Time Required: 25 minutes
Prerequisites: Graph Operations Tutorial
Difficulty: Advanced
Welcome to the Advanced Configuration Tutorial! You've mastered graph operations, and now it's time to prepare GraphMemory-IDE for production use. You'll learn security hardening, performance optimization, monitoring setup, and scaling strategies.
- Configure production-grade security with mTLS
- Optimize performance for large-scale deployments
- Set up comprehensive monitoring and alerting
- Implement backup and disaster recovery
- Configure auto-scaling and load balancing
- Harden the system against security threats
- GraphMemory-IDE development environment
- Production server or cloud environment
- SSL certificates for mTLS (or ability to generate them)
- 25 minutes of focused time
- Basic understanding of system administration
Let's start by implementing production-grade security measures.
graph TD
CLIENT[Client Application] --> LB[Load Balancer]
LB --> WAF[Web Application Firewall]
WAF --> API[API Gateway]
subgraph "Security Layers"
API --> AUTH[Authentication]
AUTH --> AUTHZ[Authorization]
AUTHZ --> RATE[Rate Limiting]
RATE --> APP[Application]
end
subgraph "Certificate Management"
CERT[Certificate Authority] --> TLS[TLS Certificates]
TLS --> MTLS[mTLS Validation]
end
subgraph "Data Protection"
APP --> ENCRYPT[Data Encryption]
ENCRYPT --> BACKUP[Encrypted Backups]
BACKUP --> AUDIT[Audit Logging]
end
MTLS --> API
AUDIT --> SIEM[Security Monitoring]
Create the mTLS configuration:
# Generate Certificate Authority
openssl genrsa -out ca-key.pem 4096
openssl req -new -x509 -days 365 -key ca-key.pem -out ca-cert.pem \
-subj "/C=US/ST=CA/L=San Francisco/O=GraphMemory/CN=GraphMemory-CA"
# Generate server certificate
openssl genrsa -out server-key.pem 4096
openssl req -new -key server-key.pem -out server-csr.pem \
-subj "/C=US/ST=CA/L=San Francisco/O=GraphMemory/CN=api.graphmemory.local"
openssl x509 -req -days 365 -in server-csr.pem -CA ca-cert.pem \
-CAkey ca-key.pem -CAcreateserial -out server-cert.pem
# Generate client certificate
openssl genrsa -out client-key.pem 4096
openssl req -new -key client-key.pem -out client-csr.pem \
-subj "/C=US/ST=CA/L=San Francisco/O=GraphMemory/CN=client"
openssl x509 -req -days 365 -in client-csr.pem -CA ca-cert.pem \
-CAkey ca-key.pem -CAcreateserial -out client-cert.pemUpdate your production configuration:
# config/production.yml
security:
mtls:
enabled: true
ca_cert_path: "/etc/ssl/certs/ca-cert.pem"
server_cert_path: "/etc/ssl/certs/server-cert.pem"
server_key_path: "/etc/ssl/private/server-key.pem"
client_verification: "require"
authentication:
jwt:
secret_key: "${JWT_SECRET_KEY}"
algorithm: "RS256"
expiration: 3600
refresh_expiration: 86400
authorization:
rbac_enabled: true
default_role: "viewer"
admin_users: ["admin@company.com"]
rate_limiting:
enabled: true
requests_per_minute: 100
burst_size: 20
cors:
allowed_origins: ["https://app.company.com"]
allowed_methods: ["GET", "POST", "PUT", "DELETE"]
allowed_headers: ["Authorization", "Content-Type"]# security/middleware.py
from fastapi import FastAPI, Request, Response
from fastapi.middleware.base import BaseHTTPMiddleware
class SecurityHeadersMiddleware(BaseHTTPMiddleware):
async def dispatch(self, request: Request, call_next):
response = await call_next(request)
# Security headers
response.headers["X-Content-Type-Options"] = "nosniff"
response.headers["X-Frame-Options"] = "DENY"
response.headers["X-XSS-Protection"] = "1; mode=block"
response.headers["Strict-Transport-Security"] = "max-age=31536000; includeSubDomains"
response.headers["Content-Security-Policy"] = "default-src 'self'"
response.headers["Referrer-Policy"] = "strict-origin-when-cross-origin"
return responseConfigure GraphMemory-IDE for high-performance production workloads.
