The Operations & Production Management category provides comprehensive documentation for running Geode successfully in production environments. From initial deployment through ongoing maintenance, these resources cover monitoring, observability, backup strategies, disaster recovery, troubleshooting, and capacity planning.

Introduction

Operating a production database requires more than just installation—it demands comprehensive monitoring, proactive maintenance, robust backup strategies, and rapid incident response capabilities. Geode provides built-in operational tooling designed for production reliability. Prometheus metrics expose detailed performance data. Structured logging enables rapid troubleshooting. Automated backup systems ensure data durability. Health check endpoints integrate with orchestration platforms. These capabilities make Geode operational excellence achievable from day one.

Production operations span the entire database lifecycle: initial capacity planning determines hardware requirements; deployment automation ensures consistent environments; monitoring systems detect issues early; backup procedures protect against data loss; disaster recovery plans enable rapid restoration; performance tuning maintains optimal throughput; and troubleshooting procedures resolve incidents quickly. This category documents all aspects of production operations, providing runbooks, best practices, and reference architectures for reliable Geode deployments.

What You’ll Find

Deployment and Configuration

Deployment Options

  • Docker containers for simple deployments
  • Kubernetes for orchestrated clusters
  • Binary installation for traditional deployments
  • Cloud-specific deployment (AWS, Azure, GCP)
  • On-premises installation guides
  • Edge deployment patterns

Configuration Management

  • Production configuration templates
  • Performance tuning parameters
  • Security hardening settings
  • Resource allocation guidelines
  • Network configuration
  • TLS certificate management
  • Environment-specific configuration

Infrastructure as Code

  • Terraform modules for cloud deployment
  • Kubernetes Helm charts
  • Docker Compose configurations
  • Ansible playbooks
  • GitOps workflows

Monitoring and Observability

Metrics Collection

  • Prometheus exposition endpoints
  • Grafana dashboard templates
  • Query performance metrics
  • Resource utilization metrics (CPU, memory, disk, network)
  • Connection pool metrics
  • Transaction statistics
  • Index performance metrics
  • Cache hit rates
  • Query plan changes

Logging

  • Structured JSON logging
  • Log levels and configuration
  • Query logging for performance analysis
  • Slow query logs
  • Error logs with stack traces
  • Audit logs for compliance
  • Log aggregation (ELK, Loki)
  • Log retention policies

Distributed Tracing

  • OpenTelemetry integration
  • Trace context propagation
  • Query execution tracing
  • Distributed transaction tracing
  • Span attributes and tags
  • Trace sampling strategies

Alerting

  • Alert rules for common issues
  • Threshold-based alerts
  • Anomaly detection alerts
  • Integration with PagerDuty, OpsGenie
  • Alert escalation policies
  • Runbook automation

Backup and Recovery

Backup Strategies

  • Full database backups
  • Incremental backups
  • Point-in-time recovery (PITR)
  • Snapshot-based backups
  • Continuous WAL archiving
  • Cross-region backup replication
  • Backup encryption
  • Backup verification and testing

Disaster Recovery

  • Recovery Time Objective (RTO) planning
  • Recovery Point Objective (RPO) planning
  • Automated failover procedures
  • Manual recovery procedures
  • Cross-datacenter replication
  • Backup restoration testing
  • Disaster recovery drills

High Availability

  • Multi-node clustering
  • Automatic failover
  • Read replicas for scaling
  • Load balancing strategies
  • Split-brain prevention
  • Quorum-based consensus
  • Zero-downtime upgrades

Performance Management

Capacity Planning

  • Workload characterization
  • Hardware sizing guidelines
  • Storage capacity planning
  • Network bandwidth requirements
  • CPU and memory sizing
  • I/O performance planning
  • Growth projections

Performance Tuning

  • Query optimization techniques
  • Index strategy optimization
  • Cache tuning
  • Connection pool sizing
  • Memory configuration
  • Disk I/O optimization
  • Network optimization

Benchmarking

  • Performance baseline establishment
  • Regression testing
  • Workload simulation
  • Stress testing
  • Capacity testing
  • Performance comparison

Troubleshooting

Diagnostic Tools

  • EXPLAIN for query plans
  • PROFILE for execution analysis
  • Health check endpoints
  • Debug logging
  • Performance counters
  • System diagnostics
  • Connection debugging

Common Issues

  • Slow query diagnosis
  • Connection exhaustion
  • Memory pressure
  • Disk space issues
  • Lock contention
  • Replication lag
  • Network connectivity

Incident Response

  • Incident detection and triage
  • Escalation procedures
  • Communication templates
  • Post-incident reviews
  • Root cause analysis
  • Preventive measures

