Spring Load Development

Spring Load Development is a project that demonstrates the integration of various modern technologies and frameworks to build a robust microservices architecture. This project showcases the use of Spring Boot for microservices, Spring WebFlux for reactive RESTful web services, Spring Data R2DBC for reactive database connectivity, Spring Cloud Config for externalized configuration management, Spring Cloud Gateway for API gateway, Resilience4J for circuit breaker patterns, and Spring AI for Model Context Protocol (MCP) server integration.

Note

Load development is used here as an example business domain to demonstrate microservices architecture and modern cloud-native patterns. In the context of competitive shooting, load development refers to the process of systematically testing and refining ammunition components (such as powder charge, bullet, primer, and case dimensions) to achieve optimal accuracy and performance for a specific firearm. This project manages and analyzes load development data as a sample use case, but the architecture and patterns are applicable to many other domains.

Technologies and Frameworks Used

Microservices using Spring Boot
Reactive RESTful Web Services using Spring WebFlux
Reactive Relational Database Connectivity using Spring Data R2DBC
Externalized Configuration Management using Spring Cloud Config
Service Discovery using Spring Cloud Netflix
Role-Based Access Control (RBAC) using Spring Security and Keycloak
API Gateway using Spring Cloud Gateway
Circuit Breaker using Spring Cloud Circuit Breaker and Resilience4J
AI Integration using Spring AI and Model Context Protocol (MCP)
Observability and Monitoring using OpenTelemetry (via Spring Boot starter), Tempo, Loki, Prometheus, and Grafana
Container Orchestration using Kubernetes and Helm

Note	All technologies are open source and widely adopted in cloud-native Java ecosystems. Version details can be found in the respective `pom.xml` files.

Current Versions

This project uses the following key versions:

Java: 25
Spring Boot: 4.0.3
Spring Cloud: 2025.1.1
Spring AI: 2.0
SpringDoc OpenAPI: 3.0.0
MapStruct: 1.6.3
OpenTelemetry Instrumentation: 2.25.0 (+ alpha)
Maven: 4.0.0-rc-5 or later
PostgreSQL: 18.2
Keycloak: 26.5
Grafana: 12.3.3
Loki: 3.6.5
Tempo: 2.10.0
Prometheus: v3.9.1
OpenTelemetry Collector: 0.145.0

Getting Started

Prerequisites

Java 25 or later
Docker and Docker Compose
Maven 4.0+ (4.0.0-rc-5 or later)
Kubernetes cluster and kubectl (for Kubernetes deployment)
Helm 3.0+ (for Helm deployment)

Deployment Options

The Spring Load Development application can be deployed in multiple ways:

Docker Compose - For local development and testing
Kubernetes with Helm - For production deployment
Manual Spring Boot - For development debugging

Option 1: Docker Compose Deployment (Recommended for Development)

Clone the repository:

git clone https://github.com/zhoozhoo/spring-load-development.git
cd spring-load-development

Build the project:

mvn clean package

Start all services using Docker Compose:

docker-compose --env-file .env up -d

This will start all infrastructure services (PostgreSQL, Keycloak, Grafana, etc.) and the Spring Boot applications.

Option 2: Kubernetes Deployment with Helm (Recommended for Production)

Install using Helm:

# Add and update helm repo (if using a helm repository)
helm repo update

# Create required namespaces first
cd helm/spring-load-development
./create-namespaces.sh

# Install the application
helm install spring-load-development . \
  --values values.yaml \
  --timeout 300s

Access services:

# Get service URLs
kubectl get services -n reloading

# For local development, port-forward to access services
kubectl port-forward -n reloading service/api-gateway 8080:8080

For detailed Helm chart configuration, see Helm Chart Documentation.

Option 3: Manual Spring Boot Services (Development/Debugging)

Start infrastructure services:

docker-compose --env-file .env up -d postgres keycloak grafana loki tempo prometheus otel-collector

Start Spring Boot services in order:

# IMPORTANT: All services default to port 8080. To run them side-by-side locally, supply a distinct port with -Dserver.port.

