
• More advanced filters
• Examples for Vue/React/Angular integration

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📜 License

MIT License © 2025 Rohit Kumar

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👉 Would you like me to also add an "Advanced Usage" section (with filtering, deep search student.classroom.name, and custom column transformations) so your README looks more complete for real-world use cases?