Cap'n Proto 1.0
capnproto.org
capnproto.org
Ian Denhardt (zenhack on HN), a lead contributor to the Go implementation, suddenly and unexpectedly passed away a few weeks ago. Before making a request to the community, I want to express how deeply saddened I am by this loss. Ian and I collaborated extensively over the past three years, and we had become friends.
As the de facto project lead, it now befalls me to fill Ian's very big shoes. Please, if you're able to contribute to the project, I could really use the help. And if you're a contributor or maintainer of some other implementation (C++, Rust, etc.), I would *REALLY* appreciate it if we could connect. I'm going to need to surround myself with very smart people if I am to continue Ian's work.
RIP Ian, and thank you. I learned so much working with you.
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P.S: I can be reached in the following places
- https://github.com/lthibault
- https://matrix.to/#/#go-capnp:matrix.org
- Telegram: @lthibault
- gmail: louist87
Peace to him.
[0]https://zenhack.net/2019/06/25/a-critique-of-the-capnproto-s...
He has left an amazing legacy that has touched a lot of people. RIP Ian.
It really sucks. And I know exactly what you mean.
https://capnproto.org/news/2013-12-13-promise-pipelining-cap...
[1] https://redis.com/ebook/part-2-core-concepts/chapter-4-keepi...
But even still, you would think it would be more popular.
I think where it shines is when interacting with stateful services. I think part of the reason everyone tries to make everything stateless is because we don't have good protocols for managing state. Cap'n Proto RPC is actually quite good at it.
Cap'n Proto actually does really well with this basic difficulty, because it treats object references as a first-class thing. When you create some state, you receive back a reference to the state, and you can make subsequent requests on that reference. The load balancer can see that this has happened, even if it doesn't know the details of the application, because object references are marked as such at the RPC layer independent of schema. Whereas in a system that returns some sort of "object ID" as a string, and expects you to pass that ID back to the server on subsequent requests, the load balancer is not going to have any idea what's going on, unless you do extra work to teach the load balancer about your protocol.
At present, the only operation allowed here is reading a nested property, and that seems to solve 99% of use cases. But one could imagine allowing other operations, like "take the Nth element of this array" or even "apply this call to all elements in the array, returning an array of results".
Or more specifically, it seems to have client-chosen file descriptors, so the client can open a file, then immediately send a read on that file, and if the open fails, the read will also fail (with EBADF). Awesome!
This is great, but "promise pipelining" also needs support in the client. Are there 9p clients which support promise pipelining? For example, if the user issues several walks, they're all sent before waiting for the reply to the first walk?
Also, it only has promise pipelining for file descriptors. That gives you a lot, definitely, but if for example you wanted to read every file in a directory, you'd want to be able to issue a read and then walk to the result of that read. Which 9p doesn't seem to support. (I actually support this in my own remote syscall protocol library thing, rsyscall :) )
(If I’m not misremembering, Mark Miller later wrote the promise proposal for JavaScript, except the planned extension for RPC never materialized and instead we got async/await, which don’t seem compatible with pipelining.)
The more recent attempts to make a distributed capability system in the image of E, like Spritely Goblins[3] and the OCapN effort[4], also try for pipelining, so maybe if you hang out on cap-talk[5] you’ll hear about a couple of other protocols that do it, if not ones with any real-world usage.
(And I again reiterate that, neat as it is, promise pipelining seems to require programming with actual explicit promises, and at this point it’s well-established how gnarly that can get.)
One idea that I find interesting and little-known from the other side—event loops and cooperatively concurrent “active objects”—is “causality IDs”[6] from DCOM/COM+ as a means of controlling reentrancy, see CoGetCurrentLogicalThreadId[7] in the Microsoft documentation and the discussion of CALLTYPE_TOPLEVEL_CALLPENDING in Effective COM[8]—I think they later tried to sell this as a new feature in Win8/UWP’s ASTAs[9]?
[1] http://erights.org/elib/distrib/captp/index.html
[2] http://erights.org/talks/thesis/index.html
[3] https://spritely.institute/goblins/
[4] https://github.com/ocapn/ocapn
[5] https://groups.google.com/g/captalk/
[6] https://learn.microsoft.com/openspecs/windows_protocols/ms-d...
[7] https://learn.microsoft.com/windows/win32/api/combaseapi/nf-...
[8] https://archive.org/details/effectivecom50wa00boxd/page/150
[9] https://devblogs.microsoft.com/oldnewthing/20210224-00/?p=10...
Pipelining is a bad idea. It reifies object instances, and thus makes robust implementation much harder. You no longer make stateless calls, but you are running functions with particular object instances.
And you immediately start getting problems. Basically, Client Joe calls Service A and then pass the promised result of the call to Service B. So that Service B will have to do a remote call to Service A to retrieve the result of the promise.
This creates immediate complications with security boundaries (what is your delegation model?). But what's even worse, it removes the backpressure. Client Joe can make thousands of calls to Service A, and then pass the not-yet-materialized results to Service B. Which will then time out because Service A is being DDoS-ed.
If you don't mind the question: will there be more work on implementations for other languages in the future? I really like the idea of the format, but the main languages in our stack aren't supported in a way I'd use in a product.
Unfortunately I can't really promise anything new here. My work on Cap'n Proto is driven by the needs of my main project, the Cloudflare Workers runtime, which is primarily C++. We do interact with Go and Rust services, and the respective implementations seem to get the job done there.
Put another way, Cap'n Proto is an open source project, and I hope it is useful to people, but it is not a product I'm trying to sell, so I am not particularly focused on trying to get everyone to adopt it. As always, contributions are welcome.
The one case where I might foresee a big change is if we (Cloudflare) decided to make Cap'n Proto be a public-facing feature of the Workers platform. Then we'd have a direct need to really polish it in many languages. That is certainly something we discuss from time to time but there are no plans at present.
