json-benchmark
Cap'n Proto
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json-benchmark | Cap'n Proto | |
---|---|---|
12 | 66 | |
169 | 11,180 | |
4.7% | 1.6% | |
4.8 | 9.2 | |
about 1 month ago | 3 days ago | |
C++ | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
json-benchmark
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Do You Know How Much Your Computer Can Do in a Second?
I don’t really understand what this is trying to prove:
- you don’t seem to specify the size of the input. This is the most important omission
- you are constructing an optimised representation (in this case, strict with fields in the right places) instead of a generic ‘dumb’ representation that is more like a tree of python dicts
- rust is not a ‘moderately fast language’ imo (though this is not a very important point. It’s more about how optimised the parser is, and I suspect that serde_json is written in an optimised way, but I didn’t look very hard).
I found[1], which gives serde_json to a dom 300-400MB/s on a somewhat old laptop cpu. A simpler implementation runs at 100-200, a very optimised implementation gets 400-800. But I don’t think this does that much to confirm what I said in the comment you replied to. The numbers for simd json are a bit lower than I expected (maybe due to the ‘dom’ part). I think my 50MB/a number was probably a bit off but maybe the python implementation converts json to some C object and then converts that C object to python objects. That might half your throughput (my guess is that this is what the ‘strict parse’ case for rustc_serialise is roughly doing).
[1] https://github.com/serde-rs/json-benchmark
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Serde Json vs Rapidjson (Rust vs C++)
But the code OP posted deserializes JSON without knowing anything about the structure, which is known to be slow in serde-json and doesn't appear to be the focus for the library. The json and json-deserializer crates should perform much better in that scenario.
- Good example of high performance Rust project without unsafe code?
- I'm a veteran C++ programmer, what can Rust offer me?
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Rust is just as fast as C/C++
Of course that doesnt mean that in practice the available libraries are as optimized. Did you try actix? It tends to be faster than rocket. Also json-rust and simd-json are usually faster than serde-json, when you don't deserialize a known structure. Here are some benchmarks: https://github.com/serde-rs/json-benchmark
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Lightweight template-based parser build system. Simple prototyping. Comfortable debugging. Effective developing.
The data for the test is taken from here: https://github.com/serde-rs/json-benchmark/tree/master/data
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Performance of serde js value conversion and reference types
Here are some benchmarks https://github.com/serde-rs/json-benchmark
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Serde zero-copy benchmarks?
I found two projects: * https://github.com/djkoloski/rust_serialization_benchmark - doesn't use Serde zero copy * https://github.com/serde-rs/json-benchmark - has copy vs borrowed, but the results were the same for both, so something's off there
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Android Developers Have A Tough Life
Rust has a good enough standard library (I’d say comparable to C++), that you don’t really need packages for a lot of stuff. Most of my projects have 1 or 2 dependencies. Most of the time I am pulling in a JS parser (serde) and a parallelization library (rayon). These are both high performance libraries that make writing very fast (serde can handle 850 MB/s on a 5 year old laptop cpu per their benchmarks). Rayon is one of the best parallelism libraries I’ve worked with.
Cap'n Proto
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Mysterious Moving Pointers
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
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Show HN: Comprehensive inter-process communication (IPC) toolkit in modern C++
- 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.)
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Condvars and atomics do not mix
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...
- Cap'n'Proto: infinitely faster than Protobuf
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I don’t understand zero copy
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/
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OpenTF Renames Itself to OpenTofu
Worked well for Cap'n Proto (the cerealization protocol)! https://capnproto.org/
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A Critique of the Cap'n Proto Schema Language
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.
- Cap'n Proto: serialization/RPC system – core tools and C++ library
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Sandstorm: Open-source platform for self-hosting web app
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
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Flatty - flat message buffers with direct mapping to Rust types without packing/unpacking
Related but not Rust-specific: FlatBuffers, Cap'n Proto.
What are some alternatives?
rust_serialization_benchmark - Benchmarks for rust serialization frameworks
gRPC - The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)
hjson-rust for serde - Hjson for Rust
Protobuf - Protocol Buffers - Google's data interchange format
simd-json - Rust port of simdjson
FlatBuffers - FlatBuffers: Memory Efficient Serialization Library
hyperjson - 🐍 A hyper-fast Python module for reading/writing JSON data using Rust's serde-json.
ZeroMQ - ZeroMQ core engine in C++, implements ZMTP/3.1
MessagePack - MessagePack serializer implementation for Java / msgpack.org[Java]
Apache Thrift - Apache Thrift
json - Strongly typed JSON library for Rust