# 1. Start Config Server (required) on 8888
java -Dserver.port=8888 -jar config-server/target/config-server-*.jar

# 2. Start Discovery Server on 8761
java -Dserver.port=8761 -jar discovery-server/target/discovery-server-*.jar

# 3. Start API Gateway on 8080 (external entrypoint)
java -jar api-gateway/target/api-gateway-*.jar

# 4. Start microservices (distinct ports)
java -Dserver.port=8081 -jar loads-service/target/loads-service-*.jar
java -Dserver.port=8082 -jar rifles-service/target/rifles-service-*.jar
java -Dserver.port=8083 -jar components-service/target/components-service-*.jar
java -Dserver.port=8084 -jar mcp-server/target/mcp-server-*.jar

Tip	You can also override ports via environment variable `SERVER_PORT` or an `application-local.yml` profile.

Accessing Services

Docker Compose Deployment

Once the services are up and running, you can access them at the following URLs:

API Gateway: http://localhost:8080
Keycloak Admin Console: http://localhost:7080
Grafana Dashboard: http://localhost:3000

Kubernetes Deployment

For Kubernetes deployment, services are accessible via NodePort or port-forwarding:

# Port-forward API Gateway
kubectl port-forward -n reloading service/api-gateway 8080:8080

# Port-forward Grafana
kubectl port-forward -n observability service/grafana-service 3000:3000

# Port-forward Keycloak
kubectl port-forward -n keycloak service/keycloak-service 7080:8080

Alternatively, if using NodePort services, check the assigned ports:

kubectl get services -A | grep NodePort

AI Integration

The project includes an MCP (Model Context Protocol) server that provides AI-assisted tools for managing loads and rifles:

Integration with GitHub Copilot through the Model Context Protocol
AI-assisted load development analysis
Intelligent rifle configuration recommendations
Natural language queries for load data

To connect GitHub Copilot to the MCP server, configure the .vscode/mcp.json file in your project directory:

{
  "servers": {
    "reloading-mcp-server": {
      "type": "sse",
      "url": "http://localhost:8080/sse"
    }
  }
}

API Documentation

API endpoints are documented using OpenAPI (Swagger). Once services are running, access the documentation at:

http://localhost:8080/swagger-ui.html

Alternatively, use the .http files in the top-level test/ directory with the VS Code REST Client extension for manual testing. (API Testing Guide)

Services Architecture

The application is composed of the following services:

Config Server: Centralized configuration management for all services
Discovery Server: Service registry and discovery using Eureka
API Gateway: Routes and filters requests to appropriate services
Rifles Service: Manages rifle data and configurations
Loads Service: Handles load development data including groups and shots
Components Service: Manages reloading components (cases, propellants/powders, primers, projectiles/bullets) with full-text search capabilities
MCP Server: Provides AI-assisted tools via Model Context Protocol for loads and rifles management (MCP Server Guide)
Common Library: Shared DTOs, mappers, utilities (not a runtime service)
Integration Tests Module: End‑to‑end verification (build-time only)

Configuration Repository

The centralized configuration for all services is stored in a separate GitHub repository: https://github.com/zhoozhoo/spring-load-development-config

The Config Server automatically picks up configuration files from this repository at startup.

Security

The application uses Keycloak for identity and access management with the following features:

Role-based access control (RBAC)
JWT token-based authentication
OAuth2/OpenID Connect integration
Predefined roles: RELOADER
Fine-grained permissions for loads and rifles management

Monitoring and Observability

The project includes a comprehensive observability stack with multiple components working together:

Observability Stack

The project uses a modern observability stack with:

OpenTelemetry Collector: Centralized collection of telemetry data (traces, logs, metrics)
Tempo: Distributed tracing backend for storing and querying traces
Loki: Log aggregation system for centralized log storage and querying
Prometheus: Metrics collection and alerting
Grafana: Unified dashboards for visualizing metrics, traces, and logs

Access the observability components at: * Grafana (unified dashboards): http://localhost:3000 * Prometheus (metrics): http://localhost:9091

Monitoring architecture

The diagram below shows the monitoring data flow used by this project. Services export metrics via the Actuator Prometheus endpoint and send telemetry (traces and logs) over OTLP to an OpenTelemetry Collector. The Collector routes traces to Tempo, logs to Loki and forwards metrics to Prometheus. Grafana visualizes metrics, traces and logs from all three backends.

