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Continuous Delivery with CircleCI, ECR and Kubernetes

As well as being a great drop-in hosting system for a lot of bare-metal and “legacy” cloud workloads, Kubernetes provides some spectacular developer tools and access to automation.  It is often very easy to do things that on other platforms would be difficult or impossible.

Continuous Delivery is an example of something that, in practice, can prove difficult to orchestrate. Even with high automation, release processes often have enough moving parts, or sufficient latency, that operating them frequently is prohibitively expensive, difficult or error-prone.

Releases in Kubernetes however are generally so rapid and so well orchestrated that this is not a problem.

This week I put together a simple CD pipeline using CircleCI which is a good example of how simple this can be.

There are three phases to a software update based on docker images: build, push and update.

Build and push

CircleCI makes orchestrating this in a Continuous Integration system really easy. We’re storing our images in AWS Elastic Container Registry (ECR), which adds a little bit of complexity, but even then it’s pretty easy. Here’s the relevant part of the CircleCI configuration:

    - image: circleci/python:3.6.1
    working_directory: ~/repo
    - checkout
    - setup_remote_docker:
        docker_layer_caching: true
    - run:
        name: Push to ECR
          command: |
            python3 -m venv venv
            . venv/bin/activate
            pip install awscli
            docker build -t local:$TAG .
            eval `aws ecr get-login | sed -e's/-e none//'`
            docker tag local:$TAG $AWS_ECR_REGISTRY:$TAG
            docker push $AWS_ECR_REGISTRY:$TAG

This has to jump through a couple of hoops. First, install the awscli, needed to log in to ECR:

python3 -m venv ven
. venv/bin/activate
pip install awscli

This is why we’re basing the build on a python image, so we have pip.

Build the local copy of the actual deployment image:

docker build -t local:$TAG .

Then do the login with some nasty shell hackery, tag the remote image and push it:

eval `aws ecr get-login | sed -e's/-e none//'`
docker tag local:$TAG $AWS_ECR_REGISTRY:$TAG
docker push $AWS_ECR_REGISTRY:$TAG

At this point, we’ve got the image in the remote registry. Authentication with AWS is handled by putting AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables with appropriate credentials in the CircleCI project environment.


Actually deploying into the cluster uses a tool we wrote a while ago, k8ecr. This uses the AWS-SDK, the Kubernetes client-go packages and the docker client to coordinate various common operations on ECR repositories and Kubernetes. In particular it can issue image updates to Kubernetes deployment resources.

It has a mode where you can tell it to update every relevant deployment in a namespace:

> k8ecr deploy stage -

Running this command will cause it to compare (using semver) all the tags in all the ECR repositories in your AWS account with all the containers in all the deployments in the specified namespace (in this case stage), and issue rolling updates for all the containers for which there is a new version. Kubernetes then does whatever is necessary to get the new code running.

So if you have previously pushed a new version of an image, and there is a deployment using an earlier version of that image, then it will get updated.

The only missing part of the orchestration is doing this regularly, and we can do this, naturally, with another kubernetes deployment. Here’s a Dockerfile:

FROM alpine
ENV AWS_REGION eu-west-2
RUN apk add --no-cache ca-certificates
ADD k8ecr /
CMD while true; do /k8ecr deploy $NAMESPACE -; sleep 60; done

and deployment resource:

apiVersion: apps/v1beta1
kind: Deployment
 name: autodeploy-stage
 replicas: 1
       app: autodeploy-stage
     - name: app
       image: isotoma/k8ecr-autodeploy
       - name: NAMESPACE
         value: stage

Every 60 seconds this will perform the checks and trigger any appropriate deployments. Voila, an auto-updating stage namespace. Now developers can do whatever is necessary in CI and magically have their stage updated. CircleCI provides filters so that, for example, only tags get deployed, and we can include the tag versions in the image versions.

If you want to use this, then an image built with that Dockerfile is available on docker hub.

Promotion to production then just requires running k8ecr with the appropriate arguments and we’re done.

