Category Archives: Scripting

Groovy, Programming

Low Expectations for the Build

I attended the talk on Gradle by Hans Doktor tonight and while I found myself agreeing that Maven is wholly unsatisfactory I did end up thinking that actually our expectations of build tools in the Java space are really low. What kind of things does Gradle offer us? Proper event interception, genuine integration with the build lifecycle, build targets dynamically defined at runtime, a directed cyclic dependency graph.

Looking at the list some of things you can’t believe are not part of our standard build package. We should be able to know when a build starts and stops and be able to attach code to those events. We should have decent target resolution that avoids duplication of tasks.

Gradle is head and shoulders about the morass that is Maven and is clearly superior to the ageing but faithful Ant but that it manages to be so on so little functionality is a shame.

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Python, Web Applications

Juno, a micro web framework for Python

I love microframeworks and I love Python to it therefore follows that I love the minimal web frameworks you can get for Python like CherryPy. However as soon as I heard about Juno, a clone of my favourite microframework Sinatra I had to give it a go.

For me the archetypal application for Sinatra is Git-Wiki and therefore to kick the tires on Juno I decided to try and do the same thing with Bazaar. Hence:  Bzr-Wiki on Juno!

You need Juno, Jinja2 and SQLAlchemy as well the source from Launchpad. Once you have it all then got to the repo directory and type bzr init. You should then be good to fire up wiki.py and muck around with it.

It is slightly ugly and lacks access to revision history, diffs and user tracking but apart from that it is a surprisingly functional wiki. It is also bi-directional in that you can add files to the Bazaar repo and they get reflected in the app.

So what was Juno like to work with? Well overall I thought this was the best Python microframework I have used so far. I really like the idea of decorating methods to avoid having to generate a mapping table. The syntax is terse and comprehensible, the conventions around the framework made sense to me.

By comparison with its progenitor I think I really missed the autoloading/dynamic evaluation that allows you to change code in Sinatra and have it immediately take effect. The function of the Request decorator was initially quite obscure (it binds all HTTP verbs to that method, if you want to map GET to another method you must specify all verbs independently) and I am still not sure it is right. I think the most specific decorator should take precedence. Other than that I think the framework ports a lot of the concepts from Sinatra in a sympathetic way.

The dependency on SQL Alchemy is also really clunky. If you specify that you are not using a database (as is the case here) then it is annoying to have to download a dependency and makes installation on Windows a pain I didn’t even want to try and tackle.

Juno is really promising though and I look forward to it developing. I think it would be a real delight to use it in an environment like Google App Engine.

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Programming, Ruby, Web Applications

Why does Heroku matter?

Heroku is an amazing cloud computing service that I think is incredibly important but which really isn’t getting the visibility it should be at the moment.

Why does Heroku matter?

Firstly it gets deployment right. What is deployment in Heroku? It is pushing a version of your app to the cloud. That’s it. The whole transformation into a deployable application slug is handled by the Heroku, pushing it out to your dynamos is handled by Heroku. Everything is handled by Heroku except writing your application.

Secondly, it is the first cloud/scalable computing targeted directly at Ruby that has the right pricing structure to encourage experimentation and innovation. It has the low barrier to entry that Google App Engine has but requires no special code and imposes no special restrictions.

Thirdly it does the right thing with your data. It respects your right to access, mine and manipulate your data and provides a mechanism for doing this easily.

For me Heroku has set a new benchmark for Cloud Computing and build and deployment in the enterprise as well.

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Ruby, Scripting, Web Applications

Semi-static CMSs

You know you have a good idea when there are half a dozen implementations of it. All essentially the same but filled with subtle nuance. The latest idea I have stumbled across is the “semi-static” website generator. The idea is so painfully obvious I cannot believe that it has taken us until 2009 to do it properly.

What is a semi-static website? Well it’s actually something like this blog. I spend ages writing and editing the articles but once I publish something I don’t generally change it unless it has factual errors. I need something that allows me to make changes but my content easily but my content doesn’t really benefit in anyway from being stored in a database or being generated dynamically on the fly because it is going to spend most of its life not being changed.

If you look at a lot of CMS systems the same is true. People want to be able to edit and reorganise material easily but they are rarely changing it more than once a week. The majority of the time the pages are static.

