.. _messaging:

======================
 Messaging in IPython
======================


Introduction
============

This document explains the basic communications design and messaging
specification for how the various IPython objects interact over a network
transport.  The current implementation uses the ZeroMQ_ library for messaging
within and between hosts.

.. Note::

   This document should be considered the authoritative description of the
   IPython messaging protocol, and all developers are strongly encouraged to
   keep it updated as the implementation evolves, so that we have a single
   common reference for all protocol details.
   
The basic design is explained in the following diagram:

.. image:: frontend-kernel.png
   :width: 450px
   :alt: IPython kernel/frontend messaging architecture.
   :align: center
   :target: ../_images/frontend-kernel.png

A single kernel can be simultaneously connected to one or more frontends.  The
kernel has three sockets that serve the following functions:

1. REQ: this socket is connected to a *single* frontend at a time, and it allows
   the kernel to request input from a frontend when :func:`raw_input` is called.
   The frontend holding the matching REP socket acts as a 'virtual keyboard'
   for the kernel while this communication is happening (illustrated in the
   figure by the black outline around the central keyboard).  In practice,
   frontends may display such kernel requests using a special input widget or
   otherwise indicating that the user is to type input for the kernel instead
   of normal commands in the frontend.

2. XREP: this single sockets allows multiple incoming connections from
   frontends, and this is the socket where requests for code execution, object
   information, prompts, etc. are made to the kernel by any frontend.  The
   communication on this socket is a sequence of request/reply actions from
   each frontend and the kernel.

3. PUB: this socket is the 'broadcast channel' where the kernel publishes all
   side effects (stdout, stderr, etc.) as well as the requests coming from any
   client over the XREP socket and its own requests on the REP socket.  There
   are a number of actions in Python which generate side effects: :func:`print`
   writes to ``sys.stdout``, errors generate tracebacks, etc.  Additionally, in
   a multi-client scenario, we want all frontends to be able to know what each
   other has sent to the kernel (this can be useful in collaborative scenarios,
   for example).  This socket allows both side effects and the information
   about communications taking place with one client over the XREQ/XREP channel
   to be made available to all clients in a uniform manner.

   All messages are tagged with enough information (details below) for clients
   to know which messages come from their own interaction with the kernel and
   which ones are from other clients, so they can display each type
   appropriately.

The actual format of the messages allowed on each of these channels is
specified below.  Messages are dicts of dicts with string keys and values that
are reasonably representable in JSON.  Our current implementation uses JSON
explicitly as its message format, but this shouldn't be considered a permanent
feature.  As we've discovered that JSON has non-trivial performance issues due
to excessive copying, we may in the future move to a pure pickle-based raw
message format.  However, it should be possible to easily convert from the raw
objects to JSON, since we may have non-python clients (e.g. a web frontend).
As long as it's easy to make a JSON version of the objects that is a faithful
representation of all the data, we can communicate with such clients.

.. Note::

   Not all of these have yet been fully fleshed out, but the key ones are, see
   kernel and frontend files for actual implementation details.

   
Python functional API
=====================

As messages are dicts, they map naturally to a ``func(**kw)`` call form.  We
should develop, at a few key points, functional forms of all the requests that
take arguments in this manner and automatically construct the necessary dict
for sending.


General Message Format
======================

All messages send or received by any IPython process should have the following
generic structure::
    
    {
      # The message header contains a pair of unique identifiers for the
      # originating session and the actual message id, in addition to the
      # username for the process that generated the message.  This is useful in
      # collaborative settings where multiple users may be interacting with the
      # same kernel simultaneously, so that frontends can label the various
      # messages in a meaningful way.
      'header' : { 'msg_id' : uuid,
                   'username' : str,
           'session' : uuid
         },

      # In a chain of messages, the header from the parent is copied so that
      # clients can track where messages come from.
      'parent_header' : dict,

      # All recognized message type strings are listed below.
      'msg_type' : str,

      # The actual content of the message must be a dict, whose structure
      # depends on the message type.x
      'content' : dict,
    }

For each message type, the actual content will differ and all existing message
types are specified in what follows of this document.


Messages on the XREP/XREQ socket
================================

.. _execute:

Execute
-------

This message type is used by frontends to ask the kernel to execute code on
behalf of the user, in a namespace reserved to the user's variables (and thus
separate from the kernel's own internal code and variables).

