Month: August 2007

Python or Python+Delphi Developer Wanted

Speaking of Python, over at oasis Digital we’re looking for a Python (subcontract) or Python+Delphi (full time) developer. For the right person this could be a great opportunity to use your preferred tools.

Plus, a tip to anyone applying for this work or any other work: when you email a resume, don’t name it “my resume.doc” or “resume.pdf”. Rather name it with your name, perhaps “Kyle Cordes resume.doc” or “Resume for Smith, John.pdf”. (I fear that someone will read this and miss the point entirely, naming their resume file with my name…)

Update: We have hired for this work – thank you to everyone who applied.

Yet Another Python Success Story

Is it OK to use programming language X in a production enterprise application? Or are fear, uncertainly, and doubt holding you back? Public “success stories” might make it more acceptable for you to do so in your environment.

In that spirit I offer our story of a production Python deployment at an Oasis Digital customer (without names or details, to protect their privacy). There are many other success stories at Python.org. In this project, the client and application server (the bulk of the system) are written in Delphi (which was much more popular when the project started, than it is today). A major subsystem (roughly 1/3rd of the overall system) is written in Python. It consists of a set modules that parse textual data from a large number of varied formats, into a common schema, another set of modules to apply (frequently adjusted) business rules, and a third set of miscellaneous modules. These are all used in background data processing, not part of a client application or a server handling requests from a client application. These modules interact with the rest of the system primarily through state stored in a database. I generally recommend against the database integration style between separate applications, but it works well in this scenario within modules of the same application, built and maintained concurrently by the same team.

The Good

We chose Python for this subsystem for a variety of reasons. First, its built-in features are well suited to the text processing task at hand. Python’s “batteries included” have generally avoided the need to find or implement add-on text processing tools (which would have been necessary in Delphi); thus a programmer needs to know and use “just” what’s in the Python box, with few external libraries to consider.

Second, Python’s built in features and compact syntax have shortened the programming time considerably, in our estimation, than would have been otherwise. It takes relatively few lines of Python to get the job done. We have many lines of Python, and would have had many more lines had we used a lower-level language. (Of course lines of code is not everything; it’s possible to come up with dense, bad code. As a general rule, though, a language in which you can express what you need to express more succinctly, is better.)

Third, Python’s interpreted nature keeps the edit-test cycles short, further speeding development. This development speed issue is especially important given the niche this project occupies, in which data format changes and rule changes sometimes arrive with no notice: new data arrives, and part of the application does not work until it is enhanced to handle the new data. Extensive use of automated unit and integration tests (many hundreds of test cases) effectively prevents the interpreted operation from causing trivial runtime errors (type errors, syntax errors).

Of course, some other languages with similar compact syntax, included libraries, and high level features, would have worked equally well. At the time(5+ years ago), though, Python appeared to have the most momentum, other than Perl. Perl would have been a good choice also from a technical point of view, but it had an (unwarranted) reputation of being hard to maintain, which I didn’t want to have to overcome.

The Bad

There are downsides to our two-language approach. The first is somewhat Python specific, and really not a big deal: Python is slow. Its primitives are fast, but when you write considerable Python code to do something, it does that something at a rather leisurely pace compared to Delphi or Java or C++, using a lot more CPU along the way. The practical impact of this has been limited, because the bottleneck on this system is not the Python code, it is the database; but still, this has been inconvenient, and has required our customer to deploy multiple machines for this subsystem where one would have been sufficient with a more efficient language/runtime. Doing so is not particularly expensive, though, and adds a measure of reliability, so we haven’t had a need to speed things up with Psyco, C modules, etc.

The other issue is more serious. It is created by the large gap in language style and features between Delphi vs. Python in particular, and low-level vs. scripting languages in general. (Those of you unfamiliar with Delphi may be thinking this is because Delphi is some hideous VB-like toy. Wrong. Delphi is a somewhat C++-like or Java-like language, statically compiled, fast, and sadly burdened with a Pascal syntax.) Personally, this gap bothers me not at all. I’ve written production code in assembly, C, C++, Delphi, Java, Python, Ruby, Javascript, Lua, a bit of Prolog, and others I forget right now; I am happy to use one language in the morning and another radically different one in the afternoon.

