Archive

Tag Archives: Django


tl;dr

When writing tests, mock out a subsystem if and only if it’s prohibitive to test against the real thing.

!tl;dr

Our product uses Redis. It’s an awesome technology.

We’ve avoided needing Redis in our unit tests. But when I added a product feature that made deep use of Redis, I wrote its unit tests to use it, and changed our development fabfile to instantiate a test Redis server when running the unit tests locally.

(A QA purest might argue that unit tests should never touch major system components outside of the unit under test. I prefer to do as much testing as possible in unit tests, provided they don’t take too long to run, and setup and teardown aren’t too much of a PITA.)

This was a contributory reason for our builds now failing on our Hudson CI server. Redis wasn’t installed on it!

Why didn’t I immediately install Redis on our CI server?

  1. Our CI server had other problems
  2. I intended to nuke it and re-create it with the latest version of Jenkins. I just needed to first clear some things off my plate
  3. Our dev team had shrunk down to just two people
  4. We were both strict about running unit tests before checking code into the pool
  5. We were up to our necks in other alligators

From a test-quality perspective, if code uses X in production, it’s better for tests to run with X than with a simulation of X.

One of the many joys of working with Ryan is that he challenges my assumptions and makes me consider alternatives. Because of a perceived lack of elegance in needing Redis on our CI server, and because his work had been temporarily blocked by my code changes, he challenged me to replace my unit tests’ use of Redis with a mock.

I walked into work yesterday and it was quiet. All our critical bugs blocking Saturday’s release were closed. I thought, why not? I’ll give it a go. Today’s a good day to see what’s involved with replacing Redis with a mock!

Read More


After some job market feedback and chin-scratching, I’ve changed our Senior Developer opening’s job description. Now it’s less about Python or Django, and more about search technologies, specifically full-text and LSI search.

We hope candidates will have some experience with Python or Django, but search technology experience (e.g., tuning, tokenizers, parsers, relevancy rank tweaking, aggregates and pivots) in now more important, and emphasized, in the the job.

Here’s the new description:

———

Founded in 2009, IP Street develops and markets software to help corporations, law firms, financial research firms, and government agencies better analyze patent information.  Our goal is to make IP data easy to get, use, and understand, so everyone can have access to high quality and transparent information.

A significant facet of our application’s capabilities are derived from Solr and other search technologies. We’re seeking a great full-text Search developer with experience in:

  • Solr, Lucene, or other search engines
  • Full-text search schemas, tokenizers, parsers, and rules for returning statistics and meaningful analytics
  • Automated workflows that process millions of objects
  • Data quality metrics and repairs

You’ll be joining us at a great time! Revenue is coming in, and we’ve done two Angel funding rounds at increasing valuations.

Key Responsibilities.

  • Enhance our Solr engine to provide more statistics and meaningful analytics to the product
  • Enhance or tune our use of other search technologies, e.g., LSI
  • Enhance and extend the existing code base to add new product features. Our application is written in Django and Python, with an almost all open-source technology stack
  • Occasionally wear testing or devops “hats,” as the needs arise
  • Write unit tests for your code, and do performance analysis
  • Demonstrate technical leadership within the team
  • Communicate well with the team, in writing and orally

Qualifications.

  • Significant experience using and tuning Solr, Lucene, or other search engines with similar capabilities
  • 3+ years related experience in Python development
  • 1+ years experience in Django development, or a strong interest in learning
  • Experience using one or more of: MongoDB, CouchDB, or another NoSQL database; Celery; Redis; PostgreSQL or another SQL database
  • Experience using latent semantic indexing search technologies would be a plus
  • Experience integrating with open-source 3rd-party libraries
  • Experience creating customer-focused software to process data and generate statistics and analytics
  • Solid troubleshooting abilities, self-directed, and proactive
  • Enjoy all aspects of software product creation — design, implementation, and debugging
  • Familiarity with using OS X as a development environment, and Linux as a production environment
  • Bachelors Degree or equivalent in Computer Science or Software Engineering
  • Excellent communication skills

Salary is DOE.

Please send resume to johnd@ipstreet.com.


My Senior Developer job description had an embarrassing mistake. It asked for 7+ years experience in Python and Django, which, as a commenter noted, limited the candidate pool to about three people on the entire planet.

I’ve fixed my goof. We’re nominally looking for at least seven years of Python experience, and at least three years of Django experience, for this slot.

 


We’re looking to hire two lucky people who desire fame and fortune. Here’s the Senior Developer opening:

Founded in 2009, IP Street develops and markets software to help corporations, law firms, financial research firms, and government agencies better analyze patent-related information.  Our goal is to make IP data easy to get, use, and understand, so everyone can have access to high quality and transparent information.

We’re seeking a great Python developer with experience in: Automated workflows that process millions of objects; data quality metrics and repairs; search, particularly with Solr or Lucene; and/or general data mining. Our stack, and development & production environments, are almost all open-source. The key technologies are Python, Django, Celery, Solr, and PostgreSQL.

Read More


Here’s another cautionary performance tale, wherein I thought I was clever but was not.

A table (“Vital”) holds widget information. Another table (“Furball”) holds other information, with an M:M relationship to Vital.

We want to do inferential computations on filtered Furball rows. So we generate a pk list from a Vital QuerySet, and call this function:

def _get_top(vitals):
    from django.db.models import Count

    TOP_NUMBER = 5

    vitalids = [x.id for x in vitals]
    top_balls = Furball.objects.filter(vital__id__in=vitalids)\
                            .annotate(count=Count('id'))\
                            .order_by('-count')[:TOP_NUMBER]
    top_list = [(x.name, x.count)for x in top_balls]

    return top_list

Read More


Django had function-based generic views through version 1.25. They were replaced in version 1.3 by class-based generic views.

Some caveats: I’m not the sharpest knife in the drawer. I’m not FSM’s gift to web development. I have a lot of experience with the function-based generic views, and little experience with the class-based ones. (Because they’re new. Duh.)

From yesterday’s experience, the new generic views use a very powerful but excessively complicated abstraction. I can only symptomatically describe the problem and I don’t have a good answer, but this is my blog so I’m going to bitch about it. If you don’t agree then move along, these aren’t the droids you’re looking for.

Read More


John Anderson has documented some nice Python interpreter tricks on his blog. Including a .pythonrc.py file hack for Djangonauts:

For Django developers when you load up the ./manage.py shell it is nice to have access to all your models and settings for testing:

# If we're working with a Django project, set up the environment
if 'DJANGO_SETTINGS_MODULE' in os.environ:
    from django.db.models.loading import get_models
    from django.test.client import Client
    from django.test.utils import setup_test_environment, teardown_test_environment
    from django.conf import settings as S

    class DjangoModels(object):
        """Loop through all the models in INSTALLED_APPS and import them."""
        def __init__(self):
            for m in get_models():
                setattr(self, m.__name__, m)

    A = DjangoModels()
    C = Client()

See his post for more interesting Python tips. Me, I’m enabling autocomplete and automatic pretty-printing right now.