Message type: ``execute_request``::

    content = {
        # Source code to be executed by the kernel, one or more lines.
    'code' : str,

    # A boolean flag which, if True, signals the kernel to execute this
    # code as quietly as possible.  This means that the kernel will compile
    # the code witIPython/core/tests/h 'exec' instead of 'single' (so
    # sys.displayhook will not fire), and will *not*:
    #   - broadcast exceptions on the PUB socket
    #   - do any logging
    #   - populate any history
    #
    # The default is False.
    'silent' : bool,

    # A list of variable names from the user's namespace to be retrieved.  What
    # returns is a JSON string of the variable's repr(), not a python object.
    'user_variables' : list,

    # Similarly, a dict mapping names to expressions to be evaluated in the
    # user's dict.
    'user_expressions' : dict,
    }

The ``code`` field contains a single string, but this may be a multiline
string.  The kernel is responsible for splitting this into possibly more than
one block and deciding whether to compile these in 'single' or 'exec' mode.
We're still sorting out this policy.  The current inputsplitter is capable of
splitting the input for blocks that can all be run as 'single', but in the long
run it may prove cleaner to only use 'single' mode for truly single-line
inputs, and run all multiline input in 'exec' mode.  This would preserve the
natural behavior of single-line inputs while allowing long cells to behave more
likea a script.  This design will be refined as we complete the implementation.

The ``user_`` fields deserve a detailed explanation.  In the past, IPython had
the notion of a prompt string that allowed arbitrary code to be evaluated, and
this was put to good use by many in creating prompts that displayed system
status, path information, and even more esoteric uses like remote instrument
status aqcuired over the network.  But now that IPython has a clean separation
between the kernel and the clients, the notion of embedding 'prompt'
maninpulations into the kernel itself feels awkward.  Prompts should be a
frontend-side feature, and it should be even possible for different frontends
to display different prompts while interacting with the same kernel.

We have therefore abandoned the idea of a 'prompt string' to be evaluated by
the kernel, and instead provide the ability to retrieve from the user's
namespace information after the execution of the main ``code``, with two fields
of the execution request:

- ``user_variables``: If only variables from the user's namespace are needed, a
  list of variable names can be passed and a dict with these names as keys and
  their :func:`repr()` as values will be returned.

- ``user_expressions``: For more complex expressions that require function
  evaluations, a dict can be provided with string keys and arbitrary python
  expressions as values.  The return message will contain also a dict with the
  same keys and the :func:`repr()` of the evaluated expressions as value.

With this information, frontends can display any status information they wish
in the form that best suits each frontend (a status line, a popup, inline for a
terminal, etc).

.. Note::

   In order to obtain the current execution counter for the purposes of
   displaying input prompts, frontends simply make an execution request with an
   empty code string and ``silent=True``.

Execution semantics
    Upon completion of the execution request, the kernel *always* sends a
    reply, with a status code indicating what happened and additional data
    depending on the outcome.

    The ``code`` field is executed first, and then the ``user_variables`` and
    ``user_expressions`` are computed.  This ensures that any error in the
    latter don't harm the main code execution.

    Any error in retrieving the ``user_variables`` or evaluating the
    ``user_expressions`` will result in a simple error message in the return
    fields of the form::

       [ERROR] ExceptionType: Exception message

    The user can simply send the same variable name or expression for
    evaluation to see a regular traceback.

Execution counter (old prompt number)
    The kernel has a single, monotonically increasing counter of all execution
    requests that are made with ``silent=False``.  This counter is used to
    populate the ``In[n]``, ``Out[n]`` and ``_n`` variables, so clients will
    likely want to display it in some form to the user, which will typically
    (but not necessarily) be done in the prompts.  The value of this counter
    will be returned as the ``execution_count`` field of all ``execute_reply```
    messages.