The economics of innovation

One of the services we provide is innovation support. We help companies of all sizes when they need help with the concrete parts of developing new digital products or services for their business, or making significant changes to their existing products.

A few weeks ago the Royal Swedish Academy of Sciences awarded the Nobel Prize for Economics to Oliver Hart and Bengt Holmström for their work in contract theory. This prompted me to look at some of his previous work (for my sins I find economics fascinating), and I came across his 1998 paper Agency Costs and Innovation. This is so relevant to some of my recent experiences I wanted to share it.

Imagine you have a firm or a business unit and you have decided that you need to innovate.

This is a pretty common situation – you know strategically that your existing product is starting to lose traction. Maybe you can see commoditisation approaching in your sector. Or perhaps, as is often the case, you can see the Internet juggernaut bearing down on your traditional business and you know you need to change things up to survive.

What do you do about it?  If you’ve been in this situation the following will probably resonate:


This describes the principal-agent problem, which is a classic in economics. This describes how a principal (who wants something) can incentivise an agent to do what they want. The agent and “contracting” being discussed here could be any kind of contracting including full time staff.

A good example of the principal-agent problem is how you pay a surgeon. You want to reward their work, but you can’t observe everything they do. The outcome of surgery depends on team effort, not just an individual. They have other things they need to do other than just surgery – developing standards, mentoring junior staff and so forth. Finally the activity itself is very high risk inherently – which means surgeons will make mistakes, no matter how competent. This means their salary would be at risk, which means you need to pay huge bonuses to encourage them to undertake the work at all.

In fact commonly firms will try and innovate using their existing teams, who are delivering the existing product. These teams understand their market. They know the capabilities and constraints of existing systems. They have domain expertise and would seem to be the ideal place to go.

However, these teams have a whole range of tasks available to them (just as with our surgeon above), and choices in how they allocate their time. This is the “multitasking effect”. This is particularly problematic for innovative tasks.

My personal experience of this is that, when people have choices between R&D type work and “normal work”, they will choose to do the normal work (all the while complaining that their work isn’t interesting enough, of course):


This leads large firms to have separate R&D divisions – this allows R&D investment decisions to take place between options that have some homogeneity of risk, which means incentives are more balanced.

However, large firms have a problem with bureaucratisation. This is a particular problem when you wish to innovate:


Together this leads to a problem we’ve come across a number of times, where large firms have strong market incentives to spend on innovation – but find their own internal incentive systems make this extremely challenging.

If you are experiencing these sorts of problems please do give us a call and see how we can help.

I am indebted to Kevin Bryan’s excellent A Fine Theorem blog for introducing me to Holmström’s work.


Hacking together a standing desk

We’ve had a few people in the office transition to standing desks recently and the process by which everyone achieved their goal has been quite interesting. Or at least interesting to me.

doug's deskDoug was the first to try it and ended up, as a lot of people do, taking the old ‘big pile of books’ approach. It’s cheap, quick and,so long as you’re using a solid and non-tottering pile of books, probably pretty safe. Luckily the Isotoma office has a pretty extensive library of books–which in Doug’s case have mostly been memorised.

(I wouldn’t let him tidy his desk before I took this photo. He wanted me to tell you.)

A different approach that David introduced (as far as I know) and which I’ve adopted is the music stand approach. It’s a wee bit more expensive and depends on you having the kind of job that doesn’t really involve paper or double monitors – but I love it. The bonus of this approach is you get to feel a little bit like a member of an nineties synth-rock band. Always a bonus.

But the canonical Isotoma standing desk was “Ikea-hacked” together by Tom. He went to town on it to such an extent that we made him do an intranet post about it:

I’ve had a couple of enquiries about the parts for my standing desk, so rather than send sporadic emails, I thought I’d just put it here.


I built my standing desk based on this article, which is great, and has assembly instructions and everything (although it’s pretty trivial to put together). The guy bought all the bits from IKEA for $22. But obviously we don’t live in America, and also I needed to double up on some of the parts due to my dual monitor setup, so here is the full parts list, with links to the UK IKEA website:

Also, you will need to find 4 screws to screw the brackets into the table legs because annoyingly, the brackets have holes drilled, but no screws to go in said holes.

