PostgreSQL Polaris

A comprehensive PostgreSQL learning environment featuring real-world urban data scenarios, advanced patterns, and hands-on exercises designed to take you from beginner to expert.

Quick Start

# Clone and setup
git clone https://github.com/SatvikPraveen/postgres-polaris.git
cd postgres-polaris
make bootstrap

# Start the environment
make up

# Access database
make psql
# OR visit http://localhost:8080 (Adminer web interface)

Learning Levels

Beginner (4-6 hours)

Perfect for SQL newcomers and those refreshing fundamentals.

1. Schema Design - Urban data modeling and relationships
2. Constraints & Indexes - Data integrity and performance fundamentals
3. DML Queries - SELECT, GROUP BY, JOINs, and advanced querying
4. Views & Materialized Views - Data abstraction and simplification

Intermediate (8-12 hours)

For developers building database-backed applications.

1. Complete Beginner Path
2. Functions & Triggers - Business logic automation with PL/pgSQL
3. JSONB & Full-Text - Modern data patterns and search
4. Geospatial - PostGIS and location analytics
5. Partitioning & Timeseries - Scaling strategies
6. Data Movement - COPY operations and foreign data wrappers

Advanced (15+ hours)

For database administrators and system architects.

1. Complete Intermediate Path
2. Transactions & MVCC - Concurrency control and isolation
3. Performance Tuning - Query optimization and statistics
4. Security & RLS - Multi-tenant security patterns
5. Backup & Replication - High availability and disaster recovery
6. Async Patterns - LISTEN/NOTIFY, advisory locks, and scheduling
7. Testing & Quality - Data validation and regression testing
8. Capstone Projects - Real-world analytical scenarios

Project Architecture

postgres-polaris/
├── docker/                    # Complete containerized environment
│   ├── docker-compose.yml     # Multi-service orchestration
│   ├── Dockerfile             # Custom PostgreSQL with extensions
│   ├── initdb/               # Database initialization scripts
│   └── pgadmin_servers.json  # Pre-configured admin interface
├── sql/                      # Progressive learning modules (16 total)
│   ├── 00_init/             # Database initialization and setup
│   ├── 01_schema_design/    # Urban data modeling foundations
│   ├── 02_constraints_indexes/ # Data integrity and performance
│   ├── 03_dml_queries/      # Advanced querying techniques
│   ├── 04_views_matviews/   # Data abstraction layers
│   ├── 05_functions_triggers/ # Business logic automation
│   ├── 06_jsonb_fulltext/   # Document storage and search
│   ├── 07_geospatial/       # PostGIS spatial analysis
│   ├── 08_partitioning_timeseries/ # Scaling and time-based data
│   ├── 09_data_movement/    # ETL and data integration
│   ├── 10_tx_mvcc_locks/    # Concurrency and isolation
│   ├── 11_perf_tuning/      # Query optimization mastery
│   ├── 12_security_rls/     # Multi-tenant security
│   ├── 13_backup_replication/ # High availability patterns
│   ├── 14_async_patterns/   # Event-driven architectures
│   ├── 15_testing_quality/  # Data validation and testing
│   └── 16_capstones/        # Applied analytical projects
├── data/                    # Urban simulation datasets
│   ├── boundaries.geojson   # Geographic boundary data
│   ├── documents.jsonb      # Sample document collections
│   ├── seeds.csv           # Demographic and commercial data
│   └── timeseries.csv      # Mobility and sensor data
├── docs/                   # Comprehensive documentation
│   ├── HOWTO_SETUP.md      # Detailed installation guide
│   ├── LEARNING_PATHS.md   # Role-based learning recommendations
│   ├── MODULE_MAP_EXERCISES.md # Exercise solutions and explanations
│   ├── EXPLAIN_PLAN_LIBRARY.md # Query optimization reference
│   └── TROUBLESHOOTING.md  # Common issues and solutions
├── examples/               # Ready-to-run demonstrations
│   ├── quick_demo.sql      # 10-minute introduction
│   ├── analytics_showcase.sql # Business intelligence patterns
│   ├── geospatial_showcase.sql # Location analysis examples
│   ├── performance_tuning_showcase.sql # Optimization techniques
│   └── security_showcase.sql # Multi-tenant security demos
├── scripts/                # Automation and utilities
│   ├── load_sample_data.sh # Data loading automation
│   ├── backup_demo.sh      # Backup and restore examples
│   ├── reset_db.sh         # Environment reset utility
│   └── run_sql.sh          # Batch SQL execution
└── tests/                  # Validation and benchmarks
    ├── schema_validation.sql # Structure integrity tests
    ├── data_integrity_checks.sql # Data quality validation
    ├── performance_benchmarks.sql # Performance regression tests
    └── regression_tests.sql # Functional regression suite

