Tag Archives: coding

Web Designers: Stop making SPAs for inherently web 1.0 style sites

It is 2017. What’s with the drive to make everything an SPA whether it needs to be or not? This is getting a little ridiculous. I’m going to ramble on below a bit because I’ve got a hankering to do so — pay this no mind.

All around the web I see sites that are best represented as a collection of inter-linked documents, and all around the web I see many of those being changed into single-page application (SPAs). Even more stupid is when the SPA in question was built by some naive dope who included a little bit of almost every JS framework in existence — including a random selection from the thousands of obsolete and dead ones.

What is the goal? What’s the deal? Do web authors today not know how the web was actually intended to work originally? That document publication is actually its reason for existence in the first place and that “web applications” are a new thing that is a backhack to an incomplete standard that only sorta-kinda-works?

Granted, the reason it only sorta-kinda-works is due mostly to the problems inherent in the fact that only a single language is allowed in scripts… which is ridiculous. Was nobody paying attention to the Guile2 approach all those years? The only lesson learned from the Java applet and Flash experience seems to have been that “it sucks to force users to install runtimes as plugins”. Ugh.

Anyway, back to web applications…

I get it. For the moment we don’t have a solid distinction between “a document browser” and “an application browser” so we are stuck with this insufficient worst-of-both-worlds nether region of “applications that masquerade as documents”. And that drives anyone nuts who has given this much thought.

Not that a lot of people have considered the difference deeply. I imagine that is probably because very few new coders today have ever written more than a line or two of code intended to run natively on a user’s local system. Nearly everyone has written thousands of lines of code intended to run natively on server-side systems, but even that is getting wonky because many youngsters today don’t know how to deploy without using Docker yet lack the faintest inkling as to what problems Docker actually is intended to solve and wind up bypassing better solutions when they exist.

Tools shine when they are used in a focused way, performing they job for which they were intended. The web is the same way. Yes, it is a big jumble of crap. So let’s just leave that there. Networks are a big jumble of crap, too, and so are human societies — so we’ve adopted dirty ways of dealing with the dirt. The jumbly pile of shit that is the web is one of our ways of dealing with that. Everything times out. Everything is sent in text. Protocols are bloated and redundant. There isn’t even a proper definition of what “valid” HTML and XML and JSON and whatever else is in most cases. Its all racing toward a singularity where everything is uniformly stupid. But… whatever, it sort of kind of still works — and humans just barely work themselves, so that’s par for the course.

The original web was designed to function as an insecure document publication system where documents could be interlinked. We realized that we could include more interesting stuff by expanding the definition of “document” to include more than just text, and quite recently with HTML5 the way in which documents can be written is only a few orders of magnitude behind, say, LaTeX, in its ability to arrange things on the screen (that’s feature lag is not entirely the fault of the HTML5 authors).

This gives a lot of freedom to website authors — perhaps too much.

If a website is a set of news articles or academic papers (or even tweets) then you really don’t need a SPA, you need a more traditional sort of “web site”. It can be dressed up all pretty with shiny things sprinkled around, of course, but we don’t want a SPA that mysteriously changes state in ways that users cannot bookmark things, can’t easily send links to one another to specific resources (something Twitter got right despite some initial confusion over how to frame their content), etc.

If a website is actually just a delivery front end for a graphical RPG, well, obviously the game part of the site is probably best designed as a SPA, but the rest of the site — the forums, armory, character pages, beastiary, fan wiki, manual, guild rankings, lore pages, etc. — are absolutely best presented outside of that SPA as an actual website.

See the difference?

The game example is actually quite useful to contemplate for a variety of reasons. I’ll probably come back and cut this post down to just that part. Either that or eventually come back and rewrite the first bits to more accurately convey the humor with which I, as a graybeard resident in cyberspace for about 30 years now, view the state of the web today.

Whatever you do, dear reader, have fun coding, and remember: Don’t outsmart yourself!

Erlangers! USE LABELS! (aka “Stop Writing Punched-in-the-Face Code Blocks”)

Do you write lambdas directly inline in the argument list of various list functions or list comprehensions? Do you ever do it even though the fun itself, or the other arguments or return assignment/assertion for the call are too long and force you to scrunch that lambda’s definition up into an inline-multiline ball of wild shit? YOU DO? WTF?!?!? AHHHH!

First off, realize this makes you look like a douchebag for not being polite to other people or your future self whenever you do it. There is a big difference for the human reading between:

%%% From shitty_inline.erl

do_whatever(Keys, SomeParameter) ->
    lists:foreach(fun(K) -> case external_lookup(K) of
                  {ok, V} -> do_side_effecty_thing(V, SomeParameter);
                  {error, R} -> report_some_failure(R)
                end
          end, Keys
    ).

and

%%% From shitty_listcomp.erl

do_whatever(Keys, SomeParameter) ->
    [fun(K) -> case external_lookup(K) of
        {ok, V} -> do_side_effecty_thing(V, SomeParameter);
        {error, R} -> report_some_failure(R) end end(Key) || Key <- Keys],
    ok.

and

%%% From less_shitty_listcomp.erl

do_whatever(Keys, SomeParameter) ->
    ExecIfFound = fun(K) -> case external_lookup(K) of
            {ok, V} -> do_side_effecty_thing(V, SomeParameter);
            {error, R} -> report_some_failure(R)
        end
    end,
    [ExecIfFound(Key) || Key <- Keys],
    ok.

