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Messaging for Parallel Computing

This is an extension of the :ref:`messaging <jupyterclient:messaging>` doc. Diagrams of the connections can be found in the :ref:`parallel connections <parallel_connections>` doc.

ZMQ messaging is also used in the parallel computing IPython system. All messages to/from kernels remain the same as the single kernel model, and are forwarded through a ZMQ Queue device. The controller receives all messages and replies in these channels, and saves results for future use.

The Controller

The controller is the central collection of processes in the IPython parallel computing model. It has two major components:

  • The Hub
  • A collection of Schedulers

The Hub

The Hub is the central process for monitoring the state of the engines, and all task requests and results. It has no role in execution and does no relay of messages, so large blocking requests or database actions in the Hub do not have the ability to impede job submission and results.

Registration (ROUTER)

The first function of the Hub is to facilitate and monitor connections of clients and engines. Both client and engine registration are handled by the same socket, so only one ip/port pair is needed to connect any number of connections and clients.

Engines register with the zmq.IDENTITY of their two DEALER sockets, one for the queue, which receives execute requests, and one for the heartbeat, which is used to monitor the survival of the Engine process.

Message type: registration_request:

content = {
    'uuid'   : 'abcd-1234-...', # the zmq.IDENTITY of the engine's sockets


these are always the same, at least for now.

The Controller replies to an Engine's registration request with the engine's integer ID, and all the remaining connection information for connecting the heartbeat process, and kernel queue socket(s). The message status will be an error if the Engine requests IDs that already in use.

Message type: registration_reply:

content = {
    'status' : 'ok', # or 'error'
    # if ok:
    'id' : 0, # int, the engine id

Clients use the same socket as engines to start their connections. Connection requests from clients need no information:

Message type: connection_request:

content = {}

The reply to a Client registration request contains the connection information for the multiplexer and load balanced queues, as well as the address for direct hub queries. If any of these addresses is None, that functionality is not available.

Message type: connection_reply:

content = {
    'status' : 'ok', # or 'error'


The hub uses a heartbeat system to monitor engines, and track when they become unresponsive. As described in :ref:`messaging <jupyterclient:messaging>`, and shown in :ref:`connections <parallel_connections>`.

Notification (PUB)

The hub publishes all engine registration/unregistration events on a PUB socket. This allows clients to have up-to-date engine ID sets without polling. Registration notifications contain both the integer engine ID and the queue ID, which is necessary for sending messages via the Multiplexer Queue and Control Queues.

Message type: registration_notification:

content = {
    'id' : 0, # engine ID that has been registered
    'uuid' : 'engine_id' # the IDENT for the engine's sockets

Message type : unregistration_notification:

content = {
    'id' : 0 # engine ID that has been unregistered
    'uuid' : 'engine_id' # the IDENT for the engine's sockets

Client Queries (ROUTER)

The hub monitors and logs all queue traffic, so that clients can retrieve past results or monitor pending tasks. This information may reside in-memory on the Hub, or on disk in a database (SQLite and MongoDB are currently supported). These requests are handled by the same socket as registration.

:func:`queue_request` requests can specify multiple engines to query via the targets element. A verbose flag can be passed, to determine whether the result should be the list of msg_ids in the queue or simply the length of each list.

Message type: queue_request:

content = {
    'verbose' : True, # whether return should be lists themselves or just lens
    'targets' : [0,3,1] # list of ints

The content of a reply to a :func:`queue_request` request is a dict, keyed by the engine IDs. Note that they will be the string representation of the integer keys, since JSON cannot handle number keys. The three keys of each dict are:

'completed' :  messages submitted via any queue that ran on the engine
'queue' : jobs submitted via MUX queue, whose results have not been received
'tasks' : tasks that are known to have been submitted to the engine, but
            have not completed.  Note that with the pure zmq scheduler, this will
            always be 0/[].

Message type: queue_reply:

content = {
    'status' : 'ok', # or 'error'
    # if verbose=False:
    '0' : {'completed' : 1, 'queue' : 7, 'tasks' : 0},
    # if verbose=True:
    '1' : {'completed' : ['abcd-...','1234-...'], 'queue' : ['58008-'], 'tasks' : []},

Clients can request individual results directly from the hub. This is primarily for gathering results of executions not submitted by the requesting client, as the client will have all its own results already. Requests are made by msg_id, and can contain one or more msg_id. An additional boolean key 'statusonly' can be used to not request the results, but simply poll the status of the jobs.

Message type: result_request:

content = {
    'msg_ids' : ['uuid','...'], # list of strs
    'targets' : [1,2,3], # list of int ids or uuids
    'statusonly' : False, # bool

The :func:`result_request` reply contains the content objects of the actual execution reply messages. If statusonly=True, then there will be only the 'pending' and 'completed' lists.

Message type: result_reply:

content = {
    'status' : 'ok', # else error
    # if ok:
    'acbd-...' : msg, # the content dict is keyed by msg_ids,
                     # values are the result messages
                    # there will be none of these if `statusonly=True`
    'pending' : ['msg_id','...'], # msg_ids still pending
    'completed' : ['msg_id','...'], # list of completed msg_ids
buffers = ['bufs','...'] # the buffers that contained the results of the objects.
                        # this will be empty if no messages are complete, or if
                        # statusonly is True.

For memory management purposes, Clients can also instruct the hub to forget the results of messages. This can be done by message ID or engine ID. Individual messages are dropped by msg_id, and all messages completed on an engine are dropped by engine ID. This may no longer be necessary with the mongodb-based message logging backend.

If the msg_ids element is the string 'all' instead of a list, then all completed results are forgotten.