Grand total: £29 (including £10 postage)

Oh and don’t forget, you’ll probably want some kind of chair to give your legs a rest every now and then (yes it’s hilarious that Tom still has a chair etc, but it does come in handy, even if it’s just for 2×30 minute spells during the day). I got a bar stool with a back from Barnitts 🙂

This concludes my blog post about standing desks. Please add photos of your sweet, customised desks below in the comments.

Just kidding.

About usIsotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.


What Heartbleed means for you

On the 8th April a group of security researchers published information about a newly discovered exploit in a popular encryption library. With some marketing panache, they called this exploit “Heartbleed”.

A huge number of Internet services were vulnerable to this exploit, and although many of them have now been patched many remain. In particular, this was an Open Source library and so many of the very largest and most popular sites were directly affected.

Attention on the exploit has so far focused on the possible use of the exploit to obtain “key material” from affected sites, but there are some more immediate ramifications and you need to act to protect yourself.

Unfortunately the attack will also reveal other random bits of webserver’s memory, which can include usernames, passwords and cookies. Obtaining this information will allow attackers to log into these services as you, and then conduct more usual fraud and identity theft.

Once the dust has settled (so later today on the 9th, or tomorrow on the 10th) you should go and change every single one of your passwords. Start with the passwords you’ve used recently and high value services.

It’s probably a good idea to clear all your cookies too once you’ve done this, to force you to re-login to every service with your new password.

You should also log out of every single service on your phone, and then re-login in, to get new session cookies. If you are particularly paranoid, wipe your phone and reinstall. Mobile app session cookies are likely to be a very popular vector for this attack.

This is an enormous amount of work, but you can use it as an opportunity to set some decent random passwords for every service and adopt a tool like LastPass, 1Password or KeePass while you are at it.

Most people are hugely vulnerable to password disclosure because they share passwords between accounts, and the entire world of black-hats are out there right now slurping passwords off every webserver they can get them from. There is going to be a huge spike in fraud and identity theft soon, and you want to make sure you are not a victim to it.

The Man-In-The-Middle Attack

In simple terms this would allow an attacker to impersonate the site’s SSL certificate, so they can show the padlock icon that demonstrates a secure connection, even though they control your connection.

They can only do this if they also manage to somehow make your browser connect to their computers for the request. This can normally only be done by either controlling part of your connection directly (hacking your router maybe), or by “poisoning” your access to the Domain Name Service with which you find out how to reach a site (there are many ways to do this, but none of them are trivial).

You can expect Internet security types to be fretting about this one for a long time to come, and there are likely to be some horrific exploits against some high-profile sites executed by some of the world’s most skilled hackers. If they do it well enough, we may never hear of it.

The impact of this exploit is going to have huge ramifications for server operators and system designers, but there is very little in practical terms that most people can mitigate this risk for their own browsing.

About us: Isotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.

Reviewing Django REST Framework

Recently, we used Django REST Framework to build the backend for an API-first web application. Here I’ll attempt to explain why we chose REST Framework and how successfully it helped us build our software.

Why Use Django REST Framework?

RFC-compliant HTTP Response Codes

Clients (javascript and rich desktop/mobile/tablet applications) will more than likely expect your REST service endpoint to return status codes as specified in the HTTP/1.1 spec. Returning a 200 response containing {‘status’: ‘error’} goes against the principles of HTTP and you’ll find that HTTP-compliant javascript libraries will get their knickers in a twist. In our backend code, we ideally want to raise native exceptions and return native objects; status codes and content should be inferred and serialised as required.

If authentication fails, REST Framework serves a 401 response. Raise a PermissionDenied and you automatically get a 403 response. Raise a ValidationError when examining the submitted data and you get a 400 response. POST successfully and get a 201, PATCH and get a 200. And so on.


