What is the best practice for testing an API that depends on data from the database?
What are the issues I need to watch out for in a "Continuous Integration" environment that runs Unit Tests as part of the build process? I mean would you deploy your database as part of the build scripts (may be run your installer) or should I go for hardcoded data [use MSTest Data Driven Unit Tests with XML]?
I understand I can mock the data layer for Business Logic layer but what if I had issues in my SQL statements in DAL? I do need to hit the database, right?
Well... that's a torrent of questions :)... Thoughts?
As far as possible you should mock out code to avoid hitting the database altogether, but it seems to me you're right about the need to test your SQL somewhere along the line. If you do write tests that hit the database, one key tip for avoiding headaches is to make sure that your setup gets the data into a known state, rather than relying on there already being suitable data available.
And of course, never test against your live database! But that goes without saying :)
As mentioned, use mocking to simulate DB calls in unit tests unless you want to fiddle with your tests and data endlessly. Testing sql statements implies more of an integration test. Run that separate from unit tests, they are 2 different beasts.
It's a good idea to automatically wipe the test database and then populate it with test harness data that will be assumed to be there for all of the tests that need to connect to the database. The database needs to be reset before each test for proper isolation - a failing test that puts in bad data could cause false failures on tests that follow and it gets messy if you have to run tests in a certain order for consistent results.
You can clear and populate the database with tools (DBUnit, DBUnit.NET, others) or just make your own utility classes to do the same thing.
As you said, other layers should be sufficiently decoupled from classes that actually hit the database, so the need for any kind of database being involved in testing is limited to tests run a small subset of your codebase. Your database accessing components can be mocked/stubbed for everything that depends on them.
One thing I did was create static methods that returned test data of a known state. I would then use a "fake" DAL to return this data as if I was actually calling the database. As for testing the sql/stored procedure, I tested it using SQL Management Studio. YMMV!
Related
thank you for reading my question.
I was just wondering about how shall i create unit tests for existing database layer. as of now my project has existing unit tests but no unit test is written for database layer or any function which inserts / updates / deletes data from database.
We are using Microsoft tests. One approach I think here is
1) We shall create database on the fly i.e. mdf file and we will keep our defaults values ready in it and in our setup method(Nunit) or initialize method(MS tests) we will mock the objects and dump the dummy data into tables.
Also we are not using any mocking framework. So i am all confuse.
i need to know how can we do this from the scratch. Also is there anything optional available for mocking framework.
Any pointers or samples would be highly appreciated.
Thank you again.
A C# unit test shall not touch the database, you should mock the database. It should be possible to execute many thousands of unit test on your local machine (without external (internet, databases, other application)) within seconds (and you want to run them when you build your code).
That leaves us kind of with your question unanswered: what should your database layer tests do? It depends on what kind of logic you have in that assembly! If you have "business or decision" logic should should test that, if you have mapping logic test that. If all your database layer does if using (whatever db framework) to put the load on you database then you might not have anything worth testing there.
If you want to test logic performed by your database (say SP's) you should do that in the database project, and most likely not using mstest.
Of course you can use mstest to setup and tear down database and perform test, but those test will not be unit tests.
I am just starting to get into unit testing and cant see an easy way to do a lot of test cases due to the interaction with a database.
Is there a standard method/process for unit testing where database access (read and write) is required in order to assert tests?
The best I can come up with so far is to have a config file used to bootstrap my app using a different db connection and then use the startup method to copy over the live db to a db used in isolation for tests?
Am I close? Or is there a better approach to this?
Your business logic shouldn't directly interact with the Database. Instead it should go through a data access layer that you can fake and mock in the context of unit testing. Look into mocking frameworks to do the mocking for you. Your tests should not depend on a database at all. Instead you should specify the data returned from your data access layer explicitly, and then ensure that your business logic behaves correctly with that information.
Testing that the program works with a DB attached is more of an integration test, and those have a lot of costs associated with them. They are slower (so it's harder to run them every time you compile), and more complicated (so they require more time and effort to maintain). If you can get simpler unit tests in place, I would recommend you do that first. Later you can add integration tests that might use the DB as well, but you'll get the most value from adding simpler unit tests first.
