Simdjson – Parsing Gigabytes of JSON per Second(github.com) |
Simdjson – Parsing Gigabytes of JSON per Second(github.com) |
That said, it also includes an event-based parser (called JSONDecoder), so if you want to handle events in order to decode into your own data structure and skip the intermediate JSON data structure, you might be able to get faster than JSONSerialization that way.
> JSON. The protocol for front-end / back-end communication, as well as between the back-end and plug-ins, is based on simple JSON messages. I considered binary formats, but the actual improvement in performance would be completely in the noise. Using JSON considerably lowers friction for developing plug-ins, as it’s available out of the box for most modern languages, and there are plenty of the libraries available for the other ones.
1: https://github.com/xi-editor/xi-editor/blob/master/README.md...
19.98 s swift_getGenericMetadata
19.15 s newJSONString
16.17 s objc_msgSend
15.33 s _swift_release_(swift::HeapObject*)
14.45 s tiny_malloc_should_clear
12.81 s _swift_retain_(swift::HeapObject*)
11.28 s searchInConformanceCache(swift::TargetMetadata<swift::InProcess> const*, swift::TargetProtocolDescriptor<swift::InProcess> const*)
10.46 s swift_dynamicCastImpl(swift::OpaqueValue*, swift::OpaqueValue*, swift::TargetMetadata<swift::InProcess> const*, swift::TargetMetadata<swift::InProcess> const*, swift::DynamicCastFlags)
I have given a bunch of talks[1] on this topic, there's also a chapter in my iOS/macOS performance book[2], which I really recommend if you want to understand this particular topic. I did really fast XML[3][4], CSV[5] and binary plist parsers[6] for Cocoa and also a fast JSON serialiser[7]. All of these are usually around an order of magnitude faster than their Apple equivalents.
Sadly, I haven't gotten around to doing a JSON parser. One reason for this is that parsing the JSON at character level is actually the smaller problem, performance-wise, same as for XML. Performance tends to be largely determined by what you create as a result. If you crate generic Foundation/Swift dictionaries/arrays/etc. you have already lost. The overhead of these generic data structure completely overwhelms the cost of scanning a few bytes.
So you need something more akin to a steaming interface, and if you create objects you must create them directly, without generic temporary objects. This is where XML is easier, because it has an opening tag that you can use to determine what object to create. With JSON, you get "{" so basically you have to know what structure level corresponds to what objects.
Maybe I should write that parser...
[1] https://www.google.com/search?hl=en&q=marcel%20weiher%20perf...
[2] https://www.amazon.com/gp/product/0321842847/
[3] https://github.com/mpw/Objective-XML
[4] https://blog.metaobject.com/2010/05/xml-performance-revisite...
[5] https://github.com/mpw/MPWFoundation/blob/master/Collections...
[6] https://github.com/mpw/MPWFoundation/blob/master/Collections...
[7] https://github.com/mpw/MPWFoundation/blob/master/Streams.sub...
I settled on a tabular-log format, which is streamed and immediately consumed most of the time, no intermediate object structures.
Then, that "text vs binary" distinction became mostly moot. The binary is slightly more efficient, but grossly less readable, so no big gain, unless at grand scale.
The intent was to open things but not publicize them at this stage but Hacker News seems to find stuff. Wouldn't surprise me if plenty of folks follow Daniel Lemire on Github as his stuff is always interesting.
Though this job is trickier than it may look. The logic to extract the "relevant" bits is often dynamic or tied to user input but for the scanner/lexer to be ultrafast it has to be tightly compiled. You can try jitting but libllvm is probably too heavyweight for parsing json.
Publishing results against this could be useful both for assessing how good this parser is and establishing and documenting any known issues. If correctness is not a goal, this can still be fine but finding out your parser of choice doesn't handle common json emitted by other systems can be annoying.
Regarding the numbers, I've run into a few cases where Jackson being able to parse BigIntegers and BigDecimals was very useful to me. Silently rounding to doubles or floats can be lossy and failing on some documents just because the value exceeds max long/in t can be an issue as well.
I lost count to broken JSON parsers which all fall to that.
Edit: to be fair, they handle a couple of other things, which many similar libraries ignore. I particulary like the support for full 64bit integers. And at least they document their limitation on NULL bytes.
Please add an issue on Github.
Edit: I went ahead and added an issue. Seems like something we should fix.