and

%%% From labeled_lambda.erl

do_whatever(Keys, SomeParameter) ->
    ExecIfFound =
        fun(Key) ->
            case external_lookup(Key) of
                {ok, Value}     -> do_side_effecty_thing(Value, SomeParameter);
                {error, Reason} -> report_some_failure(Reason)
            end
        end,
    lists:foreach(ExecIfFound, Keys).

and

%%% From isolated_functions.erl

-spec do_whatever(Keys, SomeParameter) -> ok
    when Keys          :: [some_kind_of_key()],
         SomeParameter :: term().

do_whatever(Keys, SomeParameter) ->
    ExecIfFound = fun(Key) -> maybe_do_stuff(Key, SomeParameter) end,
    lists:foreach(ExecIfFound, Keys).

maybe_do_stuff(Key, Param) ->
    case external_lookup(Key) of
        {ok, Value}     -> do_side_effecty_thing(Value, Param);
        {error, Reason} -> report_some_failure(Reason)
    end.

Which versions force your eyes to do less jumping around? How about which version lets you most naturally understand each component of the code independently? Which is more universal? What does code like this translate to after erlc has a go at it?

Are any of these difficult to read? No, of course not. Every version of this is pretty darn basic and common — you need a listy operation by require a closure over some in-scope state to make it work right, so you really do need a lambda instead of being able to look all suave with a fun some_function/1 type thing. So we agree, taken by itself, any version of this is easy to comprehend. But when you are reading through hundreds of these sort of things at once to understand wtf is going on in a project while also remembering a bunch of other shit code that is laying around and has side effects while trying to recall some detail of a standard while the phone is ringing… things change.

Do I really care which way you do it? In a toy case like this, no. In actual code I have to care about forever and ever — absolutely, yes I do. The fifth version is my definite preference, but the fourth will do just fine also.

(Or even the third, maybe. I tend to disagree with the semantic confusion of using a list comprehension to effect a loop over a list of values only for the side effects without returning a value – partly because this is semantically ambiguous, and also because whenever possible I like every expression of my code to either be an assignment or an assertion (so every line should normally have a = on it). In other words, use lists:foreach/2 in these cases, not a list comp. I especially disagree with using a listcomp when we the main utility of using a list comprehension is normally to achieve a closure over local state, but here we are just calling another closure — so semantic fail there, twice.)

But what about my lolspeed?!?

I don’t know, but let’s see. I’ve created five modules, based on the above examples:

  1. shitty_inline.erl
  2. shitty_listcomp.erl
  3. less_shitty_listcomp.erl
  4. labeled_lambda.erl
  5. isolated_functions.erl

These all call the same helpers that do basically nothing important other than having actual side effects when called (they call io:format/2). What we are interested in here is the generated assembler. What is the cost of introducing these labels that help the humans out VS leaving things all messy the way we imagine might be faster for the runtime?

It turns out that just like with using assignments to document your code, there is zero cost to label functions. For example, here is the assembler for shitty_inline.erl side-by-side with labeled_lambda.erl:

Oooh, look. The exact same stuff!

(This is a screenshot, a text file with the contents shown is here: label_example_comparison.txt)

See? All that annoying-to-read inline lambdaness buys you absolutely nothing. You’re not helping the compiler, you’re not helping the runtime, and you are hurting your future self and anyone you want to work with on the same code later. (Note: You can generate precompiler output with erlc -P and erlc -E, and assembler output with erlc -S. Here is the manpage. Play around with it a bit, BEAM and EVM are amazing platforms, wide open for exploration!)

So use labels.

As for execution speed… all of these perform basically the same, except for the last one, isolated_functions.erl. Here is the assembler for that one: isolated_functions.S. This outperforms the others, though to a relatively insignificant degree. Of course, it is only an “insignificant degree” until that part of the program is the most critical part of whatever your program does — then even a 10% difference may be a really huge win for you. In those cases it is worth it to refactor to test the speed of different representations against each version of the runtime you happen to be using — and all thoughts on mere style have to take a backseat. But this is never the case for the vast majority of our code.

(I’ve read reports in the past that indicate 99% of our performance bottlenecks tend to reside in less than 1% of our code by line count — but I can’t recall the names of any just now. If you happen to find a reference, let me know so I can update this little parenthetical blurb with some hard references.)

My point here is that breaking every lambda out into a separate named functions isn’t always worth it — sometimes an in-place lambda really is more idiomatic and easier to understand simply because you can see everything right there in the same function body. What you don’t want to see is multi-line lambdas squashed into argument lists that make things hard to read and give you the exact same result once compiled as labeling that lambda with a meaningful variable name on another line in the code and then referring to it where it is invoked later.

zUUID: An Example Erlang/OTP Project

I was talking with a friend of mine yesterday about how UUID v2 seems to have evaporated. We looked into things further and found its not actually included in RFC 4122! One thing led to another and I wound up writing an example project that is yet another UUID generator/utility in Erlang — but this time it actually has duplicate v1 and v2 detection/correction and implements as close to what I can find is defined as UUID version 2 values.

As there are already plenty of UUID projects around I focused on making this one as readable as I possibly could — to include exported documentation, in-source documentation, obvious variable names, full typespecs, my silly little “pure” notation, blatantly obvious bitstring syntax, and the obligatory github presence.