Anyway, thank you for making it open source and for working on it all this time!
How likely is this? What would be the benefits and use-cases of doing this? Would it be a standardized JS offering, or something specific to Workers that is deserialized before it hits the runtime?
Arguably Cap'n Proto RPC might be an interesting way for a Worker running on Cloudflare to talk to a back-end service, or to a service running in a container (if/when we support containers). Today you mostly have to use HTTP for this (which has its drawbacks) or raw TCP (which requires bringing your own protocol parser to run in "userspace").
That said there's obviously a much stronger case to make for supporting gRPC or other protocols that are more widely used.
I am not going to write those language implementations, I have other stuff I need to do, and gRPC is good enough. But the people who love writing language implementations might not understand why Cap'n Proto is great, or at least not understand as well as they understand Golang and Rust, so they will rewrite X in Golang and Rust instead.
Anyway, the great ideas haven't changed in whatever it is, almost 10-15 years you've been working on this, they've been right all along. So it is really about communication.
A comment on HN that really stuck with me was like: "Man dude, this is great, but try to explain to my team that it's Not React. They won't care."
I'm just a guy, I don't know how to distill how good Cap'n Proto is. But "The Unreasonable Effectiveness of Recurrent Neural Networks" is the prototype. What is the unreasonable effectiveness of Cap'n Proto? In games, which I'm familiar with, entity component systems, user generated content and their tooling have a lot in common with Cap'n Proto. "The Unreasonable Effectiveness of ECS" is deterministic multiplayer, but that is also really poorly communicated, and thus limits adoption. Maybe you are already facing the same obstacles with Cloudflare Workers. It's all very communications related and I hope you get more adoption.
I suspect a lot of things will eventually look more like Cap'n Proto and Sandstorm, but it will take a lot of time for everyone else to get there.
We will probably put out a blog post sometime soon with an update.
Feel free to take a crack at it. People would likely be rather cooperative about it. However, know that it's just simply a lot of work.
> In fact, you are using Cap’n Proto right now, to view this site, which is served by Cloudflare, which uses Cap’n Proto extensively (and is also my employer, although they used Cap’n Proto before they hired me)
If I had to guess, they looked at the landscape a bit like I do and regarded Cap'n Proto, flatbuffers, SBE, etc. as being in one category apart from other data formats like Avro, protobuf, and the like.
So once you're committed to record'ish shaped (rather than columnar like Parquet) data that has an upfront parse time of zero (nominally, there could be marshalling if you transmogrify the field values on read), the list gets pretty short.
https://capnproto.org/news/2014-06-17-capnproto-flatbuffers-... goes into some of the trade-offs here.
Cap'n Proto was originally made for https://sandstorm.io/. That work (which Kenton has presumably done at Cloudflare since he's been employed there) eventually turned into Cloudflare workers.
Another consideration: https://github.com/google/flatbuffers/issues/2#issuecomment-...
But other than who worked on them, and sharing some technology choices under the hood, there's mostly no relationship between Workers and Sandstorm.
To this day, Cloudflare's data pipeline (which produces logs and analytics from the edge) is largely based on Cap'n Proto serialization. I haven't personally been much involved with that project.
As for Cloudflare Workers, of course, I started the project, so I used my stuff. Probably not the justification you're looking for. :)
That said, I would argue the extreme expressiveness of Cap'n Proto's RPC protocol compared to alternatives has been a big help in implementing sandboxing in the Workers Runtime, as well as distributed systems features like Durable Objects. https://blog.cloudflare.com/introducing-workers-durable-obje...
Anyway. Cloudflare's always been pretty cost efficient machine wise, so it was a natural choice given the performance needs we had. In my time in the data team there, Cap'n Proto was always pretty easy to work with, and sharing proto definitions from a central schema repo worked pretty well, too. Thanks for your work, Kenton!
For my part, I'm a fan of OCapN, but I am not sure how much time I can personally commit to it, with everything on my plate.
I wish I had better news here. This was a tragic loss for all of us.
For my part I stopped pushing monthly Sandstorm updates this year as there hasn't really been anything to push. Unfortunately Sandstorm's biggest dependencies can't even be updated anymore because of breaking changes that would take significant effort to work around.
I agree a blog post is probably in order.
Would you say that there have been user-friendly Docker/etc. wrappers for self-hosting LAN services? Someone has recommended casaOS (or the proprietary Umbrel), though I haven't tried either yet.
The best link I know is Christine Lemmer-Webber's post:https://octodon.social/@cwebber/110712988569475393
Also, are you on Matrix or Telegram or something of the sort? I was hoping I could ping you with the occasional question as I continue work on go-capnp.
I link it to people all the time, especially when they ask why protobuf 3 doesn't have required fields.
[1] https://capnproto.org/faq.html#how-do-i-make-a-field-require...
https://www.youtube.com/watch?v=YR5WdGrpoug
The capnproto link explains it concretely in terms of a message bus example, which is useful.
But more abstractly you can think of the shape of data (aka schema, names and types) and field presence (optional/required) as separate things
https://lobste.rs/s/zdvg9y/maybe_not_rich_hickey
First, being valid or invalid with respect to a static type system is a GLOBAL property of program -- writing a type checker will convince you of that. And big distributed systems don't have such global properties: https://news.ycombinator.com/item?id=36590799
If they did, they'd be small :) Namely you could just reboot the whole thing at once. You can't reboot say the entire Internet at once, and this also holds for smaller systems, like the ones at say Google (and I'm sure Cloudflare, etc.).
So the idea is that the shape/schema is a GLOBAL property -- you never want two messages called foo.RequestX or two fields called "num_bar" with different types -- ever, anywhere.
But optional/required is LOCAL property. It depends on what version of a schema is deployed in a particular binary. Inherently, you need to be able to handle a mix of inconsistent versions running simultaneously.