As far as unit-test go, I think whatever works for you in practice is the way to go. It's important that unit tests give you some value and improve the quality of your system and your ability to develop and maintain it.
I would suggest you probably don't want to be copying the live db over to your test db. There's probably no guarantees that your live database will contain suitable data that will cause your unit-tests to consistently run. The unit-tests should test that your code works, they shouldn't be testing that the live database happens to contain suitable data that causes them to pass, because as it's live, your users might change the content of it so that your tests fail.
You're unit test code itself should probably populate your test db with data required that simulates the scenarios you want to write unit tests for. I messed around with some ruby on rails code a few years ago; the test framework for that would have a test class which setup the db with some fake data, then multiple test methods from the class would be written to run against that data, and the tear-down method would wipe data from the database. So, different test-classes (or sometimes people call them fixtures) would run against a certain data setup, that meant you could run a number of tests against the same data setup instead of creating it for every test case you wanted to run. Setting up data for every test could end up causing your tests to run slowly, such that you get bored of waiting for them to run and stop bothering with them.
This question is more or less programming language agnostic. However as I'm mostly into Java these days that's where I'll draw my examples from. I'm also thinking about the OOP case, so if you want to test a method you need an instance of that methods class.
A core rule for unit tests is that they should be autonomous, and that can be achieved by isolating a class from its dependencies. There are several ways to do it and it depends on if you inject your dependencies using IoC (in the Java world we have Spring, EJB3 and other frameworks/platforms which provide injection capabilities) and/or if you mock objects (for Java you have JMock and EasyMock) to separate a class being tested from its dependencies.
If we need to test groups of methods in different classes* and see that they are well integration, we write integration tests. And here is my question!
At least in web applications, state is often persisted to a database. We could use the same tools as for unit tests to achieve independence from the database. But in my humble opinion I think that there are cases when not using a database for integration tests is mocking too much (but feel free to disagree; not using a database at all, ever, is also a valid answer as it makes the question irrelevant).
When you use a database for integration tests, how do you fill that database with data? I can see two approaches:
Store the database contents for the integration test and load it before starting the test. If it's stored as an SQL dump, a database file, XML or something else would be interesting to know.
Create the necessary database structures by API calls. These calls are probably split up into several methods in your test code and each of these methods may fail. It could be seen as your integration test having dependencies on other tests.
How are you making certain that database data needed for tests is there when you need it? And why did you choose the method you choose?
Please provide an answer with a motivation, as it's in the motivation the interesting part lies. Remember that just saying "It's best practice!" isn't a real motivation, it's just re-iterating something you've read or heard from someone. For that case please explain why it's best practice.
*I'm including one method calling other methods in (the same or other) instances of the same class in my definition of unit test, even though it might technically not be correct. Feel free to correct me, but let's keep it as a side issue.
I prefer creating the test data using API calls.
In the beginning of the test, you create an empty database (in-memory or the same that is used in production), run the install script to initialize it, and then create whatever test data used by the database. Creation of the test data may be organized for example with the Object Mother pattern, so that the same data can be reused in many tests, possibly with minor variations.
You want to have the database in a known state before every test, in order to have reproducable tests without side effects. So when a test ends, you should drop the test database or roll back the transaction, so that the next test could recreate the test data always the same way, regardless of whether the previous tests passed or failed.
The reason why I would avoid importing database dumps (or similar), is that it will couple the test data with the database schema. When the database schema changes, you would also need to change or recreate the test data, which may require manual work.
If the test data is specified in code, you will have the power of your IDE's refactoring tools at your hand. When you make a change which affects the database schema, it will probably also affect the API calls, so you will anyways need to refactor the code using the API. With nearly the same effort you can also refactor the creation of the test data - especially if the refactoring can be automated (renames, introducing parameters etc.). But if the tests rely on a database dump, you would need to manually refactor the database dump in addition to refactoring the code which uses the API.
Another thing related to integration testing the database, is testing that upgrading from a previous database schema works right. For that you might want to read the book Refactoring Databases: Evolutionary Database Design or this article: http://martinfowler.com/articles/evodb.html
In integration tests, you need to test with real database, as you have to verify that your application can actually talk to the database. Isolating the database as dependency means that you are postponing the real test of whether your database was deployed properly, your schema is as expected and your app is configured with the right connection string. You don't want to find any problems with these when you deploy to production.