If you look at a number of CMS or Wiki systems they are actually quite complicated. Before you can get going and generate your content (which remember is the core of what you want to do) you might have to setup a database and get the application deployed to a webserver.

What the semi-static website generator does is provide a set of scripts that allow you to edit and create content and then “compile” your site. The result is set of static HTML, Javascript and CSS files that can be deployed to your static webserver (which is generally pretty easy to setup, most servers will come with Apache all ready to go).

When you combine it with a bit of DVCS magic deployment is even easier. You just checkin your new pages and then update the webserver copy of the content.

So if you want to get into this kind of content management what options do you have? Well you have a lot of choice indeed. Almost all of them are Ruby projects and they have a certain similarity to the Rails concept of code generation. You will be issuing commands like “create page”, “build site” and so on. You operate at one remove from it.

There are many, many, many choices but for myself I chose three to look at Nanoc, Webby and Jekyll. Nanoc I came across via Proggit and was struck by the simplicity and clarity of the documentation.

Installation of all three is via gems but there are some differences here. Nanoc is a very simple Gem and works with HTML and Erb by default, if you want to use a markup language or different templating language you will have to install the gem yourself. This means it also installs easily under JRuby too.

Webby by comparison is a very noisy install, I think it may have its dependencies slightly wrong as it installs a version of rake, flexmock and rspec which are surely development dependencies rather than anything I as a user would need to use for the framework. It also uses hpricot which means you can’t install it under JRuby.

Jekyll has a similarly demanding set of requirements but will install under JRuby. Jekyll did feel a lot like a personal solution as I was installing it. Textile is great but if I am going to be using Markdown why do I need to install RedCloth?

Overall I liked the nanoc install experience most.

Moving on to using them. Nanoc and Webby are very similar in that they use generators to create new elements in the site. Jekyll seems to use convention instead. At this point it is worth mentioning that there are some big differences between the three products in their intentions. Nanoc is a general website creator but it does have a certain amount of opinion as to how to do that. For example it generates very nice clean website urls by putting each page in a directory by itself and then creating the page as an index.html page within that directory. Make sure you have your webserver configured appropriately.

Webby is pretty flexible but comes with some built-in templates that will be build you a basic page, a tumblog style site, a normal blog and a presentation. That was a pretty neat feature and one the other frameworks didn’t have.

However the negative side to this is that the templating is based on Blueprint; which is both excellent as Blueprint is a great way of designing pages and a pain as Blueprint is actually pretty involved in terms of understanding the grid concept and what the various span-x css classes translate to. I felt that Webby did a great job of offering cross-browser styling that looked good but that in doing so it didn’t make the simple stuff simple. With my trial site my sidebar came out on the bottom of the page and I didn’t really know how to get on the right-hand side of the page. In nanoc I could just float: right it, in Webby I felt I had to understand the grid that had been created for me. I’m not sure it really allowed you to use any CSS knowledge someone might already have but the benefit is you know your site is going to look good across browsers and in print and so on. For more professional site development this might actually sway you to select Webby.

Jekyll is primarily a tool for publishing a blog style site so it has a lot of built-in support for special information relating to posts but in terms of layouts it uses general HTML templating like Nanoc does. It allows  for the creation of little chunks of reusable content across pages via includes but lacks some of the flexibility of Nanoc’s tag helpers. While focussing on blog publishing Jekyll actually encompasses a powerful set of processing tools for content that effectively allows you to use it as CMS. Jekyll is used to process GitPages on GitHub, a real demonstration of its power beyond just blogging.

For what I wanted to do I initially felt that Nanoc was the best choice, it’s flexible, powerful and generates nice sites. Webby is powerful but didn’t expose that power to me in a way that made me feel in-control. However when I started to get to grips with Jekyll I was really amazed at how powerful the use of a few conventions were. Jekyll processes your site’s pages effectively in-place. It can choose to process your files if they match a naming convention (such as having a content extension like textile or markdown) and it uses an embedded YAML section in the file to do things like provide the title of the page or select the layout that the content should be displayed within. While Nanoc produces fancy URLs Jekyll made it easy to build more conventional websites consisting of clusters of pages.

