What should be the easiest way to unit test influxdb queries - unit-testing

Question

I have a service that only make queries ( read / write ) to influxDB.
I want to unit test this, but I'm not sure how to do it, I've read a bunch of tutos talking about mocking. A lot deals with components like go-sqlmock. But as I am using influxDB, I could not use it.
I also find out other components I've tried to use like goMock or testify to be over-complicated.
What I think to do is to create a Repository Layer, an interface that should implement all the methods I need to run / test, and pass concrete classes with dependency injection.
I think it could work, but is it the easiest way to do it ?
I guess having Repositories everywhere, even for small services, just for them to be testable, seems to be over-engineered.
I can give you code if needed, but I think my question is a bit more theorical than practical. It is about the easiest way to mock a custom DB for unit testing.

Answer 1

To expand on #Markus W Mahlberg answer:
If the goal is to verify the queries are valid and actually execute against influx there's no shortcut for actually performing these against influx. These are usually considered to be "integration" tests. I have found with docker-compose that these tests can be just as reliable as unit tests, and fast enough to be integrated into CI. Having the tests execute in CI enables local engineers to easily run these tests to verify their query changes as well.
I guess having Repositories everywhere, even for small services, just for them to be testable, seems to be over-engineered.
I have found this to be pretty polarizing discussion. A test implementation IS a concrete implementation and paves the way for reliable, repeatable tests that support easily isolating and exercising specific components of your code.
I want to unit test this, but I'm not sure how to do it,
I think this is pretty nuanced, IMO unit testing queries provides negative value. Value comes from using a repository interface to allow your unit tests to explicitly configure responses that you would receive from influx in order to fully exercise your application code. This provides no feedback on influx, which is why the integration tests are essential in order to verify that your application can validly configure, connect, and query against influx. This validation implicitly happens when you deploy your application, at which point it becomes way more expensive in terms of feedback than verifying it locally and in CI with integration tests.
I created a diagram to try and illustrate these differences:
Unit tests with repository are focused on your application code and provide little feedback/value on anything to do with influx. Integration tests are useful for verifying your client (perhaps being extended to your application depending on where the tests are exercising but I prefer to bound it to the client since you already have the static feedback from go on the interfaces and calls). Then finally, as #Markus points out, the step to e2e tests is pretty small from integration tests, and allow you to test your full service.

Answer 2

By its very definition, if you test your integration with an external resource, we are talking of integration tests, not unit tests. So we have two problems to solve here.
Unit tests
What you typically do is to have a data access layer which accepts interfaces, which in turn are easy to mock and you can unittest your application logic.
package main
import (
"errors"
"fmt"
)
var (
values = map[string]string{"foo": "bar", "bar": "baz"}
Expected = errors.New("Expected error")
)
type Getter interface {
Get(name string) (string, error)
}
// ErrorGetter implements Getter and always returns an error to test the error handling code of the caller.
// ofc, you could (and prolly should) use some mocking here in order to be able to test various other cases
type ErrorGetter struct{}
func (e ErrorGetter) Get(name string) (string, error) {
return "", Expected
}
// MapGetter implements Getter and uses a map as its datasource.
// Here you can see that you actually get an advantage: you decouple your logic from the data source,
// making refactoring (and debugging) **much** easier WTSHTF.
type MapGetter struct {
data map[string]string
}
func (m MapGetter) Get(name string) (string, error) {
if v, ok := m.data[name]; ok {
return v, nil
}
return "", fmt.Errorf("No value found for %s", name)
}
type retriever struct {
g Getter
}
func (r retriever) retrieve(name string) (string, error) {
return r.g.Get(name)
}
func main() {
// Assume this is test code. No tests possible on playground ;)
bad := retriever{g: ErrorGetter{}}
s, err := bad.retrieve("baz")
if s != "" || err == nil {
panic("Something went seriously wrong")
}
// Needs to fail as well, as "baz" is not in values
good := retriever{g: MapGetter{values}}
s, err = good.retrieve("baz")
if s != "" || err == nil {
panic("Something went seriously wrong")
}
s, err = good.retrieve("foo")
if s != "bar" || err != nil {
panic("Something went seriously wrong")
}
}
In the example above, I actually had to implement two Getters to cover all test cases, since I could not use a mocking library, but you get the picture.
As for the over engineering: Plain and simple, no, that is not overengineering. It is what I personally call proper craftsmanship. It will pay in the long run to get used to it. Maybe not in this project, but in one to come.
Integration tests
Dodgy. What I tend to do is to make sure my queries are correct before I commit them ;)
In the rare case I really want to verify my queries in a CI for example, I usually create a Makefile which in turn spins up a docker(-compose) which provides the stuff I want to integrate against and then runs the tests.