encoding
jsoniter
encoding | jsoniter | |
---|---|---|
8 | 12 | |
964 | 13,085 | |
0.7% | 0.6% | |
3.6 | 0.0 | |
5 months ago | about 1 month ago | |
Go | Go | |
MIT License | MIT License |
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.
encoding
- Handling high-traffic HTTP requests with JSON payloads
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Rust vs. Go in 2023
https://github.com/BurntSushi/rebar#summary-of-search-time-b...
Further, Go refusing to have macros means that many libraries use reflection instead, which often makes those parts of the Go program perform no better than Python and in some cases worse. Rust can just generate all of that at compile time with macros, and optimize them with LLVM like any other code. Some Go libraries go to enormous lengths to reduce reflection overhead, but that's hard to justify for most things, and hard to maintain even once done. The legendary https://github.com/segmentio/encoding seems to be abandoned now and progress on Go JSON in general seems to have died with https://github.com/go-json-experiment/json .
Many people claiming their projects are IO-bound are just assuming that's the case because most of the time is spent in their input reader. If they actually measured they'd see it's not even saturating a 100Mbps link, let alone 1-100Gbps, so by definition it is not IO-bound. Even if they didn't need more throughput than that, they still could have put those cycles to better use or at worst saved energy. Isn't that what people like to say about Go vs Python, that Go saves energy? Sure, but it still burns a lot more energy than it would if it had macros.
Rust can use state-of-the-art memory allocators like mimalloc, while Go is still stuck on an old fork of tcmalloc, and not just tcmalloc in its original C, but transpiled to Go so it optimizes much less than LLVM would optimize it. (Many people benchmarking them forget to even try substitute allocators in Rust, so they're actually underestimating just how much faster Rust is)
Finally, even Go Generics have failed to improve performance, and in many cases can make it unimaginably worse through -- I kid you not -- global lock contention hidden behind innocent type assertion syntax: https://planetscale.com/blog/generics-can-make-your-go-code-...
It's not even close. There are many reasons Go is a lot slower than Rust and many of them are likely to remain forever. Most of them have not seen meaningful progress in a decade or more. The GC has improved, which is great, but that's not even a factor on the Rust side.
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Quickly checking that a string belongs to a small set
We took a similar approach in our JSON decoder. We needed to support sets (JSON object keys) that aren't necessarily known until runtime, and strings that are up to 16 bytes in length.
We got better performance with a linear scan and SIMD matching than with a hash table or a perfect hashing scheme.
See https://github.com/segmentio/asm/pull/57 (AMD64) and https://github.com/segmentio/asm/pull/65 (ARM64). Here's how it's used in the JSON decoder: https://github.com/segmentio/encoding/pull/101
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80x improvements in caching by moving from JSON to gob
Binary formats work well for some cases but JSON is often unavoidable since it is so widely used for APIs. However, you can make it faster in golang with this https://github.com/segmentio/encoding.
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Speeding up Go's builtin JSON encoder up to 55% for large arrays of objects
Would love to see results from incorporating https://github.com/segmentio/encoding/tree/master/json!
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Fastest JSON parser for large (~888kB) API response?
Try this one out https://github.com/segmentio/encoding it's always worked well for me
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📖 Go Fiber by Examples: Delving into built-in functions
Converts any interface or string to JSON using the segmentio/encoding package. Also, the JSON method sets the content header to application/json.
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In-memory caching solutions
If you're interested in super fast & easy JSON for that cache give this a try I've used it in prod & never had a problem.
jsoniter
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Handling high-traffic HTTP requests with JSON payloads
Since most of the time would be spent decoding json, you could try to cut this time using https://github.com/bytedance/sonic or https://github.com/json-iterator/go, both are drop-in replacements for the stdlib, sonic is faster.
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A Journey building a fast JSON parser and full JSONPath
We all know the builtin golang JSON parser is slow.
How about doing comparisons against other implementations?
Like this one: https://github.com/json-iterator/go
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Polygon: Json Database System designed to run on small servers (as low as 16MB) and still be fast and flexible.
Json-iterator (https://github.com/json-iterator/go), you can replace all of encoding/json with this. It does the same thing but it's faster.
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How can we umarshal a Big JSON effectively?
Do you want to look at every field all at the same time? If not, you can pick out individual fields. There's other packages such as https://github.com/tidwall/gjson or https://github.com/json-iterator/go that let you pass in paths such as "a.b.c" to extract single fields.
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Designing a config API for microservices applications built using Go
For each Go type used within the config, we generate a separate unmarshaller function. The unmarshallers use json-iterator to process the output from CUE, while tracking the path within the config to the unmarshalled value. This path tracking will allow the function to check if live overrides have been provided on that path and return the override instead.
- jsoniter+1.18: panic in reflect2
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What type of software do you write at your workplace?
https://github.com/json-iterator/go an alternative JSON encoding package which allows to stream (flush out) encoded data as soon as it's able to (which is in contrast with the stock package which buffers everything until the encoding is known to be complete and OK).
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Some Go(lang) tips
What to use Easyjson is about the top of the pack and it's straightforward. The downside of efficient tools is that they use code generation to create the code required to turn your structs into json to minimise allocations. This is a manual build step which is annoying. Interestingly json-iterator also uses reflection but it's significantly faster. I suspect black magic.
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What are your favorite packages to use?
jsoniter for low level access to JSON encode and decode
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What is the best solution to unique data in golang
Takes like 10 minutes to write and parses very efficiently. https://github.com/json-iterator/go looks like it can provide such simple parsing
What are some alternatives?
sonic - A blazingly fast JSON serializing & deserializing library
go-json - Fast JSON encoder/decoder compatible with encoding/json for Go
groupcache - Clone of golang/groupcache with TTL and Item Removal support
mapstructure - Go library for decoding generic map values into native Go structures and vice versa.
parquet-go - Go library to read/write Parquet files
easyjson - Fast JSON serializer for golang.
base64 - Faster base64 encoding for Go
goprotobuf - Go support for Google's protocol buffers
buntdb - BuntDB is an embeddable, in-memory key/value database for Go with custom indexing and geospatial support
GJSON - Get JSON values quickly - JSON parser for Go
hilbert - Go package for mapping values to and from space-filling curves, such as Hilbert and Peano curves.
compare-go-json - A comparison of several go JSON packages.