Database Schema

This document provides a comprehensive overview of the F4E AppSheet database schema, including table structures, relationships, and key business logic.

Schema Overview

The F4E AppSheet system uses a PostgreSQL database with 16 core tables that handle different aspects of kitchen operations. The schema is designed for scalability, data integrity, and performance.

Core Tables

1. Kitchen Management

## public.kitchen Table

Master data for all kitchens in the system.

CREATE TABLE public.kitchen (
    id VARCHAR(255) PRIMARY KEY,
    code VARCHAR(255),
    name VARCHAR(255),
    kitchen_type VARCHAR(255),
    capacity VARCHAR(255),
    location_id VARCHAR(255),
    latitude VARCHAR(255),
    longitude VARCHAR(255),
    phonenumber VARCHAR(255),
    datecreated VARCHAR(255),
    datemodified VARCHAR(255),
    status VARCHAR(255)
);

Key Fields:

• id: Unique kitchen identifier
• kitchen_type: Central, Satellite, or School
• capacity: Maximum meal preparation capacity
• location_id: Geographic location reference

## kitchen_mapping Table

Kitchen management and user assignments.

CREATE TABLE public.kitchen_mapping (
    id VARCHAR(255) PRIMARY KEY,
    kitchen_id VARCHAR(255),
    manager_email VARCHAR(255),
    manager_name VARCHAR(255),
    role VARCHAR(255),
    cluster VARCHAR(255),
    entity VARCHAR(255),
    status VARCHAR(255),
    created_at VARCHAR(255),
    updated_at VARCHAR(255)
);

2. School Management

## public.school Table

Master data for all schools in the system.

CREATE TABLE public.school (
    id VARCHAR(255) PRIMARY KEY,
    name VARCHAR(255),
    active VARCHAR(255),
    kitchen_id VARCHAR(255),
    location_id VARCHAR(255),
    program_type VARCHAR(255),
    population VARCHAR(255),
    sponsored VARCHAR(255),
    gpslatitude VARCHAR(255),
    gpslongitude VARCHAR(255),
    date_created VARCHAR(255),
    date_updated VARCHAR(255)
);

Key Fields:

• active: School status (true/false)
• kitchen_id: Associated kitchen
• program_type: Type of feeding program
• population: School population size

## school_bom Table

Bill of Materials configuration per school.

CREATE TABLE public.school_bom (
    id VARCHAR(255),
    school_id VARCHAR(255),
    bom VARCHAR(255),
    updated_at VARCHAR(255),
    updated_by VARCHAR(255),
    UNIQUE(school_id)
);

3. Meal Planning

## meal_plan Table

Central meal planning for different kitchen types.

CREATE TABLE public.meal_plan (
    id VARCHAR(255) PRIMARY KEY,
    iid INTEGER NOT NULL,
    date VARCHAR(255),
    menu VARCHAR(255),
    kitchen_type VARCHAR(255),
    status VARCHAR(255),
    confirmed VARCHAR(255),
    start VARCHAR(255),
    "end" VARCHAR(255),
    year VARCHAR(255),
    month VARCHAR(255),
    my_month VARCHAR(255),
    "Holiday" VARCHAR(255),
    created_on VARCHAR(255),
    created_by VARCHAR(255),
    updated_at VARCHAR(255),
    updated_by VARCHAR(255),
    distributed VARCHAR(255),
    UNIQUE(meal_plan_unique)
);

## plan Table

Individual kitchen-specific meal plans.

CREATE TABLE public.plan (
    id VARCHAR(255) PRIMARY KEY,
    system_id UUID DEFAULT gen_random_uuid(),
    kitchen_id VARCHAR(255),
    meal VARCHAR(255),
    "date" VARCHAR(255),
    status VARCHAR(255),
    state VARCHAR(255),
    meal_plan_id VARCHAR(255),
    projected_students VARCHAR(255),
    projected_staff VARCHAR(255),
    projected_teachers VARCHAR(255),
    actual_students VARCHAR(255),
    actual_staff VARCHAR(255),
    actual_teachers VARCHAR(255),
    total_projections VARCHAR(255),
    expected_carb VARCHAR(255),
    expected_stew VARCHAR(255),
    actual_carb VARCHAR(255),
    actual_stew VARCHAR(255),
    has_fruit VARCHAR(255),
    swelling VARCHAR(255),
    recipe_url VARCHAR(255),
    recipe_text VARCHAR(255),
    "comment" TEXT,
    created_at VARCHAR(255),
    created_by VARCHAR(255),
    updated_at VARCHAR(255),
    updated_by VARCHAR(255),
    closed_at VARCHAR(255),
    closed_by VARCHAR(255),
    UNIQUE(plan_unique)
);

4. Projection Management

## projection Table

School-level meal projections and actuals.

