Data — Implement Local SQLite Storage (Cost‑Free, Future Migration Planned)

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Introduction

In this article, we will explore the implementation of local SQLite storage as a cost-free, future migration-planned solution for persisting user meal logs. SQLite is an embedded, zero-cost datastore that can be used to store and manage data locally on a user's device. We will define SQLAlchemy models for a MealLog table and manage the schema with Alembic migrations. Additionally, we will expose CRUD functions in the data module to facilitate storing, querying, and aggregating logs.

Why SQLite?

SQLite is a popular choice for local data storage due to its simplicity, flexibility, and cost-effectiveness. It is an embedded database, meaning that it does not require a separate process or server to run. This makes it an ideal choice for applications that require local data storage, such as mobile apps or desktop applications.

Defining the MealLog Table

To define the MealLog table, we will use SQLAlchemy, a popular ORM (Object-Relational Mapping) tool for Python. We will create a MealLog model with the following fields:

  • id (Primary Key): a unique identifier for each log entry
  • user_hash: a unique identifier for the user who logged the meal
  • timestamp: the date and time when the meal was logged
  • meal_type: the type of meal (e.g. breakfast, lunch, dinner)
  • image_url: the URL of the image associated with the meal
  • calories: the number of calories in the meal
  • protein: the amount of protein in the meal
  • carbs: the amount of carbohydrates in the meal
  • fat: the amount of fat in the meal

Here is the SQLAlchemy model definition:

from sqlalchemy import Column, Integer, String, DateTime, Enum, Float
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class MealLog(Base):
    __tablename__ = 'meal_logs'

    id = Column(Integer, primary_key=True)
    user_hash = Column(String, nullable=False)
    timestamp = Column(DateTime, nullable=False)
    meal_type = Column(Enum('breakfast', 'lunch', 'dinner'), nullable=False)
    image_url = Column(String, nullable=True)
    calories = Column(Float, nullable=False)
    protein = Column(Float, nullable=False)
    carbs = Column(Float, nullable=False)
    fat = Column(Float, nullable=False)

Managing the Schema with Alembic Migrations

To manage the schema of the MealLog table, we will use Alembic, a popular migration tool for SQLAlchemy. We will create a migrations directory under the data module and define the initial migration in versions/001_initial_schema.py:

from alembic import op
import sqlalchemy as sa

revision = '001'
down_revision = None
branch_labels = None
depends_on = None

def upgrade():
    op.create_table(
        'meal_logs',
        sa.Column('id', sa.Integer(), nullable=False),
        sa.Column('user_hash', sa.String(length=255), nullable=False),
        sa.Column('timestamp', sa.DateTime(), nullable),
        sa.Column('meal_type', sa.Enum('breakfast', 'lunch', 'dinner'), nullable=False),
        sa.Column('image_url', sa.String(length=255), nullable=True),
        sa.Column('calories', sa.Float(), nullable=False),
        sa.Column('protein', sa.Float(), nullable=False),
        sa.Column('carbs', sa.Float(), nullable=False),
        sa.Column('fat', sa.Float(), nullable=False),
        sa.PrimaryKeyConstraint('id')
    )

def downgrade():
    op.drop_table('meal_logs')

Exposing CRUD Functions in the data Module

To facilitate storing, querying, and aggregating logs, we will expose the following CRUD functions in the data module:

  • save_meal_log(log: MealLog): saves a new log entry to the database
  • get_recent_logs(user_hash: str, days: int): retrieves recent log entries for a given user
  • aggregate_calories(user_hash: str, period: str): aggregates calories for a given user over a specified period

Here is the implementation of the CRUD functions:

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from data import MealLog

engine = create_engine('sqlite:///meal_logs.db')
Session = sessionmaker(bind=engine)

def save_meal_log(log: MealLog):
    session = Session()
    session.add(log)
    session.commit()

def get_recent_logs(user_hash: str, days: int):
    session = Session()
    query = session.query(MealLog).filter(MealLog.user_hash == user_hash).order_by(MealLog.timestamp.desc()).limit(days)
    return query.all()

def aggregate_calories(user_hash: str, period: str):
    session = Session()
    query = session.query(func.sum(MealLog.calories)).filter(MealLog.user_hash == user_hash).filter(MealLog.timestamp >= period)
    return query.scalar()

Future Consideration: Migrating to a Managed PostgreSQL

While SQLite is a cost-effective solution for local data storage, it may not be suitable for large-scale applications that require high availability and scalability. In the future, we may consider migrating to a managed PostgreSQL database, such as Supabase, which offers a scalable and secure solution for data storage.

