need guide to install and setup REQUEST TRACKER 5 dockerized

Question

Running RT inside Docker containers provides:

Simplified Deployment – No need to manually install all Perl dependencies and compile libraries. Docker images already contain them.
Isolation – Keeps RT and its dependencies separated from your host OS, avoiding conflicts with other apps.
Portability – You can run RT on any server with Docker, regardless of OS differences.
Scalability – Easier to scale or replicate in production (e.g., staging/production environments).
Easier Upgrades/Backups – RT, database, and webserver can be managed as separate containers.

Sarthak Varshney · Answer

To install and set up a dockerized Request Tracker 5 (RT5) instance, the easiest way is to use a pre-built Docker Compose configuration . This bundles RT5, a database, and a web server into separate containers, so you don’t have to manually deal with dependencies. Prerequisites Docker installed Docker Compose installed Step 1: Clone the RT5 Docker repository git clone https://github.com/firefart/rt-docker.git cd rt-docker This repo includes ready-to-use setups for both development and production. Step 2: Copy example configs cp RT_SiteConfig.pm.example RT_SiteConfig.pm cp Caddyfile.example Caddyfile mkdir -p msmtp && cp msmtp.conf.example msmtp/msmtp.conf cp crontab.example crontab Step 3: Configure RT Open RT_SiteConfig.pm and adjust: $RT::WebURL → your public RT URL (e.g., http://your-rt-hostname.com/rt/ ) Database settings if you’re not using the local DB provided Step 4: Configure email & web server Edit msmtp/msmtp.conf with your SMTP server details for outgoing mail Adjust Caddyfile if you need SSL or reverse proxy Step 5: Start containers For production (uses external DB/SMTP): ./prod.sh For development (includes local PostgreSQL, good for testing): ./dev.sh Step 6: Access RT Dev mode: http://localhost Prod mode: Use the URL you set in RT_SiteConfig.pm Step 7: First login Default credentials: Username: root Password: password On first login, you’ll be prompted to change the root password and finish the setup. This approach gives you an RT5 environment that’s portable, easy to maintain, and much simpler to upgrade or back up compared to a manual install.

Tuhin Paul · Answer

Part 2 So it redesigned their architecture using Docker Compose + Kubernetes : # docker-compose.yml (simplified) version: '3.8' services: rt-db: image: postgres:15 volumes: - ./volumes/client1/db:/var/lib/postgresql/data environment: - POSTGRES_DB=rt_client1 networks: - compliance-net rt-app: image: bestpractical/rt:5.0.2 ports: - "8081:80" volumes: - ./volumes/client1/rt-config:/opt/rt5/etc - ./volumes/client1/logs:/opt/rt5/var/log depends_on: - rt-db networks: - compliance-net nginx: image: nginx:alpine ports: - "8081:80" volumes: - ./volumes/client1/nginx.conf:/etc/nginx/conf.d/default.conf networks: - compliance-net After implementation, they set up the following structure: Each client is provided with a dedicated Git repository that includes their custom docker-compose.yml , RT plugins, and configuration files. The CI/CD pipeline, powered by GitHub Actions, builds a client-specific RT Docker image, which is tagged with a unique version (e.g., complianceguard/rt-client1:v2.1.3 ). Deployments are managed via ArgoCD, which deploys to regional Kubernetes clusters across various cloud providers (such as AWS in EU-Central-1, GCP in Asia-Southeast1, etc.). The solution is built as a multi-tenant SaaS compliance product, where each client operates their own isolated, auditable, and containerized instance of RT.

Tuhin Paul · Answer

A fintech startup, developed an AI-powered regulatory compliance platform tailored for mid-sized banks and neobanks across the EU and APAC regions. Their core offering was designed to automatically detect suspicious transactions (such as potential money laundering), generate comprehensive audit trails, and trigger internal tickets for compliance teams to investigate. To manage compliance investigations effectively, ComplianceGuard chose Request Tracker (RT) as their ticketing engine. RT was selected not only for its open-source flexibility and robust feature set but also for its support for custom fields, email integration, audit logging, and workflow automation. Initially, RT was deployed on bare-metal servers within the company’s own data center. However, as the platform scaled to support over 12 clients across five different countries, several operational challenges began to surface. Due to strict data sovereignty regulations—for example, German clients requiring data to remain within Frankfurt—each client required a fully isolated RT instance. This added significant overhead to deployment and management. The complexity further escalated when installing and managing RT across diverse environments: Ubuntu 20.04, 22.04, CentOS, and even RHEL 8. The team encountered extensive dependency conflicts and package mismatches, leading to what was described internally as “dependency hell,” severely hampering scalability and maintainability.

need guide to install and setup REQUEST TRACKER 5 dockerized

Answers (3)