I have discovered that most developers are not like me, though. Most Delphi developers are notably uninterested in Python and vice versa. As a result, our project team has ended up divided along the same lines as the software, with some cross training but relatively little production development crossover between the developers working in each languages. This is an obstacle to any developer taking end-to-end responsibility for features or issues that span the languages, and also an obstacle to hiring.

Python itself is not much of an obstacle to hiring: while there are far fewer Python programmers than Java (for example) programmers, there are also far fewer Python jobs than Java jobs.

The Verdict

In spite of the downsides discussed above, overall it has been a “win”, technically, to use two languages (each well suited to part of the application) in this project. More importantly, I am also confident this choice has been a win for our customer: they got a system delivered faster, and at lower cost, than they otherwise would have. They used every bit of speed we could deliver, to win business from their competitors.

However, the world has improved a lot since this decision was made; today I could probably choose a single language / toolset which meets all the needs sufficiently well, and thus avoid the downsides of the two-language solution. Alternatively, if starting today I might build the infrastructure for all subsystems in the same base language, with hooks to use Lua or Javascript scripting to accommodate the need for rapid runtime logic changes. It’s even possible that we would port the existing code to another language in the future – which would not make the original decision a mistake.

Fourteen Tools for a Productive Distributed Team

A geographically distributed software development team (“distributed team”, for short) is simply one where developers don’t work in close physical proximity (within a few hundred feet). In such a time you interact mostly via electronic means.

To some readers a distributed team will sound like an obviously ridiculous idea, while to others it will sound quite normal. A great many companies are distributed nowadays, as are essentially all open source projects, including the largest and most successful such projects. (Does open source seem like a toy to you? Not “real”? Consider this: in your real project you need large amounts of money to get everyone to work together to build something. Open source projects somehow accomplish this without the carrot and stick of money. Which is more impressive?)

Distributed teams appear to be most common in small firms, but certainly aren’t rare elsewhere. At some large firms, there are teams that work at the same campus or even in the same building, yet are inconveniently spread out enough that they are borderline distributed.

At Oasis Digital we operate mostly as a geographically distributed team, or rather as a set of teams (for various projects). We are scattered around our city, our country, and in a few cases, globally. We’ve learned a lot about how to succeed this way, summarized as 14 tools for a productive distributed team:

(1) Online meeting  / telephone. Effective online meeting tools are available inexpensively or sometimes free; telephone calls, including mobile, long distance, and conference calls are free or inexpensive. Either way, don’t hesitate to spend time together and live discussion, with at least voice and ideally video. Especially the early stages of a project, this is by far the closest match to in-person interaction, of all the tools in this list. At Oasis Digital, we typically use a regular weekly discussions as a baseline, with more discussions of all kinds as needed.  (Frequently at the beginning of a project, less frequently over tim.)

(2) Instant Messaging. Beware that your time is valuable though; don’t type out a long conversation, when it gets complex talk live instead. The key value in IM is as a substitute for the awareness of who is available and working, that would be obvious in the same office.

(3) Email is especially helpful to create a thread, a trail of key decisions. I suggest summarizing key decisions, especially when they span between different teams, in a (hopefully concise) email – even if the decision was reached by meeting, phone, IM, in-person, etc. As above, beware that email can consume unlimited amount of time, and don’t hesitate to  switch to live discussion if it gets complex.

(4) Airplane / Car / Train. The most powerful tool to help a distributed team work well is occasional in-person interaction.

(5) Documents. Communicate a complex idea, especially one that will need to be explained again and again, clearly in a document. (An aside on “template” documents: I’ve found them to be of limited value, though we use them when needed to meet customer requirements.)

(6) Screen Sharing. Using Remote Desktop, VNC, and other tools, it’s possible to do “pair programming” from half a world away. In our projects we do a only a little of this, but a few hours of pair programming on a tricky area can work wonders. Screen sharing is also extremely useful for demonstrating new features, reproducing bugs, working with customers, and much more.