Message type: purge_request:

content = {
    'msg_ids' : ['id1', 'id2',...], # list of msg_ids or 'all'
    'engine_ids' : [0,2,4] # list of engine IDs

The reply to a purge request is simply the status 'ok' if the request succeeded, or an explanation of why it failed, such as requesting the purge of a nonexistent or pending message.

Message type: purge_reply:

content = {
    'status' : 'ok', # or 'error'


There are three basic schedulers:

  • Task Scheduler
  • MUX Scheduler
  • Control Scheduler

The MUX and Control schedulers are simple MonitoredQueue ØMQ devices, with ROUTER sockets on either side. This allows the queue to relay individual messages to particular targets via zmq.IDENTITY routing. The Task scheduler may be a MonitoredQueue ØMQ device, in which case the client-facing socket is ROUTER, and the engine-facing socket is DEALER. The result of this is that client-submitted messages are load-balanced via the DEALER socket, but the engine's replies to each message go to the requesting client.

Raw DEALER scheduling is quite primitive, and doesn't allow message introspection, so there are also Python Schedulers that can be used. These Schedulers behave in much the same way as a MonitoredQueue does from the outside, but have rich internal logic to determine destinations, as well as handle dependency graphs Their sockets are always ROUTER on both sides.

The Python task schedulers have an additional message type, which informs the Hub of the destination of a task as soon as that destination is known.

Message type: task_destination:

content = {
    'msg_id' : 'abcd-1234-...', # the msg's uuid
    'engine_id' : '1234-abcd-...', # the destination engine's zmq.IDENTITY


In terms of message classes, the MUX scheduler and Task scheduler relay the exact same message types. Their only difference lies in how the destination is selected.

The Namespace model suggests that execution be able to use the model:

ns.apply(f, *args, **kwargs)

which takes f, a function in the user's namespace, and executes f(*args, **kwargs) on a remote engine, returning the result (or, for non-blocking, information facilitating later retrieval of the result). This model, unlike the execute message which just uses a code string, must be able to send arbitrary (pickleable) Python objects. And ideally, copy as little data as we can. The buffers property of a Message was introduced for this purpose.

Utility method :func:`build_apply_message` in :mod:`IPython.kernel.zmq.serialize` wraps a function signature and builds a sendable buffer format for minimal data copying (exactly zero copies of numpy array data or buffers or large strings).

Message type: apply_request:

metadata = {
    'after' : ['msg_id',...], # list of msg_ids or output of Dependency.as_dict()
    'follow' : ['msg_id',...], # list of msg_ids or output of Dependency.as_dict()
content = {}
buffers = ['...'] # at least 3 in length
                # as built by build_apply_message(f,args,kwargs)

after/follow represent task dependencies. 'after' corresponds to a time dependency. The request will not arrive at an engine until the 'after' dependency tasks have completed. 'follow' corresponds to a location dependency. The task will be submitted to the same engine as these msg_ids (see :class:`Dependency` docs for details).

Message type: apply_reply:

content = {
    'status' : 'ok' # 'ok' or 'error'
    # other error info here, as in other messages
buffers = ['...'] # either 1 or 2 in length
                # a serialization of the return value of f(*args,**kwargs)
                # only populated if status is 'ok'

All engine execution and data movement is performed via apply messages.

Raw Data Publication

display_data lets you publish representations of data, such as images and html. This data_pub message lets you publish actual raw data, sent via message buffers.

data_pub messages are constructed via the :func:`ipyparallel.datapub.publish_data` function:

from ipyparallel.datapub import publish_data
ns = dict(x=my_array)

Message type: data_pub:

content = {
    # the keys of the data dict, after it has been unserialized
    'keys' : ['a', 'b']
# the namespace dict will be serialized in the message buffers,
# which will have a length of at least one
buffers = [b'pdict', ...]

The interpretation of a sequence of data_pub messages for a given parent request should be to update a single namespace with subsequent results.

Control Messages

Messages that interact with the engines, but are not meant to execute code, are submitted via the Control queue. These messages have high priority, and are thus received and handled before any execution requests.

Clients may want to clear the namespace on the engine. There are no arguments nor information involved in this request, so the content is empty.

Message type: clear_request:

content = {}

Message type: clear_reply:

content = {
    'status' : 'ok' # 'ok' or 'error'
    # other error info here, as in other messages

Clients may want to abort tasks that have not yet run. This can by done by message id, or all enqueued messages can be aborted if None is specified.

Message type: abort_request:

content = {
    'msg_ids' : ['1234-...', '...'] # list of msg_ids or None

Message type: abort_reply:

content = {
    'status' : 'ok' # 'ok' or 'error'
    # other error info here, as in other messages

The last action a client may want to do is shutdown the kernel. If a kernel receives a shutdown request, then it aborts all queued messages, replies to the request, and exits.

Message type: shutdown_request:

content = {}

Message type: shutdown_reply:

content = {
    'status' : 'ok' # 'ok' or 'error'
    # other error info here, as in other messages


There are a few differences in implementation between the StreamSession object used in the newparallel branch and the Session object, the main one being that messages are sent in parts, rather than as a single serialized object. StreamSession objects also take pack/unpack functions, which are to be used when serializing/deserializing objects. These can be any functions that translate to/from formats that ZMQ sockets can send (buffers,bytes, etc.).

Split Sends

Previously, messages were bundled as a single json object and one call to :func:`socket.send_json`. Since the hub inspects all messages, and doesn't need to see the content of the messages, which can be large, messages are now serialized and sent in pieces. All messages are sent in at least 4 parts: the header, the parent header, the metadata and the content. This allows the controller to unpack and inspect the (always small) header, without spending time unpacking the content unless the message is bound for the controller. Buffers are added on to the end of the message, and can be any objects that present the buffer interface.