graph TD
USERS[Users] --> CDN[Content Delivery Network]
CDN --> LB[Load Balancer]
subgraph "Application Tier"
LB --> APP1[App Instance 1]
LB --> APP2[App Instance 2]
LB --> APP3[App Instance 3]
end
subgraph "Caching Layer"
APP1 --> REDIS[Redis Cluster]
APP2 --> REDIS
APP3 --> REDIS
end
subgraph "Database Tier"
REDIS --> PRIMARY[Primary DB]
PRIMARY --> REPLICA1[Read Replica 1]
PRIMARY --> REPLICA2[Read Replica 2]
end
subgraph "Storage"
PRIMARY --> SSD[NVMe SSD Storage]
REPLICA1 --> SSD
REPLICA2 --> SSD
end
Configure Kuzu for production performance:
# config/database.yml
kuzu:
performance:
# Memory configuration
buffer_pool_size: "8GB"
max_memory_usage: "16GB"
# Threading
num_threads: 16
parallel_execution: true
# Storage optimization
checkpoint_frequency: 300 # 5 minutes
wal_flush_frequency: 1 # Immediate
compression_enabled: true
# Query optimization
query_timeout: 30
max_query_memory: "2GB"
enable_query_cache: true
cache_size: "1GB"
indexes:
# Automatic index creation
auto_index: true
index_memory_limit: "512MB"
# Custom indexes for common queries
memory_indexes:
- property: "type"
- property: "created_at"
- property: "tags"
- fulltext: "content"# config/performance.py
import asyncio
from fastapi import FastAPI
from contextlib import asynccontextmanager
@asynccontextmanager
async def lifespan(app: FastAPI):
# Startup optimizations
# Connection pooling
app.state.db_pool = await create_connection_pool(
min_size=10,
max_size=50,
max_queries=1000,
max_inactive_connection_lifetime=300
)
# Cache warming
await warm_cache()
# Background tasks
asyncio.create_task(background_maintenance())
yield
# Cleanup
await app.state.db_pool.close()
# Memory management
MEMORY_CONFIG = {
"max_memory_per_request": "100MB",
"request_timeout": 30,
"connection_timeout": 5,
"read_timeout": 25,
"write_timeout": 10
}
# Async optimization
ASYNC_CONFIG = {
"max_concurrent_requests": 1000,
"worker_processes": 4,
"worker_connections": 1000,
"keepalive_timeout": 65,
"max_requests": 10000,
"max_requests_jitter": 1000
}flowchart TD
REQUEST[Incoming Request] --> CACHE_CHECK{Cache Hit?}
CACHE_CHECK -->|Yes| CACHE_RETURN[Return Cached Data]
CACHE_CHECK -->|No| DB_QUERY[Query Database]
DB_QUERY --> CACHE_STORE[Store in Cache]
CACHE_STORE --> RETURN_DATA[Return Data]
subgraph "Cache Layers"
L1[L1: Application Cache]
L2[L2: Redis Cache]
L3[L3: Database Query Cache]
end
subgraph "Cache Strategies"
TTL[Time-based Expiration]
LRU[Least Recently Used]
WRITE_THROUGH[Write-through]
WRITE_BEHIND[Write-behind]
end
CACHE_CHECK --> L1
L1 --> L2
L2 --> L3
Implement multi-layer caching:
# caching/strategy.py
import redis
from typing import Any, Optional
import json
import hashlib
class CacheManager:
def __init__(self):
self.redis_client = redis.Redis(
host='redis-cluster',
port=6379,
decode_responses=True,
max_connections=100
)
self.local_cache = {}
async def get(self, key: str) -> Optional[Any]:
# L1: Local cache
if key in self.local_cache:
return self.local_cache[key]
# L2: Redis cache
cached = self.redis_client.get(key)
if cached:
data = json.loads(cached)
self.local_cache[key] = data # Populate L1
return data
return None
async def set(self, key: str, value: Any, ttl: int = 300):
# Store in both layers
self.local_cache[key] = value
self.redis_client.setex(
key,
ttl,
json.dumps(value, default=str)
)
def cache_key(self, *args) -> str:
"""Generate consistent cache keys"""
key_string = "|".join(str(arg) for arg in args)
return hashlib.md5(key_string.encode()).hexdigest()Set up comprehensive monitoring for production operations.
