hw-react-1/node_modules/websocket-extensions/lib/pipeline

Extension pipelining

websocket-extensions models the extension negotiation and processing pipeline of the WebSocket protocol. Between the driver parsing messages from the TCP stream and handing those messages off to the application, there may exist a stack of extensions that transform the message somehow.

In the parlance of this framework, a session refers to a single instance of an extension, acting on a particular socket on either the server or the client side. A session may transform messages both incoming to the application and outgoing from the application, for example the permessage-deflate extension compresses outgoing messages and decompresses incoming messages. Message streams in either direction are independent; that is, incoming and outgoing messages cannot be assumed to 'pair up' as in a request-response protocol.

Asynchronous processing of messages poses a number of problems that this pipeline construction is intended to solve.

Overview

Logically, we have the following:

+-------------+  out  +---+     +---+     +---+       +--------+
|             |------>|   |---->|   |---->|   |------>|        |
| Application |       | A |     | B |     | C |       | Driver |
|             |<------|   |<----|   |<----|   |<------|        |
+-------------+  in   +---+     +---+     +---+       +--------+

                      \                       /
                       +----------o----------+
                                  |
                               sessions

For outgoing messages, the driver receives the result of

    C.outgoing(B.outgoing(A.outgoing(message)))

or, [A, B, C].reduce(((m, ext) => ext.outgoing(m)), message)

For incoming messages, the application receives the result of

    A.incoming(B.incoming(C.incoming(message)))

or, [C, B, A].reduce(((m, ext) => ext.incoming(m)), message)

A session is of the following type, to borrow notation from pseudo-Haskell:

type Session = {
  incoming :: Message -> Message
  outgoing :: Message -> Message
  close    :: () -> ()
}

(That () -> () syntax is intended to mean that close() is a nullary void method; I apologise to any Haskell readers for not using the right monad.)

The incoming() and outgoing() methods perform message transformation in the respective directions; close() is called when a socket closes so the session can release any resources it's holding, for example a DEFLATE de/compression context.

However because this is JavaScript, the incoming() and outgoing() methods may be asynchronous (indeed, permessage-deflate is based on zlib, whose API is stream-based). So their interface is strictly:

type Session = {
  incoming :: Message -> Callback -> ()
  outgoing :: Message -> Callback -> ()
  close    :: () -> ()
}

type Callback = Either Error Message -> ()

This means a message m2 can be pushed into a session while it's still processing the preceding message m1. The messages can be processed concurrently but they must be given to the next session in line (or to the application) in the same order they came in. Applications will expect to receive messages in the order they arrived over the wire, and sessions require this too. So ordering of messages must be preserved throughout the pipeline.

Consider the following highly simplified extension that deflates messages on the wire. message is a value conforming the type:

type Message = {
  rsv1   :: Boolean
  rsv2   :: Boolean
  rsv3   :: Boolean
  opcode :: Number
  data   :: Buffer
}

Here's the extension:

var zlib = require('zlib');

var deflate = {
  outgoing: function(message, callback) {
    zlib.deflateRaw(message.data, function(error, result) {
      message.rsv1 = true;
      message.data = result;
      callback(error, message);
    });
  },

  incoming: function(message, callback) {
    // decompress inbound messages (elided)
  },

  close: function() {
    // no state to clean up
  }
};

We can call it with a large message followed by a small one, and the small one will be returned first:

var crypto = require('crypto'),
    large  = crypto.randomBytes(1 << 14),
    small  = new Buffer('hi');

deflate.outgoing({ data: large }, function() {
  console.log(1, 'large');
});

deflate.outgoing({ data: small }, function() {
  console.log(2, 'small');
});

/* prints:  2 'small'
            1 'large' */

So a session that processes messages asynchronously may fail to preserve message ordering.

Now, this extension is stateless, so it can process messages in any order and still produce the same output. But some extensions are stateful and require message order to be preserved.

For example, when using permessage-deflate without no_context_takeover set, the session retains a DEFLATE de/compression context between messages, which accumulates state as it consumes data (later messages can refer to sections of previous ones to improve compression). Reordering parts of the DEFLATE stream will result in a failed decompression. Messages must be decompressed in the same order they were compressed by the peer in order for the DEFLATE protocol to work.

Finally, there is the problem of closing a socket. When a WebSocket is closed by the application, or receives a closing request from the other peer, there may be messages outgoing from the application and incoming from the peer in the pipeline. If we close the socket and pipeline immediately, two problems arise:

• We may send our own closing frame to the peer before all prior messages we sent have been written to the socket, and before we have finished processing all prior messages from the peer
• The session may be instructed to close its resources (e.g. its de/compression context) while it's in the middle of processing a message, or before it has received messages that are upstream of it in the pipeline

Essentially, we must defer closing the sessions and sending a closing frame until after all prior messages have exited the pipeline.

