sqshq/piggymetrics
Piggymetrics
PiggyMetrics is a demonstration project that illustrates a complete microservice architecture built with Spring Boot, Spring Cloud, and Docker, centered around a personal finance tracking application. It records incomes, expenses, and savings for individual accounts while computing spending statistics and time-series data to track cash flow dynamics over time.
The project showcases a full Spring Cloud service mesh, including an API gateway for request routing, service discovery for dynamic instance location, and a centralized configuration server that manages application settings from a single Git-backed service. It implements OAuth2 token-based authentication for user login and secure inter-service communication, along with a Hystrix circuit breaker pattern that monitors service call failures and latency to prevent cascading errors. Distributed tracing via Sleuth and Zipkin adds unique identifiers to log entries for end-to-end request tracking across services, while the ELK stack aggregates logs from all services into a searchable index.
The system packages each microservice as a Docker container and uses Docker Compose for orchestrated multi-container deployment, with an automated CI pipeline that builds tagged Docker images on each code push. Client-side load balancing distributes requests across available service instances, and health monitoring collects metrics on request latency, error rates, and circuit breaker state for each service. Email notifications store user contact information and preferences, then send scheduled reminders and backups.
Features
- Spring Boot Integrations - Building and running a distributed system of small, independently deployable services using Spring Boot and Spring Cloud.
- Microservice Demonstrations - An example project demonstrating a microservice architecture built with Spring Boot, Spring Cloud, and Docker for personal finance management.
- Personal Finance Management - Recording incomes, expenses, and savings for individual accounts and analyzing spending statistics over time.
- Personal Finance Trackers - Records incomes, expenses, and savings per account with currency conversion and spending statistics analysis.
- Spring Cloud Implementations - Illustrates service discovery, API gateway routing, centralized configuration, and circuit breaking using Spring Cloud components.
- Centralized Configuration Management - Managing application settings from a single service that serves configuration files to all microservices.
- Docker Compose Deployments - Packages each microservice as a Docker image and uses Docker Compose to orchestrate multi-container environments.
- Microservices Deployments - Packaging each microservice as a Docker container for consistent deployment and CI/CD pipeline automation.
- Service Discovery - Registers and locates microservice instances dynamically, enabling load balancing and failover without hardcoded addresses.
- Centralized Configuration Storage - Stores all microservice settings in a single Git-backed service that serves configuration files on startup.
- Distributed Tracing - Adds unique trace and span IDs to logs and sends them to Zipkin for end-to-end request tracking across services.
- Request Routing - Directs incoming requests to the appropriate backend service and aggregates results from multiple services.
- Circuit Breakers - Monitors service call failures and latency, then opens circuits to prevent cascading failures and allow graceful degradation.
- OAuth2 Providers - Issuing and validating OAuth2 tokens for user login and secure service-to-service communication.
- Inter-Service Token Issuances - Issues and validates OAuth2 tokens for user authentication and secure inter-service communication.
- Distributed System Monitoring and Resilience - Aggregating metrics, tracing requests across services, and preventing cascading failures with circuit breakers.
- Microservice Frameworks - Builds each service as a standalone Spring Boot application with embedded servers and auto-configuration.
- Netflix Stack Integrations - Uses Eureka, Hystrix, Zuul, and Ribbon to provide service discovery, circuit breaking, API gateway routing, and client-side load balancing.
- Centralized Logging Architectures - Collects logs from all services into Elasticsearch and visualizes them in Kibana for unified search and analysis.
- Sleuth & Zipkin Implementations - Adds unique trace and span IDs to logs and sends them to Zipkin for end-to-end request tracking across services.
- Cash Flow Dynamics - Computes statistics and time-series data normalized to a base currency to track cash flow dynamics.
- Service Discovery Integrations - Routes requests through a single entry point and enables services to locate each other without hardcoded addresses.
- Declarative API Clients - Declares REST clients as Java interfaces that automatically integrate with load balancing and circuit breaking.
- Time-Series Spending Metrics - Computes financial metrics and captures time-series data normalized to a base currency to track cash flow over time.
- Docker Image Builds - Builds tagged Docker images on each code push and makes them available for continuous delivery and rollback.
- Docker Container Deployments - Packages each microservice as a Docker container for consistent deployment and CI/CD pipeline automation.
- Client-Side Load Balancing - Distributes HTTP and TCP requests across available service instances without an additional network hop.
- Microservices Health Monitoring - Collects and displays metrics on request latency, error rates, and circuit breaker state for each microservice.
- Log Analysis - Aggregates logs from all services into a searchable index for identifying problems in a distributed environment.
- Request Tracing - Adds unique identifiers to log entries so you can follow a single request as it travels through multiple microservices.
- Microservice Reference Implementations - Personal finance tracker built with Spring Boot.
Star history
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