You could PATCH an existing user profile with just the field that was changed in your UI, DELETE a comment, PUT a new shopping basket, and so on. HTTP methods exist so that you don’t have to encode the nature of your request within the body of your request. REST Framework has support for these methods natively in its base ViewSet class which is used to build each of your endpoints; verbs are mapped to methods on your view class which, by default, are implemented to do everything you’d expect (create, update, delete).


The base ViewSet class looks for the Accepts header and encodes the response accordingly. You need only specify which formats you wish to support in your

Serializers are not Forms

Django Forms do not provide a sufficient abstraction to handle object PATCHing (only PUT) and cannot encode more complex, nested data structures. The latter limitation lies with HTTP, not with Django Forms; HTTP forms cannot natively encode nested data structures (both application/x-www-form-urlencoded and multipart/form-data rely on flat key-value formats). Therefore, if you want to declaratively define a schema for the data submitted by your users, you’ll find life a lot easier if you discard Django Forms and use REST Framework’s Serializer class instead.

If the consumers of your API wish to use PATCH rather than PUT, and chances are they will, you’ll need to account for that in your validation. The REST Framework ModelSerializer class adds fields that map automatically to Model Field types, in much the same way that Django’s ModelForm does. Serializers also allow nesting of other Serializers for representing fields from related resources, providing an alternative to referencing them with a unique identifier or hyperlink.


Should you choose to go beyond an AJAX-enabled site and implement a fully-documented, public API then best practice and an RFC or two suggest that you make your API discoverable by allowing OPTIONS requests. REST Framework allows an OPTIONS request to be made on every endpoint, for which it examines request.user and returns the HTTP methods available to that user, and the schema required for making requests with each one.


Support for OAuth 1 and 2 is available out of the box and OAuth permissions, should you choose to use them, can be configured as a permissions backend.


REST framework provides a browsable HTTP interface that presents your API as a series of forms that you can submit to. We found it incredibly useful for development but found it a bit too rough around the edges to offer as an aid for third parties wishing to explore the API. We therefore used the following snippet in our file to make the browsable API available only when DEBUG is set to True:



REST Framework gives you an APITestCase class which comes with a modified test client. You give this client a dictionary and encoding and it will serialise the request and deserialise the response. You only ever deal in python dictionaries and your tests will never need to contain a single instance of json.loads.


The documentation is of a high quality. By copying the Django project’s three-pronged approach to documentation – tutorial, topics, and API structure, Django buffs will find it familiar and easy to parse. The tutorial quickly gives readers the feeling of accomplishment, the high-level topic-driven core of the documentation allows readers to quickly get a solid understanding of how the framework should be used, and method-by-method API documentation is very detailed, frequently offering examples of how to override existing functionality.

Project Status

At the time of writing the project remains under active development. The roadmap is fairly clear and the chap in charge has a solid grasp of the state of affairs. Test coverage is good. There’s promising evidence in the issue history that creators of useful but non-essential components are encouraged to publish their work as new, separate projects, which are then linked to from the REST Framework documentation.



We found that writing permissions was messy and we had to work hard to avoid breaking DRY. An example is required. Let’s define a ViewSet representing both a resource collection and any document from that collection:

class JobViewSet(ViewSet):
    Handles both URLS:
    serializer_class = JobSerializer
    permission_classes = (IsAuthenticated, JobPermission)

    def get_queryset(self):
        if self.request.user.is_superuser:
            return Job.objects.all()

        return Job.objects.filter(
            Q(applications__user=request.user) |

If the Job collection is requested, the queryset from get_queryset() will be run through the serializer_class and returned as an HTTPResponse with the requested encoding.

If a Job item is requested and it is in the queryset from get_queryset(), it is run through the serializer_class and served. If a Job item is requested and is not in the queryset, the view returns a 404 status code. But we want a 403.

So if we define that JobPermission class, we can fail the object permission test, resulting in a 403 status code:

class JobPermission(Permission):
    def get_object_permission(self, request, view, obj):
    if obj in Job.objects.filter(
        Q(applications__user=request.user) |
        return True
    return False

Not only have we duplicated the logic from the view method get_queryset (we could admittedly reuse view.get_queryset() but the method and underlying query would still be executed twice), if we don’t then the client is sent a completely misleading response code.