Message type: ``execute_reply``::

    content = {
      # One of: 'ok' OR 'error' OR 'abort'
      'status' : str,

      # The global kernel counter that increases by one with each non-silent
      # executed request.  This will typically be used by clients to display
      # prompt numbers to the user.  If the request was a silent one, this will
      # be the current value of the counter in the kernel.
      'execution_count' : int,
    }

When status is 'ok', the following extra fields are present::

    {
      # The execution payload is a dict with string keys that may have been
      # produced by the code being executed.  It is retrieved by the kernel at
      # the end of the execution and sent back to the front end, which can take
      # action on it as needed.  See main text for further details.
      'payload' : dict,

      # Results for the user_variables and user_expressions.
      'user_variables' : dict,
      'user_expressions' : dict,

      # The kernel will often transform the input provided to it.  If the
      # '---->' transform had been applied, this is filled, otherwise it's the
      # empty string.  So transformations like magics don't appear here, only
      # autocall ones.
      'transformed_code' : str,
      }

.. admonition:: Execution payloads
    
   The notion of an 'execution payload' is different from a return value of a
   given set of code, which normally is just displayed on the pyout stream
   through the PUB socket.  The idea of a payload is to allow special types of
   code, typically magics, to populate a data container in the IPython kernel
   that will be shipped back to the caller via this channel.  The kernel will
   have an API for this, probably something along the lines of::

       ip.exec_payload_add(key, value)

   though this API is still in the design stages.  The data returned in this
   payload will allow frontends to present special views of what just happened.

   
When status is 'error', the following extra fields are present::

    {
      'exc_name' : str,   # Exception name, as a string
      'exc_value' : str,  # Exception value, as a string

      # The traceback will contain a list of frames, represented each as a
      # string.  For now we'll stick to the existing design of ultraTB, which
      # controls exception level of detail statefully.  But eventually we'll
      # want to grow into a model where more information is collected and
      # packed into the traceback object, with clients deciding how little or
      # how much of it to unpack.  But for now, let's start with a simple list
      # of strings, since that requires only minimal changes to ultratb as
      # written.
      'traceback' : list,
    }


When status is 'abort', there are for now no additional data fields.  This
happens when the kernel was interrupted by a signal.

Kernel attribute access
-----------------------

While this protocol does not specify full RPC access to arbitrary methods of
the kernel object, the kernel does allow read (and in some cases write) access
to certain attributes.

The policy for which attributes can be read is: any attribute of the kernel, or
its sub-objects, that belongs to a :class:`Configurable` object and has been
declared at the class-level with Traits validation, is in principle accessible
as long as its name does not begin with a leading underscore.  The attribute
itself will have metadata indicating whether it allows remote read and/or write
access.  The message spec follows for attribute read and write requests.

Message type: ``getattr_request``::

    content = {
        # The (possibly dotted) name of the attribute
	'name' : str,
    }

When a ``getattr_request`` fails, there are two possible error types:

- AttributeError: this type of error was raised when trying to access the
  given name by the kernel itself.  This means that the attribute likely
  doesn't exist.

- AccessError: the attribute exists but its value is not readable remotely.


Message type: ``getattr_reply``::

    content = {
        # One of ['ok', 'AttributeError', 'AccessError'].
        'status' : str,
	# If status is 'ok', a JSON object.
	'value' : object,
    }

Message type: ``setattr_request``::

    content = {
        # The (possibly dotted) name of the attribute
	'name' : str,
	
	# A JSON-encoded object, that will be validated by the Traits
	# information in the kernel
	'value' : object,
    }

When a ``setattr_request`` fails, there are also two possible error types with
similar meanings  as those of the ``getattr_request`` case, but for writing.
    
Message type: ``setattr_reply``::

    content = {
        # One of ['ok', 'AttributeError', 'AccessError'].
        'status' : str,
    }


Object information
------------------

One of IPython's most used capabilities is the introspection of Python objects
in the user's namespace, typically invoked via the ``?`` and ``??`` characters
(which in reality are shorthands for the ``%pinfo`` magic).  This is used often
enough that it warrants an explicit message type, especially because frontends
may want to get object information in response to user keystrokes (like Tab or
F1) besides from the user explicitly typing code like ``x??``.

Message type: ``object_info_request``::

    content = {
        # The (possibly dotted) name of the object to be searched in all
	# relevant namespaces
        'name' : str,

    	# The level of detail desired.  The default (0) is equivalent to typing
	# 'x?' at the prompt, 1 is equivalent to 'x??'.
	'detail_level' : int,
    }

The returned information will be a dictionary with keys very similar to the
field names that IPython prints at the terminal.
    
