Docker Deployments

Docker Deployments: Containerization Considerations and Best Practices for rConfig V8

Docker containerization provides rapid deployment, environment isolation, and infrastructure portability that appeal to organizations standardizing on containerized architectures. However, rConfig V8’s architecture and operational characteristics create specific challenges in containerized environments that organizations must understand before selecting Docker as their deployment platform.

This document addresses the critical considerations, known limitations, and appropriate use cases for Docker-based rConfig deployments. Organizations can leverage this guidance to make informed decisions about deployment architecture aligned with their operational requirements, risk tolerance, and support expectations.

Caution

Production Deployment Warning: Docker-based deployments are NOT recommended for high-end production environments managing hundreds or thousands of network devices. rConfig support has encountered multiple unresolvable issues in production Docker deployments that do not occur in traditional server installations. Read this document completely before proceeding with Docker-based production deployments.

Understanding Docker Deployment Challenges

Architectural Complexity

Docker introduces multiple abstraction layers between rConfig and the underlying infrastructure:

Container runtime layer: Docker engine manages container lifecycle, resource allocation, and process isolation. This layer adds overhead and introduces failure modes not present in traditional deployments.

Network abstraction: Docker networking (bridge, overlay, macvlan) creates virtual network interfaces and routing that can interfere with device connectivity, particularly for protocols requiring specific source addressing or when traversing complex network topologies.

Storage abstraction: Docker volume mounts and storage drivers (overlay2, aufs, devicemapper) add filesystem layers between application and physical storage. Configuration repositories containing tens of thousands of files create unique stress patterns on these drivers.

Process management: Docker’s PID namespace isolation and process management interact with Supervisord’s queue worker management and PHP-FPM’s process pools in ways that create edge cases difficult to predict or troubleshoot.

Each abstraction layer provides isolation benefits but also introduces potential failure points, performance overhead, and troubleshooting complexity that compound when issues arise.

Known Production Issues

rConfig support has documented persistent, often unresolvable issues in Docker-based production deployments:

Persistent Storage Complications

Issue: Docker volume mounts for configuration repositories exhibit unexpected permission issues, filesystem locking problems, and performance degradation difficult to reproduce or resolve.

Symptoms:

• Configuration downloads succeed but files cannot be read back from storage
• Intermittent “permission denied” errors despite correct container user configuration
• File corruption in configuration repository under high concurrent write load
• Inexplicable filesystem lock timeouts during compliance checks

Why this occurs: Configuration repositories in production environments contain tens of thousands of files organized in deep directory hierarchies. Docker storage drivers optimized for container image layers perform poorly with this access pattern. Permission mapping between container UIDs and host filesystem UIDs creates race conditions under concurrent access.

Impact: Configuration downloads may fail silently, compliance checks produce inconsistent results, and backup operations become unreliable. Troubleshooting requires deep understanding of both rConfig internals and Docker storage driver implementation.

Network Connectivity Challenges

Issue: Docker’s network abstraction layers sometimes interfere with device connections, particularly when managing devices across complex network topologies or using specific protocol configurations.

Symptoms:

• SSH connections to devices timeout in Docker but succeed from host
• SNMPv3 authentication failures specific to containerized deployment
• Source IP address presented to devices differs from expected/configured
• Intermittent connection failures to devices behind NAT or across VPN tunnels

Why this occurs: Docker networking modifies packet routing, source address translation, and connection tracking in ways that some network devices handle poorly. Protocols expecting specific source addressing (SNMPv3, SSH with source address restrictions) may fail. Docker’s iptables rules interact unpredictably with host firewall configurations.

Impact: Subsets of managed devices become unreachable from containerized rConfig despite network connectivity existing. Troubleshooting requires packet captures across multiple network stack layers and deep networking expertise.

Queue Worker Instability

Issue: Laravel Horizon queue workers exhibit instability in some Docker deployments, with workers becoming unresponsive, failing to process jobs correctly, or requiring frequent restarts.