Design goals

• Message order must be preserved between the protocol driver, the extension sessions, and the application
• Messages should be handed off to sessions and endpoints as soon as possible, to maximise throughput of stateless sessions
• The closing procedure should block any further messages from entering the pipeline, and should allow all existing messages to drain
• Sessions should be closed as soon as possible to prevent them holding memory and other resources when they have no more messages to handle
• The closing API should allow the caller to detect when the pipeline is empty and it is safe to continue the WebSocket closing procedure
• Individual extensions should remain as simple as possible to facilitate modularity and independent authorship

The final point about modularity is an important one: this framework is designed to facilitate extensions existing as plugins, by decoupling the protocol driver, extensions, and application. In an ideal world, plugins should only need to contain code for their specific functionality, and not solve these problems that apply to all sessions. Also, solving some of these problems requires consideration of all active sessions collectively, which an individual session is incapable of doing.

For example, it is entirely possible to take the simple deflate extension above and wrap its incoming() and outgoing() methods in two Transform streams, producing this type:

type Session = {
  incoming :: TransformStream
  outtoing :: TransformStream
  close    :: () -> ()
}

The Transform class makes it easy to wrap an async function such that message order is preserved:

var stream  = require('stream'),
    session = new stream.Transform({ objectMode: true });

session._transform = function(message, _, callback) {
  var self = this;
  deflate.outgoing(message, function(error, result) {
    self.push(result);
    callback();
  });
};

However, this has a negative impact on throughput: it works by deferring callback() until the async function has 'returned', which blocks Transform from passing further input into the _transform() method until the current message is dealt with completely. This would prevent sessions from processing messages concurrently, and would unnecessarily reduce the throughput of stateless extensions.

So, input should be handed off to sessions as soon as possible, and all we need is a mechanism to reorder the output so that message order is preserved for the next session in line.

Solution

We now describe the model implemented here and how it meets the above design goals. The above diagram where a stack of extensions sit between the driver and application describes the data flow, but not the object graph. That looks like this:

        +--------+
        | Driver |
        +---o----+
            |
            V
      +------------+      +----------+
      | Extensions o----->| Pipeline |
      +------------+      +-----o----+
                                |
                +---------------+---------------+
                |               |               |
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+

A driver using this framework holds an instance of the Extensions class, which it uses to register extension plugins, negotiate headers and transform messages. The Extensions instance itself holds a Pipeline, which contains an array of Cell objects, each of which wraps one of the sessions.

Message processing

Both the Pipeline and Cell classes have incoming() and outgoing() methods; the Pipeline interface pushes messages into the pipe, delegates the message to each Cell in turn, then returns it back to the driver. Outgoing messages pass through A then B then C, and incoming messages in the reverse order.

Internally, a Cell contains two Functor objects. A Functor wraps an async function and makes sure its output messages maintain the order of its input messages. This name is due to @fronx, on the basis that, by preserving message order, the abstraction preserves the mapping between input and output messages. To use our simple deflate extension from above:

var functor = new Functor(deflate, 'outgoing');

functor.call({ data: large }, function() {
  console.log(1, 'large');
});

functor.call({ data: small }, function() {
  console.log(2, 'small');
});

/*  ->  1 'large'
        2 'small' */

A Cell contains two of these, one for each direction:

                        +-----------------------+
                  +---->| Functor [A, incoming] |
+----------+      |     +-----------------------+
| Cell [A] o------+
+----------+      |     +-----------------------+
                  +---->| Functor [A, outgoing] |
                        +-----------------------+

This satisfies the message transformation requirements: the Pipeline simply loops over the cells in the appropriate direction to transform each message. Because each Cell will preserve message order, we can pass a message to the next Cell in line as soon as the current Cell returns it. This gives each Cell all the messages in order while maximising throughput.

Session closing

We want to close each session as soon as possible, after all existing messages have drained. To do this, each Cell begins with a pending message counter in each direction, labelled in and out below.

                          +----------+
                          | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
                |               |               |
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 0          out: 0          out: 0

When a message m1 enters the pipeline, say in the outgoing direction, we increment the pending.out counter on all cells immediately.

                          +----------+
                    m1 => | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
                |               |               |
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 1          out: 1          out: 1

m1 is handed off to A, meanwhile a second message m2 arrives in the same direction. All pending.out counters are again incremented.

                          +----------+
                    m2 => | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
            m1  |               |               |
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 2          out: 2          out: 2

When the first cell's A.outgoing functor finishes processing m1, the first pending.out counter is decremented and m1 is handed off to cell B.

                          +----------+
                          | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
            m2  |           m1  |               |
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 1          out: 2          out: 2

As B finishes with m1, and as A finishes with m2, the pending.out counters continue to decrement.

                          +----------+
                          | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
                |           m2  |           m1  |
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 0          out: 1          out: 2

Say C is a little slow, and begins processing m2 while still processing m1. That's fine, the Functor mechanism will keep m1 ahead of m2 in the output.

                          +----------+
                          | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
                |               |           m2  | m1
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 0          out: 0          out: 2

Once all messages are dealt with, the counters return to 0.

                          +----------+
                          | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
                |               |               |
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 0          out: 0          out: 0

The same process applies in the incoming direction, the only difference being that messages are passed to C first.

