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0 pointskuschku9y ago0 comments

Almost all high-scale infrastructure tooling? Are there many databases, parallelism frameworks, indexing systems, or machine learning frameworks in go?

I’d be interested if you can name any fields where non-Google go projects dominate. I only know of containers.

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cube22229y ago· 3 in thread

Service Discovery and configuration (Consul, etcd), Monitoring (Prometheus and Influx), non-container cluster management (Nomad), databases (CockroachDB), messaging (NATS, Jocko), Terraform. Other than that, I think everybody noticed the frequent posts "xxx database written in Go". Also, most high scale computing companies now use Go for their infrastructure parts.

EDIT: You can also check out all the stuff the CoreOS team puts out.

kuschkuOP9y ago

I’ve actually never used any of them, and none of the devs or ops people I usually talk to used them either.

We never even heard of any of those except for CockroachDB.

Are you sure these are "dominating the entire industry"-products, as you mentioned before? Or is it more that they’re only used in SV?

cube22229y ago

Totally not, I'm working in Poland. And, I'm sorry to say, if you haven't heard about Consul, then you're really not in a position to judge if infrastructure tools are or aren't written in Go. Consul is the de'facto industry standard for service discovery for a few years already.

EDIT: Cockroach is actually the most SV'ish of all those.

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koffiezet9y ago

In the ops world, these tools are pretty much the standard. If you look at the state of many of the tools targeted at infrastructure were, with in the best scenario a decently maintained Python code-base - it's no surprise the ops world jumped on Go.

Go's main attraction there is that it's a simple, script-like language that compiles to easily deployed static binaries. That enables people who's main job is not necessarily coding, but with a good scripting background to solve problems by writing more complex/performance-critical stuff which was previously not feasible in languages like Python, Ruby, ...

These projects requiring more performance would have required a seasoned dedicated C/C++ coder before, but now they can be started and maintainer by people initially not focusing on programming, but on solving problems they are facing in their daily job. They don't have the time to invest learning a language with complex typesystems where initially, you'll be fighting the language itself more than solving actual problems. For programmers, this investment is useful, it's their main skill and is worth something. Ops people have other things to worry about, and just want to solve problems.

It does mean wheels are being reinvented, but the major projects now have pretty smart people working on them, hitting the limits of the language and it's garbage collector, pushing them forward.

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