Data Streams with Queues¶
gather can consume and
produce standard Python
Queue objects. This is useful for processing
continuous streams of data. However, it does not constitute a full streaming
data processing pipeline like Storm.
We connect to a local Executor.
>>> from distributed import Executor >>> e = Executor('127.0.0.1:8786') >>> e <Executor: scheduler=127.0.0.1:8786 workers=1 threads=4>
We build a couple of toy data processing functions:
from time import sleep from random import random def inc(x): from random import random sleep(random() * 2) return x + 1 def double(x): from random import random sleep(random()) return 2 * x
And we set up an input Queue and map our functions across it.
>>> from queue import Queue >>> input_q = Queue() >>> remote_q = e.scatter(input_q) >>> inc_q = e.map(inc, remote_q) >>> double_q = e.map(double, inc_q)
We will fill the
input_q with local data from some stream, and then
double_q will fill with
Future objects as
data gets moved around.
We gather the futures from the
double_q back to a queue holding local
data in the local process.
>>> result_q = e.gather(double_q)
Insert Data Manually¶
Because we haven’t placed any data into any of the queues everything is empty,
including the final output,
>>> result_q.qsize() 0
But when we insert an entry into the
input_q, it starts to make its way
through the pipeline and ends up in the
>>> input_q.put(10) >>> result_q.get() 22
Insert data in a separate thread¶
We simulate a slightly more realistic situation by dumping data into the
input_q in a separate thread. This simulates what you might get if you
were to read from an active data source.
def load_data(q): i = 0 while True: q.put(i) sleep(random()) i += 1 >>> from threading import Thread >>> load_thread = Thread(target=load_data, args=(input_q,)) >>> load_thread.start() >>> result_q.qsize() 4 >>> result_q.qsize() 9
We consume data from the
result_q and print results to the screen.
>>> while True: ... item = result_q.get() ... print(item) 2 4 6 8 10 12 ...
- This doesn’t do any sort of auto-batching of computations, so ideally you batch your data to take significantly longer than 1ms to run.
- This isn’t a proper streaming system. There is no support outside of what you see here. In particular there are no policies for dropping data, joining over time windows, etc..
We can extend this small example to more complex systems that have buffers, split queues, merge queues, etc. all by manipulating normal Python Queues.
Here are a couple of useful function to multiplex and merge queues:
from queue import Queue from threading import Thread def multiplex(n, q, **kwargs): """ Convert one queue into several equivalent Queues >>> q1, q2, q3 = multiplex(3, in_q) """ out_queues = [Queue(**kwargs) for i in range(n)] def f(): while True: x = q.get() for out_q in out_queues: out_q.put(x) t = Thread(target=f) t.daemon = True t.start() return out_queues def push(in_q, out_q): while True: x = in_q.get() out_q.put(x) def merge(*in_qs, **kwargs): """ Merge multiple queues together >>> out_q = merge(q1, q2, q3) """ out_q = Queue(**kwargs) threads = [Thread(target=push, args=(q, out_q)) for q in in_qs] for t in threads: t.daemon = True t.start() return out_q
With useful functions like these we can build out more sophisticated data
processing pipelines that split off and join back together. By creating queues
maxsize= we can control buffering and apply back pressure.