Acceptance Criteria

To ensure that the implementation meets the requirements, we will define the following acceptance criteria:

  • The SQLite database is initialized via SQLAlchemy and migrations are applied on startup
  • The data module exports the required CRUD interface
  • Integration tests demonstrate storing, querying, and aggregating logs
  • The README is updated with SQLite setup instructions and a note about future DB migration

Q: What is SQLite and why is it used for local data storage?

A: SQLite is a self-contained, file-based database that can be used for local data storage. It is a popular choice for applications that require a simple, lightweight, and cost-effective solution for storing and managing data.

Q: What are the benefits of using SQLite for local data storage?

A: The benefits of using SQLite for local data storage include:

  • Cost-effectiveness: SQLite is a zero-cost solution for local data storage.
  • Lightweight: SQLite is a self-contained database that does not require a separate process or server to run.
  • Simple: SQLite is a simple database that is easy to set up and manage.
  • Flexible: SQLite can be used for a wide range of applications, from small-scale projects to large-scale enterprise applications.

Q: What are the limitations of using SQLite for local data storage?

A: The limitations of using SQLite for local data storage include:

  • Scalability: SQLite is not designed for large-scale applications and may not be suitable for applications that require high availability and scalability.
  • Concurrency: SQLite is not designed for concurrent access and may not be suitable for applications that require multiple users to access the database simultaneously.
  • Security: SQLite is not designed for secure data storage and may not be suitable for applications that require high levels of security and data protection.

Q: How do I set up SQLite for local data storage?

A: To set up SQLite for local data storage, follow these steps:

  1. Install the SQLite library: Install the SQLite library for your programming language of choice.
  2. Create a database file: Create a new database file using the SQLite library.
  3. Create tables: Create tables in the database using the SQLite library.
  4. Insert data: Insert data into the tables using the SQLite library.

Q: How do I manage the schema of my SQLite database?

A: To manage the schema of your SQLite database, use the following steps:

  1. Use a schema migration tool: Use a schema migration tool such as Alembic to manage the schema of your SQLite database.
  2. Create a migration script: Create a migration script that defines the changes to the schema.
  3. Apply the migration: Apply the migration script to the database.

Q: How do I expose CRUD functions in my SQLite database?

A: To expose CRUD functions in your SQLite database, use the following steps:

  1. Create a data access object: Create a data access object that provides CRUD functions for the database.
  2. Implement CRUD functions: Implement CRUD functions in the data access object.
  3. Expose the CRUD functions: Expose the CRUD functions in the data access object.

Q: What are the best practices for implementing local SQLite storage?

A: The best practices for implementing local SQLite storage include:

  • Use a schema migration tool: Use a schema migration tool such as Alembic to manage the schema of your SQLite database.
  • Create a data access object: Create a data access object that provides CRUD functions for the database.
  • Implement CRUD functions: Implement CRUD functions in the data access object.
  • Expose the CRUD functions: Expose the CRUD functions in the data access object.

Q: What are the future considerations for implementing local SQLite storage?

A: The future considerations for implementing local SQLite storage include:

  • Scalability: Consider the scalability of your application and whether SQLite is suitable for large-scale applications.
  • Concurrency: Consider the concurrency of your application and whether SQLite is suitable for applications that require multiple users to access the database simultaneously.
  • Security: Consider the security of your application and whether SQLite is suitable for applications that require high levels of security and data protection.
  • Migration: Consider migrating to a managed PostgreSQL database such as Supabase for high availability and scalability.