Jekyll uses a small number of special directories (indicated by a leading underscore) to hold things like layouts, cross-page snippets and posts (if you are using the dated posting functionality). However for a simple site the number of directories and configuration files was significantly less than Nanoc. In fact you could get by with just a master page layout, which would require just one special directory and page within it. Everything that should have been simple was with Jekyll and the result was very close to a conventional website you might have coded by hand but extremely fast to create. It also uses just one command to press the pages and fire up a local server to view the pages. Both Webby and Nanoc seemed to make pressing and previewing slightly harder than it needed to be and at least once I forget to execute the Nanoc commands in the right order.

So, conclusions… Going forward I will be using both Nanoc and Jekyll. Webby I would be willing to consider but I don’t really have a task for it that would reward the additional effort I have to invest in its capabilities. If I just want to bang out pages for a website but minimise my effort and make it easy to abstract common content then I will definitely be using Jekyll. It is a joy to use. For sites that are maybe more based around articles or grouped content then I think I will be using Nanoc. The default URL construction is nice for hierarchical data (as long as you can stand the many directories) and there’s a sense that the configuration files make it easy to make changes that affect the site as a whole. However it does use a lot of scaffolding to achieve the effect and there are a lot of exposed options that could have been reduced by adopting some conventions Jekyll style.

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Groovy, Java, Programming, Scripting, Software

Working with Groovy Tests

For my new project Xapper I decided to try and write my tests in Groovy. Previously I had used Groovy scripts to generate data for Java tests but I was curious as to whether it would be easier to write the entire test in Groovy instead of Java.

Overall the experience was a qualified “yes”. When I was initially working with the tests it was possible to invoke them within Eclipse via the GUnit Runner. Trying again with the more recent 1.5.7 plugin, the runner now seems to be the JUnit4 one and it says that it sees no tests, to paraphrase a famous admiral. Without being able to use the runner I ended up running the entire suite via Gant, which was less than ideal, because there is a certain amount of spin-up time compared to using something like RSpec’s spec runner.

I would really like all the major IDEs to get smarter about mixing different languages in the same project. At the moment I think Eclipse is the closest to getting this to work. NetBeans and Intellij have good stories around this but it seems to me to be a real pain to get it working in practice. I want to be able to use Groovy and Java in the same project without having Groovy classes be one of the “final products”. NetBeans use of pre-canned Ant files to build projects is a real pain here.

Despite the pain of running them though I think writing the tests in Groovy is a fantastic idea. It really brought it home to me, how much ceremony there is in conventional Java unit test writing. I felt like my tests improved when I could forget about the type of a result and just assert things about the result.

Since I tend to do TDD it was great to have the test run without having to satisfy the compiler’s demand that methods and classes be there. Instead I was able to work in a Ruby style of backfilling code to satisfy the runtime errors. Now some may regard this a ridiculous technique but it really does allow you to provide a minimum of code to meet the requirement and it does give you the sense that you are making progress as one error after another is squashed.

So why use Groovy rather than JRuby and RSpec (the world’s most enjoyable specification framework)? Well the answer lies in the fact that Groovy is really meant to work with Java. Ruby is a great language and JRuby is a great implementation but Groovy does a better job of dealing with things like annotations and making the most of your existing test libraries.

You also don’t have the same issue of context-switching between different languages. Both Groovy and Scala are similar enough to Java that you can work with them and Java without losing your flow. In Ruby, even simple things like puts versus println can throw you off. Groovy was created to do exactly this kind of job.

If the IDE integration can be sorted out then I don’t see any reason why we should write tests in Java anymore.

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Java, Programming, Python, Scripting, Web Applications, Work

JMeter or The Grinder, so which one is better, like?

Or for the benefit of Google: Apache JMeter versus The Grinder. Fight!

A while ago I got paid to put these two tools head to head and I think it’s been long enough that the people who put up the money will have got the benefit now.

I had used The Grinder in a previous job where it had done excellent service finding out what level of peak load our site could handle. It was convenient in that you script it in Jython because Jython was our chosen scripting language.

JMeter on the other hand was a whole new thing to me. It is a kind of graphical programming interface where you drop controls into a tree structure and the framework generates threads that run through the tree generating interactions with the target application via things like HttpClient.

The scripting versus component structure is a pretty major difference between the two and is probably one of the key things that is going to help you choose between two products that are both, to be honest, pretty mature, capable pieces of software.