Hopefully some folks newish to Erlang will come along and explain to me what confuses them about that code, the process of writing it, the documentation conventions, etc. so that I can become a better literate programmer. Of course, since the last thing the world needs is another UUID implementation I suppose I would have had better luck with something at least peripherally related to the web. (>.<)

Binary Search: Random Windowing Over Large Sets

Yesterday I came across a blog post from 2010 that said less than 10% of programmers can write a binary search. At first I thought “ah, what nonsense” and then I realized I probably haven’t written one myself, at least not since BASIC and Pascal were what the cool kids were up to in the 80’s.

So, of course, I had a crack at it. There was an odd stipulation that made the challenge interesting — you couldn’t test the algorithm until you were confident it was correct. In other words, it had to work the first time.

I was wary of fencepost errors (perhaps being self-aware that spending more time in Python and Lisp(s) than C may have made me lazy with array indexes lately) so, on a whim, I decided to use a random window index to guarantee that I was in bounds each iteration. I also wrote it in a recursive style, because it just makes more sense to me that way.

Two things stuck out to me.

Though I was sure what I had written was an accurate representation of what I thought binary search was all about, I couldn’t actually recall ever seeing an implementation, having never taken a programming or algorithm class before (and still owning zero books on the subject, despite a personal resolution to remedy this last year…). So while I was confident that my algorithm would return the index of the target value, I wasn’t at all sure that I was implementing a “binary search” to the snob-standard.

The other thing that made me think twice was simply whether or not I would ever breach the recursion depth limit in Python on really huge sets. Obviously this is possible, but was it likely enough that it would occur in the span of a few thousand runs over large sets? Sometimes what seems statistically unlikely can pop up as a hamstring slicer in practical application. In particular, were the odds good that a random guess would lead the algorithm to follow a series of really bad guesses, and therefore occasionally blow up. On the other hand, were the odds better that random guesses would be occasionally so good that on average a random index is better than a halved one (of course, the target itself is always random, so how does this balance)?

I didn’t do any paperwork on this to figure out the probabilities, I just ran the code several thousand times and averaged the results — which were remarkably uniform.

binsearch

I split the process of assignment into two different procedures, one that narrows the window to be searched randomly, and another that does it by dividing by two. Then I made it iterate over ever larger random sets (converted to sorted lists) until I ran out of memory — turns out a list sort needs more than 6Gb at around 80,000,000 members or so.

I didn’t spend any time rewriting to clean things up to pursue larger lists (appending guaranteed larger members instead of sorting would probably permit astronomically huge lists to be searched within 6Gb of memory) but the results were pretty interesting when comparing the methods of binary search by window halving and binary search by random window narrowing. It turns out that halving is quite consistently better, but not by much, and the gap may possibly narrow at larger values (but I’m not going to write a super huge list generator to test this idea on just now).

It seems like something about these results are exploitable. But even if they were, the difference between iterating 24 instead of 34 times over a list of over 60,000,000 members to find a target item isn’t much difference in the grand scheme of things. That said, its mind boggling how not even close to Python’s recursion depth limit one will get, even when searching such a large list.

Here is the code (Python 2).

from __future__ import print_function
import random

def byhalf(r):
    return (r[0] + r[1]) / 2

def byrand(r):
    return random.randint(r[0], r[1])

def binsearch(t, l, r=None, z=0, op=byhalf):
    if r is None:
        r = (0, len(l) - 1)
    i = op(r)
    z += 1

    if t > l[i]:
        return binsearch(t, l, (i + 1, r[1]), z, op)
    elif t < l[i]:
        return binsearch(t, l, (r[0], i - 1), z, op)
    else:
        return z

def doit(z, x):
    l = list(set((int(z * random.random()) for i in xrange(x))))
    l.sort()

    res = {'half': [], 'rand': []}
    for i in range(1000):
        if x > 1:
            target = l[random.randrange(len(l) - 1)]
        elif x == 1:
            target = l[0]
        res['half'].append(binsearch(target, l, op=byhalf))
        res['rand'].append(binsearch(target, l, op=byrand))
    print('length: {0:>12} half:{1:>4} rand:{2:>4}'\
                    .format(len(l),
                            sum(res['half']) / len(res['half']),
                            sum(res['rand']) / len(res['rand'])))

for q in [2 ** x for x in range(27)]:
    doit(1000000000000, q)

Something just smells exploitable about these results, but I can’t put my finger on why just yet. And I don’t have time to think about it further. Anyway, it seems that the damage done by using a random index to make certain you stay within bounds doesn’t actually hurt performance as much as I thought it would. A perhaps useless discovery, but personally interesting nonetheless.

Object-Relation Mismatch: Comparing Strawberries and Sunglasses

I’ve been spending a lot of time lately writing a rather large suite of business applications. The original customer was a construction company which needed a replacement for their estimation system. Then the same customer needed a facility pass management system to make the insane amount of bit-shoveling/paperwork involved in getting security clearances for workers to perform work in secure sites. Then a human resources system. Then a subcontract management system. Then a project scheduling system. Then an invoicing system.

