Running Django Tests Without Migrations


Django is a strong framework for building reliable websites. However, it is showing signs of age. Particularly, it is failing where tools such as Spring Boot excel.

This article examines how to overcome yet another issue encountered in Django, running tests without migrating databases.

Use Case

There are many times when a database migration is inappropriate for testing:

  • we have separated test, development, and production databases with custom features requiring testing
  • there are fixtures pre-loaded into a database
  • we have legwork to complete through scripts alongside database setup
  • dropping and re-creating a database eliminates data that is absolutely necessary to another function in our test environment
  • nothing we do can be included in a TestRunner, at least not reliably

Sadly, Django developers, among other issues they refuse to fix, were not willing to create a workaround until recently. The workaround does not work in the latest version.

Django Unit Test Framework

The Django unit test framework comes with many features typically reserved for tools such as or selenium.

For instance, Django provides:

from django.test import TestCase
from django.test import Client
from django.test import RequestFactory

A RequestFactory is used to perform specific tests while the Client acts as a browser would.

While there is an option to use pytest, the changing nature of Django manages to continually break this tool. Importing models from different applications using multiple databases is a broken process at the time of publication.

Unittest Format

Django provides an extensive tutorial for setting up unit tests. The setup is straightforward:

from django.test import TestCase
from django.test import Client
from django.test import RequestFactory

class AdminTestCases(TestCase):
    multi_db = True

    def setUp(self):
        self.client = Client()
        self.rf = RequestFactory()

    def test_create_superuser(self):

    def test_remove_superuser(self):

    def test_fail_create_superuser(self):

    def test_fail_remove_superuser(self):

    def tearDown(self):

This skeleton preparesĀ  a client and request factory, generates tests, and provides a tear down function. Setup and tear down appear to run before and after each test.

Multiple Databases

For security and other purposes, it is often necessary to separate databases. This offers a degree of protection for sensitive information.

Unfortunately, this also presents an issue with the tests in which a single database is used. To avoid this, each test case must include the following:

multi_db = True

This line informs the the test runner to search for different databases.

Ignoring Migrated Databases

Now, for the meat and potatoes. We want to ignore database migration altogether at times. It is still wise to have a script and drop any unused data.

Django allows for the generation of custom TestRunner classes. As of Django > 2.0. the following runner avoids migrating a database:

from django.test.runner import DiscoverRunner

class NoMigrationTestRunner(DiscoverRunner):
  """ A test runner to test without database creation """

  def setup_databases(self, **kwargs):
    """ Override the database creation defined in parent class """

  def teardown_databases(self, old_config, **kwargs):
    """ Override the database teardown defined in parent class """

TheĀ NoMigrationTestRunner extends the DiscoverRunner and directly overwrites the setup_databases and teardown_databases methods. These methods are used to setup certain connections, create database tables, and perform cleanup.

In this example, the setup and teardown methods are left blank to avoid creating new tables as well as to allow for the smooth usage of multiple databases.

Django is informed of the new test runner through a variable in the relevant settings configuration:

TEST_RUNNER = 'path_to.NoMigrationTestRunner'


Tests are executed using

python test
python<version> --settings=<settings_file>

The first example is generic and collects tests found through our DiscoverRunner. The second command runs tests using a specific python version and a path to the relevant module which is imported using this string. A settings file was also provided in the second command.


Django has extensive documentation. However, as the framework changes, certain aspects will break. This article covered how to setup tests for multiple databases, not migrate databases in test, and how to execute our tests.

Headless Testing and Scraping with Java FX

There is a lot of JavaScript in the world today and there is a need to get things moving quickly. Whether testing multiple websites or acquiring data for ETL and/or analysis, a tool needs to exist that does not leak memory as much as Selenium. Until recently, Selenium was really the only option for webkit, JCEF and writing native bindings for Chromium have been options for a while. Java 7 and Java 8 have stepped into the void with the JavaFX tools. These tools can be used to automate scraping and testing where network calls for HTML, Json, CSVs, pdfs, or what not are more tedious and difficult.