(7) Screencasts are video/audio recordings of the computer screen and a person talking; they are very useful for explaining a feature or module to another developer. Recording then viewing a screencast is not as effective as sitting alongside someone explaining in real-time, but it has the tremendous advantage of re-playability. A series of (high quality) screencasts explaining important parts of a system will get new team members up to speed quickly.

(8) Audio recordings. Record an audio explanation of a feature or bug, while walking around or driving, on an inexpensive hand-held digital voice recorder. Then send it to another developer as is, or have it transcribed. Again, it’s not quite as effective or high-fidelity (of either the audio or the ideas) as an in-person explanation, but can be replayed, sent to multiple recipients, etc. A caveat here is that people read much faster than they listen, so if you have something long-winded (or long-lived, important) to say, have it transcribed then edit it. You may find, as I have, that it’s far easier to compose a long detailed explanation this way, than via direct typing.

(9) Screen Shots. Don’t just tell; show. Show another developer on your team what you mean by taking a screenshot them marking it up.  Unlike a screen recording, it’s easy to go through a set of screenshots and update just one of them in the middle if things change.

(10) Mockups. A distributed team leaves more room for misunderstanding of desired results. Counteract this by building a mockup (paper, Excel, etc.) of what you want.

(11) Issue / Bug Tracking. If you don’t have an issue tracker for your team, stop reading this now and go get one: Mantis, Trac, Jira, there are hundreds to choose from. (Go ahead. I’ll wait.) A common approach for collocated teams is to use a tracking system loosely, updating it occasionally. In this usage model, tracker items act mostly as high level token for more detailed information exchanged in conversations. This approach can be made to work on a distributed team, but in my experience it is unwise. Instead, get every issue in to your tracker and keep it up to date aggressively. Don’t let the sun go down with your issue tracker mismatching reality. Assume that others on your team will check the issue tracker then make important decisions relying on the data therein. If they see something that was fixed 3 days ago, but still marked as broken, bad decisions can easily result. Anyone on the team should be able to see the essential status of every current issue in the tracker, not by a time consuming process of asking someone about each item.

(12) Source Control. You’re already using this, of course. For the future, consider a distributed source control tool (bzr, git, svk, Mercurial, etc.) for your distributed team – if you’ve only used centralized tools (CVS, SVN, ClearCase, etc.) you’ll be surprised how helpful it is to have the project version history available locally, among other benefits. (See Linus explain distributed source control.)

(13) Code Review. Some collocated teams enjoy social camaraderie, but squander the benefits of proximity by working in technical isolation. Your distributed team can outperform them easily: avoid technical isolation. Read each other’s code, comment on it, learn from each other.

(14) Automated Builds. Surely there is hardly anyone left by 2007 who isn’t using an automated, continuous build process on a server somewhere. The value of this is amplified in a distributed team, because the stream of successful builds (we fire off a build after each commit) helps keep everyone in sync.

A related but separate question is where distributed team members work: in their homes, in offices outside their homes, in public spaces, etc. I believe the best answer is a blend of these; I mentioned hiding in a cafe to get some work done before, and I’ll write more about the Where question in a later post.

Another related question is whether any of this is even a good idea; stayed tuned to read more on this as well.

My name is Kyle, and I’m an Infoholic

I recently read Tim Ferriss’s book The Four Hour Work Week, colloquially called 4HWW. The book is short, dense with ideas, and easily worth the $12 price. I recommend the book in spite of:

  • Questions about the veracity of Ferriss’s claimed accomplishments
  • Criticisms that some of his techniques are not as broadly applicable as he makes them sound
  • The fact that the author apparently fell for a bogus chain-letter email and reprinted it on page 284. Ooops – how embarrassing!
  • I’d guess he’s spending more like 80 hours per week promoting his book over the last few months, with many media appearances, interviews, etc.

Among his main points (outsource more, delegate more, sell products rather than services, travel, etc.), the key idea that stood out for me is the “low-information diet”: read less, watch less, surf the web less. This is nothing new of course (I even touched on it myself in an earlier post), but Ferriss makes a compelling case.