Core Features

Realistic Urban Simulation

• Complete city ecosystem with interconnected data domains
• 10,000+ citizens with demographic diversity and lifecycle events
• Commerce network including merchants, orders, and supply chains
• Transit system with real-time operations and capacity management
• Geographic data with neighborhood boundaries and spatial relationships

Progressive Learning Architecture

• 16 comprehensive modules building from basics to expert-level patterns
• Each module includes theory, practical exercises, and real-world applications
• Detailed solutions with performance analysis and optimization techniques
• Capstone projects integrating multiple advanced concepts

Production-Ready Patterns

• Enterprise security models including row-level security and audit trails
• High-availability configurations with replication and backup strategies
• Performance optimization techniques used in large-scale deployments
• Data quality frameworks with automated monitoring and validation

Modern PostgreSQL Stack

• Latest PostgreSQL features including advanced indexing and parallel processing
• PostGIS for comprehensive geospatial analysis and routing
• Full-text search with custom configurations and ranking
• JSONB document storage with validation and advanced querying

Urban Dataset Details

Civics Schema (Government Operations)

• Citizens: 10,000+ individuals with demographics, addresses, and registration history
• Permit Applications: Construction, business, and event permits with approval workflows
• Tax Records: Property assessments, payment history, and compliance tracking
• Voting Records: Election participation and ballot preferences (anonymized)
• Property Ownership: Real estate transactions and zoning classifications

Commerce Schema (Economic Activity)

• Merchants: Business directories with licenses, categories, and operational status
• Orders: Transaction processing with payment methods and fulfillment tracking
• Inventory: Product catalogs with pricing, availability, and supplier relationships
• Customer Analytics: Purchase patterns, loyalty metrics, and segmentation data
• Supply Chain: Vendor relationships and logistics tracking

Mobility Schema (Transportation Network)

• Transit Routes: Bus and rail lines with schedules and capacity specifications
• Trip Records: Real-time operational data with delays and passenger counts
• Station Management: Facility maintenance, accessibility, and usage statistics
• Sensor Data: Traffic monitoring, environmental conditions, and infrastructure health
• Routing Analysis: Optimal path calculation and network optimization

Geographic Schema (Spatial Infrastructure)

• Neighborhood Boundaries: Political districts and community areas with PostGIS polygons
• Road Networks: Street layouts with intersection data and traffic classifications
• Points of Interest: Schools, hospitals, parks, and commercial centers
• Zoning Data: Land use classifications and development restrictions
• Environmental Layers: Flood zones, green spaces, and conservation areas

Document Schema (Information Management)

• Citizen Complaints: Service requests with categorization and resolution tracking
• Policy Documents: Regulations and procedures with full-text search capabilities
• Meeting Minutes: Government proceedings with agenda tracking and decision logs
• Audit Trails: System changes and user activities with temporal tracking
• Metadata Systems: Document classification and relationship management

Available Commands

# Environment Management
make bootstrap     # Complete initial setup with dependency checks
make up           # Start all Docker services (PostgreSQL, Adminer, monitoring)
make down         # Graceful shutdown of all containers
make restart      # Full restart cycle for configuration changes
make status       # Display service health and connection information

