内容简介:omega|ml provides a straight-forward, Python-native approach to mini-batch streaming and complex-event processing that is highly scalable. Streaming primarily consists ofInstead of directly connection producers and consumers, a producer sends messages to a
Minibatch - Python Stream Processing for humans
- Pre-requisites:
-
- a running MongoDB accessible to minibatch (docker run mongodb)
omega|ml provides a straight-forward, Python-native approach to mini-batch streaming and complex-event processing that is highly scalable. Streaming primarily consists of
- a producer, which is some function inserting data into the stream
- a consumer, which is some function retrieving data from the stream
Instead of directly connection producers and consumers, a producer sends messages to a stream. Think of a stream as an endless buffer, or a pipeline, that takes input from many producers on one end, and outputs messages to a consumer on the other end. This transfer of messages happens asynchronously, that is the producer can send messages to the stream independent of whether the consumer is ready to receive, and the consumer can take messages from the stream independent of whether the producer is ready to send.
Unlike usual asynchronous messaging, however, we want the consumer to receive messages in small batches as to optimize throughput. That is, we want the pipeline to emit messages only subject to some criteria of grouping messages, where each group is called a mini-batch . The function that determines whether the batching criteria is met (e.g. time elapsed, number of messages in the pipeline) is called emitter strategy , and the output it produces is called window .
Thus in order to connect producers and consumers we need a few more parts to our streaming system:
Stream WindowEmitter Window
Note
The producer accepts input from some external system, say a Kafka queue. The producer's responsibility is to enter the data into the streaming buffer. The consumer uses some emitter strategy to produce a Window of data that is then forwarded to the user's processing code.
Creating a stream
Streams can be created by either consumers or producers. A stream can be connected to by both.
from minibatch import Stream stream = Stream.get_or_create('test')
Implementing a Producer
# a very simple producer for i in range(100): stream.append({'date': datetime.datetime.now().isoformat()}) sleep(.5)
Implementing a Consumer
# a fixed size consumer -- emits windows of fixed sizes from minibatch import streaming @streaming('test', size=2, keep=True) def myprocess(window): print(window.data) return window => [{'date': '2018-04-30T20:18:22.918060'}, {'date': '2018-04-30T20:18:23.481320'}] [{'date': '2018-04-30T20:18:24.041337'}, {'date': '2018-04-30T20:18:24.593545'} ...
In this case the emitter strategy is CountWindow
. The following strategies are
available out of the box:
-
-
CountWindow
- emit fixed-sized windows. Waits until at least n messages are - available before emitting a new window
-
-
-
FixedTimeWindow
- emit all messages retrieved within specific, time-fixed windows of - a given interval of n seconds. This guarnatees that messages were received in the specific window.
-
-
-
RelaxedTimeWindow
- every interval of n seconds emit all messages retrieved since - the last window was created. This does not guarantee that messages were received in a given window.
-
Implementing a custom WindowEmitter
Custom emitter strategies are implemented as a subclass to WindowEmitter
. The main methods
to implement are
-
-
window_ready
- returns the tuple(ready, data)
, where ready is True if there is data - to emit
-
-
-
query
- returns the data for the new window. This function retrieves thedata
part -
of the return value of
window_ready
-
See the API reference for more details.
class SortedWindow(WindowEmitter): """ sort all data by value and output only multiples of 2 in batches of interval size """ def window_ready(self): qs = Buffer.objects.no_cache().filter(processed=False) data = [] for obj in sorted(qs, key=lambda obj : obj.data['value']): if obj.data['value'] % 2 == 0: data.append(obj) if len(data) >= self.interval: break self._data = data return len(self._data) == self.interval, () def query(self, *args): return self._data
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