The point here is, they really liked my initial work, and suddenly I got further orders. Pretty soon after discussing the first few add-on requirements with the customer it became apparent that I was either going to be writing a bunch of independent systems that would eventually have to learn how to talk to each other, or a modular system that covered down on office work as much as possible and could pull data from associated modules as necessary (but by the strictest definition is not an “expert” or ERP system — note that “ERP” is now a buzzword void of meaning just like “cloud”). Obviously, a modular design is the preferred way to go here, and what that costs me in effort making sure that dependencies don’t become big globby cancer balls buys me enormous gains selling the same work, reconfigured, to other customers later and makes it really easy to quickly write add-ons to fill further needs from the same customer.

Typical story. But how am I doing it and what does this have to do with the Dreaded Object-Relation “Impedance” Mismatch? Tools, man, tools. Most of the things I wrote in the past were system level utilities, subsystems, security toys, games, one-off utilities for myself to make my previous office work disappear[1], patches to my own systems, and other odds and ends. I’d never sat down and written a huge system to automate away someone else’s problems, though it turns out this is a lot more fun than you might expect provided you actually take the time to grasp what the customers need beyond what they have the presence of mind to actually say. (And this last point is worthy of an entire series of books no one will ever pay me to write.)

And so tools. I looked around and found a sweet toolkit for ERP called Tryton. I tried it out. Its pretty cool, but the biggest stepping stones Tryton gives you out of the box are a bunch of pre-defined models. That’s fine, at first, but they are almost exclusively based on non-normalized (as opposed to denormalized) data models. This looked good going in, but turned out to suck horribly as time passed.

Almost all of the problems ultimately trace back to the loose way in which the term “model” is used in ORM. “Model” means both the object definitions and the tables that feed them[2]. Which is downright mad because object member variables are not table columns, not by a mile, and tables can’t do things. This leads to a lot of wacky stuff.

Sometimes you can’t tell if it makes sense to add a method to a model, or to write a function and call it with arguments because what you’re trying to do isn’t an inherent function of the modeled concept itself (and if you’ve been conned into using Java life sucks even more because this decision has already been made for you regardless your situation). And then later you forget some of the things you wrote (or rather, where they are located, which equates to forgetting how to call them) because it was never clear from the outset what should be a function, what should be a method, and what is data and what is an object. This makes it unclear what should be an inherited behavior and what should be part of a library (and I’ll avoid ranting about the pointlessness of libraries of Java/struct-based objects). And this all because we love OOP so much that we’re willing to overlook the obvious fact that business rules are actually all about data and processes, not about objects and methods at the expense of sane project semantics.

In other words, business rules are about data, most easily conceptualized as nouns, and not really about verbs, most easily conceptualized as functions (and this is the beginning of why using OOP for things other than interface and simulation design is stupid — because its impossible to properly subordinate verbs to nouns or vice versa).

Beginning with this conceptual problem you start running into all sorts of weirdness which principally revolves around the related problem that every ORM-based business handling system out there tries to force data into a highly un-normalized data model. I think this is in an effort to make business data modeling “easy”, but it results in conscious efforts by framework designers to prevent their users (the application developers) from ever touching or knowing about SQL. To do that, though, it is necessary to make every part of data constraint, validation, verification, consistency, integrity (even referential integrity!), etc. into methods and functions and processes which live in the application. Instead of building on the fascinating advancements that have been made in data rule systems this approach deliberately tosses them aside and reinvents the wheel, but much worse. This relegates the database itself to actually just being a million-dollar file system[3].

For example, starting out with the estimation stuff wasn’t too hard, and Tryton has a fairly easy-to-use set of invoicing, receiving, accounting and tax configuration modules you can stack on to get some sweet functionality for free. It also has a customer management model and a generalized personal information manager that is supposed to form the basis for human resources management stuff you can build yourself. So this is great, right?

Wrong. Not just wrong because of the non-normalized data, I’ll get to that in a moment, but primarily wrong because nearly everything in the system attempts to be object oriented and real data just doesn’t work that way at all. I didn’t realize this at first, being inexperienced with business applications development. At first I thought, “Ah, so if we make our person model based off the person-party-address chain we can save a lot of time writing by simply spending time understanding what’s already here”. That sort of worked out. Until the pass management request came in. (That basing the estimation module off of the existing sales/orders/invoices chain would be a ridiculous prospect was a far less obvious problem.)

Now I had a new problem. Party objects are one table in the database, people objects are a child class in the application that inherits Party but is represented in the database as a separate table that doesn’t inherit the party one (but has a pass-up key instead to make the framework portable to database backends that don’t support inheritance or other useful features — more on that mess later) and addresses are represented in the database as being a child table to the party table, but as independent objects within the OO system at the application server level.

Still doesn’t sound horrible, accept that it requires a lot of gymnastics to do handle security checks and passes this way. In particular getting security clearances for workers involves explaining two things in excruciating detail: family relationships and address histories.

The first problem has absolutely no parallel in Tryton, so writing my own solution was the only way to proceed. This actually turned out to be easier than tackling the second problem, specifically because it let me write a data model first that was unencumbered by any design assumptions inherent in the system (other than fighting with the basic OOP one-table-per-model silliness). What was really required was to understand what constitutes a family. You can’t adopt a sibling, but a parent can adopt you, and reproduction is what makes people to begin with which requires a M+F pair, and we need an extra slot each direction for adoption/step relationships. So every person who shares a parent with you is a sibling. Label them based on sex and distance and viola! we’ve got a self-mapping family model. Cake. Oh wait, that’s only cake in SQL. Its actually really, really ugly to do that from within OOP ORM code. But enough about families. That was the easy part.