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To be pedantic, I woulds say "making illegal states unrepresentable" DOES apply, but you can't do it in a STATIC type system. [1] Your Maybe<T> type is not useful for data that crosses process boundaries.
A distributed system isn't a state machine.
1. Lamport showed us one important reason why: the relative order of messages means that there is no globally coherent state. You need something like Paxos to turn a distributed system back into a state machine (and this is very expensive in general)
2. The second reason is probably a consequence of the first. You can think of deploying a binary to a node as a message to that node. So you don't have a completely consistent state -- you always have an in-between state, a mix of versions. And presumably you want your system to keep working during this time period :)
And that coarse-grained problem (code versioning and deployment) implies the fine-grained problem (whether a specific message in a field is present). This is because protobufs generate parsers with validation for you -- or they used to!
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tl;dr Think of the shape of data (aka schema) and field presence (optional/required) as different dimensions of data modeling. Maybe<T> mixes those up, which is fine in a single process, but doesn't work across processes.
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[1] A very specific example of making illegal states unrepresentable without static types - my Oils project uses a DSL for algebraic data types, borrowed from CPython. The funny thing is that in CPython, it generates C code, which doesn't have any static notion of Maybe<T>. It has tagged unions.
And in Oils we first generated dynamically typed Python at first. Somewhat surprisingly, algebraic data types are STILL useful there.
Now the generated code is statically typed with MyPy (and with C++), and we do pleasant type-driven refactorings. But using algebraic data types were still extremely useful before static typing. They made illegal states unrepresentable -- but you would get the error at runtime.
This is sort of like the difference between a text editor and a compiler. An editor has to deal with code that doesn't even parse, which is easiest if just treats it as a plain text file, but then you're missing a lot of language-specific features that we take for granted these days. Meanwhile, a compiler can require all errors to be fixed before it emits a binary, but it has to be good at reporting what the errors are, because they will certainly happen.
It's unclear to me how the type of the field can be a global property in a large system. From a text editor's point of view, you can just edit the type. How can anyone guarantee that a type is always the same?
Also, SQL tables actually do make closed-world assumptions; every record meets the schema, or it can't be stored there. If you change the schema, there is a migration step where all the old rows in the production database gets upgraded. This doesn't seem unrealistic?
I guess it's unrealistic that you only have one production database, and not also a staging database, and every developer having their own database? And they will be on different versions. As soon as you have lots of databases, things get complicated.
I started using the terminology "interior vs. exterior" for these issues, and I find it pretty helpful - https://www.oilshell.org/blog/2023/06/ysh-design.html
e.g. type systems are interior, network protocols and persisted data are exterior.
SWEs tend to reason about the interior; SREs tend to reason about the exterior. Every problem a SRE deals with has passed type checks.
I see many fallacies where programmers want to think about the interior ONLY. They want the type system to ensure correctness. But that can be taken too far -- there are some things the interior view can't (or doesn't currently) handle, like mixed versions of binaries, schema migrations, etc.
The key point with databases is that your schema is LITERALLY dynamic -- it lives in a process outside your program, outside your type system (unless your program consists entirely of stored procedures, etc.)
Of course most people have some kind of synchronization or ORM (with varying degrees of success). But the point is that the exterior reality is the thing that matters; the interior is just a guide. "When models and reality collide, reality wins"
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On the other hand, I think there can be more static tools in the future -- if they are built to understand more of the system; if they're not so parochially limited to a single process.
But I think these issues are never going away -- quite the contrary they will increase, because software tends to get more heterogeneous as it gets bigger. It's tempting to think that someday there's going to be a "unified platform" that solves it all, but I think the trend is actually the opposite.
The other issue is that while type systems can get better, they're mostly applicable when you control both sides of the wire.
Most orgs writing software do not control both sides of the wire -- they are reusing services like Google Maps or Stripe. In the last 10 years it seems like every org is now relying on 50 different third party integrations (which btw makes the web super slow ...)
As I mentioned in the previous comment, even if you can somehow go into the repo of Google Maps or Stripe, download their schema, and compile it into your binary, that STILL doesn't give you any guarantees.
They can upgrade or roll back their binaries whenever. They might switch from free-form JSON to JSON schema to Swagger, etc. You don't control that decision.
The people on the other side of the wire may have started using protobufs 10 years ago, and don't feel like switching to whatever you think is great right now. There's a lot of heterogeneity across time too, not just across different tech right now.
So fundamentally the problems are with "the world" the type system is modeling, not about or within the type system itself!
Why can this guarantee a field's type but not whether it's required? Because fields have a lifecycle. They get added to the schema, used for a while, and later become obsolete and are deleted.
The maintainers of a protobuf schema can guarantee that a field number is always associated with a certain type, and that when the field is deleted, that field number is never reused. This makes (field number, type) tuples timeless. But they can't say whether a field will be needed forever, and they can't control which versions of the schema are still being used.
Effectively, "required" means "possibly required forever." As long as there is some parser out there that treats the field as required, deserialization will break if you leave out the field.
Changing a field's type is easier, because you don't really do it. You add a new field number with a new type and maybe eventually stop emitting the old field.
This suggests a strategy: fields can be required as long as they're not top-level. You can define coordinates(42) to always be (x,y) where both x and y are required. If you change it to be a 3D coordinate, then that will be coordinates(43) = (x,y,z), and you never reuse field number 42 in that schema.
Then you start to make compromises such as defining an IncompleteCoordinate message that requires x. But what if later you calculate y first and use that to calculate x?
Etc. Etc. I find it more convenient to make everything optional at the wire format level and explicitly validate what is required at the point of use, not at the point of parse.
But yeah, for larger child messages, copying fields would be annoying. Often you want to be able to create child messages without creating the parent.
Which is definitely a counterpoint to the oft-stated argument that you should validate all data upfront.
