This is a post for a great idea called SQL University started by Jorge Segarra also famously known as SqlChicken on Twitter. It’s a collection of blog posts on different database related topics contributed by several smart people all over the world. So this week is mine and we’ll be talking about database testing and refactoring. In 3 posts we’ll cover:

SQLU part 1 - What and why of database testing

SQLU part 2 - What and why of database refactoring

SQLU part 2 – Tools of the trade

With that out of the way let us sharpen our pencils and get going.

Why test a database

The sad state of the industry today is that there is very little emphasis on testing in general. Test driven development is still a small niche of the programming world while refactoring is even smaller. The cause of this is the inability of developers to convince themselves and their managers that writing tests is beneficial. At the moment they are mostly viewed as waste of time. This is because the average person (let’s not fool ourselves, we’re all average) is unable to think about lower future costs in relation to little more current work. It’s orders of magnitude easier to know about the current costs in relation to current amount of work. That’s why programmers convince themselves testing is a waste of time.

However we have to ask ourselves what tests are really about? Maybe finding bugs? No, not really. If we introduce bugs, we’re likely to write test around those bugs too. But yes we can find some bugs with tests. The main point of tests is to have reproducible repeatability in our systems. By having a code base largely covered by tests we can know with better certainty what a small code change can break in other parts of the system. By having repeatability we can make code changes with confidence, since we know we’ll see what breaks in other tests. And here comes the inability to estimate future costs. By spending just a few more hours writing those tests we’d know instantly what broke where.

Imagine we fix a reported bug. We check-in the code, deploy it and the users are happy. Until we get a call 2 weeks later about a certain monthly process has stopped working. What we don’t know is that this process was developed by a long gone coworker and for some reason it relied on that same bug we’ve happily fixed. There’s no way we could’ve known that. We say OK and go in and fix the monthly process. But what we have no clue about is that there’s this ETL job that relied on data from that monthly process. Now that we’ve fixed the process it’s giving unexpected (yet correct since we fixed it) data to the ETL job. So we have to fix that too. But there’s this part of the app we coded that relies on data from that exact ETL job. And just like that we enter the “Loop of maintenance horror”. With the loop eventually comes blame. Here’s a nice tip for all developers and DBAs out there: If you make a mistake man up and admit to it.

All of the above is valid for any kind of software development. Keeping this in mind the database is nothing other than just a part of the application. But a big part! One reason why testing a database is even more important than testing an application is that one database is usually accessed from multiple applications and processes. This makes it the central and vital part of the enterprise software infrastructure.

Knowing all this can we really afford not to have tests?

What to test in a database

Now that we’ve decided we’ll dive into this testing thing we have to ask ourselves what needs to be tested? The short answer is: everything. The long answer is: read on! There are 2 main ways of doing tests: Black box and White box testing.

Black box testing means we have no idea how the system internals are built and we only have access to it’s inputs and outputs. With it we test that the internal changes to the system haven’t caused the input/output behavior of the system to change. The most important thing to test here are the edge conditions. It’s where most programs break. Having good edge condition tests we can be more confident that the systems changes won’t break.

White box testing has the full knowledge of the system internals. With it we test the internal system changes, different states of the application, etc… White and Black box tests should be complementary to each other as they are very much interconnected.

Testing database routines includes testing stored procedures, views, user defined functions and anything you use to access the data with. Database routines are your input/output interface to the database system. They count as black box testing. We test then for 2 things: Data and schema. When testing schema we only care about the columns and the data types they’re returning. After all the schema is the contract to the out side systems. If it changes we usually have to change the applications accessing it. One helpful T-SQL command when doing schema tests is SET FMTONLY ON. It tells the SQL Server to return only empty results sets. This speeds up tests because it doesn’t return any data to the client. After we’ve validated the schema we have to test the returned data. There no other way to do this but to have expected data known before the tests executes and comparing that data to the database routine output.

Testing Authentication and Authorization helps us validate who has access to the SQL Server box (Authentication) and who has access to certain database objects (Authorization). For desktop applications and windows authentication this works well. But the biggest problem here are web apps. They usually connect to the database as a single user. Please ensure that that user is not SA or an account with admin privileges. That is just bad.

Load testing ensures us that our database can handle peak loads. One often overlooked tool for load testing is Microsoft’s OSTRESS tool. It’s part of RML utilities (x86, x64) for SQL Server and can help determine if our database server can handle loads like 100 simultaneous users each doing 10 requests per second. SQL Profiler can also help us here by looking at why certain queries are slow and what to do to fix them.

One particular problem to think about is how to begin testing existing databases. First thing we have to do is to get to know those databases. We can’t test something when we don’t know how it works. To do this we have to talk to the users of the applications accessing the database, run SQL Profiler to see what queries are being run, use existing documentation to decipher all the object relationships, etc… The way to approach this is to choose one part of the database (say a logical grouping of tables that go together) and filter our traces accordingly. Once we’ve done that we move on to the next grouping and so on until we’ve covered the whole database. Then we move on to the next one.

Database Testing is a topic that we can spent many hours discussing but let this be a nice intro to the world of database testing. See you in the next post.