graph TD
subgraph "Application Metrics"
APP[GraphMemory-IDE] --> METRICS[Metrics Collector]
APP --> LOGS[Log Aggregator]
APP --> TRACES[Distributed Tracing]
end
subgraph "Infrastructure Metrics"
SERVER[Servers] --> SYSTEM[System Metrics]
DB[(Database)] --> DB_METRICS[DB Metrics]
CACHE[(Cache)] --> CACHE_METRICS[Cache Metrics]
end
subgraph "Monitoring Stack"
METRICS --> PROMETHEUS[Prometheus]
LOGS --> ELASTICSEARCH[Elasticsearch]
TRACES --> JAEGER[Jaeger]
SYSTEM --> PROMETHEUS
DB_METRICS --> PROMETHEUS
CACHE_METRICS --> PROMETHEUS
end
subgraph "Visualization & Alerting"
PROMETHEUS --> GRAFANA[Grafana Dashboards]
ELASTICSEARCH --> KIBANA[Kibana Logs]
JAEGER --> TRACE_UI[Tracing UI]
GRAFANA --> ALERTS[Alert Manager]
ALERTS --> SLACK[Slack Notifications]
ALERTS --> EMAIL[Email Alerts]
ALERTS --> PAGER[PagerDuty]
end
# monitoring/metrics.py
from prometheus_client import Counter, Histogram, Gauge, start_http_server
import time
from functools import wraps
# Define metrics
REQUEST_COUNT = Counter(
'graphmemory_requests_total',
'Total requests',
['method', 'endpoint', 'status']
)
REQUEST_DURATION = Histogram(
'graphmemory_request_duration_seconds',
'Request duration',
['method', 'endpoint']
)
ACTIVE_CONNECTIONS = Gauge(
'graphmemory_active_connections',
'Active database connections'
)
MEMORY_USAGE = Gauge(
'graphmemory_memory_usage_bytes',
'Memory usage in bytes'
)
GRAPH_SIZE = Gauge(
'graphmemory_graph_size',
'Number of nodes and edges',
['type']
)
def monitor_performance(func):
@wraps(func)
async def wrapper(*args, **kwargs):
start_time = time.time()
try:
result = await func(*args, **kwargs)
REQUEST_COUNT.labels(
method=request.method,
endpoint=request.url.path,
status='success'
).inc()
return result
except Exception as e:
REQUEST_COUNT.labels(
method=request.method,
endpoint=request.url.path,
status='error'
).inc()
raise
finally:
REQUEST_DURATION.labels(
method=request.method,
endpoint=request.url.path
).observe(time.time() - start_time)
return wrapper# monitoring/health.py
from fastapi import APIRouter, HTTPException
from typing import Dict, Any
import asyncio
router = APIRouter()
@router.get("/health")
async def health_check() -> Dict[str, Any]:
"""Comprehensive health check"""
checks = await asyncio.gather(
check_database(),
check_cache(),
check_memory(),
check_disk_space(),
return_exceptions=True
)
health_status = {
"status": "healthy",
"timestamp": datetime.utcnow().isoformat(),
"checks": {
"database": checks[0],
"cache": checks[1],
"memory": checks[2],
"disk": checks[3]
}
}
# Determine overall status
if any(check.get("status") == "unhealthy" for check in checks if isinstance(check, dict)):
health_status["status"] = "unhealthy"
raise HTTPException(status_code=503, detail=health_status)
return health_status
async def check_database() -> Dict[str, Any]:
try:
# Test database connectivity
result = await db.execute("SELECT 1")
return {
"status": "healthy",
"response_time_ms": 5,
"connections": get_active_connections()
}
except Exception as e:
return {
"status": "unhealthy",
"error": str(e)
}# monitoring/alerts.yml
groups:
- name: graphmemory_alerts
rules:
- alert: HighErrorRate
expr: rate(graphmemory_requests_total{status="error"}[5m]) > 0.1
for: 2m
labels:
severity: warning
annotations:
summary: "High error rate detected"
description: "Error rate is {{ $value }} errors per second"
- alert: DatabaseConnectionsHigh
expr: graphmemory_active_connections > 80
for: 1m
labels:
severity: critical
annotations:
summary: "Database connections near limit"
description: "Active connections: {{ $value }}"
- alert: MemoryUsageHigh
expr: graphmemory_memory_usage_bytes / (1024^3) > 14
for: 5m
labels:
severity: warning
annotations:
summary: "High memory usage"
description: "Memory usage: {{ $value }}GB"
- alert: SlowQueries
expr: histogram_quantile(0.95, rate(graphmemory_request_duration_seconds_bucket[5m])) > 2
for: 3m
labels:
severity: warning
annotations:
summary: "Slow queries detected"
description: "95th percentile response time: {{ $value }}s"Implement robust backup and recovery procedures.
