This section is only relevant if you want to use time-based functionality. If you are only using operations like map and accumulate then you can safely skip this section.
When using time-based flow control like
timed_window Streamz relies on the Tornado framework for concurrency.
This allows us to handle many concurrent operations cheaply and consistently
within a single thread. However, this also adds complexity and requires some
understanding of asynchronous programming. There are a few different ways to
use Streamz with a Tornado event loop.
We give a few examples below that all do the same thing, but with different styles. In each case we use the following toy functions:
from tornado import gen import time def increment(x): """ A blocking increment function Simulates a computational function that was not designed to work asynchronously """ time.sleep(0.1) return x + 1 @gen.coroutine def write(x): """ A non-blocking write function Simulates writing to a database asynchronously """ yield gen.sleep(0.2) print(x)
Within the Event Loop¶
You may have an application that runs strictly within an event loop.
from streamz import Stream from tornado.ioloop import IOLoop @gen.coroutine def f(): source = Stream(asynchronous=True) # tell the stream we're working asynchronously source.map(increment).rate_limit(0.500).sink(write) for x in range(10): yield source.emit(x) IOLoop().run_sync(f)
We call Stream with the
asynchronous=True keyword, informing it that it
should expect to operate within an event loop. This ensures that calls to
emit return Tornado futures rather than block. We wait on results using
yield source.emit(x) # waits until the pipeline is ready
This would also work with async-await syntax in Python 3
from streamz import Stream from tornado.ioloop import IOLoop async def f(): source = Stream(asynchronous=True) # tell the stream we're working asynchronously source.map(increment).rate_limit(0.500).sink(write) for x in range(10): await source.emit(x) IOLoop().run_sync(f)
Event Loop on a Separate Thread¶
Sometimes the event loop runs on a separate thread. This is common when you want to support interactive workloads (the user needs their own thread for interaction) or when using Dask (next section).
from streamz import Stream source = Stream(asynchronous=False) # starts IOLoop in separate thread source.map(increment).rate_limit('500ms').sink(write) for x in range(10): source.emit(x)
In this case we pass
asynchronous=False to inform the stream that it is
expected to perform time-based computation (our write function is a coroutine)
but that it should not expect to run in an event loop, and so needs to start
its own in a separate thread. Now when we call
await the emit call blocks, waiting on a
coroutine to finish within the IOLoop.
All functions here happen on the IOLoop. This is good for consistency, but can
cause other concurrent applications to become unresponsive if your functions
increment) block for long periods of time. You might address this by
using Dask (see below) which will offload these computations onto separate
threads or processes.
Dask is a parallel computing library that uses Tornado for concurrency and
threads for computation. The
DaskStream object is a drop-in replacement
Stream (mostly). Typically we create a Dask client, and then
scatter a local Stream to become a DaskStream.
from dask.distributed import Client client = Client(processes=False) # starts thread pool, IOLoop in separate thread from streamz import Stream source = Stream() (source.scatter() # scatter local elements to cluster, creating a DaskStream .map(increment) # map a function remotely .buffer(5) # allow five futures to stay on the cluster at any time .gather() # bring results back to local process .sink(write)) # call write locally for x in range(10): source.emit(x)
This operates very much like the synchronous case in terms of coding style (no
yield) but does computations on separate threads.
This also provides parallelism and access to a dashboard at
Dask can also operate within an event loop if preferred. Here you can get the non-blocking operation within an event loop while also offloading computations to separate threads.
from dask.distributed import Client from tornado.ioloop import IOLoop async def f(): client = await Client(processes=False, asynchronous=True) source = Stream(asynchronous=True) source.scatter().map(increment).rate_limit('500ms').gather().sink(write) for x in range(10): await source.emit(x) IOLoop().run_sync(f)