Symptoms:

• Queue depth grows continuously despite workers showing as running
• Worker processes consume 100% CPU but process no jobs
• Jobs stuck in “processing” state indefinitely
• Supervisor reports workers healthy but no job processing occurs

Why this occurs: The interaction between Supervisord (managing Horizon), Docker’s process management, PHP-FPM worker pools, and signal handling creates edge cases where processes enter unrecoverable states. Docker’s PID namespace isolation interferes with inter-process communication patterns Horizon expects.

Impact: Background operations including configuration downloads, compliance checks, and scheduled tasks cease functioning. Manual container restarts provide temporary relief but issues recur. No permanent resolution exists without abandoning containerization.

Performance Degradation Under Load

Issue: Docker’s layered filesystem and network abstractions introduce overhead that becomes pronounced when downloading thousands of configurations simultaneously or processing intensive compliance checks.

Symptoms:

• Configuration download throughput 30-50% lower than traditional deployments
• Compliance checks take significantly longer to complete
• Database query performance degraded compared to non-containerized database
• Intermittent timeouts during high concurrency operations

Why this occurs: Docker storage drivers add overhead to every filesystem operation. Network address translation and packet inspection add latency to every connection. Resource limits interact unpredictably with PHP-FPM and database connection management. These overheads compound under concurrent load.

Impact: Organizations cannot scale Docker deployments to the same device counts traditional deployments support with identical hardware. Operations that work reliably at 500 devices begin failing at 1,000 devices in containerized environments.

Resource Limit Unpredictability

Issue: Docker resource limits (memory, CPU) interact unpredictably with PHP-FPM process pools, database connection management, and queue worker allocation, creating performance cliffs.

Symptoms:

• Application performs well until sudden complete failure at specific load threshold
• Memory limits trigger container restarts during operations that succeed in traditional deployments
• CPU throttling causes cascading timeouts across application components
• Cannot determine appropriate resource limits through testing or monitoring

Why this occurs: Docker enforces hard limits triggering immediate process termination when exceeded. Traditional deployments allow processes to compete for resources with graceful degradation. The interaction between PHP memory_limit, database connection pools, and container memory limits creates complex failure scenarios.

Impact: Production deployments experience unexpected outages during routine operations. Increasing resource limits delays but does not eliminate issues. No reliable methodology exists for sizing Docker deployments.

Backup and Restore Complications

Issue: System backups including Docker volumes, container state, and orchestration configurations create complexity traditional backups avoid. Restore procedures become significantly more complex.

Symptoms:

• Backups complete successfully but cannot be restored
• Restored containers exhibit different behavior than original
• Volume backups missing files that existed in running container
• Database restore succeeds but application cannot access data

Why this occurs: Docker introduces multiple state locations (container filesystem layers, volumes, network definitions, container configuration) that must be backed up and restored consistently. Timing coordination between application backup and volume snapshot creates race conditions.

Impact: Disaster recovery procedures become unreliable. Organizations discover backup inadequacy during actual disaster recovery attempts. Extensive testing required to validate backup/restore procedures.

Logging and Monitoring Complexity

Issue: Extracting logs from containers, monitoring individual container health, and correlating events across multiple containers complicates troubleshooting compared to traditional deployments.

Symptoms:

• Cannot locate relevant logs when troubleshooting issues
• Container logs evicted before issue detected, losing critical diagnostic information
• Monitoring shows container healthy but application inside container failed
• Cannot correlate events across web, database, and queue worker containers

Why this occurs: Docker’s logging drivers buffer and rotate logs independently from application awareness. Container health checks detect process existence but not application functionality. Multi-container applications require log aggregation and correlation infrastructure.

Impact: Troubleshooting time increases dramatically. Issues that rConfig support resolves quickly in traditional deployments become multi-day investigations in Docker environments. Support effectiveness reduced due to limited access to diagnostic information.

Appropriate Docker Use Cases

Despite limitations for production environments, Docker-based rConfig deployments are appropriate for specific scenarios:

Development Environments

Ideal for: Developers building integrations, testing customizations, evaluating features, or contributing to rConfig ecosystem.

Benefits:

• Rapid environment provisioning and teardown
• Consistent development environments across team members
• Easy reset to clean state for testing
• Isolation from host system preventing conflicts

Acceptable trade-offs: Performance overhead, stability issues, and troubleshooting complexity are acceptable in development contexts where the priority is rapid iteration rather than production reliability.