My immediate thought is to compare it to rapidjson, which I've used before. The paradigm of mutating iterators seems awkward at first but should be just as powerful as rapidjson's Value. For example, both approaches end up doing a linear scan to find an object member by name.
The fact that rapidjson supports mutation of Values and simdjson does not has huge implications (as mentioned in the simdjson README scope section), I suspect this tradeoff explains most of the performance differences as I know rapidjson also uses simd internally.
I'm not sure AVX2 is as ubiquitous as the README says: "We assume AVX2 support which is available in all recent mainstream x86 processors produced by AMD and Intel."
I guess "mainstream" is somewhat subjective, but some recent Chromebooks have Celeron processors with no AVX2:
https://us-store.acer.com/chromebook-14-cb3-431-c5fm
https://ark.intel.com/products/91831/Intel-Celeron-Processor...
Firebase backups are huge JSON files and we haven’t found a good way to deal with them.
There are some “streaming JSON parsers” that we have wrestled with but they are buggy.
However they have the ability to build a tape out of the json and find the interesting marks. Perhaps it can be adapted to make a fast parser than only parses the relevant stuff but zooms through the large file in blocks.
Terabytes of "big data" get passed around as CSV.
But, the goal of my message is not to tease you with an unavailable code. It's just to say it is a lot more simpler to write a CSV parser than a JSON parser.
So, do not hesitate to write one yourself ! It's easy and a nice way to introduce yourself to SIMD instructions.
Or, perhaps a more common scenario today, it was designed by people who simply had no knowledge of binary protocols or efficiency at all --- not too long ago I had to deal with an API which returned a binary file, but instead of simply sending the bytes directly, it decided to send a JSON object containing one array, whose elements were strings, and each string was... a hex digit. Instead of sending "Hello world" it would send '{"data":["4","8"," ","6","5"," ","6","C"," " ... '
He's a professor, but his work is highly applied and immediately usable. He manages to find and demonstrate a lot of code where we assume the big-O performance, but the reality of modern processors and caching (etc.) mean very difference performance in practice.
Really? I thought they diverged specifications long enough ago (though using those extras could be discouraged in some cases).
The people complaining about dependency management in Python should try doing it in C++; there seems to be half a dozen competing ones. And three times as many build systems.
It's a hammer on rocket fuel.
Though from the readme on that module the dev says "it turns out that you’re better off using the normal Node.js/V8 implementation unless you’re operating on huge JSON.
... the bridging from V8 to C++ is a bit too costly at this stage."
It's possible to do SWAR (SIMD Within A Register) tricks to try to substitute, but on a 32-bit processor (or even a 64-bit processor) I doubt our techniques would look good. In Hyperscan, my regex project, we used SWAR for simple things (character scans) but I doubt that simdjson would work well if you tried to make it into swarjson. :-)
I think the behavior of all the code that touches is undefined (it breaks the calling convention of the ABI), and while this often results in corrupted floating point values in registers, maybe you won't see much if you are not using the FPU. Still, since the function is inline, chances that this gets inlined somewhere where it could cause trouble seem high.
You might want to look into that.
Also, I wish this would all be written in Rust, there is great portable SIMD support over there. Might make your life easier trying to target other platforms.
EDIT: as burntsushi mentions below, that's not available in stable Rust, but if you want to squeeze out the last once of performance out of the Rust compiler, chances are you won't be using that anyways.
If, once you review our codebase and verify that we are not inadvertently using a 22-year-old SIMD extension but still have undefined behavior, please write an issue on github.
I'm admiring Rust from a distance at this stage. I am comfortable enough with writing bare intrinsics and slapping a giant #ifdef around stuff.
It's not stable yet. The only stable SIMD stuff Rust supports is access to the raw x86 vendor intrinsics.
If you or anyone else has some opinions on this, please let me know! I'd really like to learn how people do this type of analysis at scale.
One thing locally is each file takes up a full block. So even if you only need 500 bytes of data in a file, and a block is 4kb, youve wasted 3.5kb of space and IO. Multiply that by a million and youre wasting gigabytes of space.
In S3, listing 12 million files takes 12 thousand http(max return is 1000 items). So that would take two minutes if you assume its 10ms per round trip. Let's say you wanted to read each file, and again each read takes 10ms.. youre looking at 1.4 days. Obviously this can be parallelized, but when you look at the raw byte size this is a huge overhead, and this is just to read one day of data.