This makes closing the sessions quite simple. When the driver wants to close the socket, it calls Pipeline.close(). This immediately calls close() on all the cells. If a cell has in == out == 0, then it immediately calls session.close(). Otherwise, it stores the closing call and defers it until in and out have both ticked down to zero. The pipeline will not accept new messages after close() has been called, so we know the pending counts will not increase after this point.

This means each session is closed as soon as possible: A can close while the slow C session is still working, because it knows there are no more messages on the way. Similarly, C will defer closing if close() is called while m1 is still in B, and m2 in A, because its pending count means it knows it has work yet to do, even if it's not received those messages yet. This concern cannot be addressed by extensions acting only on their own local state, unless we pollute individual extensions by making them all implement this same mechanism.

The actual closing API at each level is slightly different:

type Session = {
  close :: () -> ()
}

type Cell = {
  close :: () -> Promise ()
}

type Pipeline = {
  close :: Callback -> ()
}

This might appear inconsistent so it's worth explaining. Remember that a Pipeline holds a list of Cell objects, each wrapping a Session. The driver talks (via the Extensions API) to the Pipeline interface, and it wants Pipeline.close() to do two things: close all the sessions, and tell me when it's safe to start the closing procedure (i.e. when all messages have drained from the pipe and been handed off to the application or socket). A callback API works well for that.

At the other end of the stack, Session.close() is a nullary void method with no callback or promise API because we don't care what it does, and whatever it does do will not block the WebSocket protocol; we're not going to hold off processing messages while a session closes its de/compression context. We just tell it to close itself, and don't want to wait while it does that.

In the middle, Cell.close() returns a promise rather than using a callback. This is for two reasons. First, Cell.close() might not do anything immediately, it might have to defer its effect while messages drain. So, if given a callback, it would have to store it in a queue for later execution. Callbacks work fine if your method does something and can then invoke the callback itself, but if you need to store callbacks somewhere so another method can execute them, a promise is a better fit. Second, it better serves the purposes of Pipeline.close(): it wants to call close() on each of a list of cells, and wait for all of them to finish. This is simple and idiomatic using promises:

var closed = cells.map((cell) => cell.close());
Promise.all(closed).then(callback);

(We don't actually use a full Promises/A+ compatible promise here, we use a much simplified construction that acts as a callback aggregater and resolves synchronously and does not support chaining, but the principle is the same.)

Error handling

We've not mentioned error handling so far but it bears some explanation. The above counter system still applies, but behaves slightly differently in the presence of errors.

Say we push three messages into the pipe in the outgoing direction:

                          +----------+
            m3, m2, m1 => | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
                |               |               |
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 3          out: 3          out: 3

They pass through the cells successfully up to this point:

                          +----------+
                          | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
            m3  |           m2  |           m1  |
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 1          out: 2          out: 3

At this point, session B produces an error while processing m2, that is m2 becomes e2. m1 is still in the pipeline, and m3 is queued behind m2. What ought to happen is that m1 is handed off to the socket, then m2 is released to the driver, which will detect the error and begin closing the socket. No further processing should be done on m3 and it should not be released to the driver after the error is emitted.

To handle this, we allow errors to pass down the pipeline just like messages do, to maintain ordering. But, once a cell sees its session produce an error, or it receives an error from upstream, it should refuse to accept any further messages. Session B might have begun processing m3 by the time it produces the error e2, but C will have been given e2 before it receives m3, and can simply drop m3.

Now, say e2 reaches the slow session C while m1 is still present, meanwhile m3 has been dropped. C will never receive m3 since it will have been dropped upstream. Under the present model, its out counter will be 3 but it is only going to emit two more values: m1 and e2. In order for closing to work, we need to decrement out to reflect this. The situation should look like this:

                          +----------+
                          | Pipeline |
                          +-----o----+
                                |
                +---------------+---------------+
                |               |           e2  | m1
          +-----o----+    +-----o----+    +-----o----+
          | Cell [A] |    | Cell [B] |    | Cell [C] |
          +----------+    +----------+    +----------+
             in: 0           in: 0           in: 0
            out: 0          out: 0          out: 2

When a cell sees its session emit an error, or when it receives an error from upstream, it sets its pending count in the appropriate direction to equal the number of messages it is currently processing. It will not accept any messages after it sees the error, so this will allow the counter to reach zero.

Note that while e2 is in the pipeline, Pipeline should drop any further messages in the outgoing direction, but should continue to accept incoming messages. Until e2 makes it out of the pipe to the driver, behind previous successful messages, the driver does not know an error has happened, and a message may arrive over the socket and make it all the way through the incoming pipe in the meantime. We only halt processing in the affected direction to avoid doing unnecessary work since messages arriving after an error should not be processed.

Some unnecessary work may happen, for example any messages already in the pipeline following m2 will be processed by A, since it's upstream of the error. Those messages will be dropped by B.

Alternative ideas

I am considering implementing Functor as an object-mode transform stream rather than what is essentially an async function. Being object-mode, a stream would preserve message boundaries and would also possibly help address back-pressure. I'm not sure whether this would require external API changes so that such streams could be connected to the downstream driver's streams.

Acknowledgements

Credit is due to @mnowster for helping with the design and to @fronx for helping name things.