How Rust Lets Us Monitor 30k API calls/min

https://blog.bearer.sh/how-rust-lets-us-monitor-30k-api-calls-min/#.XujqagRRU9Q.hackernews

by cfabianski • 6 years ago

97 76 6 years ago

blog.bearer.sh

qrczeno• 6 years ago

That was a real issue we were struggling to solve. Feels like Rust was the right tool for the right job.

didroe• 6 years ago

I'm one of the engineers that worked on this. It was the first Rust production app code I've written so it was a really fun project.

mamcx3• 6 years ago

One of the interesting effects of using rust is saving money! I also migrate a F#/.NET ecommerce backend and can run in less RAM/CPU that make my bills lower.

chrischen2• 6 years ago

Does Rust offer much of the same language features as F#?

raphinou• 6 years ago

Can you share information about your experience? I'm currently working on a F# project, enjoying the functional approach, while having a lot of libraries available on the .Net platform. The |> operator is one I use all over the code, but Rust doesn't support custom operators. Is that annoying, or not at all? Is your code less functional and more imperative style due to Rust?

jturpin• 6 years ago

This is a point that's really attractive to me as well. Faster language and runtime means I can run on fewer, cheaper servers which means when its time to scale 10 or 100x, it's not a massive cost increase.

therealdrag0• 6 years ago

Looks like some of your screenshots are from NewRelic and the last one is from Datadog?

cfabianski• 6 years ago

Yes we switched over from one to another because NewRelic doesn’t really support Rust and DD supports StatsD and there is no lock-in. Good spot ;-)

vmarchaud2• 6 years ago

Interesting post ! From what i've understood you had only one instance of NodeJS, while i agree that Rust is more performant generally, couldn't have you just added more instance ?

eggsnbacon17• 6 years ago

They didn't mention Java as a possible solution, even though its GC's are far better than anything else out there. I have nothing against Rust but if I was at a startup I would save my innovation points for where they're mandatory

JoshTriplett• 6 years ago

I have nothing against Rust but if I was at a startup I would save my innovation points for where they're mandatory

An article published today, addressing that exact point: https://tim.mcnamara.nz/post/621040767010504704/spend-your-n...

andrewzah3• 6 years ago

Ah yes, a 236 word "article", that says to choose "boring old technology" and also to use Rust in the same breath.

This article should mention that rust isn't close to ready when it comes to web backends. As much as I love Rust, if I were running a startup or even a decently sized company I would always choose Rails. Now -that- is boring, old... and mature technology. Certain components could get re-written in Rust, certainly, but there's no reason to ignore a mature ecosystem from the start.

dep_b• 6 years ago

It's not like Elixir doesn't benefit from a battle hardened VM since it's older and has been used in these kind of high volume scenarios before Java was.

dnautics• 6 years ago

not to mention a GC that you basically don't have to hire an expensive FTE "enterprise dev" to tweak and generally give you good p99s.

andrewzah3• 6 years ago

Rust doesn't have a GC; it uses an ownership model instead.

Ultimately in a startup it comes to what people are comfortable with. I personally would use Rust for something like this because I am comfortable with Rust. It's a perfect use case for it as well, imho.

TheTank• 6 years ago

One step beyond good Java GCs is to write fully zero-GC Java code. The advantage of it is complete control over your performance which means your software is going to be consistently fast. The disadvantage is that it is relatively difficult to obtain.

If you want to see an example of fully zero-GC Java, you can check out QuestDB on Github [1] - Disclaimer I work for QuestDB.

[1]https://github.com/questdb/questdb

masklinn• 6 years ago

One step beyond good Java GCs is to write fully zero-GC Java code. The advantage of it is complete control over your performance which means your software is going to be consistently fast. The disadvantage is that it is relatively difficult to obtain.

I don't know that it's actually possible in the general case, as Java's support for value types remains wholly insufficient. IIRC the ixy folks never managed to remove all allocations from the java version.

lostcolony6• 6 years ago

They ruled out a language because it had stop the world GC, and even if it removed the bottleneck, it would likely become one later. Java has the same issue. Rust does not.