Unfortunately, upon self-examination the truth hurts:

  • I read too many books, even though I’ve gotten rid of many recently
  • I read too many magazines.
  • I read too many web sites.
  • I subscribe to too many RSS/Atom feeds.
  • I check email too often.

In my defense, I also somehow write a lot of software, solve many customer problems, and much of the information I consume is at least tangentially related to those sources of value. I read quickly, and I don’t watch television, so this excessive consumption is not as time consuming as it could be.

Still, I need something closer to Ferriss’s low-information diet. I don’t have the guts to go cold turkey, and part of the service we offer to our customers is fast response to problems, so I won’t go as far as he suggests. I will spend less time consuming input and more time producing output.

Update in 2009: This remains an ongoing struggle, but I do quite often manage entire days of producing most of the day and consuming only in short breaks.

Help! My Hierarchy is Slow – Faster Hierarchies with Nested Sets

A great many applications, including many that I’ve worked on, have a hierarchy of things: of parts, of people, of organizations, etc. The way most of us represent such hierarchy is with the first thing that generally comes to mind: make each Widget have a parent Widget, with a table like so:

This representation is called an “adjacency list”, and is simple and easy. You can readily build a tool to manipulate a hierarchy stored this way. Many off the shelf visual components, for both client-side and web applications, know how to manipulate hierarchies represented this way. Some reporting tools know how to report on hierarchies represented this way.

However, for answering common questions like “who all is under person X in the hierarchy”, the adjacency list approach is unwieldy and slow.

There are various other approaches to representing a hierarchy, most of them discussed in detail in Joe Celko’s articles and books, prominently in the book Trees and Hierarchies in SQL for Smarties. If you work with SQL and hierarchies, buy this book now.

One approach Celko is especially fond of is the “nested set” representation. You can read about it online here and here.

Of course, changing an entire application to use nested sets might be a very big deal in a mature application. That’s OK; in most cases we can get much of the benefit by building a nested-sets “cache” of the share of the hierarchy, with a table like so:

Each time the hierarchy has changed, or before each time we need to run complex queries, delete the rows in this cache table and repopulate them based on the current canonical adjacency data. Celko offers SQL code in his book to do that, which could be translated to work in the stored procedure language of the DBMS at hand. But what about DBMSs that don’t offer stored procedures, such as lightweight local databases (SQLite), MySQL, etc.? The translation must be done in application code instead.

I wrote such code in Delphi a while back, in the process of getting a full understanding of this problem; I’ve cleaned it up and now offer code for download here (DelphiAdjacencyNestedSets.zip), under an open source license (MIT license – use it all you want). I tested this today with Delphi 2007 Win32, but it should work fine at least back to Delphi 7. As far as I can tell with some searching, this is the only Delphi code for translating adjacency to nested sets available on the internet. This code doesn’t know about databases – it is a module to which you feed adjacency data, and from which you get back nested set data. It includes DUnit test cases.

I’ve also put the code on github, for easy browsing and forking.

(Update in August 2007: a new version (DelphiAdjacencyNestedSets2.zip) optionally tolerates “orphan” nodes and forests. Update in January 2008: a newer version (DelphiAdjacencyNestedSets3.zip) propagates an integer value down the hierarchy; it is named Tag as a nod to the .Tag property on VCL components. Both of these newer versions were tested with Delphi 2006/2007 also.)

The essence of the translation is a depth-first traversal of the hierarchy, and of course this can be easily implemented in other languages; the Delphi code is easy to understand, so don’t be afraid to take a look even if you need some Java or C# or PHP etc. I also stumbled across this PHP nested sets implementation, which offers a set of functions to maintain (insert, update, etc.) a hierarchy stored as nested sets, rather than only translate from adjacency to nested sets.

Another useful way to represent a hierarchy for fast querying is with a transitive closure table. I’ll write this up in a future post; it turns out to be especially useful (and necessary) to make arbitrary hierarchies work in the Mondrian OLAP server.