LatticeDB-NextGen-DBMS

LatticeDB Deployment Guide

Complete guide for deploying LatticeDB to AWS, Azure, GCP, and HashiCorp stack environments

Table of Contents

Overview

LatticeDB provides production-ready deployment configurations for major cloud providers and HashiCorp stack. Each deployment option is designed for high availability, security, and scalability.

Supported Platforms

Platform Service Features Best For
AWS ECS Fargate + ALB Auto-scaling, EFS storage, CloudWatch Enterprise workloads
Azure Container Apps Serverless, Auto-scaling, Azure Files Modern cloud-native apps
GCP Cloud Run + Cloud SQL Serverless, Global load balancer Global applications
HashiCorp Nomad + Consul + Vault Service mesh, Secret management On-premises/hybrid

Architecture Overview

graph TB
    subgraph "Load Balancer Layer"
        LB[Load Balancer]
    end

    subgraph "Application Layer"
        A1[LatticeDB Instance 1]
        A2[LatticeDB Instance 2]
        A3[LatticeDB Instance N]
    end

    subgraph "Storage Layer"
        DB[(Database)]
        FS[File Storage]
        CACHE[Cache]
    end

    subgraph "Observability"
        LOGS[Logs]
        METRICS[Metrics]
        TRACES[Traces]
    end

    LB --> A1
    LB --> A2
    LB --> A3

    A1 --> DB
    A2 --> DB
    A3 --> DB

    A1 --> FS
    A2 --> FS
    A3 --> FS

    A1 --> LOGS
    A2 --> LOGS
    A3 --> LOGS

Prerequisites

Common Requirements

Cloud-Specific Requirements

AWS

Azure

GCP

HashiCorp Stack

Quick Start

1. Clone and Prepare

git clone https://github.com/your-org/LatticeDB-DBMS.git
cd LatticeDB-DBMS

# Build the application first
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)
cd ..

2. Choose Your Platform

# AWS
cd aws && ./deploy.sh

# Azure
cd azure && ./deploy.sh

# GCP
export GCP_PROJECT=your-project-id
cd gcp && ./deploy.sh

# HashiCorp Stack
cd hashicorp && ./deploy.sh

3. Verify Deployment

Each deployment script will output connection information and management commands.

AWS Deployment

Architecture

graph TB
    subgraph "AWS Cloud"
        subgraph "VPC"
            subgraph "Public Subnets"
                ALB[Application Load Balancer]
                NAT[NAT Gateway]
            end

            subgraph "Private Subnets"
                ECS1[ECS Task 1]
                ECS2[ECS Task 2]
            end
        end

        ECR[Elastic Container Registry]
        EFS[Elastic File System]
        CW[CloudWatch]
    end

    Internet --> ALB
    ALB --> ECS1
    ALB --> ECS2
    ECS1 --> EFS
    ECS2 --> EFS
    ECS1 --> CW
    ECS2 --> CW

Quick Deployment

cd aws

# Copy and customize configuration
cp terraform.tfvars.example terraform.tfvars
# Edit terraform.tfvars with your settings

# Deploy
./deploy.sh

Configuration Options

Basic Configuration (terraform.tfvars)

# AWS Configuration
aws_region = "us-west-2"
project_name = "latticedb"
environment = "production"

# Container Configuration
container_cpu    = 1024  # 1 vCPU
container_memory = 2048  # 2GB
desired_count    = 2
min_capacity     = 1
max_capacity     = 10

# Storage
enable_efs = true

# Domain (optional)
domain_name     = "latticedb.example.com"
certificate_arn = "arn:aws:acm:us-west-2:123456789012:certificate/..."

Advanced Configuration

# Network Security
allowed_cidr_blocks = ["10.0.0.0/8", "172.16.0.0/12"]

# Monitoring
enable_monitoring   = true
notification_email  = "admin@example.com"

# Backup
enable_backup           = true
backup_retention_days   = 30

Deployment Process

  1. Infrastructure Setup: Creates VPC, subnets, security groups
  2. Container Registry: Sets up ECR repository with lifecycle policies
  3. Application Deployment: Deploys ECS service with auto-scaling
  4. Load Balancer: Configures ALB with health checks
  5. Storage: Sets up EFS for persistent data
  6. Monitoring: Configures CloudWatch logs and metrics

Management Commands

# View service status
aws ecs describe-services --cluster latticedb-production --services latticedb-production

# Scale service
aws ecs update-service --cluster latticedb-production --service latticedb-production --desired-count 5

# View logs
aws logs tail /ecs/latticedb-production --follow

# Update deployment
./deploy.sh update

Azure Deployment

Architecture

graph TB
    subgraph "Azure Cloud"
        subgraph "Resource Group"
            subgraph "Virtual Network"
                CA[Container Apps Environment]
                ACR[Container Registry]
            end