Use Cases with Code Examples

Prometheus Monitoring Setup 

# prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'geode'
    static_configs:
      - targets: ['geode-server:3141']
    metrics_path: '/metrics'
    scheme: 'https'
    tls_config:
      ca_file: /etc/prometheus/ca.crt
      cert_file: /etc/prometheus/client.crt
      key_file: /etc/prometheus/client.key

# Alert rules
rule_files:
  - 'geode_alerts.yml'

# geode_alerts.yml
groups:
  - name: geode_alerts
    interval: 30s
    rules:
      - alert: GeodeHighQueryLatency
        expr: geode_query_duration_seconds{quantile="0.99"} > 1.0
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High query latency detected"
          description: "P99 query latency is {{ $value }}s"

      - alert: GeodeHighConnectionCount
        expr: geode_active_connections > 900
        for: 2m
        labels:
          severity: warning
        annotations:
          summary: "High connection count"
          description: "Active connections: {{ $value }}"

      - alert: GeodeDowntime
        expr: up{job="geode"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "Geode server is down"
          description: "Geode instance has been down for 1 minute"

Automated Backup Script 

#!/bin/bash
# backup-geode.sh - Automated backup script

set -euo pipefail

# Configuration
GEODE_HOST="localhost"
GEODE_PORT="3141"
BACKUP_DIR="/backups/geode"
RETENTION_DAYS=30
S3_BUCKET="s3://company-backups/geode"

# Create backup directory
BACKUP_DATE=$(date +%Y%m%d-%H%M%S)
BACKUP_PATH="${BACKUP_DIR}/${BACKUP_DATE}"
mkdir -p "${BACKUP_PATH}"

# Perform backup
echo "Starting backup at $(date)"
geode backup \
  --host "${GEODE_HOST}" \
  --port "${GEODE_PORT}" \
  --output "${BACKUP_PATH}" \
  --format snapshot \
  --compress gzip \
  --verify

# Upload to S3
echo "Uploading to S3"
aws s3 sync "${BACKUP_PATH}" "${S3_BUCKET}/${BACKUP_DATE}" \
  --storage-class STANDARD_IA \
  --sse AES256

# Verify backup integrity
echo "Verifying backup"
geode backup verify --path "${BACKUP_PATH}"

# Clean up old backups
echo "Cleaning up old backups"
find "${BACKUP_DIR}" -type d -mtime +${RETENTION_DAYS} -exec rm -rf {} +

# Send notification
echo "Backup completed successfully at $(date)"
curl -X POST https://monitoring.example.com/webhook \
  -H "Content-Type: application/json" \
  -d "{\"status\": \"success\", \"backup\": \"${BACKUP_DATE}\"}"

Health Check Integration 

import aiohttp
import asyncio
from typing import Dict, Any

async def check_geode_health() -> Dict[str, Any]:
    """Comprehensive health check for Geode."""
    health = {
        'status': 'healthy',
        'checks': {}
    }

    # Check server responsiveness
    try:
        async with aiohttp.ClientSession() as session:
            async with session.get(
                'https://geode:3141/health',
                ssl=True,
                timeout=aiohttp.ClientTimeout(total=5)
            ) as resp:
                health['checks']['server'] = {
                    'status': 'up' if resp.status == 200 else 'down',
                    'latency_ms': resp.headers.get('X-Response-Time')
                }
    except Exception as e:
        health['checks']['server'] = {'status': 'down', 'error': str(e)}
        health['status'] = 'unhealthy'

    # Check database connectivity
    try:
        import geode_client
        client = geode_client.open_database('quic://geode:3141')
        async with client.connection() as conn:
            result, _ = await conn.query('MATCH (n) RETURN count(n) LIMIT 1')
            result.rows[0] if result.rows else None
            health['checks']['database'] = {'status': 'up'}
    except Exception as e:
        health['checks']['database'] = {'status': 'down', 'error': str(e)}
        health['status'] = 'unhealthy'

    # Check disk space
    try:
        import shutil
        stat = shutil.disk_usage('/data/geode')
        free_percent = (stat.free / stat.total) * 100
        health['checks']['disk'] = {
            'status': 'up' if free_percent > 10 else 'warning',
            'free_percent': round(free_percent, 2),
            'free_gb': round(stat.free / (1024**3), 2)
        }
        if free_percent < 10:
            health['status'] = 'degraded'
    except Exception as e:
        health['checks']['disk'] = {'status': 'unknown', 'error': str(e)}

