[kerng]

I have not noticed these technical shifts per se. What I have noticed is that mature engineers move on and do other things and new ones reinvent the wheel with some new fancy language or term which then becomes the new way of doing things, and the cycle repeats. Sometimes there is a great deal of value when a new level of abstraction happens but I wouldnt call this a shift, it's just progression.

Many of the underlying problems and solutions exist for decades. Database systems you mention are a good example of this.

[spamizbad]

When I talk about shifts, I'm referring to things like the proliferation of smartphones and tablets, which increase the net-demand for software and along with an entirely new specializations of knowledge.

While there are some key concepts to things like databases, the fact remains that your 1985 database would not be considered sufficient in todays world: It would have too many limitations, lack features we now take for granted, would not scale to modern data requirements, etc. Supporting all that "modern" functionality is non-trivial and requires a huge amount of effort. You can't just say "Well, we figured out space and computationally-efficient hashing, so relational databases are well on their way to being feature-complete"

There's a reason we haven't stuck with 1.0 on our platforms, and it's not just security or a desire for a bigger version number: New demands required new functionality and new ways of building things.

[jimbokun]

"When I talk about shifts, I'm referring to things like the proliferation of smartphones and tablets, which increase the net-demand for software and along with an entirely new specializations of knowledge."

iOS was basically AppKit, so anyone already developing for the Mac knew most of what they needed to know to develop for iPhone.

Pretty much every programming innovation is incremental, and doesn't require throwing out all of your previous knowledge and starting over.

[JKCalhoun]

> iOS was basically AppKit, so anyone already developing for the Mac knew most of what they needed to know to develop for iPhone.

Maybe. But AppKit was not the Mac Toolbox.

When my career began being good at memory management was a skill to be proud of. I would say now, being good at concurrency is a skill to be proud of.

I don't really have to worry about memory management any longer but didn't worry about threading when I started my career.

As I see the younger generation entering the programming field I wonder in what ways the craft will be different when they've had a few decades under their belt.

Will parameter tuning datasets for machine learning be the coveted skill? Who knows.

[pennaMan]

>so anyone already developing for the Mac knew most of what they needed to know to develop for iPhone

But the demand for developing in AppKit suddenly increased by orders of magnitude.

[convolvatron]

i'll just leave this here https://books.google.com/books/about/Distributed_Databases.h...

[nostrademons]

MongoDB didn't become a public company through innovations in fundamental distributed database technology or even through good engineering. They became a public company because once Javascript became adequate for building client software, there was a strong incentive to build MVPs using the same data structures from client to server to DB, and once you build an MVP that gets adoption there's a strong incentive not to switch databases.

That's the sort of shift in the environment that the grandparent is talking about. Fundamental CS tech was arguably better in the 1970s and 1980s, because it moved more slowly and you had time to get the details right. That doesn't matter if you're building say a mobile Ethereum wallet in 2018, because you're building for the user expectations of today, they don't care about data integrity or security as long as it doesn't fail during the period where they're deciding which tech to use, and software that solves the problem (poorly) now is better than software that doesn't exist.

[vkou]

> Fundamental CS tech was arguably better in the 1970s and 1980s, because it moved more slowly and you had time to get the details right. That doesn't matter if you're building say a mobile Ethereum wallet in 2018, because you're building for the user expectations of today, they don't care about data integrity or security as long as it doesn't fail during the period where they're deciding which tech to use, and software that solves the problem (poorly) now is better than software that doesn't exist.

I believe you are a victim of survivorship bias.

There was plenty of shitty software in the 70s and 80s. The difference between then and now is that we haven't been able to wait for 4 decades, to see what software of 2018 stood the test of time.

[kerng]

I agree, back then there was not a mindset of move fast and break things. It was foundational research, a lot results and learnings from then are still applicable today.

[sedachv]

> It was foundational research

In the 1980s there was a lot of "foundational research" (poorly re-inventing the wheel) for microcomputer people who did not know about the work done on large computers in the 1960s. Move fast and break things was also very much a thing for microcomputer manufacturers and most microcomputer software vendors. Look at how many releases software packages went through, and at what rate.

[oblio]

I think you're agreeing to disagree. His viewpoint, to me at least, is opposite to yours. Or at least parallel. He never said that today there's no foundational research.

[ams6110]

> software that solves the problem (poorly) now is better than software that doesn't exist.

That has always been the case and is about as good a one-line summary of the software industry as I can think of.

[fnord123]

It could be a one line summary of Richard Gabriel's famous essay "The Rise of "Worse is Better""

https://www.jwz.org/doc/worse-is-better.html

[chrisco255]

Done is better than good!

[tracker1]

But on the flip side of that slow movement, you wind up with things like Oracle... for good or bad.

[jskaggz]

And the mainframes that run our banks, transportation systems, healthcare, public safety.. etc etc. Use the right tool for the job, price it against what the market will bear. Pacemakers and insulin pumps driven by npm updates - shudder -

[joshuamorton]

This predates the formulation of the cap theorem by ~15 years, so I doubt it contains everything relevant to a modern distributed database.

[eropple]

It's before my time, but I'm pretty sure people had at least an intuitive understanding of what partitioning does to a datastore before Eric Brewer wrote it down.

[kerng]

I'm not sure why you get downvoted, I'll upvote you.

The "modern" database systems are now going back to the exact design principles that the books you refer to solved long time ago. There is tons of research, dissertations,.. that focuses on this from decades ago.

Its just now that the new systems realize that these problems actually exist.

If you dont know the history of a certain field and what came out, you repeat and make the same mistakes again. This seems to also apply to software engineering.

[RhodesianHunter]

I can assure you that the major theories behind modern big distributed databases were not written in decades old books.

[kerng]

Yeah, distributed database systems from the late 70s, early 80s actually had certain transactional guarantees that some of these "modern big distributed systems" you refer to still dont have.

[sedachv]

Yes, some of those theories were also applied in decades old systems. Look up Tandem Computers and Teradata.

[lumost]

In maturing parts of the software industry you'll often see a desire to stay with the times in order to maintain a competitive edge, re-inventing the wheel often looks like full/partial re-writes of a system for minor marginal gains.

A great example of this is the evolution of FB/Google/Amazon. Portions of their core tech have been completely re-written over the years for marginal gain, but there is a large premium to being the best in tech.

In other parts of the industry every new cycle enables some new area of tech, and those marginal gains become the minimum bar for entry. e.g. Deep Learning and Computer Vision, distributed systems and cloud computing/SaaS.

[hopler]

You overestimate the quality and reliability and expandability of those old systems. The 1998 10 Blue Links tech couldn't support the functionality and scale of Google today.