            SA[Storage Account]
            KV[Key Vault]
            AI[Application Insights]
        end
    end

    Internet --> CA
    CA --> SA
    CA --> KV
    CA --> AI

Quick Deployment

cd azure

# Authenticate with Azure
az login

# Copy and customize configuration
cp terraform.tfvars.example terraform.tfvars
# Edit terraform.tfvars with your settings

# Deploy
./deploy.sh

Configuration Options

Basic Configuration (terraform.tfvars)

# Azure Configuration
azure_location = "East US"
project_name   = "latticedb"
environment    = "production"

# Container Configuration
container_cpu    = 1.0
container_memory = "2Gi"
min_replicas     = 1
max_replicas     = 10

# Storage
enable_persistent_storage = true
storage_replication_type  = "LRS"

# Monitoring
enable_monitoring   = true
notification_email  = "admin@example.com"

Deployment Process

  1. Resource Group: Creates resource group for all resources
  2. Networking: Sets up VNet with private subnets
  3. Container Registry: Creates Azure Container Registry
  4. Storage: Sets up Azure Files for persistence
  5. Container Apps: Deploys to Container Apps with auto-scaling
  6. Monitoring: Configures Application Insights and Log Analytics

Management Commands

# View app status
az containerapp show --name latticedb-production --resource-group latticedb-production-rg

# Scale app
az containerapp update --name latticedb-production --resource-group latticedb-production-rg --min-replicas 2 --max-replicas 15

# View logs
az containerapp logs show --name latticedb-production --resource-group latticedb-production-rg --follow

# Update deployment
./deploy.sh update

GCP Deployment

Architecture

graph TB
    subgraph "Google Cloud"
        subgraph "Project"
            CR[Cloud Run]
            AR[Artifact Registry]
            LB[Load Balancer]
        end

        subgraph "Optional"
            SQL[Cloud SQL]
            GCS[Cloud Storage]
        end
    end

    Internet --> LB
    LB --> CR
    CR --> SQL
    CR --> GCS

Quick Deployment

cd gcp

# Set up GCP authentication
gcloud auth login
gcloud config set project YOUR_PROJECT_ID

# Copy and customize configuration
cp terraform.tfvars.example terraform.tfvars
# Edit terraform.tfvars with your settings

# Deploy
export GCP_PROJECT=your-project-id
./deploy.sh

Configuration Options

Basic Configuration (terraform.tfvars)

# GCP Configuration
project_id = "your-gcp-project-id"
gcp_region = "us-central1"
project_name = "latticedb"
environment = "production"

# Container Configuration
cpu_limit    = "2000m"
memory_limit = "2Gi"
min_instances = 0
max_instances = 10

# Database (optional)
enable_cloud_sql = false

# Domain (optional)
custom_domain = "latticedb.example.com"
enable_cdn = true

Deployment Process

  1. API Enablement: Enables required Google Cloud APIs
  2. Networking: Creates VPC and subnets
  3. Container Registry: Sets up Artifact Registry
  4. Cloud Run: Deploys serverless container service
  5. Load Balancer: Sets up Global Load Balancer (if custom domain)
  6. Monitoring: Configures Cloud Monitoring and Logging

Management Commands

# View service status
gcloud run services describe latticedb-production --region=us-central1

# Update service
gcloud run services update latticedb-production --image=gcr.io/project/latticedb:v2.0.0 --region=us-central1

# View logs
gcloud logging read 'resource.type=cloud_run_revision resource.labels.service_name=latticedb-production' --limit=50

# Scale service
gcloud run services update latticedb-production --region=us-central1 --min-instances=2 --max-instances=20

HashiCorp Stack Deployment

Architecture

graph TB
    subgraph "HashiCorp Stack"
        subgraph "Consul"
            SD[Service Discovery]
            KV[Key-Value Store]
            SM[Service Mesh]
        end

        subgraph "Vault"
            SEC[Secrets Management]
            PKI[Certificate Authority]
            DB[Dynamic Secrets]
        end

        subgraph "Nomad"
            SCHED[Job Scheduler]
            ORCH[Container Orchestration]
        end
    end

    subgraph "Application"
        APP[LatticeDB Instances]
        PROXY[Envoy Proxy]
    end