    # Check replication lag (if applicable)
    try:
        client = geode_client.open_database('quic://geode:3141')
        async with client.connection() as conn:
            result, _ = await conn.query('SHOW REPLICATION STATUS')
            row = result.rows[0] if result.rows else None
            lag_seconds = row['lag_seconds'] if row else 0
            health['checks']['replication'] = {
                'status': 'up' if lag_seconds < 10 else 'warning',
                'lag_seconds': lag_seconds
            }
            if lag_seconds > 60:
                health['status'] = 'degraded'
    except Exception as e:
        health['checks']['replication'] = {'status': 'unknown', 'error': str(e)}

    return health

# Kubernetes liveness probe
async def liveness_probe():
    """Simple liveness check."""
    try:
        import geode_client
        client = geode_client.open_database('quic://localhost:3141')
        async with client.connection() as conn:
            await conn.query('RETURN 1 AS ok')
            return {'alive': True}
    except Exception:
        return {'alive': False}

# Kubernetes readiness probe
async def readiness_probe():
    """Readiness check for load balancing."""
    health = await check_geode_health()
    return {'ready': health['status'] in ['healthy', 'degraded']}

Capacity Planning Script 

import geode_client
import asyncio
from datetime import datetime, timedelta

async def analyze_capacity():
    """Analyze current capacity and project growth."""
    client = geode_client.open_database('quic://localhost:3141')
    async with client.connection() as conn:
        # Current database size
        result, _ = await conn.query("""
            SELECT database_size_bytes,
                   wal_size_bytes,
                   index_size_bytes
            FROM system_stats
        """)
        stats = result.rows[0] if result.rows else None

        # Query rate
        result, _ = await conn.query("""
            SELECT COUNT(*) as query_count
            FROM metrics_history
            WHERE timestamp > $start
        """, {'start': datetime.now() - timedelta(hours=1)})
        query_stats = result.rows[0] if result.rows else None

        # Connection usage
        result, _ = await conn.query("""
            SELECT AVG(active_connections) as avg_connections,
                   MAX(active_connections) as peak_connections
            FROM metrics_history
            WHERE timestamp > $start
        """, {'start': datetime.now() - timedelta(days=1)})
        conn_stats = result.rows[0] if result.rows else None

        # Calculate projections
        current_size_gb = stats['database_size_bytes'] / (1024**3)
        queries_per_hour = query_stats['query_count']
        queries_per_second = queries_per_hour / 3600

        report = {
            'current_state': {
                'database_size_gb': round(current_size_gb, 2),
                'wal_size_gb': round(stats['wal_size_bytes'] / (1024**3), 2),
                'index_size_gb': round(stats['index_size_bytes'] / (1024**3), 2),
                'queries_per_second': round(queries_per_second, 2),
                'avg_connections': round(conn_stats['avg_connections'], 0),
                'peak_connections': conn_stats['peak_connections']
            },
            'projections_30_days': {
                'estimated_size_gb': round(current_size_gb * 1.15, 2),  # 15% growth
                'recommended_disk_gb': round(current_size_gb * 1.15 * 2, 2),  # 2x headroom
                'recommended_connections': int(conn_stats['peak_connections'] * 1.5)
            },
            'recommendations': []
        }

        # Generate recommendations
        if current_size_gb > 500:
            report['recommendations'].append(
                'Consider partitioning large labels for better performance'
            )

        if conn_stats['peak_connections'] > 800:
            report['recommendations'].append(
                'Connection pool approaching limit - consider increasing max_connections'
            )

        if queries_per_second > 1000:
            report['recommendations'].append(
                'High query rate - consider read replicas for scaling'
            )

        return report

# Run capacity analysis
report = asyncio.run(analyze_capacity())
print(f"Capacity Report: {report}")

Best Practices

Monitoring

  1. Set Up Alerts: Configure alerts for critical metrics
  2. Monitor Trends: Track metrics over time to identify patterns
  3. Dashboard Creation: Build comprehensive Grafana dashboards
  4. Log Aggregation: Centralize logs for easy searching
  5. Regular Review: Review metrics weekly for anomalies

Backup

  1. Automate Backups: Schedule automated backups daily
  2. Test Restores: Regularly test backup restoration
  3. Off-Site Storage: Store backups in different region/datacenter
  4. Verify Integrity: Always verify backup integrity
  5. Document Procedures: Maintain clear recovery procedures

Performance

  1. Establish Baselines: Define normal performance metrics
  2. Regular Profiling: Profile queries periodically
  3. Capacity Planning: Plan for growth 6-12 months ahead
  4. Index Maintenance: Regularly review and optimize indexes
  5. Resource Monitoring: Monitor CPU, memory, disk, network

Operations

  1. Runbooks: Maintain detailed operational runbooks
  2. Change Management: Use change control procedures
  3. Incident Response: Have clear incident response process
  4. Post-Mortems: Conduct blameless post-mortems
  5. Automation: Automate routine operational tasks

Further Reading

Related Articles