Overview
PostGIS extends PostgreSQL with spatial capabilities, providing advanced geospatial data types, functions, and indexing. It serves as the primary data store for GeoFlow’s spatial data, enabling efficient storage and querying of geospatial information.Technology Stack
- Database: PostgreSQL 15 with PostGIS 3.3 extension
- Spatial Types: Geometry, Geography, Raster data types
- Indexing: GiST and SP-GiST spatial indexes
- Functions: 300+ spatial functions for analysis
- Standards: OGC Simple Features and SQL/MM standards
Key Features
Spatial Data Types
- Geometry: Cartesian coordinate system geometries
- Geography: Spherical coordinate system for global data
- Raster: Grid-based spatial data (DEM, satellite imagery)
- Topology: Topological relationships and networks
Spatial Operations
- Measurement: Area, distance, perimeter calculations
- Relationships: Contains, intersects, touches, overlaps
- Operations: Buffer, union, intersection, difference
- Transformations: Reprojection, scaling, rotation
Advanced Features
- Spatial Indexing: R-tree indexing for fast queries
- Clustering: Group nearby features for analysis
- Partitioning: Divide large datasets for performance
- Replication: High availability and read scaling
Architecture
Database Schema
Table Organization
- Feature Tables: Store individual spatial features
- Metadata Tables: Store dataset information and properties
- Index Tables: Spatial indexes for query performance
- System Tables: PostGIS internal tables and views
Connection Management
- Connection Pooling: Efficient connection reuse
- Prepared Statements: Cached query plans
- Transaction Management: ACID compliance
- Lock Management: Minimize blocking operations
Configuration
Docker Configuration
Initialization Script
Connection Settings
Spatial Operations
Basic Operations
Advanced Queries
Raster Operations
Performance Optimization
Indexing Strategies
Query Optimization
- Bounding Box Queries: Use && operator for fast filtering
- Index-Only Scans: Include indexed columns in SELECT
- Prepared Statements: Cache query plans for repeated queries
- Partitioning: Divide large tables by spatial regions
Maintenance
Backup and Recovery
Backup Strategies
Point-in-Time Recovery
High Availability
- Streaming Replication: Real-time data synchronization
- Failover: Automatic promotion of standby servers
- Load Balancing: Distribute read queries across replicas
- Connection Pooling: PgBouncer for connection management
Monitoring
System Monitoring
Spatial-Specific Monitoring
Security
Access Control
Data Encryption
- SSL Connections: Encrypt client-server communication
- Data Encryption: Encrypt sensitive spatial data
- Backup Encryption: Encrypt database backups
- Key Management: Secure key storage and rotation
Audit Logging
Troubleshooting
Common Issues
Out of Memory: Increase PostgreSQL memory settings Slow Queries: Check query plans and add appropriate indexes Connection Issues: Verify connection pool settings Disk Space: Monitor table and index sizesPerformance Tuning
Diagnostic Queries
Integration with GeoFlow
Convex Integration
PostGIS integrates with Convex through:- Direct Queries: Convex can query PostGIS for spatial data
- Data Synchronization: Keep spatial data in sync between systems
- Complex Queries: Leverage PostGIS functions in Convex queries
- Performance: Spatial indexing for fast queries
Worker Integration
The PDAL worker uses PostGIS for:- Result Storage: Store processed spatial data
- Reference Data: Access existing spatial datasets
- Quality Assurance: Validate processing results
- Metadata: Store processing metadata and statistics
Application Integration
The GeoFlow app interacts with PostGIS through:- Data Visualization: Display spatial data on maps
- Query Interface: Allow users to construct spatial queries
- Export Functions: Generate spatial data exports
- Analysis Tools: Provide spatial analysis capabilities