CREATE TABLE public.projection (
    id VARCHAR(255) PRIMARY KEY,
    system_id UUID DEFAULT gen_random_uuid(),
    plan_id VARCHAR(255),
    school_id VARCHAR(255),
    kitchen_id VARCHAR(255),
    meal VARCHAR(255),
    plan_date VARCHAR(255),
    status VARCHAR(255),
    projected_students VARCHAR(255),
    projected_staff VARCHAR(255),
    projected_teachers VARCHAR(255),
    actual_students VARCHAR(255),
    actual_staff VARCHAR(255),
    actual_teachers VARCHAR(255),
    planned_cooked_to_deliver VARCHAR(255),
    actual_food_delivered VARCHAR(255),
    missed_meals VARCHAR(255),
    food_remaining VARCHAR(255),
    loaves VARCHAR(255),
    milk VARCHAR(255),
    extra_rice VARCHAR(255),
    extra_stew VARCHAR(255),
    "comment" TEXT,
    created_at VARCHAR(255) NOT NULL,
    created_by VARCHAR(255),
    updated_at VARCHAR(255),
    updated_by VARCHAR(255),
    closed_at VARCHAR(255),
    closed_by VARCHAR(255),
    _created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    UNIQUE(projection_unique)
);

5. Consumption Tracking

## consumption Table

Ingredient consumption tracking per projection.

CREATE TABLE public.consumption (
    id VARCHAR(255) PRIMARY KEY,
    plan_id VARCHAR(255),
    projection_id VARCHAR(255),
    item VARCHAR(255),
    product_name VARCHAR(255),
    planned_qty VARCHAR(255),
    actual_qty VARCHAR(255),
    waste VARCHAR(255),
    status VARCHAR(255),
    "comment" VARCHAR(255),
    created_at VARCHAR(255),
    created_by VARCHAR(255),
    updated_at VARCHAR(255),
    updated_by VARCHAR(255)
);

6. Picking Management

## picking Table

Picking list management for kitchen operations.

CREATE TABLE public.picking (
    id VARCHAR(255) PRIMARY KEY,
    plan_id VARCHAR(255),
    kitchen_id VARCHAR(255),
    product_id VARCHAR(255),
    product_name VARCHAR(255),
    planned_qty VARCHAR(255),
    actual_qty VARCHAR(255),
    waste VARCHAR(255),
    reason VARCHAR(255),
    status VARCHAR(255),
    "comment" TEXT,
    created_at VARCHAR(255),
    created_by VARCHAR(255),
    updated_at VARCHAR(255),
    updated_by VARCHAR(255),
    UNIQUE(picking_unique)
);

7. Product Management

## product_master Table

Master product catalog.

CREATE TABLE public.product_master (
    id VARCHAR(255) PRIMARY KEY,
    product_name VARCHAR(255),
    product_code VARCHAR(255),
    type VARCHAR(50),
    uom VARCHAR(255),
    bag INTEGER,
    created_at VARCHAR(255)
);

## ingredients Table

Recipe ingredients and quantities.

CREATE TABLE public.ingredients (
    id VARCHAR(255) PRIMARY KEY,
    product_id VARCHAR(255),
    product VARCHAR(255),
    product_code VARCHAR(255),
    meal VARCHAR(255),
    type VARCHAR(255),
    qty VARCHAR(255),
    count VARCHAR(255),
    status VARCHAR(255),
    meals_id VARCHAR(255)
);

8. Inventory Management

## stock_adjustment Table

Inventory adjustments and corrections.

CREATE TABLE public.stock_adjustment (
    id VARCHAR(255),
    uid UUID DEFAULT gen_random_uuid(),
    kitchen_id VARCHAR(255),
    item VARCHAR(255),
    item_name VARCHAR(255),
    qty VARCHAR(255),
    type VARCHAR(255),
    reason VARCHAR(255),
    "comment" VARCHAR(255),
    activity_date VARCHAR(255),
    date VARCHAR(255),
    month VARCHAR(255),
    year VARCHAR(255),
    raised_by VARCHAR(255),
    approved_by VARCHAR(255),
    approved_on VARCHAR(255),
    status VARCHAR(255),
    serial VARCHAR(255),
    linked_to VARCHAR(255)
);

## stock_count Table

Physical stock count records.

CREATE TABLE public.stock_count (
    id VARCHAR(255) PRIMARY KEY,
    system_id UUID DEFAULT gen_random_uuid(),
    kitchen_id VARCHAR(255),
    date VARCHAR(255),
    serial VARCHAR(255),
    status VARCHAR(255),
    rice VARCHAR(255),
    beans VARCHAR(255),
    maize VARCHAR(255),
    salt VARCHAR(255),
    cooking_oil VARCHAR(255),
    w_ndengu VARCHAR(255),
    gg VARCHAR(255),
    briquette VARCHAR(255),
    mwitemania VARCHAR(255),
    created_at VARCHAR(255),
    done_by VARCHAR(255),
    confirmed_by VARCHAR(255),
    confirmed_at VARCHAR(255),
    cancelled_by VARCHAR(255),
    cancelled_at VARCHAR(255),
    rejected_by VARCHAR(255),
    rejected_at VARCHAR(255),
    rejected_comment VARCHAR(255),
    updated_at VARCHAR(255),
    "comment" VARCHAR(255)
);