If you don’t feel comfortable with Python or Jython then Grinder is likely to be out. JMeter is much more friendly to non-programming testers. However this decision isn’t as clear as you might think, JMeter actually includes Javascript functions, access to BeanShell and its own expression language. As we created more complex traffic simulations we found ourselves having extremely complex expressions that often mixed a scripting language with an expression evaluation. In some ways The Grinder is more honest in telling you up front that you are going to have to program some of this stuff yourself.

Another big differentiator is in reporting, if you need to generate reports then JMeter is a much better choice as it has components that collect and collate information and you can dump data to XML for XSLT transformation into pretty much any output format you want. It is also possible to run JMeter headless once you have the test scripts so it is possible to encorporate it into a continuous integration process. Debugging slightly flaky applications is much easier with JMeter because you can easily capture a lot of result information and then just delete the data gathering component once the test flow is working.

A lot of the functionality of both products overlaps: they can both be used a browser proxy and be used to capture scripts as a user “drives” around the site. These captures are likely to form the basis of your test plans unless you have a very clear URL scheme with little session state.

Both use thread pools to generate load and both tend to get blocked on your application before they max out their local machine (although both are capable of using 90% of memory and CPU). JMeter has a few nice options around randomly ramping up the thread activation (recreating a spike in usage more closely). The Grinder tends to give more bang for the buck as its runtime is very simple and minimal.

Both are also capable of using distributed agents and collating the data from these agents back to the coordinator.

In many ways there isn’t a “bad” choice to be made between them. So what might sway your choice one way or another? Well JMeter is an Apache project and that might make a difference for some people as you have all those good things like project governance and a good chance the project is going to continue to be developed and enhanced. JMeter was also the only project to have a test suite last time I checked out both code bases.

The Grinder does have one unbeatable quality in my opinion and that’s flexibility. When you look at the features listed on the websites you might think that JMeter does all this cool stuff with HTTP, SOAP and POP3 and the like and that The Grinder is comparatively light in the feature set.

The opposite is actually true, as The Grinder website points out the The Grinder can test anything with a Java API. There really is no limit to what you can make it do or how you can execute your test plan. In fact most of the things I’ve complained about with The Grinder are fixable from within the test scripts  (what I am really complaining about is that some things like capturing HTML output per run is something that should be available as part of the standard package). On the other hand I felt that creating a new JMeter component was actually quite tricky as you have to deal with both the GUI aspects and the actual functionality you are trying to create in your test component.

If you really want to have total control of your concurrent volume testing then The Grinder is probably the best product for you.

Perhaps the last topic for consideration is The Grinder’s use of Jython. Python is a great language and you get a lot of power for some very concise code. Just as some people are going to find it off-putting others are actually going to find it very compelling.

Okay so yet another weak “horses for courses” post on the InterWeb. Perhaps the easiest summary to end on is that your test teams will probably take a shine to JMeter while your developer teams who are also building scale tests are probably going to like The Grinder; unless they dislike Python or learning a new language.

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Programming, Python, Ruby

Mocking Random

Mocking calls to random number generators is a useful and important technique. Firstly it gives you a way into testing something that should operate randomly in production and because random number generation comes from built-in or system libraries normally it is also a measure of how well your mocking library actually works.

For Ruby I tend to use RSpec and its in-built mocking. Here the mocking is simple, of the form (depending on whether you are expecting or stubbing the interaction):

Receiver.should_receive(:rand)
Receiver.stub!(:rand)

However what is tricky is determining what the receiver should be. In Ruby random numbers are generated by Kernel so rand is Kernel.rand. This means that if the mocked call occurs in a class then the class is the receiver of the rand call. If the mocked call is in a module though the receiver is properly the Kernel module.

So in summary:

For a class: MyClass.should_receive(:rand)
For a module: Kernel.should_receive(:rand)

This is probably obvious if you a Ruby cognoscenti but is actually confusing compared to other languages.

In Python random functions are provided by a module, which is unambiguous but when using Mock I still had some difficultly as to how I set the expectation. Mock uses strings for the method called by the instance of the item for the mock anchor.  This is how I got my test for shuffling working in the end.

from mock import Mock, patch_object
import random

mock = Mock()
class MyTest(unittest.TestCase):
    @patch_object(random, 'shuffle', mock)
    def test_shuffling(self):
            thing_that_should_shuffle()
            self.assertTrue(mock.called, 'Shuffle not called')

You can see the same code as a technicolor Gist.

