My reading is: people use Rust because it’s fast but then they complain about the semantics that make it fast.
In other words, be careful what you wish for.
Most people would probably be better served by a language that was a tiny bit slower but had better developer productivity. However, once you deviate from the goal of “as fast as possible”, then you have to choose which parts you want to sacrifice speed for productivity. Like Excel, everybody agrees that Rust is too complicated but nobody can agree on which 10% to remove.
> people use Rust because it’s fast but then they complain about the semantics that make it fast.
I don't think most people use Rust because it's fast - fast is nice but Rust is being thrown at a bunch of use cases (e.g. backend services and APIs) for which it replaces "slower" garbage collected languages (the language being faster doesn't always make the overall product/service faster but that's a separate question).
What Rust gives you is a viable potential alternative to C and C++ in places where you absolutely can't have a GC language, and that's a huge deal, the problems and confusion start when people try to use Rust for everything.
> everybody agrees that Rust is too complicated
I don't think this is true either - a large part of the Rust community seem to think that it's as complicated as it needs to be. As a beginner/outsider, I found it kind of cumbersome to get started with, but that's certainly not everyone's opinion.
> Most people would probably be better served by a language that was a tiny bit slower but had better developer productivity.
True, and such languages already exist and are widely used, Rust doesn't need to fit that use case.
> I don't think this is true either - a large part of the Rust community seem to think that it's as complicated as it needs to be. As a beginner/outsider, I found it kind of cumbersome to get started with, but that's certainly not everyone's opinion
With any language there’s an active part of the community and then there’s the “dark matter” of people who use the language but are not actively involved in shaping its direction, forums or subreddits, etc.
Of course the people who are actively involved are likely to be of the opinion that all the complexity is necessary, but I doubt that applies to the broader Rust userbase.
> I don't think this is true either - a large part of the Rust community seem to think that it's as complicated as it needs to be. As a beginner/outsider, I found it kind of cumbersome to get started with, but that's certainly not everyone's opinion.
Personally I feel it's not complicated enough. Where is my function overloading, variadic templates and usable compile time reflection? (Sure you can sometimes use macros but ew macros)
> Personally I feel it's not complicated enough. Where is my function overloading, variadic templates and usable compile time reflection? (Sure you can sometimes use macros but ew macros)
Indeed. Rust is really crying out for a real CTFE implementation + richer macros to replace the mess that is procmacros (I really don't want to have to run an arbitrary external binary with full system access just to manipulate the AST...)
When GP said “most”, I interpreted it more broadly. Most applications simply do not require the guarantees of a non-GC language. When you expand that horizon, list of contenders becomes considerably larger - even when restricted to statically typed languages.
Yes for example many Python users switched to Go, a native code GC language, and are satisfied with the performance.
There’s also the middle ground of Swift’s memory management which uses compiler-elided refcounting - i.e. the compiler detects when a count goes up then down again and removes those operations.
> There’s also the middle ground of Swift’s memory management which uses compiler-elided refcounting - i.e. the compiler detects when a count goes up then down again and removes those operations.
In the face of threading that's not a safe optimisation; if another thread decrements the refcount inbetween those two removed operations, boom. The compiler will have to track every variable that crosses threads or something.
Scalability and cost -- Loki stores the the actual log data on S3 and only keeps an index of a few fields. Log queries that can then efficiently target the (hopefully small) set of files containing the data and loki can re-parse those specific files from S3 to display the log results.
nushell is also amazing for exploring data quickly like this! I can't use it as a daily driver shell, but I just call it directly from whatever other shell I'm already in and then ^D back to my prior session when I'm done exploring. Works great and lets me visualize realllly nicely.
I can't live without this anymore! However, occasionally sqlite won't quite guess the type affinity as I'd hoped for a column, then I do have to resort to enumerating all the types.
I find it slightly annoying to have to switch mode back to something reasonable again, since mode impacts query results as well as imports.
Despite doing this every few weeks, I can never remember what the commands are! The Zui might improve this workflow for me a bit. Worth a shot!
Full disclosure, I currently work at Mux on the video product. Previously though, I worked at an education startup with user generated video content. Like many others commenting on this thread, I built a simple queuing system using RabbitMQ and celery, transcoding on EC2 with ffmpeg. While we might have saved some money by doing this in house, we almost certainly discouraged users from uploading content because the entire video needed to transcoded before it could be viewed. For use cases like the breaking news or high traffic user generated content, you really want to minimize wait time, and that requires some kind of special sauce. At Mux, we encode content just in time for very fast publish times. It’s very challenging to do this on your own.
I've never seen nushell as a daily driver -- more as a data exploration tool. Have a random export you want to go splunking in? `nu` from your current zsh session and go wild. When you're done, ^D back to your main zsh session, job done. Whereas `jq` is only useful for json, and `xsv` is only useful for CSVs, `nu` offers uniform syntax for exploring many different formats and producing structured data out at the end as well. Neat!
Can you saw more about this? I've never heard of anything like this and can't figure out if its genius or silly. Things I'm curious about:
* Are you working on a SAAS product or embedded/IoT project or hobby project?
* How do you aggregate the SQLite logs together from disparate machines? Seems like you probably can't use fluentbit/filebeat/etc.
* Where do you query these logs?
* How do you structure these logs? (timestamp, machine, message) or something with more columns?
* Are you able to capture stacktraces?
I _love_ the idea of leveraging SQLite for this kind of scenario and possibly skipping a lot of messy plumbing or pricy vendors, but I'm uncertain how this works.
We ship a B2B product that spools to a log.db when running on client machines. We built in-house tooling that is tailored to obtaining and analyzing copies of these databases.
Stack traces, user actions, 3rd party logs, et. al. are meticulously tracked in a schema we thought most appropriate for our business.
As a Rust fanboy, I mostly agree with this sentiment. Theoretically, Rust should prevent “security” issues arising from memory safety issues like heartbleed (maybe not actually heartbleed) but a good GC would too. I do think Rust does push a little harder on correctness via Result types and things like Sync/Send traits, but I don’t have much Java experience to fully compare.
However, type correctness won’t necessarily prevent a DDoS, stolen password, specter, timing attack, phishing attack, etc. Type safety is a nice-to-have but not sufficient for security in SAAS products.
Have you considered making this a frontend to apache arrow or datafusion? It seems like Flink or kSQLdb could use this as an alternative syntax to produce the same physical queries. Love kusto!!