If you concatenate the files together to get a reasonable size and number of files, raw json on s3 is really powerful. Point athena at it, and you just write sql and it handles the rest, and is serverless. But it does make single row lookups more expensive(supplementing with dynamodb could keep it serverless if single row lookups are frequent).
lots of optimizations will get improvements, like parquet that tobilg mentioned(binary format and columnar), but anything with a decent file size will work.
The best way to not lose messages is to minimize the work done by your log receiver. So we did. It receives the uploaded log file chunk and appends it to a file, and that's it. The "file" is actually in a cloud storage system that's more-or-less like S3. When I explained this to someone, they asked why we didn't put it in a Bigtable-like thing or some other database, because isn't a filesystem kinda cheesy? No, it's not cheesy, it's simple. Simple things don't break.
It's local storage only, limited query capabilities depending on the DB, but should be extremely fast.
if you're doing "table scan" processing of entire datasets, sure just-a-bunch-of-files would work too.
Databases can be surprisingly fast for things like that, since high performance file i/o is full of tricky/annoying stuff that databases have already optimized for.
I haven't used it but have been given a presentation by them on it, and it was very very good.
They store data in S3 and use FoundationDB for indexes. You can feed it JSON and it'll index it and let you query it on a massive scale shockingly fast.
Obviously they are not aimed at small hobby projects but if your project has money / serious product depending on your needs it's well worth looking at.
On the S3 cheaper / smaller end you can batch up data daily / weekly etc. So the landing bucket acts as a queue that gets processed creating daily batch files from the small files aggregated together. You can then take the daily batches to create weekly batches etc etc, essentially partitioning. This will reduce the total number of files needed to query. If you use deterministic names based on how you plan to query this can also reduce the number of files you need to list / parse. When batching / re-partitioning the data you can also use the Apache Parquet format to compress a little better + also import in some of the querying tools out there.
Unfortunately, the fragmentation of SIMD standards and various pitfalls in implementation (the much ballyhoo'ed "running AVX will make your processor clock to half its speed or something" exaggerations, for example) make a lot of people nervous about putting in the time to commit to developing expertise, which is a shame.
Something that can take generic grammer rules and turn it into a high performance parsing engine.
It wouldn't have to support every possible grammar or option. Json isn't that complex of a language, but even a limited set of grammar options in exchange for a performant parser could be of benefit for a very large set of problems.
We'd like to have some more examples of formats people care about - I'm interested in generalizing this work. So if you want to followup with more detail please do.
Kudos on some incredible work! :)
By the way, nativejson-benchmark (from RapidJson) has a nice conformance checker that tries various corner cases. But you probably know it.
We use RapidJSON in the high-performance mode not the funky mode that minimizes FP error (which is some astounding work - I had no idea that strtof was so involved!). Number conversion is not our #1 focus - doing it well is nice, but all implementations have access to the same FP tricks, so you don't really learn much by going wild on this aspect.
At least, you don't unless FP conversion is your focus, in which case you should share your FP conversion code with everyone!
I'm interested in this: some aspects of our very serial 'stage 2' (the parsing step) could be made parallel. This would be very interesting. Unfortunately I personally cannot be made parallel, so working on this needs to go into a big queue with a lot of other work.
I don't think it would be hard at all; it would just be extra effort that wasn't needed to run obvious comparisons.
I can't speak for Daniel's motivation.
I don't think either of us know much about android - not enough to do that. But an ARM port is very interesting.
Since I'm no longer an Intel employee I don't see why I shouldn't skill up and do a Neon port (I got interested in SVE, but since ARM doesn't seem to want to bother releasing cores that run SVE, I'm not going to go too far down that path right now). Neon, on the other hand, is in tons of places. As far as I know all the required permutes, carryless multiplies and various other SIMD bits and pieces are there on Neon. So it's a simple matter of porting.
JIT approaches make a lot of sense for lex/yacc and their numerous descendants, as these typically need to put a lot of extra logic into the process of parsing. You don't need to JIT just to look up some strings and/or parse a fairly simple hierarchical structure.
I think the problem is that to extract arbitrary keys, you really need to parse the whole thing, although you don't need to materialize nodes for the whole thing.
But if you have big JSON with a given schema, you may be able to skip things lexically. You basically need to count {} and [], while taking into account " and \ within quoted strings.