    SD --> APP
    SEC --> APP
    SCHED --> APP
    SM --> PROXY

Quick Deployment

cd hashicorp

# Ensure HashiCorp stack is running
export CONSUL_HTTP_ADDR=http://127.0.0.1:8500
export VAULT_ADDR=http://127.0.0.1:8200
export NOMAD_ADDR=http://127.0.0.1:4646

# Authenticate with Vault
vault auth

# Copy and customize configuration
cp terraform.tfvars.example terraform.tfvars
# Edit terraform.tfvars with your settings

# Deploy
./deploy.sh

Configuration Options

Basic Configuration (terraform.tfvars)

# HashiCorp Stack
consul_address    = "http://127.0.0.1:8500"
vault_address     = "http://127.0.0.1:8200"
nomad_address     = "http://127.0.0.1:4646"
consul_datacenter = "dc1"

# Service Mesh
enable_consul_connect = true
enable_consul_acl     = true

# Vault Features
enable_vault_database_secrets = true
enable_vault_pki             = true

# Application
instance_count = 2
cpu_limit      = 1000  # MHz
memory_limit   = 2048  # MB

Deployment Process

  1. Vault Setup: Creates secret mounts and policies
  2. Consul Registration: Registers service with health checks
  3. Nomad Job: Deploys containerized application
  4. Service Mesh: Configures Consul Connect (if enabled)
  5. Secrets Integration: Sets up Vault secrets integration

Management Commands

# View service status
consul catalog service latticedb-production

# View job status
nomad job status latticedb-production

# Scale job
nomad job scale latticedb-production 5

# View logs
nomad logs -f latticedb-production

# Vault operations
vault kv get latticedb/production/config

CI/CD Integration

Travis CI

The .travis.yml file provides multi-stage pipeline with:

Key Features

Jenkins

The Jenkinsfile provides comprehensive pipeline with:

Key Features

GitHub Actions (Optional)

Create .github/workflows/deploy.yml:

name: Deploy LatticeDB

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        target: [aws, azure, gcp, hashicorp]

    steps:
    - uses: actions/checkout@v3

    - name: Deploy to $
      run: |
        cd $
        ./deploy.sh
      env:
        # Add your secrets here
        AWS_ACCESS_KEY_ID: $
        AZURE_CLIENT_ID: $
        GOOGLE_PROJECT_ID: $

Monitoring and Observability

Built-in Monitoring Stack

All LatticeDB deployments include integrated Prometheus and Grafana monitoring:

Prometheus Features

Grafana Features

Monitoring Architecture

graph TB
    subgraph "Application Layer"
        APP1[LatticeDB Instance 1]
        APP2[LatticeDB Instance 2]
        APP3[LatticeDB Instance N]
    end

    subgraph "Monitoring Stack"
        PROM[Prometheus]
        GRAF[Grafana]
        AM[AlertManager]
    end

    subgraph "Infrastructure Monitoring"
        NE[Node Exporter]
        CAD[cAdvisor]
        BE[Blackbox Exporter]
    end

    subgraph "Notification Channels"
        EMAIL[Email]
        SLACK[Slack]
        PD[PagerDuty]
    end

    APP1 --> PROM
    APP2 --> PROM
    APP3 --> PROM
    NE --> PROM
    CAD --> PROM
    BE --> PROM

    PROM --> GRAF
    PROM --> AM
    AM --> EMAIL
    AM --> SLACK
    AM --> PD

Platform-Specific Monitoring

AWS Monitoring

# Access Prometheus (AWS)
kubectl port-forward svc/prometheus 9090:9090
# Access Grafana (AWS)
kubectl port-forward svc/grafana 3000:3000

Azure Monitoring

# Access Prometheus (Azure)
az containerapp exec --name prometheus --command "curl localhost:9090"
# Access Grafana (Azure)
az containerapp browse --name grafana

GCP Monitoring

# Access Prometheus (GCP)
gcloud run services proxy prometheus --port=9090
# Access Grafana (GCP)
gcloud run services proxy grafana --port=3000

HashiCorp Stack Monitoring

# Access Prometheus (HashiCorp)
consul connect proxy -service prometheus -upstream prometheus:9090
# Access Grafana (HashiCorp)
consul connect proxy -service grafana -upstream grafana:3000

Pre-configured Dashboards

LatticeDB Application Dashboard

Infrastructure Dashboard

Platform-Specific Dashboards

Alert Rules

Critical Alerts (PagerDuty)

Warning Alerts (Slack)

Info Alerts (Email)

Monitoring Configuration

Each environment includes monitoring-specific configurations:

Development Environment:

# environments/dev/monitoring.tfvars
prometheus_retention = "7d"
grafana_admin_password = "admin"
alert_channels = ["email"]
enable_pagerduty = false

Staging Environment:

# environments/staging/monitoring.tfvars
prometheus_retention = "14d"
grafana_admin_password = "secure-staging-password"
alert_channels = ["email", "slack"]
enable_pagerduty = false

Production Environment:

# environments/prod/monitoring.tfvars
prometheus_retention = "30d"
grafana_admin_password = "ultra-secure-password"
alert_channels = ["email", "slack", "pagerduty"]
enable_pagerduty = true
pagerduty_service_key = "your-pagerduty-key"

Health Checks

All deployments include comprehensive health checks:

Logging

AWS

aws logs tail /ecs/latticedb-production --follow --format short

Azure

az monitor log-analytics query -w workspace-id --analytics-query "ContainerAppConsoleLogs_CL | where TimeGenerated > ago(1h)"

GCP

gcloud logging read 'resource.type=cloud_run_revision' --limit=100

HashiCorp

nomad logs -f latticedb-production
consul monitor -log-level=INFO

Common Issues and Solutions

Deployment Issues

Issue: Terraform state lock

# Solution: Force unlock (use carefully)
terraform force-unlock LOCK_ID

Issue: Container startup failures

# Check container logs
docker logs container_name

# Check resource constraints
# Increase CPU/memory limits in terraform.tfvars

Issue: Health check failures

# Test health endpoint directly
curl -f http://service-url/health

# Check application logs for errors
# Verify database connectivity

Performance Issues

Issue: High response times

Issue: Memory leaks

Security Issues

Issue: SSL/TLS certificate issues

Issue: Network connectivity

Monitoring Dashboards

Each platform provides monitoring dashboards:

Security Considerations

Network Security

Secrets Management

Access Control

Data Protection

Security Scanning

Cost Optimization

AWS Cost Optimization

# Cost-optimized configuration
container_cpu = 512    # Reduce CPU
container_memory = 1024 # Reduce memory
desired_count = 1      # Start with fewer instances

Azure Cost Optimization

# Cost-optimized configuration
min_replicas = 0       # Scale to zero when not used
storage_replication_type = "LRS"  # Local redundancy

GCP Cost Optimization

# Cost-optimized configuration
min_instances = 0      # Scale to zero
enable_cloud_sql = false  # Use SQLite for development

HashiCorp Cost Optimization

# Cost-optimized configuration
cpu_limit = 500        # Lower CPU limit
memory_limit = 1024    # Lower memory limit

Cost Monitoring

Set up billing alerts and cost monitoring:

# AWS
aws budgets create-budget --account-id 123456789012 --budget file://budget.json

# Azure
az consumption budget create --budget-name LatticeDBBudget --amount 100

# GCP
gcloud alpha billing budgets create --billing-account=BILLING_ACCOUNT --display-name="LatticeDB Budget"

FAQ

General

Q: Can I deploy to multiple clouds simultaneously? A: Yes, each deployment is independent. You can run deployments in parallel or sequentially.

Q: How do I migrate between cloud providers? A: Use the database backup/restore functionality to migrate data between providers.

Q: What’s the recommended deployment for production? A: For production workloads, we recommend:

AWS Specific

Q: Can I use existing VPC? A: Yes, modify the Terraform configuration to reference existing VPC resources.

Q: How do I enable HTTPS? A: Provide domain_name, certificate_arn, and route53_zone_id in terraform.tfvars.

Q: Can I use RDS instead of EFS? A: Yes, modify the configuration to include RDS and update the connection string.

Azure Specific

Q: How do I use custom domain? A: Set domain_name and certificate_thumbprint in terraform.tfvars.

Q: Can I use Azure SQL? A: The configuration supports Azure Files by default. For Azure SQL, modify the database connection string.

GCP Specific

Q: How do I enable Cloud SQL? A: Set enable_cloud_sql = true in terraform.tfvars and configure database parameters.

Q: Can I use multiple regions? A: Yes, deploy to multiple regions with different Terraform state files.

HashiCorp Specific

Q: Do I need all three tools (Consul, Vault, Nomad)? A: Consul and Nomad are required. Vault is optional but recommended for production.

Q: Can I use external Consul/Vault/Nomad? A: Yes, configure the addresses in terraform.tfvars to point to your existing clusters.

Q: How do I enable mTLS? A: Enable Consul Connect and Vault PKI in terraform.tfvars for automatic mTLS.

For additional support and questions, please refer to the GitHub Issues or contact the development team.

This site is open source. Improve this page.