The FX Package

FX is much better than the television channel with some exceptions. Java created a sleeker version of Chromium based on webkit. While webkit suffers from some serious setbacks, Java FX also incorporates nearly any part of the framework. Setting SSL Handlers, proxies, and the like works the same as with Therefore, FX can be used to intercept traffic (e.g. directly stream images that are incoming to a file named by URL without making more network calls), present a nifty front end controlled by JavaScript and querying for components,


Ui4j is as equally nifty as the FX package. While FX is not capable of going headless without a lot of work, Ui4j takes the work out of such a project using Monocle or Xvfb. Unfortunately, there are some issues getting Monocle to run by setting -Dui4j.headless=true on command line or using system properties after jdk1.8.0_20. Oracle removed Monocle from the jdk after this release and forced the programs using the server to OpenMonocle. However, xvfb-run -a works equally well. The -a option automatically chooses a server number. The github site does claim compatibility with Monocle though.

On top of headless mode, the authors have made working with FX simple. Run JavaScript as needed, incorporate interceptors with ease, run javascript, and avoid nasty waitFor calls and Selanese (this is an entire language within your existing language).


There is an alternative to Ui4j in TestFX. It is geared towards testing. Rather than using an Assert after calling or with ((String) page.executeScript(“document.documentElement.innerHTML”)), methods such as verifyThat exist. Combine with Scala and have a wonderfully compact day. The authors have also managed to get a workaround for the Monocle problem.

Multiple Proxies

The only negative side effect of FX is that multiple instances must be run to use multiple proxies. Java and Scala for that matter set one proxy per JVM. Luckily, both Java and Scala have subprocess modules. The lovely data friendly language that is Scala makes this task as simple as Process(“java -jar myjar.jar -p my:proxy”).!. Simply run the command which returns the exit status and blocks until complete (see Futures to make this a better version of non-blocking) and use tools like Scopt to get the proxy and set it in a new Browser session. Better yet, take a look at my Scala macros article for some tips on loading code from a file (please don’t pass it as command line). RMI would probably be a bit better for large code but it may be possible to better secure a file than compiled code using checksums.


Throw out Selenium, get rid of the extra Selanese parsing and get Ui4J or TestFX for webkit testing. Sadly, it does not work with Gecko so Chromium is needed to replace these tests and obtain such terrific options as –ignore-certificate-errors. There are cases where fonts in the SSL will wreak havoc before you can even handle the incoming text no matter how low level you write your connections. For simple page pulls, stick to Apache HTTP Components which contains a fairly fast, somewhat mid-tier RAM usage asynchronous thread pool useable in Java or Scala. Sorry for the brevity folks but I tried to answer a question or two that was not in tutorials or documentation. Busy!

Could Watson Be Used to Dynamically Create Databases

Data collection,cleaning, and presentation are a pain, especially when dealing with a multitude of sources. When APIs aren’t available and every step is taken to keep people from getting data, it can be incredibly tedious just to get the data. Parsing in this instance, of course, can be made easier by relating terms in a dictionary and using the documents structure to your advantage. At worst it is just a few lines of regex or several expath expressions and more cleaning with Pentaho. I’ve gone a bit further by enforcing key constraints and naming conventions with the help of Java Spring.

It seems that IBM is making this process a little less time consuming with Watson. Watson appears to have the capacity to find patterns and relations with minimal effort from a CSV or other structured file.

This could really benefit database design by applying a computer to the finding and illumination of the patterns driving key creation and normalization. After all, I would love to be able to focus less on key creation in a maximum volume industry and more on pumping scripts into my automated controllers. The less work and more productivity pre person, the more profit.

Morning Joe: Normalization with Climate Data Streams


So I want to gather climate data and make some predictions on my own using a variety of factors and an Ardunio nano through assembler. That requires storing data I collect and ensuring that it can stream and is accountable. Which normalization level do I use?

  1. Only reduces horizontal redundancy so no.
  2. Only reduces vertical redundancy so no.
  3. Closer. Everything relates to the key. BNCF is even closer since the key explains everything with all candidate keys separated.
  4. Splits out multiple redundancies and further reduces data. So weather data can be separated by sensor or snow-water equivalent by area and layer.
  5. Accounts for more business-like rules. Is this overdoing it? It is semantic. Do I know enough to use it?
  6. Takes over all of the set of related values with a join. It is good for temporal data.


My data is meant to persist once it is inserted. It must be separated for easy mathematical calculations. Finally, it deals with nature, so relationships should probably not be rule defined. I n particular, it deals with a side of nature that no one really knows much about. I want to preserve all possible relationships. Therefore, 5 NF is a bit much.

I do need to relate things to keys so I can grab by specific area, day, weather, type of phenomena; whatever else I need. I also need to separate attributes into easy to grab attributes with an appropriate impact. The goal is prediction and calculation.

I am going to use 4 NF. Look back for more on this project.