# Database Operations
make psql         # Interactive PostgreSQL client connection
make reset        # Reset database to clean initial state with sample data
make backup       # Create timestamped database backup
make restore      # Restore from most recent backup file

# Development Workflow
make test         # Run comprehensive validation suite (schema, data, performance)
make bench        # Execute performance benchmarks with timing analysis
make lint         # Validate SQL code style and best practices
make docs         # Generate documentation from code comments

# Data Management
make sample-data  # Regenerate realistic sample dataset
make load-data    # Load additional datasets from data/ directory
make export-data  # Export current data for external analysis
make import-csv   # Bulk import CSV files with automatic schema detection

# Monitoring and Analysis
make logs         # Display aggregated service logs
make stats        # Database statistics and performance metrics
make explain      # Run EXPLAIN ANALYZE on sample queries
make monitor      # Start real-time performance monitoring dashboard

Quick Start Examples

Urban Analytics Query

-- Neighborhood economic analysis with spatial joins
WITH neighborhood_commerce AS (
    SELECT
        n.name as neighborhood,
        COUNT(DISTINCT m.merchant_id) as total_merchants,
        COUNT(DISTINCT o.order_id) as total_orders,
        SUM(o.total_amount) as total_revenue,
        AVG(o.total_amount) as avg_order_value,
        PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY o.total_amount) as median_order_value
    FROM geography.neighborhoods n
    LEFT JOIN commerce.merchants m ON ST_Contains(n.boundary, m.location)
    LEFT JOIN commerce.orders o ON m.merchant_id = o.merchant_id
    WHERE o.order_date >= CURRENT_DATE - INTERVAL '1 year'
    GROUP BY n.neighborhood_id, n.name
),
demographic_context AS (
    SELECT
        n.name as neighborhood,
        COUNT(c.citizen_id) as population,
        AVG(EXTRACT(YEAR FROM age(c.date_of_birth))) as avg_age,
        AVG(c.annual_income) as avg_income,
        COUNT(*) FILTER (WHERE c.employment_status = 'employed') * 100.0 / COUNT(*) as employment_rate
    FROM geography.neighborhoods n
    LEFT JOIN civics.citizens c ON ST_Contains(n.boundary, ST_Point(c.longitude, c.latitude))
    GROUP BY n.neighborhood_id, n.name
)
SELECT
    nc.neighborhood,
    nc.total_merchants,
    nc.total_orders,
    ROUND(nc.total_revenue::NUMERIC, 2) as total_revenue,
    ROUND(nc.avg_order_value::NUMERIC, 2) as avg_order_value,
    dc.population,
    ROUND(dc.avg_income::NUMERIC, 2) as avg_income,
    ROUND(dc.employment_rate::NUMERIC, 1) as employment_rate_pct,
    ROUND((nc.total_revenue / NULLIF(dc.population, 0))::NUMERIC, 2) as revenue_per_capita
FROM neighborhood_commerce nc
JOIN demographic_context dc ON nc.neighborhood = dc.neighborhood
WHERE nc.total_orders > 0
ORDER BY revenue_per_capita DESC;