Either way though, you can still have types, the question is just when and where (in a distributed system, especially) they should be checked.
The more modern approaches, like protobuf or capn proto are designed with the experience of mutating protocols over time.
It works pretty well too unless the new field changes the semantics of old field values, e.g. adding a field "payment_is_reversal_if_set" to a payment info type, which would change the meaning of the signs of the amounts. In that case, you have to reason more explicitly about when to roll out the protocol readers and when to roll out the protocol writers. Or version it, etc.
I was at Google when the "let's get rid of optional" crusade started. It didn't make sense to me then and over a decade later it still doesn't. If a program expects a field to be there then it has to be there, removing the protobuf level checking just meant that programs could now read garbage (some default value) instead of immediately crashing. But the whole reason we have types, assertions, bounds checking and so on is because, almost always, we'd like our software to NOT just blindly plough on into undefined territory when it doesn't understand something properly, so in reality it just means everyone ends up coding those very same required-ness assertions by hand.
Now, Google's business model is remarkably robust to generating and processing corrupt data, so you can argue that in the specific case of this specific company, it is actually better to silently serve garbage than to crash. This argument was made explicitly in other forms, like when they deleted all the assertions from the HTTP load balancers. But in every case where I examined an anti-required argument carefully the actual problem would turn out to be elsewhere, and removing assertions was just covering things up. The fact that so much of Google's code is written in C++ that not only starts up slowly but also just immediately aborts the entire process when something goes wrong also contributes to the brittleness that encourages this kind of thing. If Google had been built on a language with usable exceptions right from the start it'd have been easier to limit the blast radius of data structure versioning errors to only the requests where that data structure turned up, instead of causing them to nuke the entire server (and then the RPC stack will helpfully retry because it doesn't know why the server died, promptly killing all of them).
But this tolerance to undefined behavior is not true for almost any other business (except maybe video games?). In those businesses it's better to be stopped than wrong. If you don't then you can lose money, lose data, lose customers or in the worst cases even lose your life. I don't think people appreciate the extent to which the unique oddities of Google's business model and infrastructure choices have leaked out into the libraries their staffers/ex-staffers release.
The middleman software in question often needed to process some part of the message but not others. It wasn't realistic to define a boundary between what each middleman might need and what they wouldn't need, and somehow push the "not needed" part into nested encoded blobs.
I'm not sure the rest of your comment is really addressing the issue here. The argument doesn't have anything to do with proceeding forward in the face of corrupt data or undefined behavior. The argument is that validation needs to happen at the consumer. There should still be validation.
It was quite realistic to improve protobufs to help dig web search out of their "everything+dog consumes an enormous monolithic datastructure" problem, assuming that's what you're thinking of (my memory of the details of this time is getting fuzzy).
A simple brute-force fix for their situation would have been to make validation of required fields toggle-able on a per-parse level, so they could disable validation for their own parts of the stack without taking it away for everyone else (none of the projects I worked on had problems with required fields that I can recall).
A better fix would have been for protobufs to support composition. They could then have started breaking down the mega-struct into overlapping protos, with the original being defined as a recursive merge of them. That'd have let them start narrowing down semantically meaningful views over what the programs really needed.
The worst fix was to remove validation features from the language, thus forcing everyone to manually re-add them without the help of the compiler.
Really, the protobuf type system was too simple for Google even in 2006. I recall during training wondering why it didn't have a URL type given that this was a web-centric company. Shortly after I discovered a very simple and obvious bug in web search in which some local business results were 404s even though the URL existed. It had been there for months, maybe years, and I found it by reading the user support forums (nobody else did this, my manager considered me way out of my lane for doing so). The bug was that nothing anywhere in the pipeline checked that the website address entered by the business owner started with https://, so when the result was stuffed into an <a> tag it turned into <a href="www.business.com"> and so the user ended up at https://www.google.com/www.business.com. Oops. These bad strings made it all the way from the business owner, through the LBC frontend, the data pipeline, the intermediate microservices and the web search frontends to the user's browser. The URL did pass crawl validation because when loaded into a URL type, the missing protocol was being added. SREs were trained to do post-mortems, so after it got fixed and the database was patched up, I naively asked whether there was a systematic fix for this, like maybe adding a URL type to protobufs so data would be validated right at the start. The answer was "it sounds like you're asking how to not write bugs" and nothing was done, sigh. It's entirely possible that similar bugs reoccurred dozens of times without being detected.
Those are just a couple of cases where the simplicity (or primitivity) of the protobuf type system led to avoidable problems. Sure, there are complexity limits too, but the actual languages Googlers were using all had more sophisticated type systems than protobuf and bugs at the edges weren't uncommon.
Maybe there need to be levels of validation, like "it's a text file" versus "it parses" versus "it type checks."
You can also see it as a version control and awareness problem rather than a schema or serialization problem. The issues don't occur if you always have full awareness of what code is running and what's consuming what data, but that's hard especially when you take into account batch jobs.
I am.
> After all it's usually better to abort early than half way through an operation.
I realize this goes against common wisdom, but I actually disagree.
It's simply unrealistic to imagine that we can fully determine whether an operation will succeed by examining the inputs upfront. Even if the inputs are fully valid, all sorts of things can go wrong at runtime. Maybe a database connection is randomly dropped. Maybe you run out of memory. Maybe the power goes out.
So we already have to design our code to be tolerant to random failures in the middle. This is why we try to group our state changes into a single transaction, or design things to be idempotent.
Given we already have to do all that, I think trying to validate input upfront creates more trouble than it solves. When your validation code is far away from the code that actually processes the data, it is easier to miss things and harder to keep in sync.
To be clear, though, this does not mean I like dynamic typing. Static types are great. But the reason I like them is more because they make programming easier, letting you understand the structure of the data you're dealing with, letting the IDE implement auto-complete, jump-to-definition, and error checking, etc.