Addresses were way more problematic. Most software systems written in Western languages were developed in (surprise!) the West. The addressing systems in the West vary greatly and dealing with this variance is a major PITA, so most software is written to just completely ignore the interesting problem worth solving and instead pretend that addresses are always just three text strings (usually called something like “address_1”, “address_2” and “postal_code”). In line with the trend of ignoring the nature of the data that’s being dealt with, most personnel/party data management models plop the three address elements directly into the “person” (or “party” or “partner”, etc.) table directly. This is what Tryton does.

But there’s a bunch of problems here.

For one we’ve completely removed any chance of a person or party having two addresses without either adding more columns (the totally stupid, but most common approach) or adding a separate table and letting our existing columns wither on the vine. “Why not remove them?” — because removing columns in a pre-fab OOP ORM can have weird ripple effects because other objects expect the availability of those member variables on the person or party objects and the interface bits usually rely on the availability of related objects methods, etc.

Another problem is that such designs train users wrong by teaching them that whenever a person changes addresses the world actually changed as well and the right thing to do is erase the old data and replace it with something new. Which is crazy — because the old address is still the correct label for a location that didn’t move in the real world and so erasing it doesn’t mirror reality at all.

And the last statement above reveals the root problem: this isn’t how addresses really work at all. Addresses are limited in scope by time. A person or party occupies a location for a time, but the location was already there — so we need a start/end time span, not just a record linking a party and an address. Moving further, addresses are merely labels for locations. So we need a model of locations — which should be hierarchal and boundless, because that’s how real locations are. Then we need an address builder on top of that that can assemble an address by walking up the chain. This solves a ton of problems — for one we don’t have to care if a building has a street number or even a street at all (in Japan, for example, we don’t have street names, we have nested blocks of zones that define our address elements). It also solves the translation problem — which is really important for me again here because in English addresses are written from smallest element to largest, and in Japanese they are written from largest to smallest. But these representations are not the locations nor are they actually the addresses themselves — they are merely different forms of notation for the same address.

So all this stuff above is totally ignored by the typical software model of addressing — which really puts a kink in any prospect of working within the existing framework to write a background information check and pass management system. These kinds of incomplete conceptual assumptions pervade every framework I’ve dealt with, not just Tryton and make life within OOP ORM frameworks very difficult when you need to do something that the original authors didn’t think about.

This article is about mismatches, so I’ll point out that the obvious one we’re already overlooking is that the data doesn’t match reality — or come even close. And we’re only talking about addresses. This goes beyond the Object-Relation Mismatch — its the Data-Reality Mismatch. It just so happens that the Object-Relation Mismatch greatly enables the naive coder in creating ever deeper Data-Reality mismatches.

Given the way addresses are handled in most software systems we have a new data input and verification problem. With no concept of locations there is no way to let someone who is doing input link parties to common addresses. This is stupid for a lot of reasons, for one thing consider how much easier it is for a user to trace down an existing location tree until they get to a level that doesn’t exist in the database yet and then input just the new parts rather than typing in whole addresses each time.

“But typing addresses is easy!” you say. Not true. We have to track four different scripts per address element (Latin, two forms of kana, and kanji) and they all will have to come out the same way every time for the police computers to accept them. One of the core problems here is validating that person A’s address #2 which extends from the same dates as person B’s (his brother) address #4 which spans the same dates is the same in all details so that the police background checker won’t spit out an error (because they already have this data so yours had better be right). Trusting that every user is always going to input the exact same long address string all four times and never make a mistake is ridiculous. Its even more stupid when you consider that they are referencing the same places in the real world against data you already have so why on earth wouldn’t your software system just let them link to existing data rather than force them to enter unique, error-prone new stuff?

So assuming that you do the right thing and create a real data model in your database where locations are part of a tree structure and address assembled strings linked against locations and have a time reference, etc. how does all this manifest in the object code? Not at all the way that they present in the database. Consider trying to define a person’s “current address”.

There are two naive ways to do this and two right ways to do this. The most common stupid approach is to just put a boolean on it “is_current” or something similar and call it good. The other stupid way to do it is to present any NULL end dates as “current” and call it good. But what about the fact that NULL is supposed to mean “unknown” — which would most likely be the case at least some of the time anyway and therefore an accurate representation of known fact. And even more interestingly, how do we declare that a person can only have one current address? Without a programmatic rule you can’t, because making the “is_current” boolean a UNIQUE means that a person can’t have more than one false value, either, which means they can only ever have a current and a not current address (just two) and this is silly. Removing the constraint means that either the client code (really stupid) or a database trigger (sort of stupid) should check for and reject any more than a single true value per person.

The better way to handle this is to have an independent “current address” table where the foreign key to person or party is UNIQUE and a separate “address” table where you dump anything that isn’t current. This has the advantage of automatic partitioning — since you will almost never refer to old addresses anyway, you can get snappy responses to current address queries because the current address table is only as large as your person table. The other right way to do this is to create a “current address” table that doesn’t contain any address data at all but rather just a unique reference to a party and a (not unique) reference to an address. This approach is the easiest to retro-fit onto an existing schema and is probably the right solution for a datastore that isn’t going to get more than a million addresses to store anyway.