Real-Time Transit Performance Monitoring

-- Advanced transit delay analysis with predictive indicators
WITH route_performance AS (
    SELECT
        t.route_id,
        t.trip_date::DATE as service_date,
        COUNT(*) as total_trips,
        AVG(t.delay_minutes) as avg_delay,
        STDDEV(t.delay_minutes) as delay_variance,
        COUNT(*) FILTER (WHERE t.delay_minutes > 5) as delayed_trips,
        COUNT(*) FILTER (WHERE t.delay_minutes > 15) as severely_delayed_trips,
        MAX(t.delay_minutes) as max_delay
    FROM mobility.trips t
    WHERE t.trip_date >= CURRENT_DATE - INTERVAL '30 days'
    GROUP BY t.route_id, t.trip_date::DATE
),
service_reliability AS (
    SELECT
        route_id,
        AVG(avg_delay) as monthly_avg_delay,
        AVG(delayed_trips::NUMERIC / total_trips) * 100 as on_time_performance,
        CORR(extract(dow from service_date), avg_delay) as weekday_delay_correlation,
        COUNT(*) FILTER (WHERE avg_delay > 10) as problem_days
    FROM route_performance
    GROUP BY route_id
),
current_status AS (
    SELECT
        t.route_id,
        COUNT(*) as current_active_trips,
        AVG(t.delay_minutes) as current_avg_delay,
        COUNT(*) FILTER (WHERE t.delay_minutes > 10) as current_delayed_trips
    FROM mobility.trips t
    WHERE t.scheduled_arrival >= NOW() - INTERVAL '2 hours'
    AND t.scheduled_arrival <= NOW() + INTERVAL '2 hours'
    GROUP BY t.route_id
)
SELECT
    sr.route_id,
    ROUND(sr.monthly_avg_delay::NUMERIC, 2) as monthly_avg_delay_min,
    ROUND(sr.on_time_performance::NUMERIC, 1) as on_time_performance_pct,
    sr.problem_days as poor_performance_days,
    cs.current_active_trips,
    ROUND(cs.current_avg_delay::NUMERIC, 2) as current_avg_delay_min,
    cs.current_delayed_trips,
    CASE
        WHEN sr.on_time_performance < 70 THEN 'CRITICAL'
        WHEN sr.on_time_performance < 85 THEN 'NEEDS_ATTENTION'
        WHEN sr.on_time_performance < 95 THEN 'GOOD'
        ELSE 'EXCELLENT'
    END as service_grade,
    CASE
        WHEN cs.current_avg_delay > sr.monthly_avg_delay * 1.5 THEN 'DEGRADED'
        WHEN cs.current_avg_delay > sr.monthly_avg_delay * 1.2 THEN 'WATCH'
        ELSE 'NORMAL'
    END as current_status
FROM service_reliability sr
LEFT JOIN current_status cs ON sr.route_id = cs.route_id
ORDER BY sr.on_time_performance ASC, sr.monthly_avg_delay DESC;

Comprehensive Learning Objectives

Database Architecture Mastery

• Relational schema design with proper normalization and denormalization strategies
• Constraint implementation for data integrity and business rule enforcement
• Index design for optimal query performance across diverse workloads
• Partitioning strategies for horizontal scaling and maintenance efficiency

Advanced SQL Proficiency

• Complex analytical queries using window functions and common table expressions
• Recursive queries for hierarchical data and graph traversal problems
• Advanced aggregation techniques including ROLLUP, CUBE, and custom aggregates
• Query optimization techniques with EXPLAIN plan analysis and statistics management

Modern PostgreSQL Features

• JSONB document modeling with validation schemas and advanced indexing
• Full-text search implementation with custom configurations and ranking algorithms
• Array and range data types with specialized operators and indexing strategies
• Advanced data types including network addresses, geometric shapes, and custom types

Geospatial Analysis Expertise

• PostGIS installation, configuration, and spatial data type management
• Spatial indexing strategies (GiST, SP-GiST) for optimal geographic query performance
• Complex spatial analysis including buffer operations, intersection analysis, and routing
• Integration of geographic data with business intelligence and analytical workflows

Enterprise Performance Optimization

• Query performance analysis using EXPLAIN, EXPLAIN ANALYZE, and pg_stat_statements
• Index optimization including partial indexes, expression indexes, and covering indexes
• Database statistics management and automatic vacuum configuration
• Connection pooling and resource management for high-concurrency applications

Production Security Implementation

• Row-level security policies for multi-tenant applications and data isolation
• Authentication and authorization patterns including role-based access control
• Data encryption at rest and in transit with proper key management
• Audit logging and compliance frameworks for regulatory requirements