graph TD
subgraph "Backup Types"
FULL[Full Backup]
INCREMENTAL[Incremental Backup]
DIFFERENTIAL[Differential Backup]
SNAPSHOT[Point-in-time Snapshot]
end
subgraph "Backup Schedule"
DAILY[Daily Full Backup]
HOURLY[Hourly Incremental]
REALTIME[Real-time WAL Shipping]
end
subgraph "Storage Locations"
LOCAL[Local Storage]
S3[AWS S3]
GLACIER[AWS Glacier]
OFFSITE[Offsite Storage]
end
subgraph "Recovery Procedures"
PITR[Point-in-time Recovery]
FAILOVER[Automatic Failover]
MANUAL[Manual Recovery]
end
FULL --> DAILY
INCREMENTAL --> HOURLY
SNAPSHOT --> REALTIME
DAILY --> S3
HOURLY --> LOCAL
REALTIME --> S3
S3 --> GLACIER
GLACIER --> OFFSITE
PITR --> MANUAL
FAILOVER --> PITR
#!/bin/bash
# scripts/backup.sh
set -euo pipefail
BACKUP_DIR="/backups"
S3_BUCKET="graphmemory-backups"
RETENTION_DAYS=30
DATE=$(date +%Y%m%d_%H%M%S)
# Full database backup
backup_database() {
echo "Starting database backup..."
# Create backup directory
mkdir -p "${BACKUP_DIR}/${DATE}"
# Backup Kuzu database
kuzu_backup \
--source-path "/data/kuzu" \
--backup-path "${BACKUP_DIR}/${DATE}/kuzu_backup" \
--compression gzip
# Backup configuration
tar -czf "${BACKUP_DIR}/${DATE}/config_backup.tar.gz" \
/etc/graphmemory/
# Backup certificates
tar -czf "${BACKUP_DIR}/${DATE}/certs_backup.tar.gz" \
/etc/ssl/certs/ /etc/ssl/private/
echo "Database backup completed"
}
# Upload to S3
upload_to_s3() {
echo "Uploading backup to S3..."
aws s3 sync "${BACKUP_DIR}/${DATE}" \
"s3://${S3_BUCKET}/${DATE}/" \
--storage-class STANDARD_IA \
--server-side-encryption AES256
echo "Upload completed"
}
# Cleanup old backups
cleanup_old_backups() {
echo "Cleaning up old backups..."
# Local cleanup
find "${BACKUP_DIR}" -type d -mtime +7 -exec rm -rf {} +
# S3 cleanup (lifecycle policy handles this)
aws s3api put-bucket-lifecycle-configuration \
--bucket "${S3_BUCKET}" \
--lifecycle-configuration file://lifecycle.json
echo "Cleanup completed"
}
# Main execution
main() {
backup_database
upload_to_s3
cleanup_old_backups
# Send notification
curl -X POST "${SLACK_WEBHOOK}" \
-H 'Content-type: application/json' \
--data "{\"text\":\"✅ Backup completed successfully: ${DATE}\"}"
}
main "$@"# disaster_recovery/plan.yml
recovery_procedures:
rto: 15 # Recovery Time Objective (minutes)
rpo: 5 # Recovery Point Objective (minutes)
scenarios:
- name: "Database Corruption"
steps:
- "Stop application services"
- "Restore from latest backup"
- "Apply WAL logs since backup"
- "Verify data integrity"
- "Restart services"
estimated_time: "10 minutes"
- name: "Complete Server Failure"
steps:
- "Provision new server"
- "Install GraphMemory-IDE"
- "Restore from S3 backup"
- "Update DNS records"
- "Verify functionality"
estimated_time: "30 minutes"
- name: "Data Center Outage"
steps:
- "Activate secondary region"
- "Restore from cross-region backup"
- "Update load balancer"
- "Notify users of temporary service"
estimated_time: "45 minutes"
testing:
frequency: "monthly"
last_test: "2025-01-15"
next_test: "2025-02-15"
success_criteria:
- "RTO < 15 minutes"
- "RPO < 5 minutes"
- "100% data integrity"
- "All services functional"Configure automatic scaling for varying workloads.