You also want to test with both precreated data sets and empty data set. You need to test both path where your app starts with an empty database with only your default initial data and starts creating and populating the data and also with a well-defined data sets that target specific conditions you want to test, like stress, performance and so on.
Also, make sur that you have the database in a well-known state before each state. You don't want to have dependencies between your integration tests.
Why are these two approaches defined as being exclusively?
I can't see any viable argument for
not using pre-existing data sets, especially particular data that has
caused problems in the past.
I can't
see any viable argument for not
programmatically extending that data with
all the possible conditions that
you can imagine causing problems and even a
bit of random data for integration
testing.
In modern agile approaches, Unit tests are where it really matters that the same tests are run each time. This is because unit tests are aimed not at finding bugs but at preserving the functionality of the app as it is developed, allowing the developer to refactor as needed.
Integration tests, on the other hand, are designed to find the bugs you did not expect. Running with some different data each time can even be good, in my opinion. You just have to make sure your test preserves the failing data if you get a failure. Remember, in formal integration testing, the application itself will be frozen except for bug fixes so your tests can be change to test for the maximum possible number and kinds of bugs. In integration, you can and should throw the kitchen sink at the app.
As others have noted, of course, all this naturally depends on the kind of application that you are developing and the kind of organization you are in, etc.
It sounds like your question is actually two questions. Should you exclude the database from your testing? When you do a database, then how should you generate the data in the database?
When possible I prefer to use an actual database. Frequently the queries (written in SQL, HQL, etc.) in CRUD classes can return surprising results when confronted with an actual database. It's better to flush these issues out early on. Often developers will write very thin unit tests for CRUD; testing only the most benign cases. Using an actual database for your testing can test all kinds corner cases you may not have even been aware of.
That being said there can be other issues. After each test you want to return your database to a known state. It my current job we nuke the database by executing all the DROP statements and then completely recreating all the tables from scratch. This is extremely slow on Oracle, but can be very fast if you use an in memory database like HSQLDB. When we need to flush out Oracle specific issues we just change the database URL and driver properties and then run against Oracle. If you don't have this kind of database portability then Oracle also has some kind of database snapshot feature which can be used specifically for this purpose; rolling back the entire database to some previous state. I'm sure what other databases have.
Depending on what kind of data will be in your database the API or the load approach may work better or worse. When you have highly structured data with many relations, APIs will make your life easier my making the relations between your data explicit. It will be harder for you to make a mistake when creating your test data set. As mentioned by other posters refactoring tools can take care of some of the changes to structure of your data automatically. Often I find it useful to think of API generated test data as composing a scenario; when a user/system has done steps X, Y Z and then tests will go from there. These states can be achieved because you can write a program that calls the same API your user would use.
Loading data becomes much more important when you need large volumes of data, you have few relations between within your data or there is consistency in the data that can not be expressed using APIs or standard relational mechanisms. At one job that at worked at my team was writing the reporting application for a large network packet inspection system. The volume of data was quite large for the time. In order to trigger a useful subset of test cases we really needed test data generated by the sniffers. This way correlations between the information about one protocol would correlate with information about another protocol. It was difficult to capture this in the API.
Most databases have tools to import and export delimited text files of tables. But often you only want subsets of them; making using data dumps more complicated. At my current job we need to take some dumps of actual data which gets generated by Matlab programs and stored in the database. We have tool which can dump a subset of the database data and then compare it with the "ground truth" for testing. It seems our extraction tools are being constantly modified.
I've used DBUnit to take snapshots of records in a database and store them in XML format. Then my unit tests (we called them integration tests when they required a database), can wipe and restore from the XML file at the start of each test.
I'm undecided whether this is worth the effort. One problem is dependencies on other tables. We left static reference tables alone, and built some tools to detect and extract all child tables along with the requested records. I read someone's recommendation to disable all foreign keys in your integration test database. That would make it way easier to prepare the data, but you're no longer checking for any referential integrity problems in your tests.