High Availability and Disaster Recovery

• Streaming replication configuration for read replicas and failover scenarios
• Point-in-time recovery implementation with WAL archiving and restoration
• Logical replication for selective data synchronization and migration strategies
• Backup strategies including pg_dump, pg_basebackup, and continuous archiving

Operational Excellence

• Database monitoring with built-in statistics views and external tools integration
• Automated maintenance tasks using pg_cron and custom scheduling frameworks
• Performance regression testing and benchmark development
• Capacity planning and resource allocation for growth scenarios

System Requirements and Setup

Minimum System Requirements

• Operating System: Docker-compatible platform (Linux, macOS, Windows with WSL2)
• Docker: Version 20.10.0 or higher with Docker Compose 2.0+
• Memory: 4GB RAM available for container allocation
• Storage: 10GB free disk space for data, logs, and temporary files
• Network: Internet connectivity for initial setup and extension downloads

Recommended Development Environment

• Memory: 8GB+ RAM for optimal performance during complex analytical queries
• Storage: SSD storage for improved I/O performance during data loading operations
• CPU: Multi-core processor for parallel query execution and concurrent user scenarios
• Network: Stable broadband connection for documentation and community resource access

Production Deployment Considerations

• Scaling: Container orchestration support (Kubernetes, Docker Swarm) for production deployment
• Monitoring: Integration points for Prometheus, Grafana, and other monitoring solutions
• Security: Network isolation and firewall configuration for production security
• Backup: External storage integration for automated backup and archival processes

Contributing and Community

Contribution Workflow We welcome contributions that enhance the learning experience and expand the curriculum:

1. Fork and Branch: Create your own fork and feature branch for development
2. Quality Standards: Ensure all new content includes comprehensive documentation and testing
3. Code Review: Submit detailed pull requests with clear descriptions of changes and improvements
4. Testing: Add validation tests for new exercises and verify existing functionality remains intact

Content Guidelines

• Exercise Design: All new exercises must include problem statements, solution explanations, and performance analysis
• SQL Style: Follow established coding conventions for readability and maintainability
• Documentation: Update relevant documentation files when adding new modules or changing existing functionality
• Performance: Include EXPLAIN output and optimization discussion for complex queries

Community Resources and Support

• GitHub Issues: Report bugs, request features, and discuss enhancements with the development community
• Documentation: Comprehensive guides for setup, troubleshooting, and advanced configuration scenarios
• Examples Gallery: Showcase of student projects and real-world implementations using PostgreSQL Polaris
• Discussion Forum: Community-driven support for learning questions and technical discussions

License and Acknowledgments

Open Source License This project is released under the MIT License, allowing for both educational and commercial use with proper attribution. See the LICENSE file for complete terms and conditions.

Community Acknowledgments PostgreSQL Polaris builds upon the outstanding work of the global PostgreSQL community. Special recognition goes to:

• PostgreSQL Global Development Group for creating and maintaining the world's most advanced open source database
• PostGIS Development Team for enabling sophisticated geospatial analysis capabilities
• PostgreSQL Community for extensive documentation, tutorials, and best practice sharing
• Contributors and Educators who have shared knowledge and improved database education worldwide

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Ready to Begin Your PostgreSQL Journey?

Quick Setup Commands

git clone https://github.com/SatvikPraveen/postgres-polaris.git
cd postgres-polaris
make bootstrap && make up

Access Points

• Database Console: make psql for direct PostgreSQL command line access
• Web Interface: Visit http://localhost:8080 for Adminer database administration
• Documentation: Browse the docs/ directory for comprehensive learning guides
• Quick Demo: Run examples/quick_demo.sql for a 10-minute introduction to key concepts

Next Steps

1. Review the Setup Guide for detailed installation instructions
2. Choose your Learning Path based on your current experience level
3. Start with Module 01: Schema Design to understand the urban data model
4. Join our community discussions and share your learning progress

Transform your PostgreSQL expertise from beginner to advanced practitioner with hands-on experience using realistic, complex datasets that mirror real-world analytical challenges.