flowchart LR
    subgraph Services["Services / Applications"]
        GW["API Gateway"]
        LS["Loads Service"]
        RS["Rifles Service"]
        CS["Components Service"]
        MCP["MCP Server"]
        KC["Keycloak"]
    end

    subgraph OTEL["Observability Plane"]
        OC["OpenTelemetry Collector\n:4317 / :4318"]
        Tempo["Tempo\n(Trace Store)"]
        Loki["Loki\n(Log Store)"]
        Prom["Prometheus"]
        Graf["Grafana\n:3000"]
    end

    %% Services -> Collector
    GW -->|"OTLP"| OC
    LS -->|"OTLP"| OC
    RS -->|"OTLP"| OC
    CS -->|"OTLP"| OC
    MCP -->|"OTLP"| OC
    KC -->|"OTLP (traces)"| OC

    %% Collector -> backends
    OC -->|"traces"| Tempo
    OC -->|"logs"| Loki
    OC -->|"metrics"| Prom

    %% Visualisation
    Tempo -->|traces| Graf
    Loki -->|logs| Graf
    Prom -->|metrics| Graf

    classDef serviceStyle fill:#e8f5e8,stroke:#388e3c,stroke-width:2px,color:#000000
    classDef obsStyle fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#000000

    class GW,LS,RS,CS,MCP,KC serviceStyle
    class OC,Tempo,Loki,Prom,Graf obsStyle

This observability architecture shows how all telemetry data (traces, logs, and metrics) is centrally collected by the OpenTelemetry Collector and distributed to specialized backends: Tempo for traces, Loki for logs, and Prometheus for metrics. Grafana provides unified dashboards combining all three data types for comprehensive observability.

Deployment Architecture Comparison

The Spring Load Development application supports two primary deployment models: Docker Compose for local development and Kubernetes with Helm for production deployments.

Docker Compose Architecture

flowchart TB
    User(["User / Client"]) -->|REST :8080| DC_GW

    subgraph "Docker Compose - Local Development"
        subgraph "Application Services"
            DC_GW[API Gateway<br/>:8080]
            DC_LOADS[Loads Service<br/>:8081]
            DC_RIFLES[Rifles Service<br/>:8082]
            DC_COMP[Components Service<br/>:8084]
            DC_MCP[MCP Server<br/>:8083]
        end

        subgraph "Support Services"
            DC_CONFIG[Config Server<br/>:8888]
            DC_DISC[Discovery Server<br/>:8761]
        end

        subgraph "Infrastructure"
            DC_PG[(PostgreSQL<br/>:5432)]
            DC_KC[Keycloak<br/>:7080]
            DC_KC_DB[(Keycloak DB<br/>:5433)]
        end

        subgraph "Observability"
            DC_GRAF[Grafana<br/>:3000]
            DC_LOKI[Loki]
            DC_TEMPO[Tempo]
            DC_PROM[Prometheus]
            DC_OTEL[OTEL Collector]
        end
    end

    %% Gateway routes
    DC_GW --> DC_LOADS
    DC_GW --> DC_RIFLES
    DC_GW --> DC_COMP
    DC_GW --> DC_MCP

    %% Database connections (MCP Server has no DB)
    DC_LOADS --> DC_PG
    DC_RIFLES --> DC_PG
    DC_COMP --> DC_PG

    %% MCP Server calls downstream services
    DC_MCP -.->|HTTP| DC_LOADS
    DC_MCP -.->|HTTP| DC_RIFLES

    %% Auth connections
    DC_GW -->|OAuth2/UMA| DC_KC
    DC_KC --> DC_KC_DB

    %% Config & Discovery
    DC_LOADS -.-> DC_CONFIG
    DC_RIFLES -.-> DC_CONFIG
    DC_COMP -.-> DC_CONFIG
    DC_MCP -.-> DC_CONFIG
    DC_LOADS -.-> DC_DISC
    DC_RIFLES -.-> DC_DISC
    DC_COMP -.-> DC_DISC
    DC_MCP -.-> DC_DISC