Consider TypeScript, which implements static typing on JavaScript, but explicitly does not perform any runtime checks whatsoever validating types. It's absolutely possible that a value at runtime does not match the type that TypeScript assigned to it. The result is a runtime exception when you try to access the value in a way that it doesn't support (even though its type says it should have). And yet, people love TypeScript, it clearly provides value despite this.
This stuff makes a programming language theorist's head explode but it practice it works. Look, anything can be invalid in ways you never thought of, and no type system can fully defend you from that. You gotta get comfortable with the idea that exceptions might be thrown from anywhere, and design systems to accommodate failure.
1. The advantage of having it in the type system is the compiler can't forget.
2. It's quite hard to unwind operations in C++. I think delaying validation to the last moment is easier when you have robust exceptions. At the top level the frameworks can reject RPCs or return a 400 or whatever it is you want to do, if it's found out 20 frames deep into some massive chunk of code then you're very likely to lose useful context as the error gets unwound (and worse error messages).
On forgetting, the risky situation is something like this:
message FooRequest {
required string query = 1;
optional list<string> options = 2; // added later
}
TypeScript does indeed not do validation of type casts up front, but that's more because it's specifically designed to be compatible with JavaScript and the runtime doesn't do strong typing. People like it compared to raw JS.
> This stuff makes a programming language theorist's head explode but it practice it works. Look, anything can be invalid in ways you never thought of, and no type system can fully defend you from that. You gotta get comfortable with the idea that exceptions might be thrown from anywhere, and design systems to accommodate failure.
It's only possible if you're doing something wrong type-wise. In particular, when ingesting an object you're supposed to validate it before/as you assign the type to it. Delaying the error until the particular field is accessed is bad TypeScript! Those kinds of exceptions aren't supposed to be thrown from anywhere.
This is an interesting argument that I would like to see more elaboration on, because that's the obvious solution. Effectively you're building a pipeline of data processors and each stage in the pipeline reads its own information and then passes along a payload with the rest of the information to the next stage. This would preserve full static typing with required fields, but I can see how it might inhibit some forms of dynamic instrumentation, eg. turning verbose logging on/off might dynamically reconfigure the pipeline, which would affect all upstream producers if they're wrapping messages for downstream consumers.
If this were a programming language I would immediately think of row typing to specify the parts that each stage depends on while being agnostic about the rest of the content, but I'm not sure how that might work for a serialization format. Effectively, you're pulling out a typed "view" over the underlying data that contains offsets to the underlying fields (this is the dictionary-passing transform as found in Haskell).
Truly generic middleware like RPC balancers did what you are saying, but there were also a lot of service specific "middlemen" which did need to look at parts of these mega-structures.
Now due to how protobufs work, you can do what you suggest and "cast" a byte stream to multiple different types, so they could have defined subsets of the overall structures and maybe they did, I don't remember, but the issue then is code duplication. You end up defining the same structures multiple times, just as subsets. With a more advanced type system you can eliminate the duplication, but there was a strong reluctance to add features to protobufs.
These "tweaks" could be just about anything. Like: "You searched for Jaguar, but I don't know if you meant the cat or the car. The index decided that pages about the car rank higher so the first three pages of results are about the car. I'm going to pull some results about the cat from page 4 and put them near the top so just in case that's what you really wanted, you'll find it."
Google Search, at least when I worked on it, was composed of a huge number of such tweaks. People were constantly proposing them, testing if they led to an improvement, and shipping them if they do. For a variety of reasons, our middleman server was a great place to implement certain kinds of tweaks.
But what kinds of information are needed for these "tweaks"? Could be anything! It's a general-purpose platform. Search results were annotated with all kinds of crazy information, and any piece of information might be useful in implementing some sort of middleman tweak at some point.
So you couldn't very well say upfront "OK, we're going to put all the info that is only for the frontend into the special 'frontend blob' that doesn't get parsed by the middleman", because you have no idea what fields are only needed by the frontend. In fact, that set would change over time.
> If this were a programming language I would immediately think of row typing to specify the parts that each stage depends on while being agnostic about the rest of the content
Indeed, perhaps one could develop an elaborate system where in the schemas, we could annotate certain fields as being relevant to certain servers. Anywhere else, those fields would be unavailable (but passed through without modification or validation). If you needed the fields in a new place, you change the annotations.
But that sounds... complicated to design and cumbersome to maintain the annotations. Simply banning required fields solved the problem for us, and everything else just worked.
I don't think it has to be elaborate. What I was thinking was something more like, in pseudo-C#:
// the framework's general channel type from which messages are read
public interface IChannel
{
T Read<T>() where T : interface;
}
// clients declare the interface they operate on:
public interface IClientFields
{
public int Foo { get; set; }
public string? Name { get; set; }
}
...
// client middleware function
Task MiddlewareFn(IChannel chan)
{
var client = chan.Read<IClientFields>();
... // do something with client before resuming at next stage
}
This essentially means declaring the same protocol multiple times, which seems like a big pain.
In principle, this also enables type checking the whole pipeline before deployments since the interfaces can be known upfront rather than latent in the code.
In the search infrastructure example I mentioned up-thread, we had hundreds, maybe thousands of schemas involved.
> I was at Google when the "let's get rid of optional" crusade started. It didn't make sense to me then and over a decade later it still doesn't. If a program expects a field to be there then it has to be there, removing the protobuf level checking just meant that programs could now read garbage (some default value) instead of immediately crashing. But the whole reason we have types, assertions, bounds checking and so on is because, almost always, we'd like our software to NOT just blindly plough on into undefined territory when it doesn't understand something properly, so in reality it just means everyone ends up coding those very same required-ness assertions by hand.
