Különbségek

A kiválasztott változat és az aktuális verzió közötti különbségek a következők.

--- tanszek:oktatas:iss_t:messaging_systems [2023/04/24 19:00]
knehez [MQTT example]
+++ tanszek:oktatas:iss_t:messaging_systems [2023/05/14 15:23] (aktuális)
knehez [RabbitMQ example]
@@ Sor 53: / Sor 53: @@
 This code sets up a callback function that will be called every time a message is received from the 'my_queue' queue. The `auto_ack` parameter specifies whether to automatically acknowledge the message after it has been processed. Finally, the `start_consuming` method starts consuming messages from the queue.
-==== Type of "Exchange" in RabbitMQ ====
+=====  Type of "Exchange" in RabbitMQ =====
 An exchange in RabbitMQ is a messaging entity that receives messages from producers and routes them to queues based on some criteria. When a producer sends a message to RabbitMQ, it sends the message to an exchange. The exchange then examines the message's routing key and decides which queue(s) the message should be sent to.
@@ Sor 107: / Sor 107: @@
 Each exchange type has its own routing algorithm and is used in different messaging scenarios. Understanding the exchange types is important when designing RabbitMQ architectures that meet specific business requirements.
-==== MQTT example ====
+===== MQTT example =====
 Clone repository into docker playground:
     git clone https://github.com/knehez/isi.git
+    cd isis/mqtt-python
+    docker-compose up
 docker-compose.yml defines a multi-container application with three services: mqtt, consumer, and producer.
@@ Sor 149: / Sor 151: @@
     volumes:
         - .:/app
-    depen
+    depends_on:
+      - mqtt
 </code>
+**Dockerfile-consumer**
+This Dockerfile defines a simple containerized Python application that can be used as a consumer for a message broker. The dependencies are installed in the container, and the consumer code is copied into the container's /app directory. When the container is started, the consumer.py script is executed to consume messages from the broker.
+<code yml>
+FROM python:3.9-slim-buster
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY consumer.py .
+CMD ["python", "-u", "consumer.py"]
+</code>
+Here is a breakdown of the different parts of the Dockerfile:
+  * FROM python:3.9-slim-buster: This line specifies the base image for the Docker image. In this case, the image is based on Python 3.9 running on a slimmed-down version of the Debian Buster Linux distribution.
+  * WORKDIR /app: This line sets the working directory for the container to /app. This is where the consumer code and other related files will be located.
+  * COPY requirements.txt .: This line copies the requirements.txt file from the current directory on the host machine to the /app directory in the container. The requirements.txt file lists the dependencies that the consumer requires to run.
+  * RUN pip install --no-cache-dir -r requirements.txt: This line installs the dependencies listed in the requirements.txt file using pip. The --no-cache-dir option is used to ensure that pip does not cache the downloaded packages, which can help to reduce the size of the Docker image.
+  * COPY consumer.py .: This line copies the consumer.py file from the current directory on the host machine to the /app directory in the container. This is the main code file for the consumer.
+  * CMD ["python", "-u", "consumer.py"]: This line specifies the command to run when the container is started. In this case, it runs the consumer.py script using the Python interpreter (python). The -u flag is used to enable unbuffered output, which ensures that log messages are immediately visible in the console.
+**consumer.py**
+<code python>
+import paho.mqtt.client as mqtt
+broker = "mqtt"
+port = 1883
+timelive = 60
+def on_connect(client, userdata, flags, rc):
+    print("Connected with result code "+str(rc))
+    client.subscribe("/data")
+def on_message(client, userdata, msg):
+    print(msg.payload.decode())
+client = mqtt.Client()
+client.connect(broker, port, timelive)
+client.on_connect = on_connect
+client.on_message = on_message
+client.loop_forever()
+</code>
+**producer.py**
+<code python>
+# simulator device 1 for mqtt message publishing
+import paho.mqtt.client as paho
+import time
+import random
+broker = "mqtt"
+port = 1883
+def on_publish(client, userdata, result):
+    print("Device 1 : Data published.")
+client = paho.Client("admin")
+client.on_publish = on_publish
+client.connect(broker, port)
+for i in range(20):
+    d = random.randint(1, 5)
+    # telemetry to send
+    message = "Device 1 : Data " + str(i)
+    time.sleep(d)
+    # publish message
+    ret = client.publish("/data", message)
+print("Stopped...")
+</code>

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