Another problem is database schema changes. We wrote some tools that would add default values for columns that had been added since the snapshots were taken.
Obviously these tests were way slower than pure unit tests.
When you're trying to test some legacy code where it's very difficult to write unit tests for individual classes, this approach may be worth the effort.
I do both, depending on what I need to test:
I import static test data from SQL scripts or DB dumps. This data is used in object load (deserialization or object mapping) and in SQL query tests (when I want to know whether the code will return the correct result).
Plus, I usually have some backbone data (config, value to name lookup tables, etc). These are also loaded in this step. Note that this loading is a single test (along with creating the DB from scratch).
When I have code which modifies the DB (object -> DB), I usually run it against a living DB (in memory or a test instance somewhere). This is to ensure that the code works; not to create any large amount of rows. After the test, I rollback the transaction (following the rule that tests must not modify the global state).
Of course, there are exceptions to the rule:
I also create large amount of rows in performance tests.
Sometimes, I have to commit the result of a unit test (otherwise, the test would grow too big).
I generally use SQL scripts to fill the data in the scenario you discuss.
It's straight-forward and very easily repeatable.
This will probably not answer all your questions, if any, but I made the decision in one project to do unit testing against the DB. I felt in my case that the DB structure needed testing too, i.e. did my DB design deliver what is needed for the application. Later in the project when I feel the DB structure is stable, I will probably move away from this.
To generate data I decided to create an external application that filled the DB with "random" data, I created a person-name and company-name generators etc.
The reason for doing this in an external program was:
1. I could rerun the tests on by test modified data, i.e. making sure my tests were able to run several times and the data modification made by the tests were valid modifications.
2. I could if needed, clean the DB and get a fresh start.
I agree that there are points of failure in this approach, but in my case since e.g. person generation was part of the business logic generating data for tests was actually testing that part too.
Our team confront the same question recently.
Before, we were using specflow to do integration testing. With specflow, QA can write each test case inside which populating necessary test data to DB.
Now, QA want to use postman to test API, how can they populate the data? One solution is creating Apis for populating them. Another is sync historical data from Prod to test env.
Will update my answer once we try different solutions and decide which one to go.
When unit testing, is it a must to use a database when testing CRUD operations?
Can sql lite help with this? Do you have to cre-create the db somehow in memory?
I am using mbunit.
No. Integrating an actual DB would be integration testing. Not unit testing.
Yes you could use any in-memory DB like SQLite or MS SQL Compact for this if you can't abstract (mock) your DAL/DAO in any other way.
With this in mind I have to point out, that unit testing is possible all the way to DAL, but not DAL itself. DAL will have to be tested with some sort of an actual DB in integration testing.
As with all complicated question, the answer is: It depends :)
In general you should hide your data access layer behind an interface so that you can test the rest of the application without using a database, but what if you would like to test the data access implementation itself?
In some cases, some people consider this redundant since they mostly use declarative data access technologies such as ORMs.
In other cases, the data access component itself may contain some logic that you may want to test. That can be an entirely relevant thing to do, but you will need the database to do that.
Some people consider this to be Integration Tests instead of Unit Tests, but in my book, it doesn't matter too much what you call it - the most important thing is that you get value out of your fully automated tests, and you can definitely use a unit testing framework to drive those tests.
A while back I wrote about how to do this on SQL Server. The most important thing to keep in mind is to avoid the temptation to create a General Fixture with some 'representative data' and attempt to reuse this across all tests. Instead, you should fill in data as part of each test and clean it up after.
When unit testing, is it a must to use a database when testing CRUD operations?
Assuming for a moment that you have extracted interfaces round said CRUD operations and have tested everything that uses said interface via mocks or stubs. You are now left with a chunk of code that is a save method containing a bit of code to bind objects and some SQL.
If so then I would declare that a "Unit" and say you do need a database, and ideally one that is at least a good representation of your database, lest you be caught out with vender specific SQL.
I'd also make light use of mocks in order to force error conditions, but I would not test the save method itself with just mocks. So while technically this may be an integration test I'd still do it as part of my unit tests.
Edit: Missed 2/3s of your question. Sorry.
Can sql lite help with this?