That doesn't seem too hard. I think a tiny bit of http://re2c.org/ could do a good job of it.
https://gitlab.com/philbooth/bfj
The specific function of interest here is `bfj.match`, which takes a readable stream and a selector as arguments:
https://gitlab.com/philbooth/bfj#how-do-i-selectively-parse-...
It still walks the full tree like a regular parser, but just avoids creating any data items unless the selector matches. Though there is an outstanding issue to support JSONPath in the selector, currently it only matches individual keys and values.
The Morning Paper’s writeup[2] from last year provides a good summary
[1]: http://www.vldb.org/pvldb/vol11/p1576-palkar.pdf [2]: https://blog.acolyer.org/2018/08/20/filter-before-you-parse-...
Parsing the entire document lock stock and barrel is an easier thing to write about and benchmark. The problem is with skipping around and pulling out bits of JSON from a benchmarking framework is that attempting to present such data often amounts to "hey, we asked ourselves a question and then we got a really good answer for it!". It's hard to picture what a 'typical' query for some field over a JSON document would look like. Conversely, it's pretty easy to know when you finished parsing the Whole Thing.
[1]: https://github.com/circe/circe-fs2/blob/master/README.md
You could store them in some binary format, but the API response format changed over the years with various fields being added and removed, and either your binary format ends up not much better than JSON or you end up reencoding old comments because the API changed.
It’s a great way to store big json files where you only want to access a subset of data very quickly and not load the whole file into memory.
Those are other options too, eg, storing the schema separately from the records (then batching records with identical schemas in compact binary files) and defining migration rules between different schemas (eg, if schema A has required field "foo" while schema B has required field "foo" and optional field "bar" then data which follows schema A can be trivially migrated to schema B at read time without needing to reencode on disk).
Methods like this are used for batch search / summation where only a fraction of the parsed data is actually relevant during any particular run. You'll find similar approaches used in e.g. the row format parser of a database like MongoDB or Postgres
Kudos to Douglas Crockford for keeping it simple. I wish more standards committees would take a cue from him. (Looking at ECMAScript [2] and C++.)
There's been a tremendous amount of growth and value around JSON precisely because it's so simple and easy to implement.
People complain about the lack of comments and trailing commas, but I think those are really expanding on the initial use case of JSON, and the benefit isn't worth cost of change. JSON does some things super well, other things marginally well, and some not at all, and that's working as intended.
You can always make something separate to cover those use cases, and that seems to have happened with TOML and so forth.
(I recall there was an RFC that cleaned up ambiguities in Crockford's web page, but it just clarified things. No new features were added. So JSON is still as much of a subset of JavaScript as it ever was. On the other hand, JavaScript itself has grown wildly out of control.)
[1] http://json.org/
> Although Douglas Crockford originally asserted that JSON is a strict subset of JavaScript, his specification actually allows valid JSON documents that are invalid JavaScript. Specifically, JSON allows the Unicode line terminators U+2028 LINE SEPARATOR and U+2029 PARAGRAPH SEPARATOR to appear unescaped in quoted strings, while ECMAScript 2018 and older does not.
This is another useful resource, discussed here already - http://seriot.ch/parsing_json.php - which lists relevant standards. But "the" standard is static, so divergence, is any, is with other standards (different from json.org) vs. evolving JavaScript.
Yeah, I don't think JSON should include those things. I think the lack of comments makes JSON a poor format for config files, but that just means you should use another format for config files. JSON is good for machine-to-machine communication.
We had a lot of user supplied data in the strings of our API responses, some of it copied from Word documents and were ridden with U+2028 and U+2029 whitespace. Turns out that on iOS, the trigger.io library makes the all too popular assumption that any well-formated JSON can be interpreted as JS, and parses the responses with "eval", thus turning all those unicode characters _within JSON strings_ into newlines!
JSON Objects tend to have few enough values that it doesn't matter a ton anyway.
But if you want to use the nice features like parquet conversion your data can't be compressed.
If it could handle compressed data at the same price I would use a lot more of it.
Codable is desirable because it encodes/decides directly to strifes vs manually picking fields out of dicts.
Exactly. A "query" would have to define not only the path, type of the field in the source data but also the type/interface of where you want to put that data. Combining dynamic queries and typed data you get a fairly tricky problem, which is why I said this is tricky. I worked on a similar thing for protobuf and jitting was a solution I looked into (in that project libllvm was too unwieldy to use).
On another note. As a js programmer who deals with a ton of json, I would love v8 to adopt some of the tricks into their json parser.
