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Merge branch 'main' of gitlab.com:postgres-ai/postgres_ai
2 parents 3bdf14d + 60c210c commit 754cc4f

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‎docker-compose.yml‎

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POSTGRES_DB: target_database
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POSTGRES_USER: postgres
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POSTGRES_PASSWORD: postgres
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command: ["postgres", "-c", "shared_preload_libraries=pg_stat_statements", "-c", "pg_stat_statements.track=all"]
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command:
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[
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"postgres",
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"-c",
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"shared_preload_libraries=pg_stat_statements",
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"-c",
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"pg_stat_statements.track=all",
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]
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ports:
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- "55432:5432"
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volumes:
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- ./config/prometheus/prometheus.yml:/etc/prometheus/prometheus.yml
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- prometheus_data:/prometheus
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command:
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- '--config.file=/etc/prometheus/prometheus.yml'
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- '--storage.tsdb.path=/prometheus'
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- '--web.console.libraries=/etc/prometheus/console_libraries'
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- '--web.console.templates=/etc/prometheus/consoles'
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- '--storage.tsdb.retention.time=200h'
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- '--web.enable-lifecycle'
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- "--config.file=/etc/prometheus/prometheus.yml"
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- "--storage.tsdb.path=/prometheus"
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- "--web.console.libraries=/etc/prometheus/console_libraries"
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- "--web.console.templates=/etc/prometheus/consoles"
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- "--storage.tsdb.retention.time=200h"
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- "--web.enable-lifecycle"
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# PGWatch Instance 1 - Monitoring service (Postgres sink)
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pgwatch-postgres:
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image: cybertecpostgresql/pgwatch:3
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container_name: pgwatch-postgres
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command: ["--sources=/etc/pgwatch/sources.yml", "--metrics=/etc/pgwatch/metrics.yml", "--sink=postgresql://pgwatch:pgwatchadmin@sink-postgres:5432/measurements", "--web-addr=:8080"]
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command:
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[
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"--sources=/etc/pgwatch/sources.yml",
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"--metrics=/etc/pgwatch/metrics.yml",
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"--sink=postgresql://pgwatch:pgwatchadmin@sink-postgres:5432/measurements",
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"--web-addr=:8080",
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]
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ports:
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- "58080:8080"
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depends_on:
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pgwatch-prometheus:
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image: cybertecpostgresql/pgwatch:3
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container_name: pgwatch-prometheus
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command: ["--sources=/etc/pgwatch/sources.yml", "--metrics=/etc/pgwatch/metrics.yml", "--sink=prometheus://0.0.0.0:9091/pgwatch", "--web-addr=:8089"]
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command:
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[
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"--sources=/etc/pgwatch/sources.yml",
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"--metrics=/etc/pgwatch/metrics.yml",
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"--sink=prometheus://0.0.0.0:9091/pgwatch",
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"--web-addr=:8089",
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]
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ports:
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- "58089:8089"
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- "59091:9091"
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container_name: grafana-with-datasources
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environment:
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GF_SECURITY_ADMIN_USER: monitor
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GF_SECURITY_ADMIN_PASSWORD: demo
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GF_SECURITY_ADMIN_PASSWORD: ${GF_SECURITY_ADMIN_PASSWORD:-demo}
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GF_INSTALL_PLUGINS: yesoreyeram-infinity-datasource
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ports:
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- "3000:3000"

‎terraform/README.md‎

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# Terraform deployment modules
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Infrastructure as Code modules for deploying postgres_ai monitoring to cloud providers.
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## Available modules
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### AWS (EC2)
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Single EC2 instance deployment with Docker Compose.
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- **Path**: `aws/`
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- **Architecture**: Single EC2 instance with Docker Compose
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- **Best for**: Small to medium deployments (1-10 databases)
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- **Documentation**: [aws/README.md](aws/README.md)
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### GCP (Coming soon)
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Deploy to Google Cloud Platform using Compute Engine or Cloud Run.
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### Azure (Coming soon)
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Deploy to Microsoft Azure using Virtual Machines or Container Instances.
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## Quick start
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### AWS deployment
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```bash
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cd terraform/aws
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# Copy example variables
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cp terraform.tfvars.example terraform.tfvars
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# Edit variables with your settings
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vim terraform.tfvars
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# Initialize Terraform
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terraform init
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# Review the plan
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terraform plan
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# Deploy infrastructure (takes 5-10 minutes)
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terraform apply
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```
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## Architecture overview
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The AWS deployment creates:
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1. **Compute**
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- Single EC2 instance (t3.medium default)
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- Ubuntu 22.04 LTS (Jammy) with Docker and Docker Compose
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- Systemd service for automatic startup
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2. **Storage**
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- EBS volume for persistent data
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- Automated snapshots available via AWS Backup
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3. **Networking**
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- VPC with public subnet
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- Security Group with restricted access
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- Optional Elastic IP for stable addressing
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4. **Monitoring stack**
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- Runs docker-compose from cloned repository
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- Grafana accessible on port 3000
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## Security considerations
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- EC2 instance in public subnet (can be changed to private with bastion)
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- Security groups restrict access to SSH and Grafana only
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- All data encrypted at rest (EBS encryption)
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- Recommended: Use AWS Systems Manager Session Manager instead of SSH
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- Recommended: Restrict `allowed_cidr_blocks` to your office/VPN IP
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## Instance types
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Recommended instance types based on workload:
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- **t3.medium**: 2 vCPU, 4 GiB RAM - suitable for 1-3 databases (default)
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- **t3.large**: 2 vCPU, 8 GiB RAM - suitable for 3-10 databases
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- **t3.xlarge**: 4 vCPU, 16 GiB RAM - suitable for 10+ databases
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Additional options:
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- Use Spot Instances for non-critical workloads (subject to interruption)
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- Disable Elastic IP if stable address not required
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## Support
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For issues or questions:
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- Open an issue on GitLab
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- Contact PostgresAI support
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- Check documentation at https://postgres.ai
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‎terraform/aws/.gitignore‎