I have in the past used in memory databases and have been bitten as either the database I used and the live system did something different or they took quite some time to start up. I would recommend that every developer have a developer local database anyway.
Do you have to cre-create the db somehow in memory?
In the database yes. I use DbUnit to splatter data and manually keep the schema up to date with SQL scripts but you could use just SQL scripts. Having a developer local database does add some additional maintenance as you have both the schema and the datasets to keep up to data but personally I find is worth while as you can be sure that database layer is working as expected.
As others already pointed out, what you are trying to achieve isn't unit testing but integration testing.
Having that said, and even if I prefer unit testing in isolation with mocks, there is nothing really wrong with integration testing. So if you think it makes sense in your context, just include integration testing in your testing strategy.
Now, regarding your question, I'd check out DbUnit.NET. I don't know the .NET version of this tool but I can tell you that the Java version is great for tests interacting with a database. In a few words, DbUnit allows you to put the database in a known state before a test is run and to perform assert on the content of tables. Really handy. BTW, I'd recommend reading the Best Practices page, even if you decide to not use this tool.
Really, if you are writing a test that connects to a database, you are doing integration testing, not unit testing.
For unit testing such operations, consider using some typed of mock-database object. For instance, if you have a class that encapsulates your database interaction, extract an interface from it and then create an inheriting class that uses simple in-memory objects instead of actually connecting to the database.
As mentioned above, the key here is to have your test database in a known state before the tests are run. In one real-world example, I have a couple of SQL scripts that are run prior to the tests that recreate a known set of test data. From this, I can test CRUD operations and verify that the new row(s) are inserted/updated/deleted.
I wrote a utility called DBSnapshot to help integration test sqlserver databases.
If your database schema is changing frequently it will be helpful to actually test your code against a real db instance. People use SqlLite for speedy tests (because the database runs in memory), but this isn't helpful when you want to verify that your code works against an actual build of your database.
When testing your database you want to follow a pattern similar to: backup the database, setup the database for a test, exercise the code, verify results, restore database to the starting state.
The above will ensure that you can run each test in isolation. My DBSnapshot utility will simplify your code if your writing it .net. I think its easier to use than DbUnit.NET.
How are people unit testing their business applications? I've seen a lot of examples of unit testing with "simple to test" examples. Ex. a calculator. How are people unit testing data-heavy applications? How are you putting together your sample data? In many cases, data for one test may not work at all for another test which makes it hard to just have one test database?
Testing the data access portion of the code is fairly straightforward. It's testing out all the methods that work against the data that seem to be hard to test. For example, imagine a posting process where there is heavy data access to determine what is posted, numbers are adjusted, etc. There are a number of interim steps that occur (and need to be tested) along with tests afterwards that ensure the posting was successful. Some of those steps may actually be stored procedures.
In the past I've tried inserting the test data in a test database, then running the test, but honestly it's pretty painful to write this kind of code (and error prone). I've also tried just building a test database up front and rolling back the changes. That works OK but in a number of places you can't easily do this either (and many people would say that's integration testing; so be it, I still need to be able to test this somehow).
If the answer is that there isn't a nice way of handling this and it currently just sort of sucks, that would be useful to know as well.
Any thoughts, ideas, suggestions, or tips are appreciated.
My automated functional tests usually follow one of two patters:
Database Connected Tests
Mock Persistence Layer Tests
Database Connected Tests
When I have automated tests that are connected to the database, I usually make a single test database template that has enough data for all the tests. When the automated tests are run, a new test database is generated from the template for every test. The test database has to be constantly re-generated because test will often change the data. As tests are added, I usually append more data to the test database template.
There are some nice advantages to this testing method. The obvious advantage is that the tests also exercise your schema. Another advantage is that after setting up the initial tests, most new tests will be able to re-use the existing test data. This makes it easy to add more tests.
The downside is that the test database will become unwieldy. Because data will usually be added one test at time, it will be inconsistent and maybe even unrealistic. You will also end up cursing the person who setup the test database when there is a significant database schema change (which for me usually means I end up cursing myself).
This style of testing obviously doesn't work if you can't generate new test databases at will.
