This document takes you through how to build basic streams and push data through them. We start with map and accumulate, talk about emitting data, then discuss flow control and finally back pressure. Examples are used throughout.
Map, emit, and sink¶
Push data into the stream at this point
Apply a function to every element in the stream
Apply a function on every element
You can create a basic pipeline by instantiating the
object and then using methods like
from streamz import Stream def increment(x): return x + 1 source = Stream() source.map(increment).sink(print)
sink methods both take a function and apply that
function to every element in the stream. The
map method returns a
new stream with the modified elements while
sink is typically used
at the end of a stream for final actions.
To push data through our pipeline we call
>>> source.emit(1) 2 >>> source.emit(2) 3 >>> source.emit(10) 11
As we can see, whenever we push data in at the source, our pipeline calls
increment on that data, and then calls
Often we call
emit from some other continuous process, like reading lines
from a file
import json data =  source = Stream() source.map(json.loads).sink(data.append) for line in open('myfile.json'): source.emit(line)
Accumulate results with previous state
Map and sink both pass data directly through a stream. One piece of data comes in, either one or zero pieces go out. Accumulate allows you to track some state within the pipeline. It takes an accumulation function that takes the previous state, the new element, and then returns a new state and a new element to emit. In the following example we make an accumulator that keeps a running total of the elements seen so far.
def add(x, y): return x + y source = Stream() source.accumulate(add).sink(print)
>>> source.emit(1) 1 >>> source.emit(2) 3 >>> source.emit(3) 6 >>> source.emit(4) 10
The accumulation function above is particularly simple, the state that we store and the value that we emit are the same. In more complex situations we might want to keep around different state than we emit. For example lets count the number of distinct elements that we have seen so far.
def num_distinct(state, new): state.add(new) return state, len(state) source = Stream() source.accumulate(num_distinct, returns_state=True, start=set()).sink(print) >>> source.emit('cat') 1 >>> source.emit('dog') 2 >>> source.emit('cat') 2 >>> source.emit('mouse') 3
Accumulators allow us to build many interesting operations.
Allow results to pile up at this point in the stream
Flatten streams of lists or iterables into a stream of elements
Partition stream into tuples of equal size
Produce overlapping tuples of size n
Combine multiple streams into one
Avoid sending through repeated elements
You can batch and slice streams into streams of batches in various ways with
source = Stream() source.sliding_window(3, return_partial=False).sink(print) >>> source.emit(1) >>> source.emit(2) >>> source.emit(3) (1, 2, 3) >>> source.emit(4) (2, 3, 4) >>> source.emit(5) (3, 4, 5)
Branching and Joining¶
Combine multiple streams together to a stream of tuples
Combine streams together into a stream of tuples
Combine multiple streams together to a stream of tuples
You can branch multiple streams off of a single stream. Elements that go into
the input will pass through to both output streams. Note:
networkx need to be installed to visualize the stream graph.
def increment(x): return x + 1 def decrement(x): return x - 1 source = Stream() a = source.map(increment).sink(print) b = source.map(decrement).sink(print) b.visualize(rankdir='LR')
>>> source.emit(1) 0 2 >>> source.emit(10) 9 11
Similarly you can also combine multiple streams together with operations like
zip, which emits once both streams have provided a new element, or
combine_latest which emits when either stream has provided a new element.
source = Stream() a = source.map(increment) b = source.map(decrement) c = a.zip(b).map(sum).sink(print) >>> source.emit(10) 20 # 9 + 11
This branching and combining is where Python iterators break down, and projects
streamz start becoming valuable.
Processing Time and Back Pressure¶
Add a time delay to results
Limit the flow of data
Emit a tuple of collected results every interval
Time-based flow control depends on having an active Tornado event loop. Tornado is active by default within a Jupyter notebook, but otherwise you will need to learn at least a little about asynchronous programming in Python to use these features. Learning async programming is not mandatory, the rest of the project will work fine without Tornado.
You can control the flow of data through your stream over time. For example you may want to batch all elements that have arrived in the last minute, or slow down the flow of data through sensitive parts of the pipeline, particularly when they may be writing to slow resources like databases.
Streamz helps you do these operations both with operations like
timed_window, and also by passing Tornado futures back through the
pipeline. As data moves forward through the pipeline, futures that signal work
completed move backwards. In this way you can reliably avoid buildup of data
in slower parts of your pipeline.
