[manfredo]

What is the "completely different thing" you're referring to here. Between:

1. Having a static JSON string, and decoding that string.

and

2. having a static blob, and using protobufs to decode that blob.

these two things accomplish the same thing. I'm not sure why you seem to think one is a "cold start" and the other is "hot" - they're both "single, one-time object creation". The former is going to be parsing ints and floats as ascii, and reading in "true" and "false". Regardless of compression, the memory-inefficient JSON encoding is going to be used (whether it's over the wire, or just as an intermediate representation during parsing). I've used protobuf decoding for things like localizations and configurations before - the "cold start" use case you're talking about - and it does in many circumstances result in faster loading. My napkin paper reasoning is that this will be much more heavily weighted to booleans and integers that are much more efficiently encoded in protobufs than JSON, so maybe if you had a use case that almost entirely decoded strings your performance differences may not be the same.

[kllrnohj]

Are you including the cost of loading protobuf itself? You seem to be basing your argument on an assumed already present & loaded protobuf library.

You need to benchmark starting from nothing at all. Your link that you seem to be basing this off of has a loaded and fully JIT'd protobuf. That's not the start state.

[manfredo]

You can measure the impact on loading time, and the size of the protobuf implementation you're using probably has an impact on the threshold at which it becomes more efficient. I don't doubt that parsing a 500 character long JSON string is probably faster than loading a protobuf to do it instead. In fact, apparently this JSON parsing trick is only effective beyond 10K or so. But past a certain threshold memory bandwidth is more crucial than loading code. If your data consists mostly of booleans and integers then JSON can often be an order of magnitude larger in size than protobufs. If it's compressed, then decompressing it takes clock cycles and the parsing code is still parsing the larger uncompressed JSON text. A protobuf library can often skip compression altogether by virtue of using normal ints and bits for numbers and booleans. So while the protobuf library does have some additional overhead it's often higher throughput for many types of data.

[ricardobeat]

You’re repeatedly missing the point. This is about optimizing startup time.

The comparison should be:

cost of downloading payload + runtime cost of parsing JSON

Vs

cost to download protobuf lib + parse and execute JS protobuf lib + download payload + runtime cost of parsing

Specifically, the article talks about how parsing JS is more costly than JSON - this cost will apply to the protobuf library which certainly far exceeds 10KB. There is no way the math will work on your favor until you get to MBs of data.

[manfredo]

> Specifically, the article talks about how parsing JS is more costly than JSON - this cost will apply to the protobuf library which certainly far exceeds 10KB.

I would suggest reading the links I posted. The minimal protobuf library, which is suitable for working with static decoding, is 6.5KB [1]. Again, you're right that the size of the protobuf library will be an important factor in dictating the scale at which it's more effective than JSON parsing but your sense of the factors is off - a light protobuf library doesn't reach 10kB let alone "far exceeds 10KB".

Furthermore, if your pages use the protobuf library already for other uses like decoding and encoding RPC messages then loading and parsing the protobuf library is basically free - you're going to be doing this anyway.

1. https://www.npmjs.com/package/protobufjs#installation

[ricardobeat]

I currently work on a project using protobufjs. Our generated static classes are ~500KB and ~1.5MB, or around 140KB gzipped. The schemas are not that large, and this does not even include any network code (not part of protobufjs).