Mock Persistence Layer Tests
For this pattern, you create mock objects that live with the test cases. These mock objects intercept the calls to the database so that you can programmatically provide the appropriate results. Basically, when the code you're testing calls the findCustomerByName() method, your mock object is called instead of the persistence layer.
The nice thing about using mock object tests is that you can get very specific. Often times, there are execution paths that you simply can't reach in automated tests w/o mock objects. They also free you from maintaining a large, monolithic set of test data.
Another benefit is the lack of external dependencies. Because the mock objects simulate the persistence layer, your tests are no longer dependent on the database. This is often the deciding factor when choosing which pattern to choose. Mock objects seem to get more traction when dealing with legacy database systems or databases with stringent licensing terms.
The downside of mock objects is that they often result in a lot of extra test code. This isn't horrible because almost any amount of testing code is cheap when amortized over the number of times you run the test, but it can be annoying to have more test code then production code.
I have to second the comment by #Phil Bennett as I try to approach these integration tests with a rollback solution.
I have a very detailed post about integration testing your data access layer here
I show not only the sample data access class, base class, and sample DB transaction fixture class, but a full CRUD integration test w/ sample data shown. With this approach you don't need multiple test databases as you can control the data going in with each test and after the test is complete the transactions are all rolledback so your DB is clean.
About unit testing business logic inside your app, I would also second the comments by #Phil and #Mark because if you mock out all the dependencies your business object has, it becomes very simple to test your application logic one entity at a time ;)
Edit: So are you looking for one huge integration test that will verify everything from logic pre-data base / stored procedure run w/ logic and finally a verification on the way back? If so you could break this out into 2 steps:
1 - Unit test the logic that happens before the data is pushed
into your data access code. For
example, if you have some code that
calculates some numbers based on
some properties -- write a test that
only checks to see if the logic for
this 1 function does what you asked
it to do. Mock out any dependancy
on the data access class so you can
ignore it for this test of the
application logic alone.
2 - Integration test the logic that happens once you take your
manipulated data (from the previous
method we unit tested) and call the
appropriate stored procedure. Do
this inside a data specific testing
class so you can rollback after it's
completed. After your stored
procedure has run, do a query
against the database to get your
object now that we have done some
logic against the data and verify it
has the values you expected
(post-stored procedure logic /etc )
If you need an entry in your database for the stored procedure to run, simply insert that data before you run the sproc that has your logic inside it. For example, if you have a product that you need to test, it might require a supplier and category entry to insert so before you insert your product do a quick and dirty insert for a supplier and category so your product insert works as planned.
It depends on what you're testing. If you're testing a business logic component -- then its immaterial where the data is coming from and you'd probably use a mock or a hand rolled stub class that simulates the data access routine the component would have called in the wild. The only time I mess with the data access is when I'm actually testing the data access components themselves.
Even then I tend to open a DB transaction in the TestFixtureSetUp method (obviously this depends on what unit testing framework you might be using) and rollback the transaction at the end of the test suite TestFixtureTeardown.
Mocking Frameworks enable you to test your business objects.
Data Driven tests often end up becoming more of a intergration test than a unit test, they also carry with them the burden of managing the state of a data store pre and post execution of the test and the time taken in connecting and executing queries.
In general i would avoid doing unit tests that touch the database from your business objects. As for Testing your database you need a different stratergy.
That being said you can never totally get away from data driven testing only limiting the amout of tests that actually need to invoke your back end systems.
It sounds like you might be testing message based systems, or systems with highly parameterised interfaces, where there are large numbers of permutations of input data.
In general all the rules of standard unti testing still hold:
Try to make the units being tested as small and discrete as possible.
Try to make tests independant.
Factor code to decouple dependencies.
Use mocks and stubs to replace dependencies (like dataaccess)
Once this is done you will have removed a lot of the complexity from the tests, hopefully revealing good sets of unit tests, and simplifying the sample data.
A good methodology for then compiling sample data for test that still require complex input data is Orthogonal testing, or see here.
I've used that sort of method for generating test plans for WCF and BizTalk solutions where the permutations of input messages can create multiple possible execution paths.
For lots of different runs over the same logic but with different data you can use CSV, as many columns as you like for the input and the last for the output etc.