make abstract connector in pipeline

pipeline/connector/consumer.py

@@ -0,0 +1,23 @@
+from bl.transformer_runner_interface import TransforerRunnerInterface
+from connector.producer import AbstractProducer
+
+class AbstractConsumer:
+    '''Class responsible of consumning and creating kafka consumer.'''
+    def __init__(self, trasformer: TransforerRunnerInterface, producer: AbstractProducer, configuration: dict):
+        pass
+
+    def consume(self):
+        '''Function start the consuming
+            Args:
+                topic: string array of the topic to consume'''
+        pass
+
+    def shutdown(self):
+        '''Function shutdown the consumer if needed'''
+        pass
+
+    def get_message_value_as_dict(self, msg):
+        '''Function that convert raw message to dictionary for easy consume and running the logic for abstract connector
+            Args:
+                msg: the message recieve in the consumer '''
+        pass

pipeline/connector/consumer_builder.py

@@ -0,0 +1,18 @@
+import ast
+import os
+from connector.consumer import AbstractConsumer
+from connector.kafka.kafka_consumer import KafkaConsumer
+from connector.producer import AbstractProducer
+from bl.transformer_runner_interface import TransforerRunnerInterface
+
+def build_consumer(transformer: TransforerRunnerInterface, producer: AbstractProducer) -> AbstractConsumer:
+    consumer_type = os.environ.get('type')
+    if consumer_type == 'kafka':
+        kafka_config = {
+            'bootstrap.servers': os.environ.get('brokers'),
+            'group.id': os.environ.get('group_id'),
+            'topics': [ast.literal_eval(os.environ.get('input'))['topic']]
+        }
+        return KafkaConsumer(transformer, producer, kafka_config)
+    else:
+        return None

pipeline/kafka/consumer.py → pipeline/connector/kafka/kafka_consumer.py

@@ -1,33 +1,35 @@
 import ast
-import config as cfg
 from confluent_kafka import Consumer, KafkaError, KafkaException
 from utils.logger import logger
 from bl.transformer_runner_interface import TransforerRunnerInterface
-from kafka.producer import KafkaProducer
+from connector.producer import AbstractProducer
+from connector.consumer import AbstractConsumer
 
-class KafkaConsumer:
+class KafkaConsumer(AbstractConsumer):
     '''Class responsible of consumning and creating kafka consumer.'''
-    def __init__(self, trasformer: TransforerRunnerInterface, producer: KafkaProducer):
-        logger.info(f"connect to: {cfg.kafka_config['bootstrap.servers']}")
+    def __init__(self, trasformer: TransforerRunnerInterface, producer: AbstractProducer, configuration: dict):
+        logger.info(f"connect to: {configuration['bootstrap.servers']}")
         conf = {
-            'bootstrap.servers': cfg.kafka_config['bootstrap.servers'],
-            'group.id': cfg.kafka_config['group.id'],
+            'bootstrap.servers': configuration['bootstrap.servers'],
+            'group.id': configuration['group.id'],
             'auto.offset.reset': 'latest'
         }
 
+        self.topics = configuration['topics']
+
         self.consumer = Consumer(conf)
         self.transformer = trasformer
         self.producer = producer
         self.running = False
 
-    def consume(self, topics):
+    def consume(self):
         '''Function start the consuming
             Args:
                 topic: string array of the topic to consume'''
-        logger.info("start consume topics: %s", topics)
+        logger.info("start consume topics: %s", self.topics)
         self.running = True
         try:
-            self.consumer.subscribe(topics)
+            self.consumer.subscribe(self.topics)
 
             while self.running:
                 msg = self.consumer.poll(timeout=1.0)

pipeline/kafka/producer.py → pipeline/connector/kafka/kafka_producer.py

@@ -1,28 +1,17 @@
 import json
-from utils.logger import logger
 from confluent_kafka import Producer
+from connector.producer import AbstractProducer
 import config as cfg
 
-class KafkaProducer():
+class KafkaProducer(AbstractProducer):
     '''Class responsible of producing and creating kafka prooducer.'''
-    def __init__(self, topic):
+    def __init__(self, configuration: dict):
         conf = {
             'bootstrap.servers': cfg.kafka_config['bootstrap.servers'] 
         }
 
         self.producer = Producer(conf)
-        self.topic = topic
-
-
-    def produced_callback(self, err, msg):
-        '''Callback after produce
-            Args:
-                err: if the produce failed
-                msg: the msg that produced'''
-        if err is not None:
-            logger.error("Failed to deliver message: %s:%s", str(msg), str(err))
-        else:
-            logger.error("Message produced: %s", str(msg))
+        self.topic = configuration['topic']
 
     def produce(self, values):
         '''Function produce msg

pipeline/connector/producer.py

@@ -0,0 +1,23 @@
+from utils.logger import logger
+
+class AbstractProducer():
+    '''Class responsible of producing and creating prooducer.'''
+    def __init__(self, configuration: dict):
+        pass
+
+
+    def produced_callback(self, err, msg):
+        '''Callback after produce
+            Args:
+                err: if the produce failed
+                msg: the msg that produced'''
+        if err is not None:
+            logger.error("Failed to deliver message: %s:%s", str(msg), str(err))
+        else:
+            logger.error("Message produced: %s", str(msg))
+
+    def produce(self, values):
+        '''Function produce msg
+            Args:
+                values: the values that will be produce'''
+        pass

pipeline/connector/producer_builder.py

@@ -0,0 +1,13 @@
+import ast
+import os
+from connector.kafka.kafka_producer import KafkaProducer 
+from connector.producer import AbstractProducer
+
+def build_producer() -> AbstractProducer:
+    producer_type = os.environ.get('type')
+    if producer_type == 'kafka':
+        kafka_output_topic = ast.literal_eval(os.environ.get('output'))['topic']
+
+        return KafkaProducer({'topic': kafka_output_topic})
+    else:
+        return None

pipeline/main.py

@@ -1,15 +1,14 @@
 from utils.logger import logger
-from config import kafka_input_topic, kafka_output_topic
-from kafka.consumer import KafkaConsumer
-from kafka.producer import KafkaProducer
+from connector.consumer_builder import build_consumer
+from connector.producer_builder import build_producer
 from bl.transformation_builder import build_transformation
 
 def main():
     ''' The main function is the pipeline entrypoint. '''
     logger.info("starting pipeline")
     transformer = build_transformation()
-    producer = KafkaProducer(kafka_output_topic)
-    consumer = KafkaConsumer(transformer, producer)
-    consumer.consume([kafka_input_topic])
+    producer = build_producer()
+    consumer = build_consumer(transformer, producer)
+    consumer.consume()
 
 main()