This is a template project you can clone and use as a basis for your own Kotlin based microservices.
This application structure is my personal preference. I do not claim this is the best approach (whatever that means), but simply that it works for me and has seen success at several companies I have worked at. This is an opinionated project and as such will not appeal to all.
I am a mild functional programming advocate, and my viewpoints are influenced by functional programming, but try to avoid the more advanced FP features that don’t translate well to Kotlin.
No dependency injection. You simply do not need it.
- It is trivial to pass dependencies through the constructor. Services should be small enough that you don’t have layers upon layers of “services” where passing dependencies around becomes a burden.
- When you use constructors as they were intended, you never have to try and work out where a “bean” is being instantiated.
Config as data classes.
- Easy to test because you can simply create the config values you want in tests and pass in an instance of the data class.
- No confusion as to where values are being pulled from.
Tests should be real tests
- No mocks. Ever. You don’t need them (caveat – you might need them in some tiny edge cases, like testing a legacy Java interface with 200 methods).
- Test your endpoints by using a framework that treats requests as simple objects.
- Use test containers for real databases.
- Use embedded HTTP servers for upstream dependencies.
Functional error handling
- Don’t throw exceptions unless it’s truly exceptional. Expected errors, such as invalid json, are not exceptional.
Avoid interfaces for services and datastores
- You don’t need an interface for your database code. You’re not going to need to mock them, and the logic is bespoke.
- A good rule of thumb in my opinion is, if you have an implementation of an interface called
MyInterfaceImplthen you don’t need the interface because you have no natural name for the implementation.
- Unless you have a ton of CRUD to write, it’s easier to just write SQL by hand.
- Spring JDBC Template is a simple set of helpers around JDBC calls.
- Kotlin – Running the latest Kotlin release.
- ktor – a Jetbrains Kotlin based HTTP framework that natively supports coroutines. Provides http-as-a-function (almost), and a great in-memory test server. Lacks swagger integration.
- Hoplite – a Kotlin data-class-as-config loader that provides cascading fallback of config files. Config is loaded in regular data classes that you define and can pass about. This allows for easy testing, as you can provide test-time values by simply creating instances of those data classes.
- Kotest – Kotlin test framework that supports nested test layout, coroutines, idiomatic Kotlin assertions, test-container extensions, property testing, data driven testing, non-deterministic helpers, and more.
- Micrometer – Metrics collection with integration with datadog / grafana, etc and most Java libraries come with Micrometer adapters to collect metrics. many/most Java libraries.
- Hikari – High performance and robust JDBC pooling library. The go-to JVM based connection pool library in 2022.
- Tabby – a tiny functional programming accessory kit. Essentially a set of
extension functions for
Resultthat help fill in the gaps on Result.
- Kotlin Logging – a simple project that wraps slf4j but makes it easier to define a kotlin logger as a top level function.
- Logback – a simple alternative to log4j.
- Cohort – A spring-actuator style plugin for Ktor. Useful for probes for kubernetes services.
- Princeps – A validation library for Kotlin that takes the “parse don’t validate” approach.
- TestContainers – Creates disposable containers for tests, so you don’t need to mock database code, because you can test against the real thing.
- Flyway – Versioned database migrations, that you can also apply to your tests.
- Spring JDBC Template – Small set of helpers for working with JDBC calls.
Appis your ‘god’ object. It contains config parsed by Hoplite, a Micrometer registry backed by datadog, a database
java.sql.DataSource. Any dependencies you need to share should be created here.
- The micrometer registry comes preconfigured with JVM metrics such as memory, cpu and diskspace.
- The datasource is backed by a Hikari connection pool. Configure defaults in the database.yml files.
main– This is the starter function that sets some defaults and launches ktor
module– this contains a single logical grouping of ktor endpoints and plugins.
- myservice-domain – place your domain classes here so they are shared between datastores and services
- myservice-datastore – place your database or cache repositories here
- myservice-services – place your business logic here
- myservice-endpoints – place your endpoints in here
- myservice-app – an assembly module that builds a docker image for deployment. Config files and logging configuration lives here.
We define a base file, called application.yml which contains defaults that don’t change between environments. For example, port numbers or cache ttls.
Then we have a separate file per environment in the format
This app uses the powerful data-class-as-config approach from Hoplite. Files are loaded in a cascading fashion – earlier files (as defined in App) override values in later files. The file are converted to data classes and any missing values or conversion errors are immediately flagged on startup.
AWS secrets manager support is enabled by adding a config key in the form
secretsmanager//:mykey which you can see
application-prod.yml for example.
docker-compose up from the root of this project. This will start up a mysql database.
main method located in the app module. This starts up a HTTP interface on the port defined
application.yml. This ktor based http server is configured to return JSON
Three endpoints are included:
- /health returns a 200 OK suitable for use in k8
- /random returns a random
- /database returns all wibbles from the database (you need to create the wibble table and add your own wibble!)
CREATE TABLE wibble ( a TEXT, b INT ); INSERT INTO wibble (a, b) VALUES ("foo", 1), ("bar", 2 ");
./gradlew dockerBuildImage will build a docker image ready for deployment.