    %% Telemetry
    DC_GW --> DC_OTEL
    DC_LOADS --> DC_OTEL
    DC_RIFLES --> DC_OTEL
    DC_COMP --> DC_OTEL
    DC_MCP --> DC_OTEL
    DC_KC -->|traces| DC_OTEL

    DC_OTEL --> DC_LOKI
    DC_OTEL --> DC_TEMPO
    DC_OTEL --> DC_PROM

    DC_LOKI --> DC_GRAF
    DC_TEMPO --> DC_GRAF
    DC_PROM --> DC_GRAF

    classDef clientStyle fill:#e1f5fe,stroke:#0277bd,stroke-width:2px,color:#000000
    classDef appStyle fill:#e8f5e8,stroke:#388e3c,stroke-width:2px,color:#000000
    classDef supportStyle fill:#e8eaf6,stroke:#3949ab,stroke-width:2px,color:#000000
    classDef infraStyle fill:#fff3e0,stroke:#f57c00,stroke-width:2px,color:#000000
    classDef obsStyle fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#000000

    class User clientStyle
    class DC_GW,DC_LOADS,DC_RIFLES,DC_COMP,DC_MCP appStyle
    class DC_CONFIG,DC_DISC supportStyle
    class DC_PG,DC_KC,DC_KC_DB infraStyle
    class DC_GRAF,DC_LOKI,DC_TEMPO,DC_PROM,DC_OTEL obsStyle

Docker Compose Benefits:

Single command deployment: docker-compose up -d
Automatic service networking and discovery
Easy local debugging with port mapping
Fast iteration during development
Minimal resource requirements
Simple configuration via .env file

Kubernetes Architecture

flowchart TB
    User(["User / Client"]) -->|REST :30090| K8S_GW

    subgraph "Kubernetes Cluster"
        subgraph "Namespace: reloading"
            K8S_CM[/ConfigMaps/]
            K8S_GW[API Gateway<br/>NodePort: 30090]
            K8S_LOADS[Loads Service]
            K8S_RIFLES[Rifles Service]
            K8S_COMP[Components Service]
            K8S_MCP[MCP Server]
        end

        subgraph "Namespace: postgres"
            K8S_PG[(PostgreSQL StatefulSet<br/>8Gi PVC)]
        end

        subgraph "Namespace: keycloak"
            K8S_KC_PG[(Keycloak PostgreSQL<br/>StatefulSet<br/>8Gi PVC)]
            K8S_KC[Keycloak<br/>NodePort: 30080]
        end

        subgraph "Namespace: observability"
            K8S_GRAF[Grafana<br/>NodePort: 30000<br/>5Gi PVC]
            K8S_LOKI[Loki<br/>10Gi PVC]
            K8S_TEMPO[Tempo<br/>10Gi PVC]
            K8S_PROM[Prometheus<br/>10Gi PVC]
            K8S_OTEL[OTEL Collector<br/>NodePort: 30317]
        end
    end

    %% Gateway routes
    K8S_GW --> K8S_LOADS
    K8S_GW --> K8S_RIFLES
    K8S_GW --> K8S_COMP
    K8S_GW --> K8S_MCP

    %% Database connections (MCP Server has no DB)
    K8S_LOADS --> K8S_PG
    K8S_RIFLES --> K8S_PG
    K8S_COMP --> K8S_PG

    %% MCP Server calls downstream services
    K8S_MCP -.->|HTTP| K8S_LOADS
    K8S_MCP -.->|HTTP| K8S_RIFLES

    %% ConfigMaps (replaces Config Server in K8s)
    K8S_CM -.-> K8S_GW
    K8S_CM -.-> K8S_LOADS
    K8S_CM -.-> K8S_RIFLES
    K8S_CM -.-> K8S_COMP
    K8S_CM -.-> K8S_MCP

    %% Auth
    K8S_GW -->|OAuth2/UMA| K8S_KC
    K8S_KC --> K8S_KC_PG

    %% Telemetry
    K8S_GW --> K8S_OTEL
    K8S_LOADS --> K8S_OTEL
    K8S_RIFLES --> K8S_OTEL
    K8S_COMP --> K8S_OTEL
    K8S_MCP --> K8S_OTEL
    K8S_KC -->|traces| K8S_OTEL

