Sep 08, 2022

Embracing Imperfection

Jon Pither's past mistakes, and how to cope with real data in an imperfect world

author picture
Jon Pither
CEO & Co-founder

I’m haunted by the mess I made earlier on in my career. I saw software as a means to putting in place a rationalised model of reality, whereby the codification of business rules and processes would straighten out and consolidate how things should and would work when software runs my corner of the world.

I would code up everything in a Domain Driven Design frenzy. Some little bit of data needs to enter our system? I’d create a class for it. Let’s codify all those little fields, and mark whether they are optional or mandatory, and what type they are. If this bit of incoming data is part of some kind of philosophical tree of abstraction, then let’s capture this too and make an inheritance hierarchy for all the different abstract concepts our system knows about. I experimented with statically typed MixIns, with questionable success.

At the time it felt like I was doing my employers a service, playing the role of validation-checker, contract-enforcer, and rationality-bringer. Doing anything else, such as using dynamic typing or generic data structures was unthinkable, even if occasionally, I did think it.

In one example that I think about often, our system sat in the middle of a large enterprise, collecting lots of data for reporting, storage, and triggering of events. The trouble was that the definitions of the data we ingested would evolve on a regular basis, such that we were always having to make production releases to catch-up. Worse, we had to synchronise our releases with other upstream and downstream teams and systems doing the same thing. OK, we’ll go first to add a field as optional, so we can be flexible as to when you make a release to your system to introduce the same corresponding field. Then we’ll do a subsequent release to make the field mandatory, to lock down the contract when your release has successfully gone out.

Yikes. Adding a simple field to a payload, end-to-end, would take weeks.

But the upside was I felt busy. And busy I was. The business was constantly evolving the data schemas, which was kind of annoying, and it meant that we were constantly having to play catch-up to move our model of the business world closer to the new reality. When we did add new fields to our schema, we would often have to migrate large subsets of our data, to pretend that this new field always existed. We had to do this so that the UI would work, as would data-reconciliations. Little bits of code all over the place expected a field to be there, and bad things happened otherwise.

As Orwell put it in 1984 when the Party is constantly trying to rewrite history: “Every record has been destroyed or falsified, every book rewritten, every picture has been repainted, every statue and street building has been renamed, every date has been altered. And the process is continuing day by day and minute by minute.”

If I had a time machine and was allowed to experiment with forking history, I would go back to this time in my career. I’d make the schema, that was hard-baked into shippable code, live in the system as data instead, which could be updated via user configuration. I’d make the entire system more flexible and forgiving by, for example, reducing the number of extremely specific UIs, and by making the reporting screens more generic, able to cope with shapes of data that were slightly inconsistent.

A bigger prize would be to make the whole database immutable. Looking back, it was insane to rewrite the past just because we’d got a new field in the model. We should have appended data rather than munging it in place. At the time we did consider this, but we backed off because we didn’t have experience of immutable database designs, and consequently we were scared off.

It would have been worth it though. Just try dealing with complex support queries when the data is changing underneath you. Yes, it would have been worth it.

I now understand that bitemporality - an unglamorous word I’ve grown to dislike - is really about living with immutability in the real world. In an imperfect environment, where we genuinely learn more about our past data and want to subsequently enrich it, and also where operational things like database migrations are a fact of life, we come to understand that we do need to be able to update the past. But at the same time, for a whole host of reasons, we still want to be able to access the original, unaltered data. This is why you need two timelines - one you can edit, and one you can’t.

When it comes to correcting the past, bitemporality allows you to have your immutable cake and eat it. It gives you the best of both worlds: the raw, unedited, original data, living in the database, but also new versions of the data that you’ve remodeled, enriched and evolved. Both the history you want and the history you need.

So it’s a useful concept. But the reason I dislike the word, bitemporality, is that it is bamboozling, intimidating, and once you’ve grokked it, somewhat dull. Bitemporality is like some foundational construction element that goes in underneath your patio, like the mix of steel and sand or whatever that makes it all work. You know it’s there and that you need it, but it’s not something you pay much attention to day-to-day.

The truly exciting thing about bitemporality, is what it enables: being able to drop the first two letters and to focus on temporality. This is stuff of dreams, the promise of the SQL 2011 specification, which allows me to find out when the hell did stuff happen?

I should be able to ingest documents into my data-lake, and then run queries such as when did this field/value first show up? When did field X and field Y have the same value? When did field X and field Y NOT have the same value? Looking at Allen Intervals, one can dream all kinds of ways of querying your data across the timeline.

I believe now that you shouldn’t need to carry out extensive pre-emptive modeling - putting timestamp columns everywhere - in order to access this temporal power. What embracing imperfection means, is being able to ingest data near its purest, original form and make sense of it later. We can give up on the never-ending, tantalisingly out-of-reach attempt to model reality perfectly. We can flip this dream on its head.

Taken to an extreme, firehose your database with documents, regardless of schema, query later. If you need to patch up the data at a later time to make things more consistent, go ahead.

This approach will not be for everyone. Some may see it as giving up, making APIs and systems too permissive, and too generic/flexible. I’ve just seen the opposite cost of locking everything down and attempting to keep pace with modeling out the business domain, in all its minutiae, tirelessly, forever.


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