Yeah, that's what stuck out to me from the linked explanation as well; the issue wasn't that the field was required, it was that the message bus was not doing what was originally claimed. It sounds like either having the message bus _just_ process the header and not the entire message or having the header have a version number that indicated which fields are required (with versions numbers that are newer than the latest the bus was aware of being considered to have no required fields). I don't claim that it's never correct to design a protocol optimizing for robustness when consumed by poorly written clients, but I similarly struggle to see how making that the only possible way to implement a protocol is the only valid option. Maybe the goal of cap'n proto is to be prescriptive about this sort of thing, so it wouldn't be a good choice for uses where there's more rigor in the implementation of services using the protocol, but if its intended for more general usage, I don't understand this design decision at all.
No, because static typing exists in all sorts of places. This argument is primarily about cases where you're exchanging data, which is a very specific use case.
I'm not rejecting static types entirely, and yes I was discussing exchanging data here, as Alan Kay's OOP is inherently distributed. It's much closer to Erlang than it is to Java.
OK I guess I'm having a hard time reconciling that with:
> basically all static typing
1. Type info which is available before runtime, but not at runtime (compiled away).
2. Type info which is available at runtime, but not at compile time (input, statistics, etc.).
3. Type info which is available both at compile time and runtime (say like a Java class).
When you have a JIT optimizer that can turn [3] and [2] into [1], there's no longer a reason to have [1], except if you're micro-optimizing embedded code for some device with 64kb RAM or whatever. We've carried through legacy practices, and we don't even question them, and try to push them way out of their league into large-scale distributed software.
When I say we don't need [1], this doesn't mean I deny [3], which is still statically analyzable type information. It's static types, but without throwing away flexibility and data at runtime, that doesn't need to be thrown away.
> there's no longer a reason to have [1]
I guess if you're assuming the value of static types is just performance? But it's not, not by a long shot - hence 'mypy', a static typechecker that in no way impacts runtime.
I think this conversation is a bit too confusing for me so I'm gonna respectfully walk away :)
From the linked article the post: "The right answer is for applications to do validation as-needed in application-level code. If you want to detect when a client fails to set a particular field, give the field an invalid default value and then check for that value on the server. Low-level infrastructure that doesn’t care about message content should not validate it at all."
(I agree that "static typing" isn't exactly the right term here. But protobuf dynamic validation allows the programmer to then rely on static types, vs having to dynamically check those properties with hand-written code, so I can see why someone might use that term.)
Static type systems in programming languages are designed to break at compilation-time. The reason this works is because all users are within the same “program unit”, on the same version.
In other words, static typing allows more validation to be automated, and removes the need for multiple simultaneous versions, but assumes that the developer has access and ability to change all other users at the same “time” of their own change.
I find this whole topic fascinating. It seems like programmers are limited to an implicit understanding of these differences but it’s never formalized (or even properly conceptualized). Thus, our intuition often fails with complex systems (eg multiple simultaneous versions, etc). Case in point: even mighty Google distinguished engineers made this “billion-dollar mistake” with required fields, even though they had near-perfect up-front knowledge of their planned use-cases.
The whole lesson is that you can’t apply the lessons from static type systems in PLs when you have multiple versions and fragmented validation across different subsystems. Counter-intuitively! Everyone thought it was a good idea, and it turned out to be a disaster.
Why are we not having this debate about "everything must be optional" for Internet Packets (IP) for example? Because it's just binary load. If you want to ensure integrity you checksum the binary load.
the bigger challenge with proto3 is that people use it both for rpc and storage, in some cases directly serializing rpc payloads. Disregarding how awful a choice that is, you likely want to trade off flexible deserialization of old data at the expense of rigidity, and conformance.
It's easy to imagine any statically typed language having a general-purpose JSON type. You could imagine all functions accepting and returning such objects.
Now it's your turn to implement the sum(a,b) function. Would you like to allow the caller to pass anything in as a and b?
I will never use Avro again on a MQ. I also found the schema resolution mechanism anemic.
Avro was (is?) popular on Kafka, but it is such a bad fit that Confluent created a whole additional piece of infra called Schema Registry [1] to make it work. For Protobuf and JSON schema, it's 90% useless and sometimes actively harmful.
I think you can also embed the schema in an Avro message to solve this, but then you add a massive amount of overhead if you send individual messages.
[1] https://docs.confluent.io/platform/current/schema-registry/i...
That seems like a weird way to describe it. It is assumed that a schema registry would be present for something like Avro. It's just how it's designed - the assumption with Avro is that you can share your schemas. If you can't abide by that don't use it.
Wrapping a message with its schema version so that you can look up that version is a really sensible way to go. A uuid is way more than what's needed since they could have just used a serial integer but whatever, that's on Kafka for building it that way, not Avro.
And now you can't trivially port your data between environments.
There is no difference between a random 128bit integer vs a sequentual 64bit integer except that the 64bit integer is smaller.
Sounds weird as hell.
In Protobuf or JSON this is not a problem at all, the new field is ignored. With Avro you cannot read the message.
A simpler option is to just publish the schema in to the queue periodically. Say every 30 seconds, and then receivers can cache schemas for message types they are interested in.
I dunno, that sounds extremely burdensome to me, especially if the actual payload is small.
And how exactly does exchanging schemas solve the problem? If my version of the schema says this field is required but yours says it is optional, and so you don't send it, what am I supposed to do?
It's not worth the boatload of problems it bring in all other and normal use cases though. Having the default value in the app or specified by the protocol is good enough.
The spec for evolving schemas is also full of ambiguity and relies on the canonical Java implementation. I've built an Avro decoder from scratch and some of the evolution behaviour is counter-intuitive.
https://github.com/stepchowfun/typical#asymmetric-fields-can...
>To help you safely add and remove required fields, Typical offers an intermediate state between optional and required: asymmetric. An asymmetric field in a struct is considered required for the writer, but optional for the reader. Unlike optional fields, an asymmetric field can safely be promoted to required and vice versa.