    K8S_OTEL --> K8S_LOKI
    K8S_OTEL --> K8S_TEMPO
    K8S_OTEL --> K8S_PROM

    K8S_LOKI --> K8S_GRAF
    K8S_TEMPO --> K8S_GRAF
    K8S_PROM --> K8S_GRAF

    classDef clientStyle fill:#e1f5fe,stroke:#0277bd,stroke-width:2px,color:#000000
    classDef appStyle fill:#e8f5e8,stroke:#388e3c,stroke-width:2px,color:#000000
    classDef configStyle fill:#e8eaf6,stroke:#3949ab,stroke-width:2px,color:#000000
    classDef infraStyle fill:#fff3e0,stroke:#f57c00,stroke-width:2px,color:#000000
    classDef obsStyle fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#000000

    class User clientStyle
    class K8S_GW,K8S_LOADS,K8S_RIFLES,K8S_COMP,K8S_MCP appStyle
    class K8S_CM configStyle
    class K8S_PG,K8S_KC_PG,K8S_KC infraStyle
    class K8S_GRAF,K8S_LOKI,K8S_TEMPO,K8S_PROM,K8S_OTEL obsStyle

Kubernetes Benefits:

Production-ready with high availability support
Automatic scaling and self-healing
Persistent storage with StatefulSets
Namespace isolation for security
ConfigMaps and Secrets management
Rolling updates and rollbacks
Resource limits and health checks
Helm chart for easy management

Comparison Summary

Aspect	Docker Compose	Kubernetes
Deployment	Single file, one command	Helm chart with multiple resources
Scaling	Manual container scaling	Horizontal pod autoscaling
Networking	Simple port mapping	Services, NodePorts, Ingress
Storage	Docker volumes	PersistentVolumeClaims, StatefulSets
Configuration	Environment variables, .env files	ConfigMaps, Secrets
Use Case	Local development, testing	Production, staging, multi-node
Resource Overhead	Minimal	Requires cluster infrastructure
Service Discovery	Docker DNS	Kubernetes DNS, Service discovery
Updates	Manual restart	Rolling updates, zero downtime

High-Level Architecture

flowchart LR
    subgraph Clients
        User[User]
        Copilot[GitHub Copilot]
    end

    APIGateway["API Gateway<br/>:8080<br/>CircuitBreaker"]

    subgraph Microservices
        LoadsService[Loads Service]
        RiflesService[Rifles Service]
        ComponentsService[Components Service]
        MCPServer[MCP Server]
    end

    subgraph DockerOnly["Infrastructure - Docker/Local only"]
        ConfigServer["Config Server<br/>:8888"]
        DiscoveryServer["Discovery Server<br/>:8761"]
    end

    Keycloak["Keycloak Auth<br/>:7080"]

    subgraph Observability
        OTEL[OTel Collector]
        Prom[Prometheus]
        Loki[Loki]
        Tempo[Tempo]
        Graf[Grafana]
    end

    Postgres[(PostgreSQL 18)]

    User -->|REST| APIGateway
    Copilot -->|MCP SSE| APIGateway
    APIGateway --> LoadsService
    APIGateway --> RiflesService
    APIGateway --> ComponentsService
    APIGateway --> MCPServer

    %% Database connections (MCP Server has no DB)
    LoadsService --> Postgres
    RiflesService --> Postgres
    ComponentsService --> Postgres

    %% MCP Server calls downstream services, not DB
    MCPServer -.->|HTTP| LoadsService
    MCPServer -.->|HTTP| RiflesService

    %% Auth
    APIGateway -->|OAuth2/UMA| Keycloak
    LoadsService -->|JWT| Keycloak
    RiflesService -->|JWT| Keycloak
    ComponentsService -->|JWT| Keycloak
    MCPServer -->|JWT| Keycloak

    %% Config & Discovery (Docker/Local only)
    LoadsService -.->|Config| ConfigServer
    RiflesService -.->|Config| ConfigServer
    ComponentsService -.->|Config| ConfigServer
    MCPServer -.->|Config| ConfigServer
    LoadsService -.->|Register| DiscoveryServer
    RiflesService -.->|Register| DiscoveryServer
    ComponentsService -.->|Register| DiscoveryServer
    MCPServer -.->|Register| DiscoveryServer