Not sure why they'd consider Java, given that concern.

eggsnbacon17• 6 years ago

Shenandoah and ZGC collectors have worse case pauses of ~10ms and average pauses of 0.5ms . The average pause is faster than malloc() sometimes in C, so you won't really be introducing more latency than C does.

The other option is to avoid allocating memory at all, which you could do in C/Rust but also in Java. The vast majority of shops given the choice for low/no allocation performance use Java anyways (HFT)

zozbot2343• 6 years ago

They did mention Go that also has a modern GC, and does not need to run your app under a software VM. But ultimately, even Go is just another GC language. They had no real need for GC and the like so why use Go, let alone Java?

eggsnbacon17• 6 years ago

Go's GC is notoriously low tech. Its design dates back to papers published in the 70s. Java's Parallel collector is most similar to Go's GC and its deprecated for removal due to poor performance.

Go collects VERY frequently to keep average pause times low, which hurts throughput. It also has pathological worst-case pause times, which is what the author ran into.

My suggestion of Java is just because its been used for high performance REST for decades. Rust has Actix and a few other frameworks, in Java you have 20+ options. Its a lot easier to get something off the ground when you're 99% sure you won't have to build anything except some glue code. Yes, Rust is faster, but is 30% better performance worth treading the wilds while you're trying to keep a startup afloat, I don't think so personally.

Using Java with Shenandoah or ZGC collector would directly handle their GC issue with rather boring technology

meritt• 6 years ago

Sorry, I must be missing something in this blog post because the requirements here sound incredibly minimal. You just needed an HTTP service (sitting behind an Envoy proxy) to process a mere 500 requests/second (up to 1MB payload) and pipe them to Kinesis? How much data preparation is happening in Rust? It sounds like all the permission/rate-limiting/etc happens between Envoy/Redis before it ever reaches Rust?

I know this comes across as snarky but it really worries me that contemporary engineers think this is a feat worthy of a blog post. For example, take this book from 2003 [1] talking about Apache + mod_perl. Page 325 [2] shows a benchmark: "As you can see, the server was able to respond on average to 856 requests per second... and 10 milliseconds to process each request".

And just to show this isn't a NodeJS vs Rust thing, check out these webframework benchmarks using various JS frameworks [3]. The worst performer on there still does >500 rps while the best does 500,000.

It's 2020, the bar needs to be much higher.

[1] https://www.amazon.com/Practical-mod_perl-Stas-Bekman/dp/059...

[2] https://books.google.com/books?id=i3Ww_7a2Ff4C&pg=PT356&lpg=...

[3] https://www.techempower.com/benchmarks/#section=data-r19&hw=...

lostcolony6• 6 years ago

They list out what is being done by the service - "It would receive the logs, communicate with an elixir service to check customer access rights, check rate limits using Redis, and then send the log to CloudWatch. There, it would trigger an event to tell our processing worker to take over."

That sounds like a decent amount of work for a service, and without more detail it's very hard to say whether or not a given level is efficient or inefficient (we don't know exactly what was being done; we can assume that they're using pretty small Fargate instances though since the Node one came in at 1.5G). They also give some number; 4k RPM was their scaleout point for Node (that's not necessarily the maximum, but the point they felt load was sufficiently high to warrant a scaleout; certainly, their graph shows an average latency > 1 second). Rewriting in Rust, that number was raised to 30k RPM; 100 mb of memory, < 40ms average latency (and way better max), and 2.5% of CPU.

Given all that, it sounds like, yes, GC was the issue (both high memory and CPU pressure), and with the Rust implementation (no GC) they're nowhere near any CPU or memory limit, and so the 30k is likely a network bottleneck.

That said, while I agree that sounds like a terrible metric on the face of it, with what data they've provided (and without anything else), it also sounds like it may be due to they're just operationally dealing with very large amounts of traffic. They may want to consider optimizing the network pipe; not familiar enough with Fargate, but if it's like EC2, there may be a sizing of cpu/memory that also gives you a better network connection (EC2 goes from 1 GBPS to a 10 GBPS network card at one instance type)

theikkila• 6 years ago

wasn't that 4k requests per minute?

wahern2• 6 years ago

That sounds like a decent amount of work for a service

5+ years ago I wrote a real-time transcoding, muxing streaming radio service that did 5000 simultaneous connections with inline, per-client ad spot injection (every 30 seconds in my benchmark). Using C and Lua. On 2 Xeon E3 cores--1 core for all the stream transcoding, muxing, and HTTP/RTSP setup, 1 core for the Lua controller (which was mostly idle). The ceiling was handling all the NIC IRQs.

