Cap'n Proto VS Apache Thrift

Yeah I pretty much only use my own alternate container implementations (from KJ[0]), which avoid these footguns, but the result is everyone complains our project is written in Kenton-Language rather than C++ and there's no Stack Overflow for it and we can't hire engineers who know how to write it... oops.

[0] https://github.com/capnproto/capnproto/blob/v2/kjdoc/tour.md

- may massively reduce the latency involved.

Those sharing Cap'n Proto-encoded data may have particular interest. Cap'n Proto (https://capnproto.org) is fantastic at its core task - in-place serialization with zero-copy - and we wanted to make the IPC (inter-process communication) involving capnp-serialized messages be zero-copy, end-to-end.

That said, we paid equal attention to other varieties of payload; it's not limited to capnp-encoded messages. For example there is painless (<-- I hope!) zero-copy transmission of arbitrary combinations of STL-compliant native C++ data structures.

To help determine whether Flow-IPC is relevant to you we wrote an intro blog post. It works through an example, summarizes the available features, and has some performance results. https://www.linode.com/blog/open-source/flow-ipc-introductio...

Of course there's nothing wrong with going straight to the GitHub link and getting into the README and docs.

Currently Flow-IPC is for Linux. (macOS/ARM64 and Windows support could follow soon, depending on demand/contributions.)

FWIW, my C++ toolkit library, KJ, does the same thing.[0]

But presumably you could still write a condition predicate which looks at things which aren't actually part of the mutex-wrapped structure? Or does is the Rust type system able to enforce that the callback can only consider the mutex-wrapped value and values that are constant over the lifetime of the wait? (You need the latter e.g. if you are waiting for the mutex-wrapped value to compare equal to some local variable...)

[0] https://github.com/capnproto/capnproto/blob/e6ad6f919aeb381b...

The second one is to encode data in such a way that you can read it and operate on it directly from the buffer. You write data in a layout that is the same, or easily transformed as types in memory. To do that you usually need to encode with a known schema, only Sized types to efficiently compute fields locations as offsets in the buffer, and you usually represent pointers as offset into the encode. You can look at capnproto protocol for instance https://capnproto.org/

Worked well for Cap'n Proto (the cerealization protocol)! https://capnproto.org/

With all due respect, you read completely wrong.

* The very first use case for which Cap'n Proto was designed was to be the protocol that Sandstorm.io used to talk between sandbox and supervisor -- an explicitly adversarial security scenario.

* The documentation explicitly calls out how implementations should manage resource exhaustion problems like deep recursion depth (stack overflow risk).

* The implementation has been fuzz-tested multiple ways, including as part of Google's oss-fuzz.

* When there are security bugs, I issue advisories like this:

https://github.com/capnproto/capnproto/tree/v2/security-advi...

* The primary aim of the entire project is to be a Capability-Based Security RPC protocol.

I like how they use capability-based security [0] and use Cap'n Proto protocol. This is another technology that is slow to get broad adoption, but has many things going for when compared to e.g. Protocol Buffers (Cap'n Proto is created by the primary author of Protobuf v2, Kenton Varda).

[0] https://sandstorm.io/how-it-works#capabilities

[1] https://capnproto.org

Related but not Rust-specific: FlatBuffers, Cap'n Proto.

There are various notable implementations of RPC like Apache Thrift and gRPC.

> That's just your choice of how to build your app, right? You could've avoided this by rendering templates on the server and sending static HTML to the client, keeping the business logic on the server.

No, that's a requirement on most business cases, my comment stated 'complex and dynamic web apps'. Re-rendering the whole page everytime the user checks a box or clicks a button is (a) terrible UX, (b) hard to track the state between page refresh, (c) wrong practice and (d) bad performance.

> Here's just one of ten-thousand other battle-tested options you can use: https://github.com/apache/thrift/

Sure, I should setup a complex and huge dependency for just one of the many problems I highlighted. What a great idea

Thus, we released Baseplate.py v2.1 with deadline propagation. Each request between Baseplate services has an associated THeader, which includes relevant information for Baseplate to fulfill its functionality, such as tracing request timings. We added a “Deadline-Budget” field to this header that propagates the remaining timeout so that information is available to the following request, and this timeout continues to get updated with every new request made. With this update, we save production costs by allowing resources to work on requests awaiting a response, and gain overall improved latency.

Here’s an example of the Thrift changelog. Knock yourself out. Or you can get your sense of productivity by actually doing something of value.

The biggest addition is native async reading via futures::AsyncRead and futures::AsyncSeek, which required a lot of (to be merged) changes upstream (changes to thrift rust compiler and parquet-format-rs). I placed those changes on a temporary crate until things are released there.

We were sharing instances of a Thrift TDeserializer across threads. We knew TProtocol was not thread-safe, but the TDeserializer constructor accepts a TProtocolFactory, so we naively assumed the deserialize method would use that to create a new instance of TProtocol for each invocation, but unfortunately, the TDeserializer constructor immediately creates TProtocol and stores it in a member variable, so TDeserializer is not actually thread-safe.