graph TD
subgraph "Load Balancing"
LB[Application Load Balancer]
LB --> TG1[Target Group 1]
LB --> TG2[Target Group 2]
end
subgraph "Auto Scaling Groups"
TG1 --> ASG1[Auto Scaling Group 1]
TG2 --> ASG2[Auto Scaling Group 2]
ASG1 --> APP1[App Instance 1]
ASG1 --> APP2[App Instance 2]
ASG1 --> APP3[App Instance 3]
ASG2 --> APP4[App Instance 4]
ASG2 --> APP5[App Instance 5]
end
subgraph "Scaling Triggers"
CPU[CPU Utilization > 70%]
MEMORY[Memory Usage > 80%]
REQUESTS[Request Rate > 1000/min]
LATENCY[Response Time > 2s]
end
subgraph "Database Scaling"
PRIMARY[(Primary DB)]
READ1[(Read Replica 1)]
READ2[(Read Replica 2)]
READ3[(Read Replica 3)]
end
CPU --> ASG1
MEMORY --> ASG1
REQUESTS --> ASG2
LATENCY --> ASG2
APP1 --> PRIMARY
APP2 --> READ1
APP3 --> READ2
APP4 --> READ3
APP5 --> PRIMARY
# infrastructure/autoscaling.yml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: graphmemory-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: graphmemory-api
minReplicas: 3
maxReplicas: 20
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 80
- type: Pods
pods:
metric:
name: requests_per_second
target:
type: AverageValue
averageValue: "100"
behavior:
scaleUp:
stabilizationWindowSeconds: 60
policies:
- type: Percent
value: 100
periodSeconds: 15
scaleDown:
stabilizationWindowSeconds: 300
policies:
- type: Percent
value: 10
periodSeconds: 60# nginx/load_balancer.conf
upstream graphmemory_backend {
least_conn;
server app1.internal:8000 max_fails=3 fail_timeout=30s;
server app2.internal:8000 max_fails=3 fail_timeout=30s;
server app3.internal:8000 max_fails=3 fail_timeout=30s;
# Health check
keepalive 32;
}
server {
listen 443 ssl http2;
server_name api.graphmemory.com;
# SSL configuration
ssl_certificate /etc/ssl/certs/server-cert.pem;
ssl_certificate_key /etc/ssl/private/server-key.pem;
ssl_client_certificate /etc/ssl/certs/ca-cert.pem;
ssl_verify_client on;
# Security headers
add_header Strict-Transport-Security "max-age=31536000; includeSubDomains" always;
add_header X-Content-Type-Options nosniff always;
add_header X-Frame-Options DENY always;
# Rate limiting
limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;
limit_req zone=api burst=20 nodelay;
location / {
proxy_pass http://graphmemory_backend;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
# Timeouts
proxy_connect_timeout 5s;
proxy_send_timeout 60s;
proxy_read_timeout 60s;
# Health check
proxy_next_upstream error timeout http_500 http_502 http_503;
}
location /health {
access_log off;
proxy_pass http://graphmemory_backend/health;
}
}Set up configuration management for different environments.
graph TD
subgraph "Configuration Sources"
ENV[Environment Variables]
FILES[Configuration Files]
VAULT[HashiCorp Vault]
K8S[Kubernetes Secrets]
end
subgraph "Environment Profiles"
DEV[Development]
STAGING[Staging]
PROD[Production]
end
subgraph "Configuration Validation"
SCHEMA[Schema Validation]
SECURITY[Security Checks]
COMPLIANCE[Compliance Verification]
end
ENV --> DEV
FILES --> STAGING
VAULT --> PROD
K8S --> PROD
DEV --> SCHEMA
STAGING --> SECURITY
PROD --> COMPLIANCE
# config/production.yml
environment: production
server:
host: "0.0.0.0"
port: 8000
workers: 4
max_requests: 10000
timeout: 30
keepalive: 2
database:
kuzu:
path: "/data/kuzu"
buffer_pool_size: "8GB"
num_threads: 16
checkpoint_frequency: 300
security:
mtls:
enabled: true
ca_cert_path: "${CA_CERT_PATH}"
server_cert_path: "${SERVER_CERT_PATH}"
server_key_path: "${SERVER_KEY_PATH}"
jwt:
secret_key: "${JWT_SECRET_KEY}"
algorithm: "RS256"
expiration: 3600
rate_limiting:
enabled: true
requests_per_minute: 100
burst_size: 20
monitoring:
metrics:
enabled: true
port: 9090
path: "/metrics"
logging:
level: "INFO"
format: "json"
file: "/var/log/graphmemory/app.log"
max_size: "100MB"
backup_count: 10
tracing:
enabled: true
jaeger_endpoint: "http://jaeger:14268/api/traces"
sample_rate: 0.1
backup:
enabled: true
schedule: "0 2 * * *" # Daily at 2 AM
retention_days: 30
s3_bucket: "graphmemory-backups"
encryption: true
performance:
cache:
redis:
host: "redis-cluster"
port: 6379
max_connections: 100
ttl: 300
connection_pool:
min_size: 10
max_size: 50
max_queries: 1000
max_inactive_lifetime: 300# config/validator.py
from pydantic import BaseModel, validator
from typing import Optional, List
import os
class SecurityConfig(BaseModel):
mtls_enabled: bool = True
ca_cert_path: str
server_cert_path: str
server_key_path: str
jwt_secret_key: str
@validator('ca_cert_path', 'server_cert_path', 'server_key_path')
def validate_cert_files(cls, v):
if not os.path.exists(v):
raise ValueError(f"Certificate file not found: {v}")
return v
@validator('jwt_secret_key')
def validate_jwt_secret(cls, v):
if len(v) < 32:
raise ValueError("JWT secret key must be at least 32 characters")
return v
class DatabaseConfig(BaseModel):
path: str
buffer_pool_size: str = "8GB"
num_threads: int = 16
@validator('path')
def validate_db_path(cls, v):
os.makedirs(v, exist_ok=True)
return v
class ProductionConfig(BaseModel):
environment: str = "production"
security: SecurityConfig
database: DatabaseConfig
@validator('environment')
def validate_environment(cls, v):
if v not in ['development', 'staging', 'production']:
raise ValueError("Invalid environment")
return v
def load_config() -> ProductionConfig:
"""Load and validate production configuration"""
config_data = load_yaml_config()
return ProductionConfig(**config_data)Congratulations! You've configured GraphMemory-IDE for production:
✅ Security Hardening - mTLS, authentication, authorization, security headers
✅ Performance Optimization - Database tuning, caching, connection pooling
✅ Monitoring Setup - Metrics, logging, alerting, health checks
✅ Backup & Recovery - Automated backups, disaster recovery procedures
✅ Auto-Scaling - Load balancing, horizontal scaling, resource optimization
✅ Configuration Management - Environment-specific configs, validation
You now have a robust, scalable production deployment:
graph TD
PRODUCTION[Production System] --> SECURITY[Security Hardened]
PRODUCTION --> PERFORMANCE[High Performance]
PRODUCTION --> RELIABILITY[Highly Reliable]
SECURITY --> MTLS[mTLS Authentication]
SECURITY --> RBAC[Role-Based Access]
SECURITY --> ENCRYPTION[Data Encryption]
PERFORMANCE --> CACHING[Multi-layer Caching]
PERFORMANCE --> SCALING[Auto-scaling]
PERFORMANCE --> OPTIMIZATION[Query Optimization]
RELIABILITY --> MONITORING[24/7 Monitoring]
RELIABILITY --> BACKUP[Automated Backups]
RELIABILITY --> RECOVERY[Disaster Recovery]
Your production system is ready for enterprise use:
- Integration Tutorial - Custom integrations and workflows
- Deploy to production - Apply your configuration to live environment
- Set up monitoring dashboards - Create operational visibility
- Train your team - Share operational procedures and troubleshooting
Problem: High memory usage
# Solution: Adjust buffer pool size
# Edit config/production.yml
database:
kuzu:
buffer_pool_size: "4GB" # Reduce if needed
max_memory_usage: "8GB"Problem: SSL certificate errors
# Solution: Verify certificate chain
openssl verify -CAfile ca-cert.pem server-cert.pem
openssl x509 -in server-cert.pem -text -noout
# Check certificate expiration
openssl x509 -in server-cert.pem -noout -datesProblem: Database connection timeouts
# Solution: Adjust connection pool settings
performance:
connection_pool:
min_size: 5 # Reduce minimum
max_size: 25 # Reduce maximum
timeout: 10 # Increase timeoutFor production issues:
- Check the Troubleshooting Guide for detailed solutions
- Review monitoring dashboards for system health
- Contact support with production logs and metrics
- Integration Tutorial - Custom integrations and automated workflows
- Operations Guide - Complete production operations manual
- Troubleshooting Guide - Production issue resolution
🎯 Ready for integrations? Continue with the Integration Tutorial to learn custom integrations and automated workflows.
Outstanding work! You now have a production-ready GraphMemory-IDE deployment with enterprise-grade security, performance, and reliability.