Lets consider the following example that reads JSON data from a file and inserts it into a database using an async-aware insertion function.
async def write_to_database(...): ... # build pipeline source = Source() source.map(json.loads).sink(write_to_database) async def process_file(fn): with open(fn) as f: for line in f: await source.emit(line) # wait for pipeline to clear
As we call the
write_to_database function on our parsed JSON data it
produces a future for us to signal that the writing process has finished.
Streamz will ensure that this future is passed all the way back to the
source.emit call, so that user code at the start of our pipeline can await
on it. This allows us to avoid buildup even in very large and complex streams.
We always pass futures back to ensure responsiveness.
But wait, maybe we don’t mind having a few messages in memory at once, this
will help steady the flow of data so that we can continue to work even if our
sources or sinks become less productive for brief periods. We might add a
buffer just before writing to the database.
And if we are pulling from an API with known limits then we might want to introduce artificial rate limits at 10ms.
Operations like these (and more) allow us to shape the flow of data through our pipelines.
Modifying and Cleaning up Streams¶
When you call
Stream you create a stream. When you call any method on a
Stream.map, you also create a stream. All operations can
be chained together. Additionally, as discussed in the section on Branching,
you can split multiple streams off of any point. Streams will pass their
outputs on to all downstream streams so that anyone can hook in at any point,
and get a full view of what that stream is producing.
If you delete a part of a stream then it will stop getting data. Streamz
follows normal Python garbage collection semantics so once all references to a
stream have been lost those operations will no longer occur. The one counter
example to this is
sink, which is intended to be used with side effects and
will stick around even without a reference.
Sink streams store themselves in
can remove them permanently by clearing that collection.
>>> source.map(print) # this doesn't do anything >>> source.sink(print) # this stays active even without a reference >>> s = source.map(print) # this works too because we have a handle to s
Recursion and Feedback¶
By connecting sources to sinks you can create feedback loops. As an example, here is a tiny web crawler:
from streamz import Stream source = Stream() pages = source.unique() pages.sink(print) content = pages.map(requests.get).map(lambda x: x.content) links = content.map(get_list_of_links).flatten() links.connect(source) # pipe new links back into pages >>> source.emit('http://github.com') http://github.com http://github.com/features http://github.com/business http://github.com/explore http://github.com/pricing ...
Execution order is important here, as if the print was ordered after
map; get node then the print would never run.
Streamz adds microsecond overhead to normal Python operations.
from streamz import Stream source = Stream() def inc(x): return x + 1 source.sink(inc) In : %timeit source.emit(1) 100000 loops, best of 3: 3.19 µs per loop In : %timeit inc(1) 10000000 loops, best of 3: 91.5 ns per loop
You may want to avoid pushing millions of individual elements per second through a stream. However, you can avoid performance issues by collecting lots of data into single elements, for example by pushing through Pandas dataframes instead of individual integers and strings. This will be faster regardless, just because projects like NumPy and Pandas can be much faster than Python generally.
In the following example we pass filenames through a stream, convert them to Pandas dataframes, and then map pandas-level functions on those dataframes. For operations like this Streamz adds virtually no overhead.
source = Stream() s = source.map(pd.read_csv).map(lambda df: df.value.sum()).accumulate(add) for fn in glob('data/2017-*-*.csv'): source.emit(fn)
Streams provides higher level APIs for situations just like this one. You may want to read further about collections
Metadata can be emitted into the pipeline to accompany the data as a list of dictionaries. Most functions will pass the metadata to the downstream function without making any changes. However, functions that make the pipeline asynchronous require logic that dictates how and when the metadata will be passed downstream. Synchronous functions and asynchronous functions that have a 1:1 ratio of the number of values on the input to the number of values on the output will emit the metadata collection without any modification. However, functions that have multiple input streams or emit collections of data will emit the metadata associated with the emitted data as a collection.
Reference Counting and Checkpointing¶
Checkpointing is achieved in Streamz through the use of reference counting. With this method, a checkpoint can be saved when and only when data has progressed through all of the the pipeline without any issues. This prevents data loss and guarantees at-least-once semantics.
Any node that caches or holds data after it returns increments the reference counter associated with the given data by one. When a node is no longer holding the data, it will release it by decrementing the counter by one. When the counter changes to zero, a callback associated with the data is triggered.
References are passed in the metadata as a value of the ref keyword. Each metadata object contains only one reference counter object.