encoding VS ndjson.github.io

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.

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

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.

Would love to see results from incorporating https://github.com/segmentio/encoding/tree/master/json!

Try this one out https://github.com/segmentio/encoding it's always worked well for me

Converts any interface or string to JSON using the segmentio/encoding package. Also, the JSON method sets the content header to application/json.

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.

However, since every JSON document can be represented in a single line, something like newline-delimited JSON / JSON Lines feels like it would've been more suitable for that kind of data.

No one uses that format for streamed json, see ndson and jsonl

http://ndjson.org/

The size complaint is overblown, as repeated fields are compressed away.

As other folks rightfully commented, csv is a mine field. One should assume every CSV file is broken in some way. They also don't enumerate any of the downsides of CSV.

What people should consider is using formats like Avro or Parquet that carry their schema with them so the data can be loaded and analyzed without have to manually deal with column meaning.

What is ndjson? Newline delimited JSON is the format the Algolia CLI reads from and writes to files. This means that any command that passes ndjson formatted data as output or accepts it as input can be piped together with an Algolia CLI command! We’ll see more of this in the next example

You might consider using json-lines format (also known as newline-delimited JSON), in which each line is a separate JSON document so they can be loaded individually.

The format you're getting is known as Newline-Delimited JSON. Instead of trying to parse the whole input and pass that to the JSON Decoder, you can use something like bufio.Scanner to get and parse it line by line.

This is in oposition to NDJSON, which allows to split records without deserializing JSON itself, via e.g. read_lines. fwiw CSV suffers from the same problem as JSON - generally not possible to break into records without deserializing. It is worse than NDJSON because the character \n may appear at any position within an item, thus forbidding read_lines.

I've always seen it referred to as ndjson

I think this would be fine, as long as the CSV layer was still parsable using the RFC 4180, then you could still use a normal CSV parser to parse the CSV layer and a normal JSON parser to parse the JSON layer. My worry with your example is that it is nether format, so it will need custom serialisation and deserialisation logic as it is essentially a bran new format.

https://datatracker.ietf.org/doc/html/rfc4180

If you’re looking for line-oriented JSON, another option would be ndjson: http://ndjson.org/