Python库 | watermill-3.6.3.tar.gz

# Watermill data stream organizing framework Assume that you have numerous stream data producers and processors. If data streams are logically linked together you may want to organize processing of joined data stream. Watermill helps to get streams joined in a clear, lightweight way.  ## User guide ### Briefly Input data stream is configured using processing function signature. ```python def calculate_robot_speed( prev_location: RobotLocation, next_locations: Mapping[RobotLocation, List[RobotLocationNextStep]] ) -> RobotSpeed: pass ``` First argument can have type of data stream element or list of stream elements. Subsequent arguments are mapping of types of stream elements when using stream joins. In the example above we want to get all robot locations joined with joined locations of the next time step. If there is no result data stream simply omit return value or explicitly specify NoReturn type. If you want to return several results for every processing function call you can return generator. Note that you have to yield at least one valid element when returning generator. Data themselves can be stored in any shared storage like Kafka, file system, databases.  Watermill doesn't introduce any dependency/coupling between services based on it. Every service or simply process can independently decide which actual data stream it wants to input and how to join data streams together. The only shared key of data streams is file name of file in FS, topic name for Kafka, etc. Smallest piece of data of which data streams consists is dictionary. To describe data stream elements in code python dataclasses are used. Very often you have to get reference to external objects in the processing function. Dependency injection is used for that. It is enough to specify an argument with the type of the required object in the processing function and DI will provide this argument with reference to the object. ```python def store_robot_path(location: RobotLocation, db_connection: Connection) -> NoReturn: pass ``` Message brokers are used to access to streams data in storages. Watermill package includes two commonly used brokers. JSON file broker and Kafka message broker. Both of them can use default serializers and deserializers. Moreover Watermill users can implement deserializers that receive only the required fields.  ### Stream joins For the simplest case when there is only one data stream data processing function looks like ```python def is_inside_area(location: RobotLocation) -> InsideArea: return InsideArea( x=location.x, y=locatin.y, inside=check_inside_area(location.x, location.y) ) ``` But usually there are several input data streams that should be combined by some condition. Here 2 data streams are joined and falls into the processing function as combined elements. For example join condition can be `audio_stream.frame_index = video_stream.frame_index`. In the code it looks like ```python def merge_audio_video( audio_frame: AudioFrame, video_frames: Mapping[AudioFrame, List[VideoFrame]] ) -> AudioVideoFrame: pass ``` The first argument describes left data stream in the join and is called the master stream. Iterating over the master stream returns every element just once. Elements from secondary streams can be skipped if join condition is not met. Unlike SQL joins Watermill supports only inner joins. So if there are no matching data in secondary streams there will be no data at all for the processing. Furthermore, if there are no data at all in secondary streams the processing function will never be executed. It may be easier to understand streams join if you thing about join tree with a master stream as the root node. To get next data for processing Watermill goes through the join tree and returns linked streams elements only when there is at least one element found for each join tree node. As can be seen from the processing function signature secondary streams can return more than one element. Join tree can have no loops or duplicate nodes. ### End of stream Stream elements are processed until there are elements in every input stream. This is quite simple to understand where the data stream for files in file system ends. But originally _indefinite_ data streams like Kafka or RabbitMQ should be provided with some **End Of Stream** mark explicitly. `KafkaMessageBroker` supports special type of elements `EndOfStream` defined in `message_brokers.message_broker` module. Data stream processing is immediately stopped as soon as `EndOfStream` message appears in any of joined data streams. `EndOfStream` message is then automatically put to output stream to state that there will be no more data. You can also manually return instance of `EndOfStream` dataclass from the processing function to indicate that processing loop should be stopped. `EndOfStream` is allowed to be the last element of yielded by processing function generator. ### Join conditions When joining streams join condition is evaluated for left and right elements. Every element must consist of key. It can be ordinary field of expression based on one or more fields. Join conditions are defined as a parameter to Watermill constructor. The following examples demonstrates how to join streams based on conditions with expressions. ```python @dataclass class AudioFrame: time_seconds: int data: float @dataclass class VideoFrame: time_ms: int data: float frame_index: int first_frame_ts_seconds: int ... mill = WaterMill( ... process_func=merge_audio_video, join_tree=join_streams( AudioFrame, JoinWith( with_type=VideoFrame, left_expression=get_field('time_seconds'), right_expression=get_field('time_ms') // 1000 ) ) ) mill = WaterMill( ... process_func=merge_audio_video, join_tree=join_streams( AudioFrame, JoinWith( with_type=VideoFrame, left_expression=get_field('time_seconds'), right_expression= get_field('frame_index') * FRAME_DURATION_SECONDS + get_fields('first_frame_ts_seconds') ) ) ) ``` Expressions calculation implementation is trivial and does not support operator precedence or parentheses. Computed key value for every stream must increase monotonously. Based on this rule Watermill can detect streams divergence. For example at the start of data processing left (master) stream key less than right (secondary) stream key. In this case Watermill will skip right stream elements until stream elements synchronizes. During data streams design you should keep in mind key comparison rules to correctly identify what the key will be for every data stream. Special case for joining streams is when the stream appears in join tree several times, e.g. self-join of the stream. In this case separate class have to be defined for every join tree node of this stream type. Refer to [kafka_join_streams](https://gitlab.com/kraevs/watermill/-/blob/master/examples/kafka_join_streams.py) example. ### Windows Sometimes you may want to get master stream elements combined into lists based on some condition before processing. For example collect one minute statistics and then process whole bunch of elements at once. Thus processing function will get list of elements every time. Below is the illustration of appropriate stream configuration. ```python def calculate_robot_speed(prev_location: List[RobotLocation]) -> RobotSpeed: pass ... mill = WaterMill( ... process_func=calculate_robot