A gotcha along the same path. Deserialization of things not needed as what you get with generated clients. An aspect of interfaces in Go I really like. Remotely type what I use. Skip the rest. Not fun to have incidents caused by changes to a contract that is not even used by a service. Also hard to find.
> The right answer is for applications to do validation as-needed in application-level code.
It would've been nice to include a parameter to switch "required message validation" on and off, instead of relying on application code. Internally in an application, we can turn this off, the message bus can turn it off, but in general, developers would really benefit from this being on.
[1] https://capnproto.org/faq.html#how-do-i-make-a-field-require...
The zero copy parsing is less of a benefit than you'd expect - pretty unlikely you're going to want to keep your data as a Capnp data structure because of how awkward it is to use. 99% of the time you'll just copy it into your own data structures anyway.
There's also more friction with the rest of the world which has more or less settled on Protobuf as the most popular binary implementation of this sort of idea.
I only used it for serialisation. Maybe the RPC stuff is more compelling.
I really wish Thrift had taken off instead of Protobuf/gRPC. It was so much better designed and more flexible than anything I've seen before or since. I think it died mainly due to terrible documentation. I guess it also didn't have a big name behind it.
It has established itself as the de-facto standard, with a few other places using SBE instead.
In any case the main problems with binary serialization are:
- schemas and message version management
- delta-encoding
If you ignore these, flat binary serialization is trivial.
No library provides a good solution that covers the two points above.
(I've randomly run into Cap'n Proto maybe 2-3 times but to be fair I'm probably more likely to notice that.)
I recently started a job doing work on autonomy systems that run in tractors, and was surprised to see we use it (flatbuffers) in the messaging layer (in both C++ and Rust)
Encoding JSON or MessagePack will be about the same speed, although I would expect MessagePack to be marginally faster from what I’ve seen over the years. It’s easy to encode data in most formats, compression excluded.
Parsing is the real problem with JSON, and no, it isn’t even close. MessagePack knows the length of every field, so it is extremely fast to parse, an advantage that grows rapidly when large strings are a common part of the data in question. I love the simple visual explanation of how MessagePack works here: https://msgpack.org/
Anyone who has written parsing code can instantly recognize what makes a format like this efficient to parse compared to JSON.
With some seriously wild SIMD JSON parsing libraries, you can get closer to the parsing performance of a format like MessagePack, but I think it is physically impossible for JSON to be faster. You simply have to read every byte of JSON one way or another, which takes time. You also don’t have any ability to pre-allocate for JSON unless you do two passes, which would be expensive to do too. You have no idea how many objects are in an array, you have no idea how long a string will be.
MessagePack objects are certainly smaller than JSON but larger than compressed JSON. Even compressed MessagePack objects are larger than the equivalent compressed JSON, in my experience, likely because the field length indicators add a randomness to the data that makes compression less effective.
For applications where you need to handle terabytes of data flowing through a pipeline every hour, MessagePack can be a huge win in terms of cost due to the increased CPU efficiency, and it’s a much smaller lift to switch to MessagePack from JSON than to switch to something statically typed like Protobuf or CapnProto, just due to how closely MessagePack matches JSON. (But, if you can switch to Protobuf or CapnProto, those should yield similar and perhaps even modestly better benefits.)
Compute costs are much higher than storage costs, so I would happily take a small size penalty if it reduced my CPU utilization by a large amount, which MessagePack easily does for applications that are very data-heavy. I’m sure there is at least one terribly slow implementation of MessagePack out there somewhere, but most of them seem quite fast compared to JSON.
Some random benchmarks in Go: https://github.com/shamaton/msgpack#benchmark
Also take note of the “ShamatonGen” results, which use codegen before compile time to do things even more efficiently for types known ahead of time, compared to the normal reflection-based implementation. The “Array” results are a weird version that isn’t strictly comparable, the encoding and decoding steps assume that the fields are in a fixed order, so the encoded data is just arrays of values, and no field names. It can be faster and more compact, but it’s not “normal” messagepack.
I’ve personally seen crazy differences in performance vs JSON.
If you’re not handling a minimum of terabytes of JSON per day, then the compute costs from JSON are probably irrelevant and not worth thinking too hard about, but there can be other benefits to switching away from JSON.
Sometimes you can't gzip for various reasons. There were per-message deflate bugs in Safari and Brave somewhat recently. Microsoft is obsessed with the decades old CRIME/BREACH for some reason(I've never heard any other company or individual even mention them) so signalR still doesn't have the compression option yet..
That said I personally have always been much more excited about the RPC protocol than the serialization. I think the RPC protocol is actually a paradigm shift for almost any non-trivial use case.
Currently Ruby, PHP, and Python are backed by upb.
Disclosure: I work on the protobuf team, and created the upb library.
But every time I've tried to prototype or implement around this model I've run into conceptual blocks. It's a tricky paradigm to fully wrap one's head around, and to squeeze into existing toolsets.
FWIW, gRPC certainly has this problem too, but it’s very clearly distinct from protobuf, although pb has gRPC-related features.
That entanglement makes me lean towards flatbuffers or even protobuf every time I weigh them against capnp, especially since it means that fb and pb have much simpler implementations, and I place great value on simplicity for both security and maintenance reasons.
I think the lack of good third-party language implementations speaks directly to the reasonability of that assessment. It also makes the bus factor and longevity story very poor. Simplicity rules.
You don't have to use it. You could just use it just as a 'serialization' layer but if you're writing services you could be missing half the advantage, really. And if you're writing in C++ you'll end up having to use their KJ library anyways.
If you take the whole package the zero copy, capability-security, and asynchrouny (a word I just coined!) all fit together nicely.
> but it bothers me that what is ultimately a message encoding protocol has an opinion on how I should architect my server.