Message type: ``object_info_reply``::

    content = {
        # Flags for magics and system aliases
    'ismagic' : bool,
    'isalias' : bool,

    # The name of the namespace where the object was found ('builtin',
    # 'magics', 'alias', 'interactive', etc.)
    'namespace' : str,

    # The type name will be type.__name__ for normal Python objects, but it
    # can also be a string like 'Magic function' or 'System alias'
    'type_name' : str,

    'string_form' : str,

    # For objects with a __class__ attribute this will be set
    'base_class' : str,

    # For objects with a __len__ attribute this will be set
    'length' : int,

    # If the object is a function, class or method whose file we can find,
    # we give its full path
    'file' : str,

    # For pure Python callable objects, we can reconstruct the object
    # definition line which provides its call signature.  For convenience this
    # is returned as a single 'definition' field, but below the raw parts that
    # compose it are also returned as the argspec field.
    'definition' : str,

    # The individual parts that together form the definition string.  Clients
    # with rich display capabilities may use this to provide a richer and more
    # precise representation of the definition line (e.g. by highlighting
    # arguments based on the user's cursor position).  For non-callable
    # objects, this field is empty.
    'argspec' : { # The names of all the arguments
                  args : list,
		  # The name of the varargs (*args), if any
                  varargs : str,
		  # The name of the varkw (**kw), if any
		  varkw : str,
		  # The values (as strings) of all default arguments.  Note
		  # that these must be matched *in reverse* with the 'args'
		  # list above, since the first positional args have no default
		  # value at all.
		  func_defaults : list,
		  },

    # For instances, provide the constructor signature (the definition of
    # the __init__ method):
    'init_definition' : str,
    
    # Docstrings: for any object (function, method, module, package) with a
    # docstring, we show it.  But in addition, we may provide additional
    # docstrings.  For example, for instances we will show the constructor
    # and class docstrings as well, if available.
    'docstring' : str,

    # For instances, provide the constructor and class docstrings
    'init_docstring' : str,
    'class_docstring' : str,

    # If detail_level was 1, we also try to find the source code that
    # defines the object, if possible.  The string 'None' will indicate
    # that no source was found.
    'source' : str,
    }

    
Complete
--------

Message type: ``complete_request``::

    content = {
        # The text to be completed, such as 'a.is'
    'text' : str,

    # The full line, such as 'print a.is'.  This allows completers to
    # make decisions that may require information about more than just the
    # current word.
    'line' : str,

    # The entire block of text where the line is.  This may be useful in the
    # case of multiline completions where more context may be needed.  Note: if
    # in practice this field proves unnecessary, remove it to lighten the
    # messages.
    
    'block' : str,

    # The position of the cursor where the user hit 'TAB' on the line.
    'cursor_pos' : int,
    }

Message type: ``complete_reply``::

    content = {
        # The list of all matches to the completion request, such as
    # ['a.isalnum', 'a.isalpha'] for the above example.
    'matches' : list
    }

    
History
-------

For clients to explicitly request history from a kernel.  The kernel has all
the actual execution history stored in a single location, so clients can
request it from the kernel when needed.

Message type: ``history_request``::

    content = {
    
      # If True, also return output history in the resulting dict.
      'output' : bool,

      # If True, return the raw input history, else the transformed input.
      'raw' : bool,

      # This parameter can be one of: A number,  a pair of numbers, None
      # If not given, last 40 are returned.
      #  - number n: return the last n entries.
      #  - pair n1, n2: return entries in the range(n1, n2).
      #  - None: return all history
      'index' : n or (n1, n2) or None,
    }

Message type: ``history_reply``::

    content = {
      # A dict with prompt numbers as keys and either (input, output) or input
      # as the value depending on whether output was True or False,
      # respectively.
      'history' : dict,
    }
    
Messages on the PUB/SUB socket
==============================

Streams (stdout,  stderr, etc)
------------------------------

Message type: ``stream``::

    content = {
        # The name of the stream is one of 'stdin', 'stdout', 'stderr'
    'name' : str,
    
    # The data is an arbitrary string to be written to that stream
    'data' : str,
    }

When a kernel receives a raw_input call, it should also broadcast it on the pub
socket with the names 'stdin' and 'stdin_reply'.  This will allow other clients
to monitor/display kernel interactions and possibly replay them to their user
or otherwise expose them.

Python inputs
-------------

These messages are the re-broadcast of the ``execute_request``.

