节点JS流：了解数据串联

Question

One of the first things you learn when you look at node's http module is this pattern for concatenating all of the data events coming from the request read stream:

let body = [];
request.on('data', chunk => {
  body.push(chunk);
}).on('end', () => {
  body = Buffer.concat(body).toString();
});

However, if you look at a lot of streaming library implementations they seem to gloss over this entirely. Also, when I inspect the request.on('data',...) event it almost ever only emits once for a typical JSON payload with a few to a dozen properties.

You can do things with the request stream like pipe it through some transforms in object mode and through to some other read streams. It looks like this concatenating pattern is never needed.

Is this because the request stream in handling POST and PUT bodies pretty much only ever emits one data event which is because their payload is way below the chunk partition size limit?. In practice, how large would a JSON encoded object need to be to be streamed in more than one data chunk?

It seems to me that objectMode streams don't need to worry about concatenating because if you're dealing with an object it is almost always no larger than one data emitted chunk, which atomically transforms to one object? I could see there being an issue if a client were uploading something like a massive collection (which is when a stream would be very useful as long as it could parse the individual objects in the collection and emit them one by one or in batches).

I find this to probably be the most confusing aspect of really understanding the node.js specifics of streams, there is a weird disconnect between streaming raw data, and dealing with atomic chunks like objects. Do objectMode stream transforms have internal logic for automatically concatenating up to object boundaries? If someone could clarify this it would be very appreciated.

Answer 1

The job of the code you show is to collect all the data from the stream into one buffer so when the end event occurs, you then have all the data.

request.on('data',...) may emit only once or it may emit hundreds of times. It depends upon the size of the data, the configuration of the stream object and the type of stream behind it. You cannot ever reliably assume it will only emit once.

You can do things with the request stream like pipe it through some transforms in object mode and through to some other read streams. It looks like this concatenating pattern is never needed.

You only use this concatenating pattern when you are trying to get the entire data from this stream into a single variable. The whole point of piping to another stream is that you don't need to fetch the entire data from one stream before sending it to the next stream. .pipe() will just send data as it arrives to the next stream for you. Same for transforms.

Is this because the request stream in handling POST and PUT bodies pretty much only ever emits one data event which is because their payload is way below the chunk partition size limit?.

It is likely because the payload is below some internal buffer size and the transport is sending all the data at once and you aren't running on a slow link and .... The point here is you cannot make assumptions about how many data events there will be. You must assume there can be more than one and that the first data event does not necessarily contain all the data or data separated on a nice boundary. Lots of things can cause the incoming data to get broken up differently.

Keep in mind that a readStream reads data until there's momentarily no more data to read (up to the size of the internal buffer) and then it emits a data event. It doesn't wait until the buffer fills before emitting a data event. So, since all data at the lower levels of the TCP stack is sent in packets, all it takes is a momentary delivery delay with some packet and the stream will find no more data available to read and will emit a data event. This can happen because of the way the data is sent, because of things that happen in the transport over which the data flows or even because of local TCP flow control if lots of stuff is going on with the TCP stack at the OS level.

In practice, how large would a JSON encoded object need to be to be streamed in more than one data chunk?

You really should not know or care because you HAVE to assume that any size object could be delivered in more than one data event. You can probably safely assume that a JSON object larger than the internal stream buffer size (which you could find out by studying the stream code or examining internals in the debugger) WILL be delivered in multiple data events, but you cannot assume the reverse because there are other variables such as transport-related things that can cause it to get split up into multiple events.

It seems to me that objectMode streams don't need to worry about concatenating because if you're dealing with an object it is almost always no larger than one data emitted chunk, which atomically transforms to one object? I could see there being an issue if a client were uploading something like a massive collection (which is when a stream would be very useful as long as it could parse the individual objects in the collection and emit them one by one or in batches).

Object mode streams must do their own internal buffering to find the boundaries of whatever objects they are parsing so that they can emit only whole objects. At some low level, they are concatenating data buffers and then examining them to see if they yet have a whole object.

Yes, you are correct that if you were using an object mode stream and the object themselves were very large, they could consume a lot of memory. Likely this wouldn't be the most optimal way of dealing with that type of data.

Do objectMode stream transforms have internal logic for automatically concatenating up to object boundaries?

Yes, they do.

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FYI, the first thing I do when making http requests is to go use the request-promise library so I don't have to do my own concatenating. It handles all this for you. It also provides a promise-based interface and about 100 other helpful features which I find helpful.