The neatest way to solve this issue seems to be to use the DjangoObjectPermissionsFilter together with the django-guardian package. Not only will this allow you to define object permissions independently of your views, it’ll also allow you filter querysets using the same logic. Disclaimer: I’ve not tried this solution, so it might be a terrible thing to do.

Nested Resources

REST Framework is not built to support nested resources of the form /baskets/15/items. It requires that you keep your API flat, of the form /baskets/15 and /items/?basket=15.

We did eventually choose to implement some parts of our API using nested URLs however it was hard work and we had to alter public method signatures and the data types of public attributes within our subclasses. We required entirely highly modified Router, Serializer, and ViewSet classes. It is worth noting that REST Framework deserves praise for making each of these components so pluggable.

Very specifically, the biggest issue preventing us pushing our nested resources components upstream was REST Framework’s decision to make lookup_field on the HyperlinkedIdentityField and HyperlinkedRelatedField a single string value (e.g. “baskets”). To support any number of parent collections, we had to create a NestedHyperlinkedIdentityField with a new lookup_fields list attribute, e.g. ["baskets", "items"].


REST Framework is great. It has flaws but continues to mature as an increasingly popular open source project. I’d whole-heartedly recommend that you use it for creating full, public APIs, and also for creating a handful of endpoints for the bits of your site that need to be AJAX-enabled. It’s as lightweight as you need it to be and most of what it does, it does extremely well.

About us: Isotoma is a bespoke software development company based in York and London specialising in web apps, mobile apps and product design. If you’d like to know more you can review our work or get in touch.

PloneConf2010 – and there’s more

I’m here at PloneConf2010 too, and I’ve been to mostly different talks to Mitch, so here’s my write up too.  It’s taken a bit of time to find the time to write this up properly!

Calvin Hendryx-Parker: Enterprise Search in Plone using Solr

We’ve been using Solr for a few years here at Isotoma, but so far we’ve not integrated it with Plone.  Plone’s built-in Catalog is actually pretty good, however one thing it doesn’t do fantastically well is full-text search.  It is passable in English, but has very limited stemming support – which makes it terrible in other languages.

Calvin presented their experience of using Solr with Plone. They developed their own software to integrate the Plone catalog with Solr, instead of using collective.solr, which up till then was the canonical way of connecting them. Their new product alm.solrindex sounds significantly better than collective.solr.  Based on what I’ve heard here, you should definitely use alm.solrindex.

To summarise how this all hangs together, you need an instance of Solr installed somewhere that you can use.  You can deploy a solr specifically for each site, in which case you can deploy it through buildout.  Solr is Java, and runs inside various Java application servers.

You can also run a single Solr server for multiple Plone sites – in which case you partition the Solr database.

You then configure Solr, telling it how to index and parse the fields in your content. No configuration of this is required within Plone.  In particular you configure the indexes in Solr not in Plone.

Then install alm.solrindex in your plone site and delete all the indexes that you wish to use with Solr. alm.solrindex will create new indexes by inspecting Solr.

Then reindex your site, and you’re done!  It supports a lot of more complex use cases, but in this basic case you get top-end full text indexing at quite low cost.

Dylan Jay, PretaWeb: FunnelWeb

Funnelweb sounds invaluable if you want to convert an existing non-Plone site into a Plone site, with the minimum effort.

Funnelweb is a tool based on transmogrifier. Transmogrifier provides a “pipeline” concept for transforming content. Pipeline stages can be inserted into a pipeline, and these stages then have the ability to change the content in various ways.

Dylan wrote funnelweb to use transmogrifier and provide a harness for running it in a managed way over existing websites.  The goal is to create a new Plone site, using the content from existing websites.

Funnelweb uploads remotely to Plone over XML-RPC, which means none of transmogrifier needs to be installed in a Plone site, which is a significant advantage.  It is designed to be deployed using buildout, so a script will be provided in your build that executes the import.