While I think what I did was cool, I know people can eke much more performance out of their hardware than I can. And I wasn't even trying too hard--my emphasis is always on writing clear code and simple abstractions (though that often translates into cache-friendly code).

At my day job, in the past two months I've seen two services in a "scalable" k8s clusters fall over because the daemons were running with file descriptor ulimits of 1024. "Highly concurrent" Go-based daemons. For all the emphasis on scale, apparently none of the engineers had yet hit the teeny, tiny 1024 descriptor limit.

We really do need to raise our expectations a little.

I haven't written any Rust but I have recently helped someone writing a concurrent Rust-based reverse proxy service debug their Rust code and from my vantage point I have some serious criticisms of Tokio. Some of the decisions are clearly premature optimization chosen by people who probably haven't actually developed and pushed into production a process that handles 10s of thousands of concurrent connections, single-threaded or multi-threaded. At least not without a team of people debugging things and pushing it along. For example, their choice of defaulting to edge-triggered instead of level-triggered notification shows a failure to appreciate the difficulties of managing backpressure, or debugging lost edge-triggered readiness state. These are hard lessons to learn, but people don't often learn them because in practice it's cheaper and easier to scale up with EC2 than it is to actually write a solid piece of software.

jeffbee5• 6 years ago

Seriously, 500 qps was something we used to do in interpreted languages on the Pentium Pro. But this kind of blog post is a whole genre: How [ridiculous startup name] serves [trivial traffic] using only [obscenely wasteful infrastructure] in [trendy runtime framework that's a tiny niche or totally unknown in real industry].

jen20• 6 years ago

totally unknown in real industry

Microsoft, Apple, Amazon, Oxide, Mozilla, Dropbox and CloudFlare would like a word...

hu3• 6 years ago

It took your comment to make me notice it wasn't 30k requests/second but minute instead.

500 requests per second is what I would expect of a default PHP + Apache installation on a small Ubuntu server.

I too have a hard time grasping whats special here. For example I saw cached Wordpress setups handle 400 to 500 requests per second. And Wordpress isn't known for performance even with caching plugins.

varjag• 6 years ago

To put it further into perspective, C10K challenge was circa 1999:

http://www.kegel.com/c10k.html

hobbescotch• 6 years ago

Having never dealt with issues relating to garbage collection before, how do you go about diagnosing GC issues in a language where that’s all handled for you?

the84722• 6 years ago

In the java realm you have very fine-grained GC logging that provides insight into the overall behavior of the GC and its different subcomponents. Then there are recording/debugging facilities that allow you to trace allocations, how long objects live, analyze the entire heap (including unreachable but not yet collected objects). And higher-level monitoring APIs separate from the logging and debugging stuff. You can also choose between collectors with different characteristics, trading between overall heap size, latency, utilization of CPU cores and other factors.

013a• 6 years ago

There are some general tricks that are language-agnostic, like allocating a huge "buffer" object when the app starts, the size of which is some significant portion of the memory you allow the process to use, which always has a reference, then storing references to other objects you need in that big object. In other words, circumvent the garbage collector.

Of course, this has its own issues, but I've seen it done in e.g. Go before. Its likely you'll inevitably end up with leaks, but if your service is fungible and can tolerate restarts, basically what you're doing is moving the "GC Pause" to be a "Container Restart" pause, which may be slower, but would happen less often. Some languages have ways to manually call the GC (Node is not one of them, afaik).

the84722• 6 years ago

Node allows you to call global.gc() if you enable that functionality with a separate argument. In many cases this is an anti-pattern that would make your application behavior worse rather than better, that's why you have to opt into that.

eggsnbacon17• 6 years ago

There are some general tricks that are language-agnostic, like allocating a huge "buffer" object when the app starts

I've only seen this done in Go :)

vmarchaud2• 6 years ago

In the case of NodeJS, which use the V8 Engine [0], you have access to the diagonistics API [1] that allows you profile your cpu or memory consumption. There are some tools that make that easier (see [2] or [3]) but you often left to interpret the result yourself

[0] https://v8.dev/

[1] http://nodejs.org/dist/latest-v12.x/docs/api/inspector.html#...

