In my previous post I explained why we should write more unit tests for our database code. Databases contain crucial business logic that is often developed and maintained in tandem with the code that depends on it. If we want to validate complex SQL statements in isolation and in detail, that means writing much more automatic tests than we normally do. Testing against a real database server (not an in-memory emulation) is by nature slower and more cumbersome to set up and run than basic unit tests. Here I will explain some practical strategies to make that process faster and more manageable.
The tips and tricks fall into two categories. First, I’ll look at ways to arrange your code under test so that the database is only spun up and accessed when it is actually needed. Secondly there are tips on how to prepare your database container with schemas and data, so setup time is kept to a minimum.
The test pyramid is a well-known visualization aid to classify software tests. As you climb the steps towards greater integration of components, you proceed from many, detailed, fast and isolated tests towards fewer, slower and more global tests that validate the system as a whole. It makes good sense in principle but it’s harder to explain how the stratification between unit-, integration and end-to-end tests is supposed to work. Opinions differ on what parts to integrate at which layer in the pyramid. You would think the database belongs in the upper strata, because it is expensive to set up and run. Yet it also makes sense to integrate it at the lower, detailed stage in the pyramid when it contains business-critical logic that requires detailed validation. It often needs the kind of rigorous validation that you cannot leave to a few slow, global integration tests. But let’s start with a recap of the definition of a unit test.
Summary: It’s deceptively simple to enable caching in Spring, but you must investigate actual production usage in order to configure it properly. There are four areas of concern: peak-time load, uniqueness of requests, savings in time and resources, and the longevity of cacheable values. An example project shows these concerns in action.
I like the topic of caching in Java code. It’s a technique where the Platonic world of clean code meets actual usage. You can never tell just by looking at code whether caching is a good idea or unnecessary optimization. You need to measure or at least predict realistic production loads.
Maybe the Spring framework had made it a little too easy. With minimal boilerplate you can configure any managed object to return cached responses upon repeated requests. Call it once to run the method body, call it twice and the framework intervenes and returns the result of the first call. You can (well, must) plug in a full featured third-party implementation like Caffeine or ehcache to enable things like disk overflow and automatic eviction.
SUMMARY: Any web service needs to export their public API if consumers want to make the best use of that service. A developer-friendly approach to do so if you work in the Java ecosystem is to package DTOs and endpoint interfaces in an API jar file and use the Retrofit framework to create type-safe clients for integration testing. This article discusses a complete example.
If you’ve worked in enterprise Java projects you will remember good old Web Services Description Language, theXML based format for describing network services by IBM and Microsoft. Maybe you still work with it? WSDL and its twin XML Schema are among those W3C standards that seasoned developers love to hate. Its specification files are not very human readable, let alone human writable. Fortunately you don’t have to. They can be generated by your server endpoint and fed straight into a code generator to create transfer objects (dtos) and service stubs.
SUMMARY: There’s plenty of free advice on how to become a better developer. You can safely skip much of it. You’ve probably heard it all before in different guises. Moreover, the huge scope of present-day IT is such that most advice has limited relevance. It’s rarely universally useful, and even if it is, knowing your golden tips doesn’t mean people live by them.
Blogs catering to developers are bursting with well-meaning bullet point lessons on how you too can join the pick of the bunch. Tell-tale warning signs to help you spot slackers, sociopaths and generic time wasters in your team are another favourite. Many of these articles strike me as hunches based on shreds of personal and/or anecdotal evidence. Who are you to take this authoritative tone with me? I can think up my own Ten Commandments of Seven Deadly Sins, easy. It would be a hodgepodge of stuff I experienced first-hand or read, ordered and filtered by my own subconscious preferences, hoping you, the reader, will find it useful. I won’t do it.