Eventuate example microservices applications

Eventuate™ is a family of platforms that solve the distributed data management problems inherent in the microservice architecture.

Eventuate™ consists of two platforms:

Key benefits of Eventuate

Maintain data consistency using sagas

Implement commands that update data in multiple microservices by using Sagas, which are sequences of local transactions coordinated using messages

Implement queries using CQRS

Implement queries that retrieve data from multiple services by using CQRS views, which are easily queryable replicas maintained using events

Communicate using transactional messaging

Reliably send and receive messages and events as part of a database transaction by using either the Transactional Outbox or Event Sourcing patterns

Eventuate Tram Spring Boot/JPA examples

The Eventuate Tram platform enable you to easily develop microservices that use traditional JDBC/JPA-based persistence.

Eventuate Tram supports the following technologies:

  • Frameworks - Spring Boot and Micronaut
  • Databases - MySQL, Postgres, and Microsoft SQL server
  • Message brokers - Apache Kafka, ActiveMQ, RabbitMQ, and Redis Streams

There are numerous example applications:


Eventuate Event Sourcing examples

The Eventuate Local platform let’s you easily write microservices that use Event Sourcing, Sagas and Command Query Responsibility Segregation (CQRS). It’s an open-source Event store that supports the following technologies:

  • Frameworks - Spring Boot
  • Databases - MySQL, and Postgres
  • Message brokers - Apache Kafka

There are several example applications on github.com that illustrate how to use the Eventuate Platform:

  • Todo List application - the hello world application for the Eventuate™ Platform. It illustrates how you can use the platform to write an application that uses Event Sourcing and Command Query Responsibility Segregation (CQRS). The Todo List application lets users maintain a todo list.
  • Java version of the Orders and Customers example - an application that I’ve used in my talks (e.g. Gluecon 2016) to illustrate how to achieve eventual consistency using event sourcing.
  • AWS Lambda Java Echo - illustrates how you can write an AWS Lambda function that consumes Eventuate events. It is deployed using Serverless and subscribes to events published by the Todo List application described above.
  • AWS Lambda Todo List - the AWS Lambda version of the Todo List application described above. It consists of a set of lambdas that are deployed using Serverless.
  • Restaurant management - Food to Go is a fictitious, on-demand logistics company from Chris Richardson’s book POJOs in Action. It delivers takeout orders from restaurants to customers. A key part of the application is the restaurant management service, which maintains a database of restaurants that can be queried for availability to deliver an order to a customer at a particular time. This version of the restaurant management service has an architecture based on microservices, event sourcing and Command Query Responsibility Segregation (CQRS). It is written in Java and uses Spring Boot, and Redis.
  • Money Transfer - shows how to transfer money between two bank accounts using an event-driven, eventually consistent transaction. There are Java and Scala versions of the code. The application has a microservices architecture and is written using Spring Boot.
  • Kanban Board - an example of a real-time, multi-user collaborative application. The Kanban Board application enables users to collaboratively create and edit Kanban boards and tasks. Changes made by one user to a board or a task are immediately visible to other users viewing the same board or task. It has a microservices architecture and is written using Java and Spring Boot and uses MongoDB for CQRS views. The application uses STOMP-over-WebSockets to push events from the event store to an AngularJS front-end.
  • Scala Customers and Orders - the example code from Chris Richardson’s Scala By the Bay 2015 presentation, which describes a Scala typeclass-based design for Aggregates that use Event Sourcing.

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