Tuesday, January 16, 2018

I've decided on Windows -- Please help justify my choice

After many words, the email chain I received netted out to this:
  1. I can't teach myself data science on my crappy old Windows machine.
  2. I've decided to get a new Windows machine. Here are the specs.
My response was a mixture of incredulity and bafflement.

It appears that two things happened while I wasn't paying attention.
  1. Apple ceased to exist.
  2. The cloud ceased to exist.
I'm aware that many people think the Apple alternative is a non-starter. They are sure that after 40+ years selling computes, Apple is doomed, and we'll only have Windows on the desktop. Seriously.

Some people have farcical explanations for why Apple Cannot be Taken Seriously.

In this specific instance, there was a large investment in Python and Java that somehow couldn't be rebuild in Mac OS X. Details were explicitly not provided. Which is a way of saying there were no tangible "requirements" for this upgrade. Just specification numbers.

Import note. None of this involved "data" or "science." That was the baffling part. No objective measurement of anything. No list of software titles. No projects. No dataset sizes. Nothing.

The anti-cloud argument was even stranger than the anti-Mac argument.

Somehow, a super-large AWS server -- let's say it was a x1.16xlarge -- being used an hour a day (365 day*1 hr/day*$1.82/hr = $664) was deemed *more* expensive that a 64Gb 6-core home-based machine that would sit idle 23 hours each day.

The best part of $664/yr being *more* expensive?

Expert Judgement.

No "data". No "science". No measurement. No supporting details.

I wish I'd kept the email describing how someone who knew something said something about pricing.  It was marvelous Highest Paid Person's Opinion nonsense.

AFAIK, they were using 8,766 hours per year to compare AWS computing vs. at-home computing. This meant that an m5.4xlarge should be considered as costing $1,939 each year. Presumably because they'd never shut it off.

It included terms like "half-way decent performance."

There's a depth of wrongness to this that's hard to characterize beyond no "data" and no "science".

Tuesday, January 9, 2018

Code Rewrites and How They Create Value -- Stop Fighting Against It

TL;DR -- To remove doubts and questions, rewrite it.

Many, many people are confronted with the request to maintain someone else's code.

Either it's open source, and you have to make formal PR's visible to the world.

Or it's "enterprise" in-house software, and you have to make PR's visible to a work team.


It's "work group" in-house software, and you have source that may not be under proper source-code control installed on a server where you're taking over someone else's carefully built structure of porcelain components.

In the first case -- public code -- a rewrite is a challenge. People depend on it having a well-known interface. A small change here could alienate large swaths of the user community. On the other hand. A small change here could make the project *more* useful to *more* people. This is challenging. My advice here is limited.

When we look at enterprise software, however, a rewrite won't have quite the same "blast radius."

When we look at work-group software, there is no blast radius.

Benefits of a Rewrite

Why rewrite? Three reasons.

  1. You can understand it. (This is HUGE.)
  2. You can make it objectively better (i.e., higher PyLint scores, better documentation coverage.)
  3. You can add or expand the test cases. 

The first and foremost reason -- understanding -- can't be understated.


It's a HUGE fight every time. The standard argument is "If It Ain't Broke, Don't Fix It."

This is, of course, based on misunderstanding the level of "broke." A delicately-balanced tower of porcelain components that worked once last month is -- in effect -- already pre-broken if any change will disturb the structure and ruin everything.

There are lots of examples.

  • The app only works with Pandas 0.12.0 and will not work with 0.13.x or the 0.22.0 you have in your default Conda environment.
  • The app only works when you provide --someoption=False, and no one can figure out why.
  • Some test cases have @skip because they don't work. But should.
  • The setup.py doesn't work and you can only run it using PYTHONPATH.
  • The default logging initialization is "somehow" not right and requires a manual override in the app.

I know. I've created all of these problems.

On one hand, we have management: "It ain't broke."

On the other hand, we have everyone else: "It's a fragile nightmare of pre-broken components that cannot be touched."

The Script

Here's how it plays out. In the Real World.

Management: "It ain't broke. Don't fix it."
You: "I can't make it work."
Management: "It ran last month."
You: "I made one small change and it doesn't run this month."
Management: "Back out the change."
You: "The results are then useless."

After much Grrr and Gnashing...

Management: "Identify the problems and we'll prioritize."
You: "Here are a dozen things."
Management: "These are too vague. Be more specific."
You: "Here are a score of things."
Management: "You're in the weeds. Bring it up a level to where business people can understand it."

After more Grrr and Gnashing...

Management: "What's the smallest change we can get away with?"
You: "The one I made that broke everything."
Management: "Let's have lots of peer review and design walkthroughs."
You: "Cool, then you'll see how broken it is."
Management: "Okay. Let's not. Instead, make the smallest change you can."
You: "I made one small change and it doesn't run this month."
Management: "Back out the change."
You: "The results are then useless."

Can we break the cycle of uselessness?


We may be struggling with management folks who are set against fixing what's obviously broken. They're living a rich fantasy life that you can't really change.


The Grrr and Gnashing part of the dialog represents time in which useful stuff can be done. Specifically. Rewrites.

Rewriting Strategy

There are three important parts of rewriting.
  • Understand what it's doing and why.
  • Describe it with test cases.
  • Make it objectively clear (i.e., high PyLint scores, complete documentation, etc.)
The effort often involves multiple passes. I like to describe it as Test-Driven Reverse Engineering (TDRE).

  1. Create (or expand) the test cases.
  2. Rewrite the code.
  3. Repeat until it's better.
It's essential to do these in order. Without test cases, rewrites are only more breakage. With test cases, rewrites are guaranteed to produce the same results as the previous mess of horrible code.

Sometimes the test cases are really a kind of "system test" where the whole application is run against some known inputs to produce some expected outputs. This is better than nothing. It supports building fine-grained unit test cases that conform to the system test case.

Other times, the test cases may be proper unit tests and the rewrites can be at a finer level of granularity. In this case, test coverage may have to be expanded to include the fragile bits. In some cases, the rewrites may be necessary to make the code testable in the first place.

Adding test cases is objectively valuable work.

Even the dumbest of "It ain't broke" managers can recognize this value. The rewrites are a beneficial consequence of adding test cases. You may be able to achieve a goal of fixing something without ever being seen as "fixing" it. All you did was improve test case coverage and improve the "design for testability."

Costs and Benefits

Consider the cost of struggling vs. the cost of rewriting.

It's the same 80 hours of effort.

In one case, you struggled with something management insisted wasn't broken. Eventually, you found ways to make it work.

In the other case, you rewrite something management insisted wasn't broken. In the end, you actually understood it and created objective improvements in the code.

Which is better?