But wait… you can’t really do that in an ORM. I mean, you can make an ORM play along with the idea, but you can’t actually create this idea in a simple way from within ORM code, and from OOP ORM code it is really a much huger PITA to coerce the database into giving you what you want than just writing your tables and rules in SQL yourself and some views to massage them into a complete answer for easy coexistence with an ORM. In particular, its easiest to have the objects actually have an “is_current” boolean and the database just lie to the ORM and tell it that this is the case on the database end as well. Without knowing anything about how databases work, though, you’d never know that this is the right way to do things, and you’d never know that the ORM is actually obstructing you from doing a good job at data modeling instead of enabling you to do a good job.

So here’s another mismatch: good data design predicts that objects are inherited one way in Python and the tables follow a significantly different schema in the database. Other than the problem above (which is really a problem of forcing addresses to be children of parties/people and not children of a separate concept of location as we have it in the real world) the object/relation weirdness creates a lot of situations where you’re trying to query something that is conceptually simple, but winds up requiring a lot of looping or conditional logic in the application to sort things out.

As for the looping — here be dragons. If you just trust the ORM completely each iteration may well involve one query, which is really silly once you think about it. And if you do think about it (I did) you’ll write a larger query domain initially and loop over that in the application and save yourself a bunch of round trips. But either way this is silly, because isn’t SQL itself designed to be a language that permits the asking of detailed data questions in the first place? Why am I doing this stuff in Python (or Ruby or Lisp or Haskell or whatever)?

But I digress. Let me briefly return to the fact that the tables are inherited one way and the objects another. The primary database used for Tryton is Postgres. This is a great choice. That shows that somebody thought about things before pulling the trigger. Tryton was rewritten from old TinyERP/OpenERP (the word “open” here is misleading, by the way — OpenERP’s terms don’t come close to adhering to the OSS guidelines whereas TinyERP actually did, or was very close) and the main project leader spent a lot of time cleaning out funky cruft — another great sign. But somewhere in there a heavy impulse to be “database agnostic” or “portable” or some other dreamy urge got in there and screwed things up.

See, Tryton supports MySQL and a few other database systems besides that don’t have a very complete feature set. What this means is that to make the ORM-generated SQL Postgres uses similar to the ORM-generated SQL that MySQL uses you have to settle for the lowest-common feature set between the two. So any given cool feature that you could really benefit from in one that doesn’t exist in the other must be ditched for all database backend code or else maintaining the ORM becomes a nightmare.

This means that each time you say you want your framework to be “portable” across databases you are ditching every advanced feature that one system has got that any of the others don’t, resulting in least-common-denominator system design. So every benefit to using Postgres is gone. Poof. Every detriment to using a fast, naive system like MySQL is inherited. Every benefit to a fast, naive system like MySQL is also gone, because nothing is actually written against the retrieval speed optimizations built into that system at the expense of losing all the Big Kid features in a system like Postgres. Given this environment, paying enormous fees for Oracle isn’t just stupid because Postgres can very nearly match it anyway — its doubly stupid because you’re not even going to use any cool features that any database provides anyway if you write “database agnostic” framework code.

I had many a shitty epiphany over time as I learned more about data storage concepts in general, relational database systems in particular, and Postgres, Oracle, DB2 and MySQL specifically. (And in that process I grew to love Postgres and generally like DB2.)

So there is a lesson here not related directly to the OOP/relational theme, but worth stating in a general way because its important to nearly all software projects that depend on some infrastructure piece external to the project itself:

Pick a winner. If someone else in your project wants to use systemX because they like it, they can spend time making the ORM code work, but that should be an extension to the subsystem list, not a guarantee of the project because you’ve got more important things to do. This could be MySQL vs Postgres or Windows vs Linux. It doesn’t matter — pick one and specialize. Even better, pick whichever one gives the biggest boost to a specific layer of your application stack and use that there.

So far the above thinking has had me settling more on Postgres over anything else and more on Qt at the application level than anything else.

Back to my story. The addressing thing introduced enough problems that I eventually had to ditch it entirely and write my own that was based on normalized location data that carried natural data (parent-child relationships within the hierarchy of physical locations) with an address table that carried human-invented administrative data about those locations (if they have a postal code, and other trivia) and a junction table that connects parties (people or organizations) to those locations via the addresses and carries timeline and other data.

When I did this and mentioned it to some other Tryton folks they flipped out. Not because I had done this in the core project — no, this was my own substitute module — but because:

  1. I had written SQL, and not just dabbled in some CREATE TABLE statements
  2. I had normalized the data model (well, a very small part of it)

I wrote the SQL to carry the definitions where the ORM just didn’t have a way to express what I wanted (or was really funky to grok once it was written). Apparently this was a big taboo in ORM Land, though I didn’t know that going in. SQL seems to have this forbidden quality that excites as much as it instills fear these days, but I have no idea why. Again, I’m a n00b, so maybe I just don’t get why ORM is so much better. Also, mind you, there was no hostility from anyone, just shock and some sense of the aghast query “what have you done?” (The Tryton community is actually a very warm place to play around and the project leader is enormously helpful, and despite me being an American (and a Texan, no less!) living in Japan and them all snooty Euro types, we got along swell. If any FOSS ERP system has some glimmer of hope as of July 2012 its Tryton.)