This does the job and the decorator is a nice way of setting out the expectation for the test but it was confusing as to whether I am patching or patch_object’ing and wouldn’t it be nice if that mock variable could be localised (maybe it is and I haven’t figured it out yet).

Next time I’m starting some Python code I am going to give mocktest a go. It promises to combine in-built mock verification which should simplify things. It would be nice to try and combine it with Hamcrest matchers to get good test failure messages and assertions too.

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Python, Web Applications

Django in 24 hours

Last Saturday afternoon I decided to learn Django. It was 2pm on the first day of SiCamp 2008 in London and being the only developer in the room at that point I decided that I should do whatever I felt would be the best option to get an application running by 2pm the next day.

Previously I have done some Google App Engine and the experience convinced me to give Django a go after I found myself, by intuition, creating a GAE project structure of handlers.py (views), models.py and a directory called templates that contained templates. I was then disappointed to find the whole world had got there before me.

So, Django in 24 hours, baptism of fire. What do I think now looking back on the experience?

Everyone has told me that the Django documentation is good and I think I have to concur. Not everything is so clear that when you’re speed reading in one window and typing in another it works first time but importantly nothing in the documentation is actually wrong. When stuff is not working a second, careful look at the documentation got me back on track.

Importantly Django’s core model of web development is sound and intuitive. My editor had around ten files open for the project and the flow of adding something to the application did naturally flow from url to handler to view to model. Maybe the only quibble I have is that the views.py file is deceptively named in MVC terms.

The core of the framework is amazingly concise, I spent the majority of my time thinking about the problem not about the framework API. Binding a URL to function made sense, having to specify a template instead of having one inferred from the method name was maybe my one criticism in the method handler but on the plus side it does allow for flexibility in handling requests. Handing off from one request handler to another was very easy.

Django templates are both amazing and annoying. The syntax and principles are amazing, it was easy to play around with the pages and the template inheritance was really powerful for avoiding duplication. However when I transferred the application from self-serving to mod_python the template generation was very wobbly when compiling changes from the file system. Of course this could also have been mod_python but it was the latest 3 series stable source compiled for the machine. I’ve used Jinja2 previously when generating HTML in Python and might be tempted to stick with it in future.

Django models are great, I hate ORM but I really liked syntax for defining persistence properties and I liked the way that you don’t have the fact that you are really dealing with SQL hidden away from you. It genuinely seemed a more convenient way of expressing the data model rather than an OO wallpaper over relational data storage. I didn’t feel the need to add domain logic to the models but I felt like it wasn’t really polluting the model to do that either.

One thing that didn’t work at all was changing the relations between models; it took me two or three attempts to finally model the relationships between the data concepts. Each time I changed a Foreign Key or Many to Many relationship I ended up deleting the database (SQLite3) as I couldn’t figure how to migrate from the old schema to the new.

One reason for choosing Django was the idea that I wouldn’t have to write the backend code as the admin stuff would be right there for me. It took me a while to get the 1.0 admin to fire up but once it was running it did perform as advertised. One of the attractive things about the application was that the data model followed the conceptual language of the solution in a really powerful way. You could use the admin interface to have a Devotee perform Devotion to an Action. My geek excitement peaked anyway, YMMV.

So them’s the highlights of the experience. Overall Django delivered me a rapid web development process in an intuitive, powerful way and lived up to nearly all of the claims made on the tin. Deploying to Apache/mod_python was painful but most of the pain surrounded the infrastructure of my box (multiple versions of Python, Apache config files) and my lack of mad Apache admin skillz.

I would happily tackle another project in it again.

Perhaps of interest is how the Django development experience matches up against Rails or GAE  which would have been the other obvious choices. GAE would have been very similar but the deployment would have been better and I wouldn’t have had any automatic admin. In retrospect it may have been a better choice for a hack party type event. It certainly would have been my choice for personal projects for easy of deployment but now I have one Django app running perhaps that isn’t as relevant any more. Certainly the thing that has kept me from GAE before, the pain of data migration, doesn’t seem that better in Django (except that you control the datastore and its contents).

Compared to Rails?

