PostgreSQL Indexing and Partitioning Performance Optimization

A PostgreSQL performance testing framework that benchmarks indexing and partitioning strategies. Helps DBAs and developers measure the real-world impact of optimization techniques through automated scenarios.

📊 Example Report

You can view an example report showcasing the output format and performance insights:

➡️ View Example Report

🚀 Features

• Comprehensive Performance Scenarios: Comprehensive testing of various PostgreSQL optimization techniques
• Automated Benchmarking: Time-based performance measurements with detailed reporting
• Docker Integration: Easy setup with PostgreSQL 15 container
• Rich CLI Interface: Beautiful terminal output with progress tracking and HTML reports
• Realistic Data Generation: 1M+ synthetic user records for meaningful performance testing
• Clean Architecture: Modular design with separate setup and execution phases

📊 Performance Scenarios

Indexing Strategies

• S01: Single Column Indexes - Basic B-tree index performance
• S02: Composite Indexes - Multi-column index optimization
• S03: Covering Indexes - Include-only queries for maximum performance
• S04: Unique Indexes - Constraint enforcement performance
• S05: Partial Indexes - Conditional indexing strategies
• S06: GIN Indexes - Full-text search and array operations
• S07: Foreign Key Indexes - Referential integrity performance
• S08: Bulk Loading Strategies - Data import optimization
• S09: Expression Indexes - Computed column indexing
• S10: Index Bloat - Maintenance and optimization

Partitioning Strategies

• S11: Range Partitioning - Date-based table partitioning
• S12: List Partitioning - Categorical data partitioning
• S13: Materialized Views - Pre-computed result caching

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• Docker and Docker Compose
• Git

Setup

1. Clone the repository

   git clone <repository-url>
   cd postgres-indexing-partitioning-performance-optimization

2. Start PostgreSQL container

   docker-compose up -d

3. Install Python dependencies

   cd src
   pip install -r requirements.txt

4. Verify setup

   python cli.py list

🎯 Usage

List Available Scenarios

python cli.py list

Run a Specific Scenario

python cli.py run S01_single_column_index

Run All Scenarios

python cli.py run-all

📁 Project Structure

postgres-indexing-partitioning-performance-optimization/
├── docker-compose.yml              # PostgreSQL container configuration
├── LICENSE                         # MIT License
├── README.md                       # This file
├── media/
│   └── example_reports/            # Generated HTML reports
└── src/
    ├── cli.py                      # Command-line interface
    ├── requirements.txt            # Python dependencies
    ├── scenario_manager.py         # Scenario orchestration
    ├── core/                       # Core functionality
    │   ├── base.py                 # Abstract base classes
    │   ├── benchmark.py            # Performance timing utilities
    │   ├── database.py             # Database connection management
    │   └── logger.py               # Rich console output
    ├── datasets/
    │   └── generator.py            # Synthetic data generation
    └── scenarios/                  # Performance test scenarios
        ├── base_data_setup.py      # Common data setup
        ├── S01_single_column_index/
        ├── S02_composite_indexes/
        ├── S03_covering_indexes/
        ├── S04_unique_indexes/
        ├── S05_partial_indexes/
        ├── S06_gin_indexes/
        ├── S07_foreign_key_indexes/
        ├── S08_bulk_loading_strategies/
        ├── S09_expression_indexes/
        ├── S10_index_bloat/
        ├── S11_range_partitioning/
        ├── S12_list_partitioning/
        └── S13_materialized_views/

🔧 Configuration

Database Connection

The tool connects to PostgreSQL using these default settings:

• Host: localhost
• Port: 5432
• Database: master_db
• Username: service_user
• Password: service_password

Data Generation

• Default dataset: 1,000,000 user records
• Data types: Users with demographics, timestamps, and location data

🧪 Testing Methodology

1. Setup Phase: Create tables and populate with synthetic data
2. Execution Phase: Measure performance without optimization,Apply optimization technique and re-measure
3. Cleanup Phase: Removes data, indexes, temperory tables created during setup phase

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

Author: Siva Sai Krishna