Writing SQL deeper than a raw() query here and there is one thing, but normalizing the data model is something altogether on a different plane of foul according to the rites of the Holy ORM. I was continually told that this would hurt me in the future if I continued on with Tryton. But on the other hand, they weren’t looking at the small mountain of application code I would need to maintain and forward port forever to get around the non-normalized data issue(s). And anyway, once you normalize data all the way, you don’t normalize it further. There actually is a conclusion to that exercise. I’ve found that my normalized data models tend to endure and changes wind up being modified by additions instead of the painful process of moving things around (and this still seems mysteriously, wonderfully magical and relieving to me — but probably because I’m not actually educated in relational algebra and so can’t see the underlying sense to why normalized data is so easy to extend (I mean, conceptually its obvious, but how, precisely?)).

Their arguments about “the future” disregarded the application layer entirely because they were only thinking about Tryton, but for me it wasn’t just one place where non-normalized data started hurting me (it also disregarded that this predicted that I’d wind up leaving Tryton). The original concept for the estimation program didn’t really jibe with the way that a(nother) very obvious customer need could be served by putting meaningful links between what was contained in CAD files, what existed in the product database, and how the units of measure get computed among them. This meant that my real need wasn’t a single application as much as it was a single data store that remained coherent regardless what application happened to be talking to it at the time (I’m not even going to get into security in this post, but that is another thing that is enormously simplified by submitting to The Postgres Way instead of resisting).

And this brings me to another problem — in fact, the real kicker. I started realizing as I wrote all these things that while the Tryton client program is pretty slick, its not the end of the road to handle all needs. For one things a lot of it involves writing screens in XML. Yuk. That’s about as annoying as it gets, and I’ll leave that there. But most importantly there was no way I was ever going to be able to port the Tryton client to, say, Android (and maintain it) or embed the CAD programs we’re using (one easy to port C++/Qt program, and one black-box Windows contraption we run in Wine that is currently a must-have) and make things run smoothly. I was also going to have my work cut out for me if I wanted to use the same data store to start doing things like drive dashboard snapshot reporting over http or especially provide some CRUD capabilities over the Web for guys out of the office (and this issue goes all the way to the security model here as well).

Anyway, long(er) story short, Tryton just didn’t meet my needs going forward. I could have forced it to fit at a greater cost in time than I am willing to pay, but it just wasn’t a total fit for my needs, and part of that was the way that data in objects don’t really jibe with how data in the real world works.

But the fact that I could code this stuff up in SQL in a sane way without any magic intrigued me. Greatly. The bit that I did with the addresses made so much sense compared to every other model I’ve seen for addresses that I couldn’t ignore it. In reality people move, but locations stay right where they are. Address definitions might change, but this is an administrative concern which leaves a historical record. My model perfectly captures this and permits now the asking of questions both about the location, about the parties which were involved with the location, and even about the administrative situation surrounding the location over time (and that questions of proximity are easily answered as well and nest cleanly with PostGIS extensions is magical and worth noting). All without a long string of crazy dot-joined noSQL stuff going on and all without a single null value stored anywhere. It was really easy to see that this made sense. Beyond that, I didn’t have a bunch of meta data that documented my code, which should be incidental, but instead just a hard definition of how my data should look. From there I could do whatever I wanted in whatever application I wanted. Having a truly sane data model started making so much sense to me that I tried writing a few different applications on top of the same data model as an experiment. And it worked amazingly well.

Writing a PyQt application, for example, I can just ask the database for some information against a view. I can get the query back as a dictionary or a list or whatever I want and display it or manipulate it any way I want. Doing it from a Django web face is pretty easy to. Actually, really easy. Django has an ORM that can make life easier if you ditch the “this class is a table” idea and make them all unmanaged models which actually just call views in the database. Its even easier overall if they are the exact same views that your other applications call (or not, but usually this winds up being a useful situation). If you remember to not do any processing in the application code, but instead have the database build your view for you and just let Django be the way it gets into a web page then you’re really cooking with gas and can safely take advantage of all the automatic stuff Django can do. (Or even better than web pages, use Django to render OpenDocument files for you, which turns out to be a super easy easy way to woo your customers because its so much more useful than generating web pages. I should probably write a post about how to do this later because its just that cool.) Its even more cool to do this from Snap than Django — but that’s a whole ‘nother story.

This was just retrieving data, though. I got curious about entering data. And its really easy as well. But it involves a few extra things. Like careful definitions of the data model (ensure actual normalization, which is sometimes surprisingly counter-intuitive in how intuitive it is), multi-column unique constraints, check constraints, really understanding what a foreign-key is for, etc. all while still leaving room for a (now otherwise meaningless) numeric ID column for frameworks that may require it — and this whole numeric-keys-for-everything bit will seem more weird the longer you spend dealing with solid data models.

Basically, use all the tools in the Postgres bag and your life will get easier. And that’s actually not hard at all. The Postgres feature list (even the DB2 feature list) is pretty small compared to the vastness of the entire Python API coupled with the combined might (and confusion, usually) of whatever framework(s) you’re writing around. Doing it right also requires that you learn how to handle the various exceptions that the database will throw back at you as a result of your constraints and rules and things you’ve put in the database. But this makes programming the application layer really easy. Like incredibly easy. And anyway, learning how to handle a single set of database exceptions is a lot easier than trying to remember every stupid little exception condition your framework can produce multiplied by the number of frameworks you have.