    %% Telemetry
    APIGateway -->|Telemetry| OTEL
    LoadsService -->|Telemetry| OTEL
    RiflesService -->|Telemetry| OTEL
    ComponentsService -->|Telemetry| OTEL
    MCPServer -->|Telemetry| OTEL
    OTEL --> Prom
    OTEL --> Loki
    OTEL --> Tempo
    Prom --> Graf
    Loki --> Graf
    Tempo --> Graf

    classDef clientStyle fill:#e1f5fe,stroke:#0277bd,stroke-width:2px,color:#000000
    classDef serviceStyle fill:#e8f5e8,stroke:#388e3c,stroke-width:2px,color:#000000
    classDef infraStyle fill:#e8eaf6,stroke:#3949ab,stroke-width:2px,color:#000000
    classDef authStyle fill:#fff3e0,stroke:#f57c00,stroke-width:2px,color:#000000
    classDef dataStyle fill:#fff3e0,stroke:#f57c00,stroke-width:2px,color:#000000
    classDef obsStyle fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#000000
    class User,Copilot clientStyle
    class APIGateway,LoadsService,RiflesService,ComponentsService,MCPServer serviceStyle
    class ConfigServer,DiscoveryServer infraStyle
    class Keycloak authStyle
    class Postgres dataStyle
    class OTEL,Prom,Loki,Tempo,Graf obsStyle

Database Schema

The application uses PostgreSQL 18.0 with the following schema design. Measurement columns use JSONB to store JSR-385 Quantity values (e.g. {"value": 175.5, "unit": "grain"}) and JSR-354 MonetaryAmount values (e.g. {"amount": 45.99, "currency": "USD"}). The component tables (projectiles, propellants, primers, cases) include full-text search via PostgreSQL’s tsvector type with GIN indexes, enabling efficient natural language searches across manufacturer names, types, and other attributes. Tables are grouped by owning service — each service uses its own schema.

erDiagram
    %% ── Loads Service ──
    LOADS {
        BIGSERIAL id PK
        VARCHAR owner_id NOT NULL
        VARCHAR name NOT NULL
        TEXT description
        VARCHAR powder_manufacturer NOT NULL
        VARCHAR powder_type NOT NULL
        VARCHAR bullet_manufacturer NOT NULL
        VARCHAR bullet_type NOT NULL
        JSONB bullet_weight NOT NULL "Quantity Mass"
        VARCHAR primer_manufacturer NOT NULL
        VARCHAR primer_type NOT NULL
        JSONB distance_from_lands "Quantity Length"
        JSONB case_overall_length "Quantity Length"
        JSONB neck_tension "Quantity Length"
        BIGINT rifle_id FK
    }
    GROUPS {
        BIGSERIAL id PK
        VARCHAR owner_id NOT NULL
        BIGSERIAL load_id FK NOT NULL
        DATE date NOT NULL
        JSONB powder_charge NOT NULL "Quantity Mass"
        JSONB target_range NOT NULL "Quantity Length"
        JSONB group_size "Quantity Length"
    }
    SHOTS {
        BIGSERIAL id PK
        VARCHAR owner_id NOT NULL
        BIGSERIAL group_id FK NOT NULL
        JSONB velocity "Quantity Speed"
    }

    %% ── Rifles Service ──
    RIFLES {
        BIGSERIAL id PK
        VARCHAR owner_id NOT NULL
        VARCHAR name NOT NULL
        TEXT description
        VARCHAR caliber NOT NULL
        JSONB barrel_length "Quantity Length"
        VARCHAR barrel_contour
        JSONB rifling "twistRate and twistDirection"
        JSONB zeroing "sightHeight and zeroDistance"
    }