A bunch of pipeline steps are provided to simplify the process of importing entire sites.  In particular funnelweb has a clustering algorithm that attempts to identify which parts of pages are content and which are templates.  This can be configured by providing xpath expressions to identify page sections, and then extract content from them for specific content fields.

It supports the concept of ordering and sorts, so that Ordered Folder types are created correctly.  It supports transmogrify.siteanalyser.attach to put attachments closer to pages and transmogrify.siteanalyser.defaultpage to detect index pages in collections and to make them folder indexes in the created sites.

Finally it supports relinking, so that pages get sane urls and all links to those pages are correctly referenced.

Richard Newbury: The State of Plone Caching

The existing caching solution for Plone 3 is CacheFu, which is now pretty long in the tooth.  I can remember being introduced to CacheFu by Geoff Davis at the Archipelago Sprint in 2006, where it was a huge improvement on the (virtually non-existent) support for HTTP caching in Plone.

It’s now looking pretty long in the tooth, and contains a bunch of design decisions that have proved problematic over time, particularly the heavy use of monkeypatching.

This talk was about the new canonical caching package for Plone, It was built by Richard Newbury, based on an architecture from the inimitable Martin Aspeli.

This package is already being used on high-volume sites with good results, and from what I saw here the architecture looks excellent.  It should be easy to configure for the general cases and allows sane extension of software to provide special-purpose caching configuration (which is quite a common requirement).

It provides a basic knob to control caching, where you can select strong, moderate or weak caching.

It can provide support for the two biggest issues in cache engineering: composite views (where a page contains content from multiple sources with different potential caching strategies) and split views (where one page can be seen by varying user groups who cannot be identified entirely from a tuple of URL and headers listed in Vary).

It provides support for nginx, apache, squid and varnish.  Richard recommends you do not use buildout recipes for Varnish, but I think our recipe isotoma.recipe.varnish would be OK, because it is sufficiently configuration.  We have yet to review the default config with though.

Richard recommended some tools as well:

  • funkload for load testing
  • browsermob for real browsers
  • HttpFox instead of LiveHttpHeaders
  • Firebug, natch
  • remember that hitting refresh and shift-refresh force caches to refresh.  Do not use them while testing!

Jens Klein: Plone is so semantic, isn’t it?

Jens introduced a project he’s been working on called Interactive Knowledge Stack (IKS), funded by the EU.  This project is to provide an open source Java component for Content Management Systems in Europe to help the adoption of Semantic concepts online.  The tool they have produced is called FISE. The name is pronounced like an aussie would say “phase” 😉

FISE provides a RESTful interface to allow a CMS to associate semantic statements with content.  This allows us to say, for example that item X is in Paris, and in addition we can state that Paris is in France.  We can now query for “content in France” and it will know that this content is in France.

The provide a generic Python interface to FISE which is usable from within Plone.  In addition it provides a special index type that integrates with the Plone Catalog to allow for updating the FISE triple store with the information found in the content.  It can provide triples based on hierarchical relationships found in the plone database (page X is-a-child-of folder Y).

Jens would like someone to integrate the Aloha editor into Plone, which would allow much easier control by editors of semantic statements made about the content they are editing.

Querying Webtrends ODBC from the command line with WebtrendsQT

As I alluded to yesterday, and in my post about SQLAWebtrends, I’ve recently been doing a lot of work with the Webtrends analytics service, concerned mostly with getting data out of it via the old Windows ODBC drivers.

While turn around on new data available from reports could cause Methuselah to yawn, it could still be exceedingly time consuming loading up a spreadsheet app, defining queries in an ODBC query builder, and waiting for data to populate sheets; or at best writing several Python functions to query the last data; I would still have to spend tedious amounts of time tweaking and re-tweaking queries for different reports and/or datasets.

This lead me to make WebtrendsQT, a psql/mysql-like command line query tool for Webtrends using pyODBC.

