Apr 19, 2018

Unit testing and Python

0 comments

You might have read a post from me once-or-twice where I've advocated using Python for quickly prototyping solutions, exploring data etc... But regardless of whether you're using it for building a quick prototype or create a larger application it is, as with all languages, best practice to write some unit tests. Like most languages there are a wide range of libraries for performing unit testing in Python but a great starting point (and maybe even a finishing point) is the unittest framework which comes as part of the standard library, providing unit testing and mocking capabilities. Of course once you then have your unit tests you can run them not only on your local development environment but also in your continuous integration environment. If you're a user of Visual Studio Code then there's pretty good integration thanks to the Python Extension provided by Microsoft. I'm a big fan of VSCode so that's what I'm using. There's a few conventions I tend to follow which make auto-discovery of tests a bit easier if you don't want to mess around with additional configuration settings. First name all test files as "*_test.py", you can use others but I tend to find this easier to follow and makes things clearer when there's a lot of files, the other part to this is using the name of the module as the prefix (e.g. a module called "demo.py" would have a test file of "demo_test.py"). The next is prefixing all test methods with "test_", this helps with auto_discovery but also helps me think about naming the method, such as "test_all_values_are_true". In VSCode you can can run all of your unit tests, simply open the command palette (SHIFT+CTRL+P on windows) and type in "Python run tests" to find the option.

The first time you do this you'll be walked through configuring your environment for tests, such as configuring the root folder, your test file naming convention etc... From there you can then run all (or some) of your tests.

 

For a quick walk-through I prepared a sample solution which looks as follows.

from math import ceil, sqrt
from typing import Union, List

def __check_is_prime(n:int):
  if n < 2:
    return False
   elif n == 2:
     return True

  if n % 2 == 0:
    return False
 
  for i in range(3, ceil(sqrt(n))+1, 2):
    if n % i == 0:
      return False
 
  return True

def is_prime(n:Union[int, List[int]]):
  if type(n) is list:
    return [__check_is_prime(x) for x in n if type(x) is int]
  elif type(n) is int:
    return __check_is_prime(n)
  else:
    raise TypeError

There's some more that can be done to make this more python-esque but it serves the purpose. You can find the full code over on Github.

 

From this you can then run your tests as described above to run them all, or use the code-sense links to run individual tests or suites of tests.

Once up and running you can quickly see if you have any failed tests and re-run just the tests which have failed.

 

As with other features in VSCode if you get an error in the output then you can click the file location link to be taken straight to the line of code which failed, useful for larger projects.

 

If you're going to write tools and utilities in Python then backing them up with tests is always a great idea. Understanding what to test in Python as well is crucial as it's a duck-typed language, just because you intended for an integer to be passed to a method doesn't mean it actually will, even with type hints.

 

 

 

New Posts
  • Something that comes up quite frequently when people start using Spark is "How can I filter my DataFrame using the contents of another DataFrame?". People with SQL experience will immediately look to trying to replicate the following. SELECT * FROM table_a a WHERE EXISTS (SELECT * FROM table_b b WHERE b.Id = a.Id) So how do you do this in Spark? Well, some people will try to use the Column.isin method which uses varargs, this is okay for a small set of values but if you have a couple of large DataFrames then it's less than optimal as each row needs to be evaluated against the list. So what's the other choice? We can use joins to do the same thing. There are 2 we can use, a SEMI JOIN which is equivalent to our above example of running EXISTS; the other is ANTI JOIN which is equivalent to a NOT EXISTS. Using the above example and keeping the table names as DataFrame names we could re-write this in Scala as: table_a.join(table_b, Seq("Id"), "left_semi") These 2 joins are unique in that they only return the output of the left DataFrame, without any content from the right DataFrame. So what does this look like in practice. Well using Azure Databricks we can quickly create some sample data to try them out. First lets create a couple of DataFrames. First lets runs a simple query to find heroes which have an arch-enemy. This uses the SEMI JOIN to keep records in the left DataFrame where there is a matching record in the right DataFrame. Now, lets have look for heroes who've been a little more active and have removed their arch-enemies (for now). This time we've used an ANTI JOIN to keep only those records in the left DataFrame where there are no matching records in the right DataFrame. You'll notice that in the examples the join condition uses the slightly longer form, that's because in this example the columns we're joining on have different names, and also because there is a column in both DataFrames which have the same name.
  • Recently I needed to deploy an Azure Data Lake Store - Gen 2 instance and thought I'd take the opportunity to use some custom ARM template functions . These aren't something you often see in the example templates but can be really useful if there's a complex expression which you find yourself writing repeatedly within a template. If, for instance, you routinely create resource names based on a prefix, unique name and a suffix then this could save you a few keystrokes. In essence you are simply parameterizing the expression as follows: In this way you can use this simpler expression where you would have previously used the more complex version. [namespace.function(parameter1, parameter2)] If you want to see what this looks like in a full template then checkout this simple ARM template I put together for creating a Data Lake Store - Gen 2 instance over on GitHub.
  • Documentation is not something people often spend time reading, or if they do then its to quickly find the one thing their after and then get out as quickly as possible, very similar to how I do my Christmas shopping. Sometimes it's worth spending time reading the documentation though as there can be some useful bits of information hidden in summary descriptions, links etc... One such item is the Azure Data Lake Store client. If you find yourself reading or writing a lot of files and your doing it in multiple tasks (or threads, but you should be using Tasks if possible), then reading the docs can really help you out. For instance this snippet taken from the description at the top of the documentation page . If an application wants to perform multi-threaded operations using this SDK it is highly recomended to set ServicePointManager.DefaultConnectionLimit to the number of threads application wants the sdk to use before creating any instance of AdlsClient. By default ServicePointManager.DefaultConnectionLimit is set to 2. Okay, so how bad can things be if you don't read this? Well, to answer that I created an ADLS instance and uploaded a number of small parquet files. Then wrote an application to read each file (using the excellent Parquet .NET ) and return the number of records in the file, each file is processed in it's own Task and each uses the same AdlsClient instance. The simple process being followed here is to get a list of files, call " ProcessPath " on each and then when all the files have been process output the results. The output of this initial version is as follows: It's not too bad, but with multiple tasks I would have expected it to be better. Looking at the documentation snippet above it suggests we need to change the ServicePointManager.DefaultConnectionLimit value, but what to? Well doing some digging around came across a suggestion from Microsoft Support which, for ASP.NET, is to limit the number of requests that can execute at the same time to 12 per CPU (or 12 per core). So let's give that a go and see what happens. The code change for this is pretty simple and we can use System.Environment to get the number of processors available. So does it make much of a difference? Well, yes, quite a lot of difference actually. I ran the code in both variations a few more times to check it wasn't intermittent networking issues, other processes on my laptop interfering etc... but no, it really does make that much of a difference. So next time you're working with multiple tasks sharing resources, maybe spend a bit of time reading the documentation to see if there's anything which can make a difference to your application.