[2] https://github.com/davidmarkclements/0x

[3] https://github.com/vmarchaud/openprofiling-node

PS: Disclamer i wrote one of tool i mentionned above [3]

foxknox• 6 years ago

500 requests a second.

jvehent• 6 years ago

Which any programming language can handle easily. The architecture here is more interesting than the language choice.

akoutmos2• 6 years ago

Great article and thanks for sharing! There are a couple of things that stand out at me as possible architecture smells (hopefully this comes across as positive constructive criticism :)).

As someone who has been developing on the BEAM for long time now, it usually sticks out like a sore thumb any time I see Elixir/Erlang paired with Redis. Not that there is anything wrong with Redis, but most of the time you can save yourself the additional Ops dependency and application network hop by bringing that state into your application (BEAM languages excel at writing stateful applications).

In the article you write that you were using Redis for rate limit checks. You could have very easily bundled that validation into the Elixir application and had for example a single GenServer running per customer that performs the rate limiting validation (I actually wrote a blog post on this using the leaky bucket and token bucket algorithms https://akoutmos.com/post/rate-limiting-with-genservers/). Pair this with hot code deployments, you would not lose rate limit values across application deployments.

I would be curious to see how much more mileage you could have gotten with that given that the Node application would not have to make network calls to the Elixir service and Redis.

Just wanted to share that little tidbit as it is something that I see quite often with people new to the BEAM :). Thanks again for sharing!

eggsnbacon17• 6 years ago

I would push rate limiting to the load balancer, HAProxy or Nginx, but that's just me. If you have a round-robin LB in front you just set each instance to limit at 1/nodes rate, that way you don't have to share any state.

If you're load balancing on IP hash you can set each instance to limit at full rate and not worry about it.

Shared state in rate limiting becomes a bottleneck very quickly. If you're trying to mitigate spam/DDOS you could easily get 100,000 requests a second. You're going to max out your shared state db way faster than 10gig lines

akoutmos2• 6 years ago

That is definitely a valid route to go so long as your rate limiting is not dependent on much business logic. If rate limiting is per user or per user per instance/service, I would personally bring that kind of concern into the application where it is closer to the persistence layer where those things are defined (and again handling the business logic inside per customer GenServers).

I have never used this product so just speculation. But I imagine there is some sort of auth token that valid agents send to tell Bearer that this is a valid/invalid request so that things can be trivially rejected to mitigate a DoS/DDoS to an extent.

trimbo• 6 years ago

After some more research, we appeared to be another victim of a memory leak in the AWS Javascript SDK.

Did you try using the kinesis REST API directly: https://docs.aws.amazon.com/kinesis/latest/APIReference/API_...

DevKoala• 6 years ago

There is a couple things I see in this post that I wouldn’t do at all, and I maintain a couple services with orders of magnitude higher QPS. I feel that replacing Node.js with any compiled language would have had the same positive effect.

ecoqba11• 6 years ago

Totally!

newobj• 6 years ago

500qps. i think the more interesting story here is what language/framework COULDN'T do this, than which one could.

cybervasi• 6 years ago

GC of 500 request/s could not have possibly caused a performance issue. Most likely the problem was due to JS code holding on to the 1MB requests for the duration of the asynchronous Kinesis request or a bug in the Kinesis JS library itself. With timeout of 2 minutes, you may end up with up to 30K/min x 2min x 1mb = 60GB RAM used. GC would appear running hot during this time but it is only because it is has to scrape more memory somewhere while up to 60gb is being in use.

zerubeus• 6 years ago

Feels like a HN post being upvoted just bcz it contains Rust in the title (after reader the article) ...

How Rust Lets Us Monitor 30k API calls/min

Related Stories