And this is what is solving my core problem. I’m discovering that not only is SQL pretty darn easy, but that it solves my core business logic problems without actually writing any business logic. I think this is what the relation guys at IBM knew they were on to decades ago when they thought this idea up in the first place.

Consider the “current address” issue above. I didn’t use booleans, logical processes or any other trick to figure out whether an address was current or not, nor did I have to write a special rule that states that a person can only have a single current address at once but any arbitrary number of non-current addresses, nor did I have to write a single spot of application code. The problem is solved by the structure of the data alone — which is always the most efficient solution since it involves zero processing.

THis blows all that “use this framework to build your apps in 5 easy steps with Rails!” bullshit away. But I am a little put out that the concepts themselves don’t have more support within the trendier parts of the software development world. It seems everyone is jumping on the out of control bandwagon that marketers overloaded with Java Beans and Hopes and Dreams all those years ago and sent tumbling down the hill. Its like the Obama campaign infected the software industry (because he totally earned that Nobel Prize and Hawking doesn’t deserve one). Its still rocketing down the hill, distracting faculty, investors, budding programmers and the marketing world almost completely. Its really amazing. I am a little upset that discovering a really sane way to manage data was so hard and took so long among the enormous volume of siren screams and other noise on the wire in the development community. Of course, now that I know what I’m looking for locating good discussions and resources isn’t that hard — though it is a little odd to note that the copyright dates on most of them predate my own existence.

So now, as I convert a mishmash of previously written independent application models into a central data concept I am finding something amazing: I haven’t found a single business rule yet that isn’t actually easier to express in terms of data structure than it is to put in application code. I’m also finding that importing the data from the (now legacy) application databases is also usually not that hard, either, but requires more mental effort than anything else on my plate now.

Most amazing of all is the ease of writing application code. Even if I’m writing one application in C++/Qt, another in PyQt, another in Django, another in CL and another in Haskell that run variously across the spectrum of servers, tablets, phones and desktops[4], they can all live under the same guarantees and are super easy to understand because of the extreme lightness of all their code. I’m not doing anything but showing stuff to the user from the database, and putting stuff back in the database, and adjusting based on whether or not the database accepted what was given.

This makes application development fun again. Previously I has been bogged down in trying to define business logic rules as processes, and that was boring, especially since the magic sauce really should have just been a data model forcing me to be correct in the first place instead of me chasing exceptional cases through a bunch of logical code paths in the application (which had to be duplicated across all applications!). Also, this effort tended to put horse-blinders on me as far as interface went. Once I wrote a web interface, the enormous freedom that native application development gives you is suddenly invisible and you’re thinking in terms of “what is the parallel widget in Qt or GTK to the HTML SELECT” or whatever. That’s just lame. But its what starts happening when you spend so much brainpower worrying about conditional business logic that you forget all the cool stuff you can do in a native application (like 3D flowcharts, or 3/4D projections of project management data throughout time that you can “paw through” with the mouse or even a game controller, or a million other kickass ideas we usually only ever get to see in vidya games).

Getting your data model right gives you not only the mental freedom to start exploring what native UI can do that goes so far beyond the pitiful bag of cheap tricks that “web app development” has made standard today (or the convoluted mess of JavaScript and AJAX trash that supports it), it also gives you the confidence to step out and do some cool stuff in your client applications because, hey, the data model part of the problem is already solved. All you have to do is serialize the data in your application — which means in the application if you want to have objects, go for it, but make sure they are based on a view of derived data, not a 1-for-1 mapping of objects to relations. That serialization is an easy problem to have gives you the focus to do cool stuff nobody else is doing — and it all comes down to doing data right and escaping from the ridiculous house of mirrors that ORMs lead you into.

There is a conceptual mismatch between the object world and the relational world that is so vast that it is not worth trying to bridge. I’m saying there isn’t an Object-Relation Mismatch. They just aren’t even the same thing, so how could we have ever thought that comparing them against the same criteria ever made sense to begin with?

 

[1. Both when I was a desk jockey for a while and when I was still in the Army — being an SF engineer involves a good bit of math that you know about going in (and no calculators, so programming is no help there anyway) but is also huge amounts of paperwork that they never tell you about until after you walk thousands of miles to get your floppy green hat.]

[2. This is every bit as damaging as the way that leftist political thinkers loosely throw around the word “society”. As in “you owe it to society” and “society owes it to them” or “society must regulate X, Y, and Z” when what they really mean in some cases is actually “your community” and other cases as “the government”, which convolutes the discussion enough that obviously unacceptable things can seem acceptable — which is similar to how obviously non-OO things have been massaged into a OO-ish shape in the minds of thousands, but still remain just as procedural or functional as ever they were in reality.]

[3. This mistake is somewhat comically enshrined in the new NoSQL stuff, which consists principally of reinventing pre-relational data systems IBM already worked on and largely discarded decades ago.]

[4. In fairness, almost everything is running on Linux, and this makes development much easier than if I were trying to tackle the full spectrum of device OSes out there. Who wants to write a 3D reporting face for Blackberry that needs to work and look the same way it does on Android, KDE on Linux, or iOS (or Windows Phone… haha!).]