Add scdf-python-app. fix polyglot-python-task issue
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@@ -16,8 +16,10 @@ def get_cmd_arg(name):
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for k, v in ((k.lstrip('-'), v) for k, v in (a.split('=') for a in sys.argv[1:])):
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d[k].append(v)
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return d[name][0]
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if bool(d[name]):
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return d[name][0]
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else:
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return None
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def get_db_url():
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"""Computes sqlalchemy connection URL
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@@ -0,0 +1,9 @@
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FROM python:3.7.3-slim
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RUN pip install kafka-python
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RUN pip install flask
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ADD /util/* /util/
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ADD barista_app.py /
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ENTRYPOINT ["python","/barista_app.py"]
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CMD []
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25
dataflow-website/recipes/polyglot/scdf-python-app/README.md
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25
dataflow-website/recipes/polyglot/scdf-python-app/README.md
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@@ -0,0 +1,25 @@
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Build docker image and push to Docker Hub.
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```bash
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docker build -t tzolov/scdf_python_app:0.1 .
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docker push tzolov/scdf_python_app:0.1
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```
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NOTE: replace `tzolov` with your docker hub prefix.
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Register the docker image as SCDF `app` application:
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```bash
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app register --type app --name barista-app --uri docker://tzolov/scdf_python_app:0.1
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```
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Build the Bar pipelines:
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```bash
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stream create --name orders --definition "customer: time > :orders" --deploy
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stream create --name cold-drink-line --definition ":coldDrinks > cold-drinks: log" --deploy
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stream create --name hot-drink-line --definition ":hotDrinks > hot-drinks: log" --deploy
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stream create --name bar --definition "barista-app"
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stream deploy --name bar --properties app.barista-app.spring.cloud.stream.bindings.orders.destination=orders,app.barista-app.spring.cloud.stream.bindings.hot.drink.destination=hotDrinks,app.barista-app.spring.cloud.stream.bindings.cold.drink.destination=coldDrinks
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```
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@@ -0,0 +1,71 @@
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from kafka import KafkaConsumer, KafkaProducer
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from kafka.admin import KafkaAdminClient, NewTopic
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from kafka.errors import TopicAlreadyExistsError
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from util.actuator import Actuator
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from util.arguments import get_kafka_brokers, get_env_info, get_channel_topic
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class Barista:
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"""Barista continuously process input drink orders and servers hot drinks for the even order numbers and cold drink
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for the ood orders.
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The orders come through the `orders` input channel. The hot and cold drinks are served through either the
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`hot.drink` or the `cold.drink` output channels.
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:param info: Information about the app configuration.
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:param kafka_brokers: Kafka brokers connection uri.
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:param orders: Orders topic name.
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:param hot_drinks: Hot drinks topic name.
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:param cold_drinks: Cold drinks topic name.
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"""
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def __init__(self, info, kafka_brokers, orders, hot_drinks, cold_drinks):
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self.kafka_brokers = kafka_brokers
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self.orders_topic = orders
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self.hot_drink_topic = hot_drinks
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self.cold_drink_topic = cold_drinks
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# Serve the liveliness and readiness probes via http server in a separate thread.
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Actuator.start(port=8080, info=info)
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# Ensure the output topics exist.
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self.__create_topics_if_missing([self.orders_topic, self.hot_drink_topic, self.cold_drink_topic])
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self.consumer = KafkaConsumer(self.orders_topic, bootstrap_servers=self.kafka_brokers)
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self.producer = KafkaProducer(bootstrap_servers=self.kafka_brokers)
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def __create_topics_if_missing(self, topic_names):
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admin_client = KafkaAdminClient(bootstrap_servers=self.kafka_brokers, client_id='test')
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for topic in topic_names:
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try:
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new_topic = NewTopic(name=topic, num_partitions=1, replication_factor=1)
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admin_client.create_topics(new_topics=[new_topic], validate_only=False)
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except TopicAlreadyExistsError:
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print ('Topic: {} already exists!')
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def process_orders(self):
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"""
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Continuously consumes orders form the input channel and send hot or cold drinks to the output channels.
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The even order number stands for a hot drink request while an odd number stands for a cold drink request.
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"""
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while True:
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for message in self.consumer:
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if message.value is not None:
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if self.is_even_order(message.value):
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self.producer.send(self.hot_drink_topic, b'Serve Hot drink for order:' + message.value)
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else:
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self.producer.send(self.cold_drink_topic, b'Serve Cold drink for order:' + message.value)
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@staticmethod
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def is_even_order(value):
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return int(value[-1:]) % 2 == 0
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Barista(
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get_env_info(),
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get_kafka_brokers(),
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get_channel_topic('orders'),
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get_channel_topic('hot.drink'),
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get_channel_topic('cold.drink')
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).process_orders()
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@@ -0,0 +1,3 @@
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kafka
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kafka-python
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flask
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11
dataflow-website/recipes/polyglot/scdf-python-app/test.py
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11
dataflow-website/recipes/polyglot/scdf-python-app/test.py
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#
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import time
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from util.actuator import Actuator
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from util.arguments import get_env_info
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Actuator.start(9191, get_env_info())
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#
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print('OK')
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time.sleep(10000)
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print('STOPPED')
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@@ -0,0 +1,56 @@
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import sys
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import threading
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from flask import Flask, Response
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class Actuator:
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"""Actuator is used to expose operational information about the running application, such as `health/liveliness`,
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`info`, `env`, etc. It uses HTTP endpoints to enable us to interact with it.