  • Admin is much more awesome than scaffolding.
  • Django ORM is much less complex than Active Record, all the data required to create, deploy and use the object is in one place. Django doesn’t have Migrations but has its own brand of database versioning pain.
  • RSpec is awesome (despite its monkey patching of Object) so you aren’t going to beat Rails for easy testing.
  • Django templates are more powerful and easier to use than Erb but you have a lot of Ruby templating options so it’s hard to make a complete comparision. They probably both similar in the sense that you can find a template library that suits your preferences. Django is the purely solution out of the box.
  • Routing and Controllers are much less involved in Django than Rails.
  • Django is less opinionated about how you structure your application directories, which I like.
  • Django doesn’t bake-in AJAX components but is “batteries included”, Rails probably generates better Web2.0 style apps for less effort.
  • Finally Django uses only a few code generators because its basic structure is far less involved. It also generates far less “stuff” for each MVC element which I quite like as I don’t tend to use everything Rails generates.

Okay detailed analysis over, what’s the high-level view? Django and Rails are similar experiences but I think the major differences between them are almost what you could say about Python and Ruby. In Django you are going to get simplicity, clarity and a real choice of how you plug your infrastructure components together. In Rails you are going to get magic up front which is cool but also magic at the back end, which is not cool. Ultimately I think the answer is how opinionated you like your software. Well punk? How opinionated do you like it?

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Ruby

Ruby Manor

So first things first. Ruby Manor was a huge success and a real credit to James and Murray who created and ran it. Thank you guys very much for doing it.

The talks really varied a lot, and ironically I went away most interested in Rabbit MQ and Neo4J, both of which were only tangentially related to Ruby. George Palmer’s talk on Nanite was probably the most interesting of the day but during it I picked up a sense that the audience was mostly focussed on web development within a Rails space.

This reached some kind of nadir in Alex Maccaw’s frankly unreal implementation of recommendation analysis as a Rails plugin which really did look like someone hammering a square peg through a round hole. It did however generate some interesting post conference blogging.

I also enjoyed the Shoes and Monkeybars talk although they did end up confirming my reservations about both libraries. Monkeybars really seems quite a complex setup and kind of lays an MVC framework on top of Swing which already has an MVC like framework. However it does use Matisse which is still an amazing constraints based GUI builder. Shoes is awesome, until you get stuck – then there isn’t enough feedback to understand why your awesome has disappeared. Seriously fun to play with though.

So it has been proven you can have a cheap, community-led conference in London for Ruby. Is it now time for someone to step forward and organise the Python Snake Pit?

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Groovy, Programming, Software

Using Gant to build Java Projects

I know I said I would be taking a step by step introduction to Gant in my last post on the subject but sometimes the devil drives and you need things done in a less systematic way.

Recently I have been building Java and Scala projects with Gant. I think it has been a successful exercise so I am just going to jump on and show you some example buildfiles. The first one is going to be a Java project. This project is obviously toy code but I think if you just download the sample project and start filling in your own code (it’s an Eclipse project, Intellij and NetBeans should both import it successfully) you will be happy with how little you have to change the build file.

First have a look at the Gant file itself and then I am going to talk about the things that I think make Gant such a powerful and productive tool. The first thing to point out is the line count, this represents a complete build file for a Java SE project in under 100 lines. That includes the Hello World target I’ve left in as an echo test. Gant might have a slightly weird syntax if you are unfamiliar with Groovy but it isn’t verbose.

Targets and dependencies were in the last post so this time the new thing is using AntBuilder. Any method call that starts with Ant is a invocation of an Ant task. These are nothing but thin wrappers around the normal Ant task (and in fact I usually write them using the Ant Task documentation). Things that are attributes in Ant XML become hash properties in the parameter list. Things that would normally be nested elements are calls to the enclosing builder.

One area where Gant wins big is the way you can mix normal variables, Groovy string interpolation and Ant properties. Declaring the directory paths as Groovy variables near the head of the file allows me to create new paths via interpolation and assign the variable to the properties of an Ant task. Ant XML has properties and macro interpolation but this is both clearer and easier.

I am also using the Gant built-in clean task and in the course of using it, Groovy’s operator overloading for lists. I’m not a huge fan of operator overloading but if it’s clear enough here then it is great to have a DRY list assignment.

I also like the way that the directories are created in the init task. This kind of closure looping over a list again shows some of the power and conciseness that can be achieved by a language rather than a configuration file. If you don’t read Groovy then the each method iterates over the list its attached to and each item resulting from the iteration is stored in the whimsical “it” local variable.

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