Even for output, there is the common case where your clients expect JSON because its the de facto standard and is super accessible (every language has parsers for it), so you have little choice but to serve your data as JSON.
1) there's a distinction between integers and floating point values;
2) you can semantically tag values (yes, this is a text string, but treat it as a date; this is a binary string, but treat it as a big number; etc.);
3) you can have maps with non-text keys.
I'm not sure what Carsten Bormann's ego has to do with CBOR, but I found RFC-7049 one of the better written specs, with plenty of encoding examples. It made it real easy to write a encoder/decoder [1] and use the examples as test cases.
I agree that having lots of examples in the spec is good.
Um, use another language? I use Lua, which can deal with non-text keys. As for decoding dates (if they're semantically tagged, which you can with CBOR) I convert it to a datetime object, on the grounds that if I care about tagged dates, I'm going to be using them in some capacity.
But that's not to say you have to use the flexibility of CBOR. But for me, having distinct integer and floating point values, plus distinct text and binary data, is enough of a win to use it over JSON.
"Faster" meaning faster than Chromebooks do now; 2.2 GB/s may simply be unachievable hardware-wise with these cheap processors. They're kinda slow, so any speed increase would be welcome.
This looks mostly applicable to server scenarios where the runtime environment is highly controlled.
Are you talking about state transition penalties that can occur if you forget a vzeroupper? That's the only thing I'm aware of which kind of matches that.
But almost all real JSON documents are subsets of JavaScript, unless they happen to have those characters.
And the salient point is that if JSON never changes, then no further divergence from JavaScript is possible.
That assertion is indeed incorrect.
avmich then wrote "I thought they diverged specifications".
That is also correct. JSON was meant to be a perfect subset of JavaScript. Instead, and by accident, it diverged from the relevant specification.
Your comment instead was mostly focused on opposition to changing the existing JSON specification, which is a different topic.
My code has parsed a lot JSON and that is new data to me. Thank you for that!
Do you know the historical reasoning for this particular deviation? Are there any infamous bugs or common use cases this departure impacts?
Also, they are already relying on "unstable" (non-standard conforming) C++ features (e.g. the code uses non-standard attributes behind macros, etc.). Using nightly Rust isn't worse than that per se.
Using Rust does have downsides. For the type of code they are writing, the main downside would probably be losing an alternative GCC backend, which might or might not be better than LLVM for their application.
Still, they would win portable SIMD and being able to target not only x86_64 but also ARM, Power, RISCV, WASM, etc., which is always cool to show in research papers.
I'm not suggesting that Rust is a perfect trade-off, only that it's an interesting one depending on what they want to do.
I do think stable Rust is perfectly capable though. I don't generally target nightly Rust and am happy with how much I can squeeze out of it. :-) (Check out the benchmarks for the memchr crate, which use SIMD internally and should be competitive with glibc's x86_64 implementation that's in Assembly.)
Unstable Rust sounds very dangerous, like something that breaks every day. Definitely more dangerous than stable Rust.
Yet if one is in the Rust loop, one knows that this is often not the case. I've been using some unstable features on nightly, like const fn, specialization, function traits, etc. for years (3 years?), and I've never had a CI build job fail due to a change to the implementation of these features.
Yet some features in stable Rust like Rust2018 uniform_paths or stable SIMD have caused many build job breaks and undefined behavior due to bugs in the compiler over the last months.
So whatever stability means, it does not mean "using this feature won't result in your code not breaking". It also doesn't mean "you have to use a nightly toolchain to use the feature".
An unstable Rust feature is more like a "compiler extension" in C / C++. It is just something that hasn't fully gone through the process of standardization.
I don't think it is a fair characterization that code that uses this extensions is not Rust. Pretty much all C++ code uses compiler extensions, and nobody says that this code is not C++ just because it uses one of them.
Explaining all of this when telling someone "Rust is a technology that allows you to solve problem X nicely" isn't helpful.
Many people vocal about Rust seem to think that Rust is the end in of itself. The goal isn't solving a problem, but using Rust to solve it. I see many of these people argue that unstable Rust isn't Rust, and that people should be using stable Rust etc. For most people, using Rust isn't the goal, solving their problem is. Whether one or many compiler extensions have to be enabled for that is pretty much irrelevant to them. Sure it would be nice if one didn't need to do that, but it isn't a big deal either. The big embedded community is living proof of that. Only a small minority of this community cares about the language enough to participate in its evolution. Most people don't care enough about that, they have more interesting problems to solve.
