How Fast is Finch?
Turns out I’ve never mentioned anything about Finch, a library I’m working on most of my free time, in my personal blog. So I decided to finally fix that and write a small note on what I think about Finch’s performance as an HTTP library/server. To be more precise, I want to comment the most recent results of the TechEmpower benchmark and perhaps, give some insights on why Finch is ranked so high there.
A couple of days ago, results from the most recent run of the TechEmpower benchmark were published. While I was expecting Finch to perform well there, I didn’t expect it to be the second fastest HTTP library written in Scala.
Impressive results by #Finch (now #Scala 2nd fastest HTTP library) running @techempower benchmark (430k QPS peak): pic.twitter.com/YeBMnJeQ5W— Vladimir Kostyukov (@vkostyukov) February 27, 2016
With that said, I’ll go ahead and answer my own question in the title of this post, “How Fast is Finch?”. Looking at the chart, it’s obvious that Finch is fast enough to perform really well on 99.99% of your business problems. At least, in comparison with other Scala libraries.
The most interesting part of this discussion is trying to understand why it performs so well? Why that insane level of indirections, Finch involves on top of Finagle services, doesn’t add much overhead? The quick answer would be: Finch owns most of its high performance to the fast and battle-proven libraries it depends on (Finagle, Circe, Cats and Shapeless). The secret recipe is quite simple - take fast components (no matter functional or imperative) and glue them together using the rock-solid (pure) functional abstractions that are easy to test and reason about.
Thus, Finch is fast because …
Finagle is fast
Finch was designed with one goal in mind: provide an easy to use API (i.e., combinators API) on top one that’s easy to implement (Finagle services). Obviously, it should involve some overhead on top of bare metal Finagle. And it does: by our latest measurements it adds 10% of allocations and 5% of running time on top of Finagle, which is not so dramatic and, I’d say, pretty good for a pre 1.0 library.
When it comes to Finagle, there is no doubt in its performance. The Finagle team at Twitter (which I’m luckily a part of) puts a lot of effort to make sure that Finagle’s performance is constantly improving. There is a number of micro-benchmarks we run on each commit to critical components. There are integration tests we write using the internal framework called Integ to load test different Finagle topologies. Finally, there is https://twitter.com that stress tests Finagle 24/7 doing millions of queries per second (DC-wise).
Finagle itself is built by the same principles Finch is. It reuses the industry’s best practices and runs its IO layer on Netty, which is well-know as the best thing that happened to JVM in years. Netty is everywhere: I’d be surprised if Netty code doesn’t handle at least 10% of your everyday traffic. You send at tweet - Finagle and Netty take care of it. You upload your photos to iCloud, talk to Siri - Netty handles it. This list is almost limitless, I’m not sure I know any JVM shop around that doesn’t use Netty in some way.
Circe is fast
While Finch is designed to be agnostic to a concrete serialization library, there is one that plays really nicely with Finch. Circe is relatively new JSON library, started as fork of Argonaut, but ended up as a completely standalone and mature project. It promotes type-full programming and provides compile-time mechanisms for deriving JSON codecs for sealed traits and case classes.
Even though Circe is young, it’s already one of the fastest Scala JSON libraries around, which is quite mind-blowing given how nice, thoughtful and boilerplate-less its API is. Part of the Circe’s great performance comes from the library it uses to parse JSON strings into JSON ASTs. This library is called Jawn and it’s one of the fastest (if not the fastest) ways to parse JSON on JVM.
Shapeless is fast
Shapeless is a generic programming library used by many Scala projects (including Circe, Spray, scodec, etc) to implement generic API (e.g., abstract over tuple arity) in a boilerplate-less manner.
While it might seem like Shapeless does a lot of work and does add a lot of overhead, it’s not really the case. Most of the Shapeless-related work happens at compile time and does not affect program running time. It shouldn’t be a surprise that Shapeless-powered code does increase compilation time, but it almost never increase running time. Finch benchmarks of the derived vs. custom written endpoints only conform that - the performance is literally the same.
Finch is fast
The bottom line is - Finch is in a good company. There is a team or a person, behind every single library Finch uses, dedicated to its future and performance. I’m confident in those people and I’m confident in libraries they maintain. Finch takes a lot from the OSS community and tries to pay it back with a good performance.
The fact that Finch performs so well gives me a hope that the abstractions we’ve chosen in the beginning are not completely broken performance-wise. And this makes me confident in Finch’s future performance. We haven’t stopped yet. In fact, we haven’t started yet and the actual performance work is only planned as a post 1.0 activity.