Chrome Network Stack Common Coding Patterns

Combined error and byte count into a single value

At many places in the network stack, functions return a value that, if positive, indicate a count of bytes that the the function read or wrote, and if negative, indicates a network stack error code (see net_error_list.h). Zero indicates either net::OK or zero bytes read (usually EOF) depending on the context. This pattern is generally specified by an int return type.

Many functions also have variables (often named result or rv) containing such a value; this is especially common in the DoLoop pattern described below.

Sync/Async Return

Many network stack routines may return synchronously or asynchronously. These functions generally return an int as described above. There are three cases:

  • If the value is positive or zero, that indicates a synchronous successful return, with a zero return value indicating either zero bytes/EOF or indicating net::OK, depending on context.
  • If the value is negative and != net::ERR_IO_PENDING, it is an error code specifying a synchronous failure.
  • If the return value is the special value net::ERR_IO_PENDING, it indicates that the routine will complete asynchronously. A reference to any provided IOBuffer will be retained by the called entity until completion, to be written into or read from as required. If there is a callback argument, that callback will be called upon completion with the return value; if there is no callback argument, it usually means that some known callback mechanism will be employed.


The DoLoop pattern is used in the network stack to construct simple state machines. It is used for cases in which processing is basically single-threaded and could be written in a single function, if that function could block waiting for input. Generally, initiation of a state machine is triggered by some method invocation by a class consumer, and that state machine is driven (possibly across asynchronous IO initiated by the class) until the operation requested by the method invocation completes, at which point the state variable is set to STATE_NONE and the consumer notified.

Cases which do not fit into this single-threaded, single consumer operation model are generally adapted in some way to fit the model, either by multiple state machines (e.g. independent state machines for reading and writing, if each can be initiated while the other is outstanding) or by storing information across consumer invocations and returns that can be used to restart the state machine in the proper state.

Any class using this pattern will contain an enum listing all states of that machine, and define a function, DoLoop(), to drive that state machine. If a class has multiple state machines (as above) it will have multiple methods (e.g. DoReadLoop() and DoWriteLoop()) to drive those different machines.

The characteristics of the DoLoop pattern are:

  • Each state has a corresponding function which is called by DoLoop() for handling when the state machine is in that state. Generally the states are named STATE_<STATE_NAME> (upper case separated by underscores), and the routine is named Do<StateName> (CamelCase). For example:

     enum State {
     int DoInit();
     int DoFoo();
     int DoFooComplete(int result);
  • Each state handling function has two basic responsibilities in addition to state specific handling: Setting the data member (named next_state_ or something similar) to specify the next state, and returning a net::Error (or combined error and byte count, as above).

  • On each DoLoop() iteration, the function saves the next state to a local variable and resets to a default state (STATE_NONE), and then calls the appropriate state handling based on the original value of the next state. This looks like:

       do {
         State state = io_state_;
         next_state_ = STATE_NONE;
         switch (state) {
           case STATE_INIT:
             result = DoInit();

    This pattern is followed primarily to ensure that in the event of a bug where the next state isn‘t set, the loop terminates rather than loops infinitely. It’s not a perfect mitigation, but works well as a defensive measure.

  • If a given state may complete asynchronously (for example, writing to an underlying transport socket), then there will often be split states, such as STATE_WRITE and STATE_WRITE_COMPLETE. The first state is responsible for starting/continuing the original operation, while the second state is responsible for handling completion (e.g. success vs error, complete vs. incomplete writes), and determining the next state to transition to.

  • While the return value from each call is propagated through the loop to the next state, it is expected that for most state transitions the return value will be net::OK, and that an error return will also set the state to STATE_NONE or fail to override the default assignment to STATE_DONE to exit the loop and return that error to the caller. This is often asserted with a DCHECK, e.g.

        case STATE_FOO:
            DCHECK_EQ(result, OK);
            result = DoFoo();

    The exception to this pattern is split states, where an IO operation has been dispatched, and the second state is handling the result. In that case, the second state's function takes the result code:

        case STATE_FOO_COMPLETE:
            result = DoFooComplete(result);
  • If the return value from the state handling function is net::ERR_IO_PENDING, that indicates that the function has arranged for DoLoop() to be called at some point in the future, when further progress can be made on the state transitions. The next_state_ variable will have been set to the proper value for handling that incoming call. In this case, DoLoop() will exit. This often occurs between split states, as described above.

  • The DoLoop mechanism is generally invoked in response to a consumer calling one of its methods. While the operation that method requested is occuring, the state machine stays active, possibly over multiple asynchronous operations and state transitions. When that operation is complete, the state machine transitions to STATE_NONE (by a DoLoop() callee not setting next_state_) or explicitly to STATE_DONE (indicating that the operation is complete and the state machine is not amenable to further driving). At this point the consumer is notified of the completion of the operation (by synchronous return or asynchronous callback).

    Note that this implies that when DoLoop() returns, one of two things will be true:

    • The return value will be net::ERR_IO_PENDING, indicating that the caller should take no action and instead wait for asynchronous notification.
    • The state of the machine will be either STATE_DONE or STATE_NONE, indicating that the operation that first initiated the DoLoop() has completed.

    This invariant reflects and enforces the single-threaded (though possibly asynchronous) nature of the driven state machine--the machine is always executing one requested operation.

  • DoLoop() is called from two places: a) methods exposed to the consumer for specific operations (e.g. ReadHeaders()), and b) an IO completion callbacks called asynchronously by spawned IO operations.

    In the first case, the return value from DoLoop() is returned directly to the caller; if the operation completed synchronously, that will contain the operation result, and if it completed asynchronously, it will be net::ERR_IO_PENDING. For example (from HttpStreamParser, abridged for clarity):

         int HttpStreamParser::ReadResponseHeaders(
             const CompletionCallback& callback) {
           DCHECK(io_state_ == STATE_NONE || io_state_ == STATE_DONE);
           int result = OK;
           io_state_ = STATE_READ_HEADERS;
           result = DoLoop(result);
           if (result == ERR_IO_PENDING)
             callback_ = callback;
           return result > 0 ? OK : result;

    In the second case, the IO completion callback will examine the return value from DoLoop(). If it is net::ERR_IO_PENDING, no further action will be taken, and the IO completion callback will be called again at some future point. If it is not net::ERR_IO_PENDING, that is a signal that the operation has completed, and the IO completion callback will call the appropriate consumer callback to notify the consumer that the operation has completed. Note that it is important that this callback be done from the IO completion callback and not from DoLoop() or a DoLoop() callee, both to support the sync/async error return (DoLoop and its callees don't know the difference) and to avoid consumer callbacks deleting the object out from under DoLoop(). Example:

         void HttpStreamParser::OnIOComplete(int result) {
           result = DoLoop(result);
           if (result != ERR_IO_PENDING && !callback_.is_null())
  • The DoLoop pattern has no concept of different events arriving for a single state; each state, if waiting, is waiting for one particular event, and when DoLoop() is invoked when the machine is in that state, it will handle that event. This reflects the single-threaded model for operations spawned by the state machine.

Public class methods generally have very little processing, primarily wrapping DoLoop(). For DoLoop() entry this involves setting the next_state_ variable, and possibly making copies of arguments into class members. For DoLoop() exit, it involves inspecting the return and passing it back to the caller, and in the asynchronous case, saving any passed completion callback for executing by a future subsidiary IO completion (see above example).

This idiom allows synchronous and asynchronous logic to be written in the same fashion; it‘s all just state transition handling. For mostly linear state diagrams, the handling code can be very easy to comprehend, as such code is usually written linearly (in different handling functions) in the order it’s executed.

For examples of this idiom, see