WebtrendsQT is mostly just the ODBC extra tool provided by pyODBC, with some WT-specific changes. Namely the introduction of a “p” command, which issues the {Call wtGetProfileList()} stored procedure against the WTSystem schema (via the system_cursor property), returning a list of profiles.
Similarly do_l (the handler for “l”) instead of listing real schemas, lists the Webtrends ODBC equivalent templates.

do_c (“c”) will work as you’d expect, taking a “schema” (e.g. template), and changing cursor to point to it, but also takes profile GUID as an optional first option to switch both profile and template (profiles define the data source and which report templates are available).

It took me some time to figure out that PyODBC‘s lovely columns() method wouldn’t work with the Webtrends driver, as some metadata isn’t provided by the driver and causes a segfault. Instead my hack is to use the DB API Cursor.description to get name and type details for columns on a table, unfortunately in order to get this information I need a cursor that specifically targets the table in question; and to get around this I make a simple query against the table that won’t return any information, but will still return a cursor:

def get_columns(self, name):
    columns = [['Column name', 'Type', 'Size',]]
    row = self.cursor.execute(
        'SELECT * FROM %s LIMIT 0' % (name,)
    for r in row.cursor_description:
    return columns

cursor_description is PyODBC’s special “always available even after query-set has been closed” reference to the cursor.description instance.

Unlike pyDBCLI.extras.odbc, WebtrendsQT takes a set of arguments rather than a single DSN string, due to the ODBC driver requiring a specific set of details to connect.

You most likely just want to install and run the tool under Windows, which if you have any experience with Python on Windows should be easy enough using easy_install or the included; if however you don’t have any Python-Windows experience and just want to get up and running with WebtrendsQT, the FAQ has a 5 step simple guide, including a pre-rolled pair of Windows scripts, that will install everything and create a batch script with all the Python paths set up to use.
When installed just type wtqt in the cmd.exe Window, provided by the batch script, and away you go.

C:UserstestDesktop> wtqt

ERROR: Must have a profile GUID, -p

Usage: [-u <user>] [-p <pass>] -d <system DSN> -h <host> [-P <port>] -t <template> -p <profile>

  -d, --systemdsn: Predefined system DSN
  -p, --profile : Webtrends profile GUID
  -t, --template : Template/schema
  -h, --host : Webtrends web instance
  -P, --port : Optional server port (default: 80)
  -u, --username: Optional username
  -k, --password: Optional password

Installing Postgis on Ubuntu Karmic Koala (9.10)

Karmic has Postgres 8.4 as it’s default version, and 8.3 can prove tricky to install.
Unfortunately, not all of the contrib packages were upgraded to run on the new version in time.

One of these is postgis, the geodatabase extensions. You have two options, build it from source, or install the Lucid (10.4) package.
Obviously, Lucid isn’t released yet, but I have had success with just installing the package. Your Mileage May Vary.

i386 Package
AMD Package

Hope this helps someone!

Django templates derived from the view docstring for rapid prototyping

The rather verbose title says it all really.

We had a need the other week to churn out the skeleton of a site to see how the different areas fitted together.
As it was being written in Django anyway I put together this quick ‘n dirty utility that renders the reStructuredText docstring of a view to the returned response, so you can quickly put in page furniture and links to other views without having to go to the effort of creating templates.

def docview(fn):
    from docutils.core import publish_string
    from django.http import HttpResponse

    r = HttpResponse(publish_string(
    return lambda rtn: r

Then decorate your method:

def fast_view(request):
A Fast View

With links to:

 * `An even faster view </faster_view>`_
 * `Somewhere else <>`_

Which will render:

A Fast View

With links to:

Code Coverage in Testing

Getting an idea of much code your test covers varies in how complex your code is. I have found from Ned Batchelder to be a great tool.

The usage confused me slightly for a while, so I thought I`d share with you how I used it.

First step was to created my Python test. I created mine using the standard unittest framework in Python. I then ran ‘coverage -e -x’ which executes the test.

To extract the coverage of the test run ‘coverage -b -i -d htmlcov ../’. This creates a directory of HTML output containing an index.html. Open this in your browser and you will be able to see the overall percentage of covered code. On this page is a list of the modules you tested. Clicking on a module highlights in red the statements not covered by your test.