Lab and Testing Environments

Ideal for: Testing device configurations, evaluating compliance policies, training personnel, or demonstrating rConfig capabilities.

Benefits:

• Quick deployment for temporary testing scenarios
• Safe isolation for potentially disruptive operations
• Easy cleanup after testing complete
• Portable environments for training sessions

Acceptable trade-offs: Occasional instability or performance limitations do not impact production operations. Issues in lab environments provide learning opportunities rather than business impact.

Proof-of-Concept Deployments

Ideal for: Organizations evaluating rConfig with small device counts (10-50 devices) before committing to production deployment architecture.

Benefits:

• Fast initial setup for evaluation
• Low barrier to entry for assessment
• Demonstrates core capabilities adequately
• Allows functional evaluation before infrastructure decisions

Acceptable trade-offs: Limited scalability and potential stability issues are acceptable during evaluation phase. Organizations must plan migration to traditional deployment before production use.

Non-Critical Environments

Ideal for: Deployments where occasional instability, performance degradation, or troubleshooting complexity is acceptable trade-off for containerization benefits.

Benefits:

• Aligns with organizational containerization standards
• Leverages existing container orchestration infrastructure
• Satisfies compliance requirements for containerized deployments
• Utilizes container management tooling investments

Acceptable trade-offs: Understood and accepted that some issues may be unresolvable without migrating to traditional deployment. Operational teams prepared for increased troubleshooting complexity.

Production Deployment Recommendations

Traditional Server Deployment

For production environments managing 100+ devices, especially in enterprise or mission-critical contexts, deploy rConfig V8 on traditional Linux servers:

Physical dedicated servers:

• Highest performance and reliability
• No virtualization or containerization overhead
• Simplest troubleshooting and support
• Recommended for largest deployments (1,000+ devices)

Virtual machines (VMware, Hyper-V, KVM, Xen):

• Infrastructure flexibility and resource efficiency
• Snapshot and backup capabilities
• Live migration for maintenance
• Excellent balance of flexibility and performance
• Recommended for most production deployments (100-1,000+ devices)

Cloud VMs (AWS EC2, Azure VMs, GCP Compute Engine):

• Cloud-native deployments without containerization complexity
• Leverage cloud infrastructure benefits
• Elastic scaling through VM sizing
• Managed services (RDS, Azure Database) for database tier
• Recommended for cloud-first organizations

Migration Path from Docker

Organizations currently running production rConfig in Docker should plan migration to traditional deployment:

Step 1: Assess current deployment:

• Document device count, user count, and operational patterns
• Identify any stability or performance issues
• Evaluate criticality of rConfig to operations

Step 2: Provision traditional infrastructure:

• Deploy VM or physical server meeting system requirements
• Install supported Linux distribution (Ubuntu, Debian, RHEL, Rocky, AlmaLinux)
• Configure networking, storage, and backup infrastructure

Step 3: Plan migration maintenance window:

• Schedule sufficient downtime for migration and validation
• Communicate maintenance window to stakeholders
• Prepare rollback procedures if migration encounters issues

Step 4: Execute migration:

• Backup Docker-based deployment completely
• Export database from containerized database
• Copy configuration repository files from Docker volumes
• Install rConfig on traditional server following installation documentation
• Import database and configuration files
• Validate all functionality before restoring service

Step 5: Monitor post-migration:

• Observe performance improvements
• Confirm stability over multiple weeks
• Document lessons learned and benefits realized

Contact rConfig support for migration assistance: [email protected]

Docker Deployment Best Practices (If Used Despite Recommendations)

Organizations choosing Docker deployment despite recommendations must implement these practices to minimize risk:

Container Resource Allocation

Generous resource limits: Set container memory limits to 2-3x expected peak usage to avoid hard limit failures. Set CPU limits allowing burst capacity for concurrent operations.

Avoid resource sharing: Deploy rConfig in dedicated containers without competing workloads. Co-locating rConfig with other applications amplifies resource contention issues.

Monitor actual utilization: Track container resource consumption over time to identify when limits become constraining. Increase limits proactively before hitting constraints.

