Production Raphtory

Deploy, monitor, and scale graph intelligence in production

From Docker Compose to Kubernetes, comprehensive guides for running Raphtory at scale.

Production Checklist

Before deploying Raphtory to production:

• Deployment: Containerized with resource limits
• Monitoring: Metrics, logs, and alerts configured
• Performance: Benchmarked for your data scale
• Security: Authentication, authorization, network policies
• Disaster Recovery: Backups and restoration tested

Quick Start by Environment

Production Topics

🚀 Deployment

Get Raphtory running in your infrastructure:

• Docker Compose - Single server setup with monitoring
• Kubernetes with Helm - Production K8s deployment
• Auto-Scaling - Horizontal pod autoscaling
• Resource Limits - CPU/memory configuration
• AWS Deployment - EKS, ECS, EC2
• GCP Deployment - GKE, Cloud Run
• Azure Deployment - AKS, Container Instances

📊 Observability

Monitor health and troubleshoot issues:

• Metrics - What to monitor for graph intelligence
• Prometheus + Grafana - Complete monitoring stack
• Logging - Structured logging best practices
• Distributed Tracing - Request tracing across services

Key Metrics to Track:

• Graph size (nodes, edges)
• Algorithm runtime
• Memory usage per algorithm
• Query throughput
• P95/P99 latency

⚡ Performance

Optimize for your scale:

• Benchmarking - How to measure performance
• Optimization Guide - Query and algorithm tuning
• Scaling Patterns - Horizontal vs vertical scaling

Performance Baselines (single server, 16 cores, 64GB RAM):

• Graph building: 1M edges/sec from Pandas
• PageRank: 10M edges in ~5 seconds (20 iterations)
• Louvain: 10M edges in ~8 seconds
• Memory: ~500MB per 1M edges

🔒 Security

Protect your graph intelligence:

• Authentication - User authentication methods
• Authorization - Role-based access control
• Network Security - Network policies, TLS
• Compliance - GDPR, SOC2, HIPAA considerations

Architecture Patterns

Pattern 1: Batch Intelligence

Use case: Daily fraud detection, nightly risk scoring

Pattern 2: Real-Time Intelligence

Use case: Live fraud detection, instant risk scoring

Pattern 3: Interactive Analytics

Use case: GraphQL exploration, analyst workflows

Getting Started

1. Choose Your Deployment

• Small scale (<10M edges): Docker Compose
• Medium scale (10M-100M edges): Kubernetes (3-5 nodes)
• Large scale (100M+ edges): Kubernetes cluster with auto-scaling

2. Set Up Monitoring

Start with Prometheus + Grafana to track:

• Graph intelligence job completion
• Memory usage trends
• Algorithm performance

3. Benchmark Your Workload

Use benchmarking tools to:

• Establish performance baselines
• Identify bottlenecks
• Plan capacity

4. Secure Your Deployment

Implement authentication and network policies.

Production Best Practices

Resource Management

• Memory: Allocate 2x graph size in RAM for algorithms
• CPU: Scale horizontally for parallel workloads
• Storage: Use SSD for persistent graphs

Reliability

• Graceful degradation: Cache algorithm results
• Circuit breakers: Protect downstream services
• Retries: Idempotent graph operations

Operations

• Version control: Pin Raphtory versions in production
• Rolling updates: Zero-downtime deployments
• Rollback plan: Test rollback procedures

Example: Production-Ready Docker Compose

Full deployment guide →

Support & Resources

• Performance Tuning - Optimize for your use case
• Troubleshooting - Common issues and solutions
• Community Slack - Get help from the team