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The `/actuator/health` and `/actuator/info` handles the Kubernetes liveness and readiness probes requests.
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Kubernetes expects HTTP 200 status code to consider the application live and ready.
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:param port: the HTTP port used by the Actuator.
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:param info_content: The text response to be returned by the /actuator/info endpoint.
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"""
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def __init__(self, port=8080, info_content='Info'):
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self.http_app = Actuator.__create_http_app(info_content)
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self.port = port
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print(info_content)
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sys.stdout.flush()
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def __run(self):
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self.http_app.run(port=self.port, host='0.0.0.0')
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@staticmethod
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def __create_http_app(info_description):
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app = Flask(__name__)
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app.debug = False
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app.use_reloader = False
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@app.route('/actuator/health')
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def health():
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return Response('Alive', status=200)
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@app.route('/actuator/info')
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def info():
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return Response(info_description, status=200, content_type='text/plain')
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return app
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@staticmethod
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def start(port=8080, info='Info'):
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"""Starts the `Actuator` in a separate thread.
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:param port: the HTTP port used by the Actuator. Defaults to 8080.
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:param info: The text response to be returned by the /actuator/info endpoint.
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"""
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try:
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thread = threading.Thread(target=Actuator(port, info).__run)
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thread.setDaemon(True)
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thread.start()
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print('Actuator started!')
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except KeyboardInterrupt:
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sys.exit(0)
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@@ -0,0 +1,76 @@
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import os
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import sys
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from collections import defaultdict
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def get_cmd_arg(name):
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"""Extracts argument value by name. (@author: Chris Schaefer)
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Assumes the exec (default) spring-cloud-deployer-k8s argument passing mode.
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Args:
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name: argument name.
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Returns:
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value of the requested argument.
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"""
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d = defaultdict(list)
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for k, v in ((k.lstrip('-'), v) for k, v in (a.split('=') for a in sys.argv[1:])):
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d[k].append(v)
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if bool(d[name]):
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return d[name][0]
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else:
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return ''
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def get_stream_app_label():
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return get_cmd_arg('spring.cloud.dataflow.stream.app.label')
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def get_stream_name():
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return get_cmd_arg('spring.cloud.dataflow.stream.name')
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def get_channel_topic(channel_name):
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"""
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For given channel name returns the message broker destinations (e.g. Kafka topics or RabbitMQ exchanges).
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We adopt the Spring Cloud Stream using the following format:
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spring.cloud.stream.bindings.<channelName>.destination=<value>.
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The <channelName> represents the name of the channel being configured (for example, input or output).
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:param channel_name: logical channel name as defined in the application.
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:return: The target destination of a channel on the bound middleware (for example, the RabbitMQ exchange or Kafka
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topic). If the channel is bound as a consumer, it could be bound to multiple destinations, and the
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destination names can be specified as comma-separated String values.
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"""
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return get_cmd_arg('spring.cloud.stream.bindings.{}.destination'.format(channel_name))
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def get_kafka_brokers():
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return os.getenv('SPRING_CLOUD_STREAM_KAFKA_BINDER_BROKERS', '')
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def get_kafka_zk_nodes():
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return os.getenv('SPRING_CLOUD_STREAM_KAFKA_BINDER_ZK_NODES', '')
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def get_application_guid():
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return os.getenv('SPRING_CLOUD_APPLICATION_GUID', '')
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def get_application_group():
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return os.getenv('SPRING_CLOUD_APPLICATION_GROUP', '')
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def get_env_info():
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props = ' stream-name={}\n app-name={}\n app-guid={}\n app-group={}\n kafka-brokers={}\n ' \
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'kafka-zk={}\n'.format(get_stream_name(), get_stream_app_label(), get_application_guid(),
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get_application_group(), get_kafka_brokers(), get_kafka_zk_nodes())
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channels = ' Inputs:\n orders={}\n Outputs: \n hot.drink={}\n cold.drink={}\n'.format(
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get_channel_topic('orders'), get_channel_topic('hot.drink'), get_channel_topic('cold.drink'))
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args = '\n '.join(sys.argv)
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envs = ''
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# envs = '\n '.join(list(map(lambda k: '{}={}'.format(k, os.environ[k]), os.environ)))
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return 'Properties\n{0}\nChannels\n{1}\nArguments\n {2}\n\nEnvironment\n {3}'.format(
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props, channels, args, envs)
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