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# Terraform files
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*.tfstate
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*.tfstate.*
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*.tfvars
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!terraform.tfvars.example
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.terraform/
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.terraform.lock.hcl
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crash.log
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override.tf
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override.tf.json
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*_override.tf
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*_override.tf.json
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tfplan
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plan.log
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# OS files
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.DS_Store
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Thumbs.db
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# IDE files
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.idea/
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.vscode/
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*.swp
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*.swo
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*~
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# Backup files
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*.bak
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*.backup
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# SSH keys
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*.pem
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*.key

‎terraform/aws/QUICKSTART.md‎

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# Quick start
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## Prerequisites
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```bash
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# Create SSH key
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aws ec2 create-key-pair --key-name postgres-ai-key \
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--query 'KeyMaterial' --output text > ~/.ssh/postgres-ai-key.pem
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chmod 400 ~/.ssh/postgres-ai-key.pem
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# Configure AWS credentials
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aws configure
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```
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## Configure
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```bash
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cd terraform/aws
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# Copy example config
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cp terraform.tfvars.example terraform.tfvars
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vim terraform.tfvars
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```
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Uncomment and set all required parameters:
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- `ssh_key_name` - your AWS SSH key name
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- `aws_region` - AWS region
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- `environment` - environment name
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- `instance_type` - EC2 instance type (e.g., t3.medium)
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- `data_volume_size` - data disk size in GiB
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- `data_volume_type` / `root_volume_type` - volume types (gp3, st1, sc1)
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- `allowed_ssh_cidr` / `allowed_cidr_blocks` - CIDR blocks for access
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- `use_elastic_ip` - allocate Elastic IP (true/false)
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- `grafana_password` - Grafana admin password
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- `postgres_ai_version` - git branch/tag (optional, defaults to "main")
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## Add monitoring instances
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Edit `terraform.tfvars` to add PostgreSQL instances to monitor:
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```hcl
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monitoring_instances = [
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{
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name = "prod-db"
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conn_str = "postgresql://monitor:pass@db.example.com:5432/postgres"
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environment = "production"
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cluster = "main"
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node_name = "primary"
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}
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]
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```
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## Deploy
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```bash
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# Initialize and validate
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terraform init
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terraform validate
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# Review changes
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terraform plan
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# Deploy
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terraform apply
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# Get access info
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terraform output grafana_url
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terraform output ssh_command
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```
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## Access
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```bash
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# Grafana dashboard
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open $(terraform output -raw grafana_url)
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# Login: monitor / <password from terraform.tfvars>
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# SSH
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ssh -i ~/.ssh/postgres-ai-key.pem ubuntu@$(terraform output -raw external_ip)
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```
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## Operations
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```bash
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# View logs
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ssh ubuntu@IP "sudo cat /var/log/user-data.log"
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# Restart services
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ssh ubuntu@IP "sudo systemctl restart postgres-ai"
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# Destroy
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terraform destroy
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```
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## Troubleshooting
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```bash
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# Check installation log
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ssh ubuntu@IP "sudo cat /var/log/user-data.log"
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# Check service status
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ssh ubuntu@IP "sudo systemctl status postgres-ai"
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# Check containers
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ssh ubuntu@IP "sudo docker ps"
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```
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## Security notes
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Credentials (passwords, connection strings) are stored in `terraform.tfstate` in plain text. For one-off/dev deployments this is acceptable if you clean up after `terraform destroy`:
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```bash
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terraform destroy
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rm -rf .terraform/ terraform.tfstate*
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```
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For production deployments, consider:
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- Using environment variables: `export TF_VAR_grafana_password=...`
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- Remote state with encryption (S3 + encryption)
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- Configuring monitoring instances manually after deployment
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