Storage Configuration

Use dedicated volumes: Mount dedicated Docker volumes for configuration repository, database data, and logs. Avoid sharing volumes across containers or bind mounts to complex host directories.

Select appropriate storage driver: Test different storage drivers (overlay2, zfs, btrfs) for your workload. Performance characteristics vary dramatically with file access patterns.

Monitor storage performance: Track volume I/O metrics and latency. Storage performance degradation indicates driver limitations requiring volume backend changes.

Networking Configuration

Use host networking when possible: For single-container deployments, host networking eliminates abstraction layers interfering with device connectivity. Acceptable trade-off of reduced isolation for improved reliability.

Static IP allocation: Assign static IP addresses to containers communicating with devices expecting specific source addresses. DHCP-assigned IPs create intermittent connectivity as addresses change.

Network troubleshooting tools: Include network diagnostic tools (tcpdump, netcat, traceroute) in container image for troubleshooting connectivity issues.

Queue Worker Management

Dedicated queue worker container: Separate queue workers into dedicated container with its own resource allocation and monitoring. Isolates queue instability from web interface.

Supervisor configuration: Tune Supervisor to aggressively restart failed workers. Set short worker lifetimes (1-2 hours) to prevent memory accumulation and unrecoverable states.

Worker health checks: Implement application-level health checks validating workers process jobs, not just that processes exist. Alert when workers healthy but queue not processing.

Monitoring and Alerting

Comprehensive container monitoring: Monitor container status, resource consumption, restart counts, and health check results. Container appearing healthy does not guarantee application functionality.

Log aggregation: Implement centralized logging aggregating logs from all containers. Container log rotation can evict critical diagnostic information before issues detected.

Multi-layer health checks: Check container health, application responsiveness, database connectivity, and queue processing. Each layer must be independently monitored.

Alert escalation: Configure aggressive alerting for containerized deployments. Issues requiring immediate attention in traditional deployments become critical in containerized environments.

Backup and Disaster Recovery

Test restore procedures: Validate backup/restore procedures monthly. Do not assume backups are recoverable until tested.

Multiple backup mechanisms: Implement both container volume backups and traditional application backups. Redundancy increases recovery options.

Document restore procedures: Create detailed runbooks for restore procedures including volume restoration, container recreation, and configuration validation.

Offsite backup storage: Store backups outside Docker infrastructure. Container orchestration failures should not impact backup accessibility.

Troubleshooting Docker-Specific Issues

Symptom: Configuration Files Disappear After Download

Diagnosis: Check volume mount permissions and storage driver health.

Possible Causes:

• Volume mount permission mismatch between container and host
• Storage driver filesystem corruption
• Volume not properly persisted across container restarts
• Race condition between write and sync to persistent storage

Resolution Steps:

1. Verify volume mount configuration:

   docker inspect <container_name> | grep -A 10 Mounts

2. Check volume permissions match container user:

   docker exec <container_name> ls -la /var/www/html/rconfig8/current/storage

3. Test volume persistence:

   docker exec <container_name> touch /var/www/html/rconfig8/current/storage/test
   docker restart <container_name>
   docker exec <container_name> ls /var/www/html/rconfig8/current/storage/test

4. Review storage driver logs for errors:

   docker info | grep "Storage Driver"
   journalctl -u docker | grep storage

5. If unresolved, contact rConfig support with full diagnostic output

Symptom: Device Connections Fail from Container but Succeed from Host

Diagnosis: Network abstraction interfering with device connectivity.

Possible Causes:

• Docker network mode incompatible with device protocol requirements
• Source IP address translation confusing device authentication
• Docker iptables rules blocking required ports or protocols
• MTU mismatch between container and physical network

Resolution Steps:

1. Test connectivity from host system:

   ssh admin@device-ip

2. Compare with container connectivity:

   docker exec <container_name> ssh admin@device-ip

3. Switch to host networking mode:

   network_mode: host

4. Capture packets on host and container to compare:

   tcpdump -i any -w host.pcap host device-ip
   docker exec <container_name> tcpdump -i any -w container.pcap host device-ip

5. Review packet captures for addressing or protocol differences

6. Contact rConfig support with network capture files if unresolved

Symptom: Queue Workers Stuck Processing No Jobs

Diagnosis: Worker process management failure in containerized environment.

