Existing by coincidence, programming deliberately
This post is an expansion of
a comment I made on HN recently
and continues my Extreme Learning series.
Previously I discussed how I’ve managed to break production
with Redis,
with PostgreSQL
and with healthchecks.
Now I’ll show you how I did it with Cloud Run too.
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When engineers discuss program design and system architecture,
a common source of misunderstanding is concurrency.
Often that’s because we make internal assumptions about it,
which we presume to be self-evident.
But we don’t all make the same assumptions,
so you can end up in a situation
where multiple conflicting beliefs are held
about the concurrency of a system
and nobody realises.
Left unchecked,
these misunderstandings can lurk
until much later in the development process,
when they’re more expensive to fix.
You can prevent these misunderstandings from happening
by making concurrency explicit up-front,
in a diagram.
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Every technology stack has idiosyncrasies
that can trip people up
when they first encounter them.
For whatever reason,
I’ve particularly experienced this
with engineers coming to Node.js from other platforms.
Node is versatile,
but there’s a specific paradigm of usage
that can be surprising to the uninitiated.
So, having been through this journey with people a few times now,
here is my primer for anyone new to Node.
The paradigm it talks about is asynchronous programming,
or async for short.
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There’s a fascinating chapter in Keith Johnstone’s Impro,
called Status.
The context is improvisational theatre
but it contains some real wisdom about life in general.
I’ve thought about it a lot over the years
and it’s gradually informed some opinions I hold
about healthy and unhealthy ways
that engineering teams work together.
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This post is an extension
to a talk I gave recently at work.
It was arranged at short notice
and the audience were experienced engineers,
so I kept it brief and tried not to be patronising.
But the feedback afterwards was quite positive
and there were some questions too,
so here’s the extended version
for anyone interested.
Note that our team conducts pre-merge reviews,
and some of the suggestions are specific to that context.
I actually prefer post-merge reviews,
but am yet to persuade everyone I work with
that they’re better.
Oh, and trigger warning:
this post contains opinions.
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Recently I built a system
that uses vector search to logically truncate long documents
and retain the most significant parts
according to some search term.
I’m a dummy,
with no background in machine learning or mathematics,
so there were new concepts for me to understand
and implementation details to figure out.
This post summarises what I learned.
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For the last ten months or so
I’ve worked on integrating GPT
(various flavours)
with a large production codebase.
It’s been one of the most chaotic periods of my career,
featuring numerous false starts,
changes of direction and
rapid improvements followed by major setbacks.
This is what I’ve learned.
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Production outages are great
at teaching you how not to cause production outages.
I’ve caused plenty and hope that by sharing them publicly,
it might help some people bypass part one
of the production outage learning syllabus.
Previously I discussed ways I’ve broken prod
with PostgreSQL
and with healthchecks.
Now I’ll show you how I’ve done it with Redis too.
Read more