1. Are they easier to use than JSON?
2. Is it just putting strings into a big array?
3. Do you have an examples of them being used?
2. Is it just putting strings into a big array?
3. Do you have an examples of them being used?
2. No, writers can directly embed structs and primitive data types into binary buffers at runtime through an API generated from an IDL file. Readers use direct memory access to pull values out of the buffers. If you set it up right, this can result in a massive perf boost by eliminating the encoding and decoding steps.
3. Facebook uses them in their mobile app. Another commenter mentioned use of the in the Arrow format. The flatbuffers website isn't the best, but clearly documents the flatbuffers IDL.
They aren't hard to use, but they aren't the easiest thing either.
Once you get the gist of the API through the tutorial, the other important topics that come up immediately are version control; git repo design; headers and framing.
In production, they've been bulletproof. As long as you account for the compile time issues (schema versioning, repo, headers and framing, etc.).
Just too many drawbacks.
For server to server, they might be fine, but to client just stick with JSON. (which when compressed is pretty efficient).
Also browser has JSON parsing built in. Less dependencies. Easier tooling overall.
In my experience people overuse protobuf. But I also worked at Google, where it's the hammer in constant search of any nail it can find.
At the very least, endpoints should provide the option to provide a JSON form through content representation negotiation.
We moved to flatbuffers and back to JSON because in the end of the day, for our data, data compression with JSON+gzip was similarly-sized than the original one (which had some other fields that we were not using) and 10-20 times faster to decode.
That said, the use case for flatbuffers and capnproto isn't really about data size, it's about avoiding unnecessary copies in the processing pipeline. "Zero copy" really does pay dividends where performance is a concern if you write your code the right way.
Most people working on typical "web stack" type applications won't hit these concerns. But there are classes of applications where what flatbuffers (and other zerocopy payload formats) offer is important.
The difference in computation time between operating on something sitting in L1 cache vs not-in-cache is orders of magnitude. And memory bandwidth is a bottleneck in some applications and on some machines (particularly embedded.)
I did this one for reading json on the fast path, the sending system laid out the arrays in a periodic pattern in memory that enabled parseless retrieval of individual values.
Also I assume you'd have to have some sort of binary portion bundled with it to hold the field offsets, no?
I think I'd take a different approach and send along an "offset map" index blob which maps (statically known in advance based on a schema that both client and server would need to agree to) field IDs to memory offsets&lengths into a standard JSON file.
Then you have a readable JSON, but also a fast way O(1) to access the fields in a zero-copy environment.
Done right the blob could even fit in an HTTP response header, so standard clients could use the msg as is while 'smart' clients could use the index map for optimized access.
But as I said would suffer from numeric values being text encoded. And actually 'compiling' the blob would be an extra step. Wouldn't have all the benefits of flatbuffers or capnproto, but could be an interesting compromise.
I'd be surprised if this isn't already being done somewhere.