    %% ── Components Service ──
    PROJECTILES {
        BIGSERIAL id PK
        VARCHAR owner_id NOT NULL
        VARCHAR manufacturer NOT NULL
        JSONB weight NOT NULL "Quantity Mass"
        VARCHAR type NOT NULL
        JSONB cost NOT NULL "MonetaryAmount"
        INTEGER quantity_per_box NOT NULL
        TSVECTOR search_vector "GIN indexed"
    }
    PROPELLANTS {
        BIGSERIAL id PK
        VARCHAR owner_id NOT NULL
        VARCHAR manufacturer NOT NULL
        VARCHAR type NOT NULL
        JSONB cost "MonetaryAmount"
        JSONB weight_per_container "Quantity Mass"
        TSVECTOR search_vector "GIN indexed"
    }
    PRIMERS {
        BIGSERIAL id PK
        VARCHAR owner_id NOT NULL
        VARCHAR manufacturer NOT NULL
        VARCHAR type NOT NULL
        VARCHAR size NOT NULL
        JSONB cost NOT NULL "MonetaryAmount"
        JSONB quantity_per_box NOT NULL "Quantity Dimensionless"
        TSVECTOR search_vector "GIN indexed"
    }
    CASES {
        BIGSERIAL id PK
        VARCHAR owner_id NOT NULL
        VARCHAR manufacturer NOT NULL
        VARCHAR caliber NOT NULL
        VARCHAR primer_size NOT NULL
        JSONB cost NOT NULL "MonetaryAmount"
        JSONB quantity_per_box NOT NULL "Quantity Dimensionless"
        TSVECTOR search_vector "GIN indexed"
    }

    %% Relationships
    LOADS ||--o{ GROUPS : "has"
    GROUPS ||--o{ SHOTS : "has"
    RIFLES ||--o{ LOADS : "uses"

Component Search Capabilities

The Components Service provides full-text search functionality for all component types (cases, powders, primers, bullets). Each component table includes a search_vector column of type tsvector that is automatically maintained and indexed for fast searching.

Search Features:

Natural language queries across manufacturer names, types, and attributes
Fuzzy matching and relevance ranking
Case-insensitive searches
Support for partial word matching
PostgreSQL GIN indexes for optimal performance

Example Search Queries:

Find all Lapua cases: GET /api/cases/search?query=Lapua
Search for H4350 powder: GET /api/propellants/search?query=H4350
Find 140 grain bullets: GET /api/projectiles/search?query=140
Search for CCI primers: GET /api/primers/search?query=CCI

For more details on search functionality and API usage, refer to the API Testing Guide.

TODO

❏ Update MCP server to support resources and prompts
❏ Add brass case attributes such as neck tension, headspace, etc.
❏ Implement load comparison and analysis tools
❏ Add shooting session tracking and analytics
❏ Enhance full-text search capabilities for loads and rifles
❏ Add ballistic coefficient calculations and external ballistics

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Name		Name	Last commit message	Last commit date
Latest commit History 182 Commits
.github/workflows		.github/workflows
.mvn/wrapper		.mvn/wrapper
.vscode		.vscode
api-gateway		api-gateway
components-service		components-service
config-server		config-server
discovery-server		discovery-server
docker		docker
helm/spring-load-development		helm/spring-load-development
loads-service		loads-service
mcp-server		mcp-server
rifles-service		rifles-service
shared-integration-testing		shared-integration-testing
shared-security		shared-security
shared		shared
test		test
.env		.env
.gitignore		.gitignore
LICENSE		LICENSE
README.adoc		README.adoc
docker-compose.yml		docker-compose.yml
mvnw		mvnw
mvnw.cmd		mvnw.cmd
pom.xml		pom.xml
spotbugs-exclude.xml		spotbugs-exclude.xml

License

zhoozhoo/spring-load-development

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Spring Load Development

Technologies and Frameworks Used

Current Versions

Getting Started

Prerequisites

Deployment Options

Option 1: Docker Compose Deployment (Recommended for Development)

Clone the repository:

Build the project:

Start all services using Docker Compose:

Option 2: Kubernetes Deployment with Helm (Recommended for Production)

Install using Helm:

Access services:

Option 3: Manual Spring Boot Services (Development/Debugging)

Start infrastructure services:

Start Spring Boot services in order:

Accessing Services

Docker Compose Deployment

Kubernetes Deployment

AI Integration

API Documentation

Services Architecture

Configuration Repository

Security

Monitoring and Observability

Observability Stack

Monitoring architecture

Deployment Architecture Comparison

Docker Compose Architecture

Kubernetes Architecture

Comparison Summary

High-Level Architecture

Database Schema

Component Search Capabilities

TODO

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