Table Relationships

Primary Relationships

Key Relationships

1. Kitchen → Plan: One kitchen can have multiple plans
2. Plan → Projection: One plan can have multiple projections (one per school)
3. Projection → Consumption: One projection generates multiple consumption records (per item)
4. Plan → Picking: One plan generates one picking list
5. School → School_BOM: One school has one BOM configuration (Primary/Highschool)
6. Product_Master → Ingredients: Products used in ingredient definitions

Data Volume Statistics

Based on current system analysis:

Table	Size	Row Count	Key Operations
consumption	296 MB	~40M rows	High-frequency inserts/updates
projection	81 MB	~5M rows	Medium-frequency updates
picking	17 MB	~1M rows	Medium-frequency operations
plan	4 MB	~100K rows	Low-frequency, high-impact
school	1.5 MB	~10K rows	Low-frequency updates
kitchen	160 KB	~100 rows	Very low-frequency updates

Indexes and Performance

Critical Indexes

-- Plan table indexes
CREATE INDEX idx_plan_status_date_id ON plan(status, date, id);
CREATE INDEX idx_plan_kitchen_id ON plan(kitchen_id);

-- Projection table indexes
CREATE INDEX idx_projection_plan_id ON projection(plan_id);
CREATE INDEX idx_projection_school_id ON projection(school_id);
CREATE INDEX idx_projection_plan_id_status ON projection(plan_id, status);

-- Consumption table indexes
CREATE INDEX idx_consumption_plan_item_status ON consumption(plan_id, item, status);
CREATE INDEX idx_consumption_projection_id ON consumption(projection_id);

-- Picking table indexes
CREATE INDEX idx_picking_plan_id_kitchen_id_product_id ON picking(plan_id, kitchen_id, product_id);

-- School table indexes
CREATE INDEX idx_school_kitchen_active ON school(kitchen_id, active);

Performance Considerations

1. High-Volume Tables: consumption and projection tables require careful indexing
2. Frequent Queries: Status-based queries are most common
3. Aggregation Queries: Plan totals require efficient aggregation
4. Time-Based Queries: Date-based filtering is frequent

Data Integrity

Constraints

1. Primary Keys: All tables have unique primary keys
2. Foreign Keys: Referential integrity maintained through foreign key constraints
3. Unique Constraints: Business-unique combinations enforced
4. Check Constraints: Data validation at database level

Audit Fields

All tables include audit fields:

• created_at: Record creation timestamp
• created_by: User who created the record
• updated_at: Last update timestamp
• updated_by: User who last updated the record

Data Synchronization

External System Integration

1. School Database Sync: Regular sync with external school database
2. Kitchen Data Sync: Periodic sync of kitchen master data
3. Real-time Updates: Critical data updated in real-time
4. Batch Processing: Non-critical data processed in batches

Sync Procedures

-- School data sync procedure
CREATE OR REPLACE PROCEDURE public.sync_school_data()
LANGUAGE plpgsql
AS $procedure$
BEGIN
    -- Sync logic for school data
END;
$procedure$;

-- Kitchen data sync procedure
CREATE OR REPLACE PROCEDURE public.sync_kitchen_data()
LANGUAGE plpgsql
AS $procedure$
BEGIN
    -- Sync logic for kitchen data
END;
$procedure$;

Business Logic Implementation

Trigger Functions

The schema includes 11 trigger functions that implement business logic:

1. Data Consistency: Maintains referential integrity
2. Business Rules: Enforces business logic automatically
3. Calculations: Performs complex calculations
4. Status Management: Manages status transitions
5. Audit Logging: Tracks all changes

Materialized Views

For performance optimization:

-- Projection totals materialized view
CREATE MATERIALIZED VIEW mv_projection_totals AS
SELECT
    plan_id,
    COALESCE(SUM(CAST(projected_students AS INTEGER)), 0) as total_projected_students,
    COALESCE(SUM(CAST(projected_staff AS INTEGER)), 0) as total_projected_staff,
    COALESCE(SUM(CAST(projected_teachers AS INTEGER)), 0) as total_projected_teachers,
    COALESCE(SUM(CAST(actual_students AS INTEGER)), 0) as total_actual_students,
    COALESCE(SUM(CAST(actual_staff AS INTEGER)), 0) as total_actual_staff,
    COALESCE(SUM(CAST(actual_teachers AS INTEGER)), 0) as total_actual_teachers,
    MAX(updated_at) as last_projection_update
FROM projection
GROUP BY plan_id;

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This database schema provides a robust foundation for the F4E AppSheet system, ensuring data integrity, performance, and scalability while supporting complex business processes.