Foundations

You should read through the quickstart before reading this document.

Distributed computing is hard for two reasons:

  1. Consistent coordination of distributed systems requires sophistication

  2. Concurrent network programming is tricky and error prone

The foundations of dask.distributed provide abstractions to hide some complexity of concurrent network programming (#2). These abstractions ease the construction of sophisticated parallel systems (#1) in a safer environment. However, as with all layered abstractions, ours has flaws. Critical feedback is welcome.

Concurrency with Tornado Coroutines

Worker and Scheduler nodes operate concurrently. They serve several overlapping requests and perform several overlapping computations at the same time without blocking. There are several approaches for concurrent programming, we’ve chosen to use Tornado for the following reasons:

  1. Developing and debugging is more comfortable without threads

  2. Tornado’s documentation is excellent

  3. Stackoverflow coverage is excellent

  4. Performance is satisfactory

Endpoint-to-endpoint Communication

The various distributed endpoints (Client, Scheduler, Worker) communicate by sending each other arbitrary Python objects. Encoding, sending and then decoding those objects is the job of the communication layer.

Ancillary services such as a Bokeh-based Web interface, however, have their own implementation and semantics.

Protocol Handling

While the abstract communication layer can transfer arbitrary Python objects (as long as they are serializable), participants in a distributed cluster concretely obey the distributed Protocol, which specifies request-response semantics using a well-defined message format.

Dedicated infrastructure in distributed handles the various aspects of the protocol, such as dispatching the various operations supported by an endpoint.

Servers

Worker, Scheduler, and Nanny objects all inherit from a Server class.

class distributed.core.Server(handlers, blocked_handlers=None, stream_handlers=None, connection_limit=512, deserialize=True, io_loop=None)[source]

Dask Distributed Server

Superclass for endpoints in a distributed cluster, such as Worker and Scheduler objects.

Handlers

Servers define operations with a handlers dict mapping operation names to functions. The first argument of a handler function will be a Comm for the communication established with the client. Other arguments will receive inputs from the keys of the incoming message which will always be a dictionary.

>>> def pingpong(comm):
...     return b'pong'

>>> def add(comm, x, y):
...     return x + y

>>> handlers = {'ping': pingpong, 'add': add}
>>> server = Server(handlers)  
>>> server.listen('tcp://0.0.0.0:8000')

Message Format

The server expects messages to be dictionaries with a special key, ‘op’ that corresponds to the name of the operation, and other key-value pairs as required by the function.

So in the example above the following would be good messages.

  • {'op': 'ping'}

  • {'op': 'add', 'x': 10, 'y': 20}

RPC

To interact with remote servers we typically use rpc objects which expose a familiar method call interface to invoke remote operations.

class distributed.core.rpc(arg=None, comm=None, deserialize=True, timeout=None, connection_args=None, serializers=None, deserializers=None)[source]

Conveniently interact with a remote server

>>> remote = rpc(address)  
>>> response = yield remote.add(x=10, y=20)

One rpc object can be reused for several interactions. Additionally, this object creates and destroys many comms as necessary and so is safe to use in multiple overlapping communications.

When done, close comms explicitly.

>>> remote.close_comms()

Examples

Here is a small example using distributed.core to create and interact with a custom server.

Server Side

import asyncio
from distributed.core import Server

def add(comm, x=None, y=None):  # simple handler, just a function
    return x + y

async def stream_data(comm, interval=1):  # complex handler, multiple responses
    data = 0
    while True:
        await asyncio.sleep(interval)
        data += 1
        await comm.write(data)

s = Server({'add': add, 'stream_data': stream_data})
s.listen('tcp://:8888')   # listen on TCP port 8888

asyncio.get_event_loop().run_forever()

Client Side

import asyncio
from distributed.core import connect

async def f():
    comm = await connect('tcp://127.0.0.1:8888')
    await comm.write({'op': 'add', 'x': 1, 'y': 2})
    result = await comm.read()
    await comm.close()
    print(result)

>>> asyncio.get_event_loop().run_until_complete(g())
3

async def g():
    comm = await connect('tcp://127.0.0.1:8888')
    await comm.write({'op': 'stream_data', 'interval': 1})
    while True:
        result = await comm.read()
        print(result)

>>> asyncio.get_event_loop().run_until_complete(g())
1
2
3
...

Client Side with rpc

RPC provides a more pythonic interface. It also provides other benefits, such as using multiple streams in concurrent cases. Most distributed code uses rpc. The exception is when we need to perform multiple reads or writes, as with the stream data case above.

import asyncio
from distributed.core import rpc

async def f():
    # comm = await connect('tcp://127.0.0.1', 8888)
    # await comm.write({'op': 'add', 'x': 1, 'y': 2})
    # result = await comm.read()
    with rpc('tcp://127.0.0.1:8888') as r:
        result = await r.add(x=1, y=2)

    print(result)

>>> asyncio.get_event_loop().run_until_complete(f())
3