Possible Causes:

• Supervisor unable to manage workers correctly in container
• Signal handling broken between Docker and Supervisor
• Worker processes in unrecoverable state requiring restart
• Database connection pool exhaustion from host perspective

Resolution Steps:

1. Check Supervisor status:

   docker exec <container_name> supervisorctl status

2. Review Horizon status:

   docker exec <container_name> php artisan horizon:status

3. Restart workers:

   docker exec <container_name> supervisorctl restart all

4. If issue persists, restart container:

   docker restart <container_name>

5. Monitor for recurrence and contact support if issue repeats

Symptom: Performance Significantly Worse Than Traditional Deployment

Diagnosis: Docker overhead exceeding application requirements.

Possible Causes:

• Storage driver overhead on filesystem operations
• Network translation overhead on device connections
• Container resource limits constraining operations
• Memory or CPU throttling under load

Resolution Steps:

1. Compare resource utilization:

   docker stats <container_name>

2. Test with increased resource limits:

   mem_limit: 16g
   cpus: 8

3. Benchmark storage performance:

   docker exec <container_name> dd if=/dev/zero of=/var/www/html/rconfig8/current/storage/test bs=1M count=1000

4. Consider migration to traditional deployment if performance remains inadequate

Support Considerations

Support Limitations for Docker Deployments

rConfig support provides limited assistance for Docker-specific issues:

Supported: Application-level issues reproducible in traditional deployments, configuration guidance, functional troubleshooting.

Limited support: Docker-specific networking issues, storage driver problems, container orchestration complications, performance optimization in containerized environments.

Not supported: Custom Docker configurations, Docker Swarm or Kubernetes issues, third-party container orchestration tools, Docker infrastructure problems.

When contacting support about Docker deployments:

1. Clearly indicate Docker-based deployment in support request
2. Provide complete Docker configuration (Dockerfile, docker-compose.yml)
3. Include container logs, not just application logs
4. Document reproduction steps in both containerized and traditional environments if possible
5. Be prepared for recommendation to migrate to traditional deployment

Escalation for Unresolvable Issues

If Docker-specific issues prove unresolvable:

Option 1: Migration to traditional deployment (strongly recommended)

• Support provides migration assistance
• Eliminates Docker-specific complications
• Returns to fully supported configuration
• Restores full functionality and performance

Option 2: Acceptance of limitations

• Document known issues and workarounds
• Adjust operational procedures around limitations
• Accept reduced support effectiveness
• Continue at increased operational risk

Option 3: Professional Services engagement

• Dedicated engineering resources for complex scenarios
• Custom configuration development for specific requirements
• Enhanced support for containerized deployments
• Billed engagement beyond standard support

Contact [email protected] to discuss options for problematic Docker deployments.

Related Documentation

• System Requirements - Infrastructure specifications and recommendations
• Installation Guide - Traditional installation procedures
• System Monitoring - Monitoring Docker and traditional deployments
• High Availability Options - HA architectures avoiding Docker complications
• Backups - Backup strategies for different deployment types

Summary

Docker containerization introduces architectural complexity, known stability issues, and support limitations that make it inappropriate for production rConfig deployments managing significant device inventories. While Docker provides benefits for development, testing, and proof-of-concept scenarios, organizations requiring reliable network configuration management operations should deploy rConfig on traditional Linux servers (physical or virtual machines).

Key takeaways for Docker deployment decisions:

• Avoid Docker for production environments managing 100+ devices or in mission-critical contexts
• Known issues exist that rConfig support cannot resolve without migrating to traditional deployment
• Docker is appropriate for development, lab, testing, and proof-of-concept scenarios
• Traditional deployments provide superior performance, stability, and supportability
• Migration path exists for organizations needing to move from Docker to traditional deployment

Organizations choosing Docker deployment despite recommendations accept increased operational risk, reduced support effectiveness, and potential limitations that may require eventual migration to traditional deployment. Make informed decisions balancing organizational containerization preferences against operational reliability requirements.

For questions about deployment architecture or assistance with migration planning, contact rConfig support at [email protected].