Message type: ``pyin``::

    content = {
        # Source code to be executed, one or more lines
    'code' : str
    }

Python outputs
--------------

When Python produces output from code that has been compiled in with the
'single' flag to :func:`compile`, any expression that produces a value (such as
``1+1``) is passed to ``sys.displayhook``, which is a callable that can do with
this value whatever it wants.  The default behavior of ``sys.displayhook`` in
the Python interactive prompt is to print to ``sys.stdout`` the :func:`repr` of
the value as long as it is not ``None`` (which isn't printed at all).  In our
case, the kernel instantiates as ``sys.displayhook`` an object which has
similar behavior, but which instead of printing to stdout, broadcasts these
values as ``pyout`` messages for clients to display appropriately.

Message type: ``pyout``::

    content = {
        # The data is typically the repr() of the object.
    'data' : str,
    
    # The counter for this execution is also provided so that clients can
    # display it, since IPython automatically creates variables called _N (for
    # prompt N).
    'execution_count' : int,
    }
    
Python errors
-------------

When an error occurs during code execution

Message type: ``pyerr``::

    content = {
       # Similar content to the execute_reply messages for the 'error' case,
       # except the 'status' field is omitted.
    }

Kernel crashes
--------------

When the kernel has an unexpected exception, caught by the last-resort
sys.excepthook, we should broadcast the crash handler's output before exiting.
This will allow clients to notice that a kernel died, inform the user and
propose further actions.

Message type: ``crash``::

    content = {
       # Similarly to the 'error' case for execute_reply messages, this will
       # contain exc_name, exc_type and traceback fields.

       # An additional field with supplementary information such as where to
       # send the crash message
       'info' : str,
    }


Future ideas
------------
    
Other potential message types, currently unimplemented, listed below as ideas.
    
Message type: ``file``::

    content = {
    'path' : 'cool.jpg',
    'mimetype' : str,
    'data' : str,
    }

    
Messages on the REQ/REP socket
==============================

This is a socket that goes in the opposite direction: from the kernel to a
*single* frontend, and its purpose is to allow ``raw_input`` and similar
operations that read from ``sys.stdin`` on the kernel to be fulfilled by the
client.  For now we will keep these messages as simple as possible, since they
basically only mean to convey the ``raw_input(prompt)`` call.

Message type: ``input_request``::

    content = { 'prompt' : str }

Message type: ``input_reply``::

    content = { 'value' : str }
    
.. Note::

   We do not explicitly try to forward the raw ``sys.stdin`` object, because in
   practice the kernel should behave like an interactive program.  When a
   program is opened on the console, the keyboard effectively takes over the
   ``stdin`` file descriptor, and it can't be used for raw reading anymore.
   Since the IPython kernel effectively behaves like a console program (albeit
   one whose "keyboard" is actually living in a separate process and
   transported over the zmq connection), raw ``stdin`` isn't expected to be
   available.

   
Heartbeat for kernels
=====================

Initially we had considered using messages like those above over ZMQ for a
kernel 'heartbeat' (a way to detect quickly and reliably whether a kernel is
alive at all, even if it may be busy executing user code).  But this has the
problem that if the kernel is locked inside extension code, it wouldn't execute
the python heartbeat code.  But it turns out that we can implement a basic
heartbeat with pure ZMQ, without using any Python messaging at all.

The monitor sends out a single zmq message (right now, it is a str of the
monitor's lifetime in seconds), and gets the same message right back, prefixed
with the zmq identity of the XREQ socket in the heartbeat process. This can be
a uuid, or even a full message, but there doesn't seem to be a need for packing
up a message when the sender and receiver are the exact same Python object.

The model is this::

    monitor.send(str(self.lifetime)) # '1.2345678910'

and the monitor receives some number of messages of the form::

    ['uuid-abcd-dead-beef', '1.2345678910']

where the first part is the zmq.IDENTITY of the heart's XREQ on the engine, and
the rest is the message sent by the monitor.  No Python code ever has any
access to the message between the monitor's send, and the monitor's recv.


ToDo
====

Missing things include:

* Important: finish thinking through the payload concept and API.

* Important: ensure that we have a good solution for magics like %edit.  It's
  likely that with the payload concept we can build a full solution, but not
  100% clear yet.

* Finishing the details of the heartbeat protocol.

* Signal handling: specify what kind of information kernel should broadcast (or
  not) when it receives signals.

.. include:: ../links.rst