To me, this is like saying "Using JSON is unfortunate because it has an opinion that I should use HTTP" when I don't think anyone would argue that at all, and I don't see the argument for capnp much either.
Making this work nicely requires some integration between the serialization layer and the RPC layer, though it's certainly possible to imagine Protobuf being extended with some sort of hooks for this.
The Rust version also comes with its own normative lifestyle assumptions; many of which make sense in the context of its zero-copy world but still make a lot of things hard to express, and the documentation was hard to parse.
I tend to reach for flatbuffers instead, for this reason alone.
Still I think someday I hope to have need and use for cap'n'proto; or at least finish one of several hobby projects I've forked off to try to use it over the years. There's some high quality engineering there.
I didn't initially intend for KJ to become as all-encompassing as it has. I guess I kept running into things that didn't work well about the standard library, so I'd make an alternative that worked well, but then other parts of the standard library would not play nicely with my alternative, so it snowballed a bit.
At the time the project started, C++11 -- which completely changed the language -- was brand new, and the standard library hadn't been updated to really work well with the new features.
The KJ Promise library in particular, which made asynchronous programming much nicer using the newly-introduced lambdas, predated any equivalent landing in the standard library by quite a bit. This is probably the most opinionated part of KJ, hardest to integrate with other systems. (Though KJ's even loop does actually have the ability to sit on top of other event loops, with some effort.)
And then I ended up with a complete ecosystem of libraries on top of Promises, like KJ HTTP.
With the Workers Runtime being built entirely in that ecosystem, it ends up making sense for me to keep improving that ecosystem, rather than try to make things work better across ecosystems... so here we are.
This is why these days I just work in Rust :-) Less heterogenous of an environment (so far).
The name Cap'n Proto actually originally meant "Capabilities and Protobufs" -- it was a capability-based RPC protocol based on Protocol Buffers. However, early on I decided I wanted to try a whole different serialization format instead. "Proto" still makes sense, since it is a protocol, so I kept the name.
The pun "cerealization protocol" is actually something someone else had to point out to me, but I promptly added it to the logo. :)
https://en.wikipedia.org/wiki/Capability-based_security
https://capnproto.org/rpc.html#distributed-objects
The idea really goes way beyond security and RPC. It's hard to explain concisely but it's sort of a way of thinking about software architecture.
* Specification of the number of bits I want to cap out a field at during serialization, ie: `int` that only uses 3 bits.
* Delta encoding for serialization and deserialization, this would further decrease the size of each message if there is an older message that I can use as the initial message to delta encode/decode from.
CBOR approximates this, since it has several different widths for integers.
> an older message that I can use as the initial message to delta encode/decode from.
General-purpose compression on the encoded stream would do something toward this goal, but some protocol buffers library implementations offer merge functions. The question is what semantics of "merge" you expect. For repeated fields do you want to append or clobber?
And if someone is asking for exact bit sizes and deltas, they probably don't want a format that embeds every key in every message.
By delta compression you mean across messages? Yeah I've never seen that but it's hard to imagine a scenario where it would be useful and worth the insane complexity.
[1] http://zserio.org/doc/ZserioLanguageOverview.html#bit-field-...
https://github.com/cloudflare/workerd/blob/main/src/workerd/...
this changed my world how i think about computing on the web.
if there was just a good enough js library as for lua and you could directly send capnp messages to workerd instead of always going through files. I guess one day i have to relearn c++ and understand how the internals actually work.
Are there any particular pain points that you want to call attention to?
- https://arxiv.org/abs/2201.02089
- https://arxiv.org/abs/2201.03051
Not something that aims to compete with Cap'n Proto (different use cases), but I've been also working on a binary serialization format that is pure JSON-compatible with a focus on space-efficiency: https://www.jsonbinpack.org.
Benchmarks here: https://arxiv.org/abs/2211.12799
I assume you are talking about the cancellation change. This is interesting, actually. When originally designing Cap'n Proto, I was convinced by a capabilities expert I talked to that cancellation should be considered dangerous, because software that isn't expecting it might be vulnerable to attacks if cancellation occurs at an unexpected place. Especially in a language like C++, which lacks garbage collection or borrow checking, you might expect use-after-free to be a big issue. I found the argument compelling.
In practice, though, I've found the opposite: In a language with explicit lifetimes, and with KJ's particular approach to Promises (used to handle async tasks in Cap'n Proto's C++ implementation), cancellation safety is a natural side-effect of writing code to have correct lifetimes. You have to make cancellation safe because you have to cancel tasks all the time when the objects they depend on are going to be destroyed. Moreover, in a fault-tolerant distributed system, you have to assume any code might not complete, e.g. due to a power outage or maybe just throwing an unexpected exception in the middle, and you have to program defensively for that anyway. This all becomes second-nature pretty quick.
So all our code ends up cancellation-safe by default. We end up with way more problems from cancellation unexpectedly being prevented when we need it, than happening when we didn't expect it.
EDIT: Re-reading, maybe you were referring to the breaking changes slated for 2.0. But those are primarily changes to the KJ toolkit library, not Cap'n Proto, and is all about API design... I'd say API design is not a constrained problem.
It was contributed by Marek Vavruša from CloudFlare in 2017: https://github.com/ClickHouse/ClickHouse/pull/1387
Since then, we extended and improved the support: added it for export (initially, it was only for import) and improved the performance.
About strange stuff in the library - it uses a non-obvious approach for exception handling, and their C++ code feels like they too much focusing on some non-orthodox approaches.
There are some difference between the language libraries and documentation can be lacking around those language specific solutions. I'm hoping to add blog articles and or contribute back to the example of these repositories to help future users who want to dabble.
Check out my repo here for how I use it across Rust and Python, with Golang coming soon: https://github.com/lucidmq/lucidmq