I totally get json for public internet services where you want to have lots of consumers and using a more efficient format would be significant friction, but writing an editor frontend is a very large endeavor -- it seems like the extra work of adopting something more efficient than json (like flatbuffers or whatever) would really be in the noise.
Given equally-high quality JSON and binary serdes, JSON is sufficiently fast. Raphlinus is saying that Swift's built-in deserialiser is obnoxiously slow.
This is not the general case for unstable features. And promoting the use of them too heavily can cause a lot of problems. It undermines trust in the language, especially given rust’s pre-1.0 reputation (which was well deserved at the time.)
Stuff that’s unstable isn’t in Rust; that’s why it can be changed or even wholesale removed at any time. The distinction is very important.
I've seen you talk about "writing an OS kernel in Rust", but never heard you phrase that as "writing an OS in kernel in _unstable_ Rust". I've never seen you stating: "correction: what you are using for embedded development, networking, etc. is not Rust, _but unstable Rust_" on any of the many blog posts, announcements, news, etc. about these topics over the past couple of years. I've seen you reply with that argument every now and then, when someone like me downplays the importance of the distinction, but I've never seen you address the source of that behavior.
If the distinction between Rust, and unstable Rust, is important. Why are the people at the top not making it? If you are working on the compiler, servo, etc. you are actually not programming in Rust, but in _unstable_ Rust all of the time. Are they hypocrites? I don't think so.
If I reflect on why I feel that this distinction is not important, the first thing I realize is that I do think the distinction is important. But this distinction is not binary _to me_, as opposed to how you and burntsushi are putting it.
As you mentioned, some unstable features change more than others. There is a wide range of how much continues breakage does using certain unstable features cause downstream users. Some features break every day, some features haven't broken anything in 3 years.
Are unstable features that haven't broken anything in 3 years stable? No, by definition, they aren't.
Are they practical to use? The answer isn't yes or no, the answer is "depends on how much breakage you are willing to accept". We upgrade C++ compiler ~twice per year, and even though we only write 100% standard compliant code, we have to always fix breakage due to the upgrade. Yet I wouldn't say that standard compliant C++ is an unstable programming language.
So, if consider bi-yearly breakage is stable enough for our professional C++ projects in practice, why would I judge Rust stable / unstable features using a different bar? This does not mean that I believe that using unstable (or only stable) features will never cause breakage, since that is impossible.
I've had stable Rust CI jobs break because the standard library added some new trait method, and that caused an ambiguity that broke in my stable Rust code. The answer was: your code was correct, but we are allowed to break it in this way.
In my opinion, it is not "stable vs unstable", but 99% vs what degree of stability does your project need, where choosing more stability than what it needs puts it at a technical disadvantage. It doesn't matter whether one is talking here about Rust unstable features, or using the super unstable next-gen stable Rust web framework.
The stability line does not lie where I or anybody else decides to arbitrarily put it. It lies exactly on the amount of stability that a particular project can tolerate, and it is up to the judgement of the developers of that particular project to find out where that is.
Telling someone that a particular project is not Rust because the stability line for that project does not fall where your line does feels just wrong. Particularly when those doing it don't make that distinctions about themselves and the projects their work on.
There's nothing binary about my position. My only point is to mitigate an expectation mismatch. People get pissed off when they're led to believe that a feature is baked and ready to use, when it actually isn't. Honestly, you've turned a simple correction into a ranty spiraling sub-thread. It's obnoxious.
You're also getting way too hung up on what stability means. "stability" in Rust, in the context of API availability, is a statement of intent and commitment, not a statement of how often a build will break. Of course, there may be a strong correlation between them!
In this case there's a lot of work already put into fast JSON parsers, but in general JSON is not a very friendly format to work with or write efficient, generalized implementations of. Maybe it's not worth switching to something else. I'm not saying you should, it seems like a fine choice to me. But clever implementations don't come free and representation choice has a big impact on how "clever" you need to be.
My guess is it's easier to write an efficient flatbuffers (or similar) serializer+deserializer than an efficient json serializer+deserializer. And the top-end of performance definitely higher.
So if you're already reaching the point of needing to write your own json deserializers...
(† Unless you're talking about some hand-written bespoke binary format, but that would almost certainly be crazy.)