--- /dev/null
+# Python image to use.
+FROM python:3.8
+
+# Set the working directory to /src/hdfs-writer
+WORKDIR /src/inferenceApp
+
+# Install librdkafka
+RUN mkdir /librdkafka-dir && cd /librdkafka-dir
+RUN git clone https://github.com/edenhill/librdkafka.git && \
+cd librdkafka && \
+./configure --prefix /usr && \
+make && \
+make install
+
+#RUN export PYTHONPATH="/usr/bin/python3:/src/python-kafkaconsumer-inference-app/"
+
+# copy the requirements file used for dependencies
+COPY requirements.txt .
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --trusted-host pypi.python.org -r requirements.txt
+
+RUN pip install confluent-kafka
+RUN pip install python-dateutil
+
+# Install ptvsd for debugging
+RUN pip install ptvsd
+
+
+
+# Copy the rest of the working directory contents into the container at /app
+COPY . ./
+
+# Start the server when the container launches
+CMD ["python3", "-m", "ptvsd", "--host", "localhost", "--port", "5000", "--wait", "/src/inferenceApp/main.py"]
\ No newline at end of file
--- /dev/null
+import logging
+from confluent_kafka import Consumer
+import json
+
+logging.basicConfig(format='%(asctime)s::%(process)d::%(levelname)s::%(message)s', level=logging.INFO, datefmt='%d-%b-%y %H:%M:%S')
+
+
+class CustomKafkaConsumer:
+ def __init__(self):
+ self.output_map = dict()
+ self.topic_name = "metrics3"
+ #self.topic_name = "adatopic1"
+ self.consumer = Consumer({
+ 'bootstrap.servers': 'kafka-cluster-kafka-bootstrap:9092',
+ #'bootstrap.servers': '172.25.103.6:31610',
+ 'group.id': 'grp1',
+ 'auto.offset.reset': 'earliest'
+ })
+ self.duration = 31536000 #50
+ self.time_format = 'timestamp' #or 'iso'
+ # duration may be equal to no_of_recs_wanted, say we gurantee 50 secs generate 50 recs
+ self.no_of_recs_wanted = 3
+
+
+ def processMessage(self, msg_key, msg_val):
+ python_obj = {}
+ try:
+ python_obj = json.loads(msg_key)
+ except ValueError:
+ pass
+ try:
+ python_obj = json.loads(msg_val)
+ except ValueError:
+ pass
+ #print(python_obj["labels"]["__name__"])
+ metric_name = python_obj["labels"]["__name__"]
+ ip = python_obj["labels"]["instance"]
+ if self.time_format == 'iso':
+ logging.info("Time_format is ISO-FORMAT")
+ iso_time = python_obj["timestamp"]
+ logging.info("iso_time:: {}".format(iso_time))
+ import dateutil.parser as dp
+ parsed_datetime_obj = dp.parse(iso_time)
+ from datetime import datetime
+ now_datetime_obj = datetime.now()
+ st_datetime_obj = now_datetime_obj - datetime.timedelta(seconds= self.duration)
+ en_datetime_obj = now_datetime_obj
+ if st_datetime_obj <= parsed_datetime_obj and parsed_datetime_obj <= en_datetime_obj:
+ logging.info("Parsed a relevant record")
+ if metric_name in self.output_map:
+ if ip in self.output_map[metric_name]:
+ self.output_map[metric_name][ip].append(python_obj)
+ logging.info("::Appended a record to existing time series data::")
+ else:
+ self.output_map[metric_name][ip] = list()
+ self.output_map[metric_name][ip].append(python_obj)
+ logging.info("::Appended a recorded to existing time series data with a new ip::")
+ else:
+ self.output_map[metric_name] = dict()
+ self.output_map[metric_name][ip] = list()
+ self.output_map[metric_name][ip].append(python_obj)
+ logging.info("::Inserted the first record to a new time series::")
+ else:
+ logging.info("Time_format is timestamp")
+ parsed_timestamp = python_obj["timestamp"]
+ logging.info("parsed_timestamp:: {}".format(parsed_timestamp))
+ from datetime import datetime, timedelta
+ now_datetime_obj = datetime.now()
+ st_datetime_obj = now_datetime_obj - timedelta(seconds=self.duration)
+ en_datetime_obj = now_datetime_obj
+ st_timestamp = int(st_datetime_obj.timestamp()*1000)
+ en_timestamp = int(en_datetime_obj.timestamp()*1000)
+
+ logging.info("st_timestamp:: {}".format(st_timestamp))
+ logging.info("en_timestamp:: {}".format(en_timestamp))
+ if st_timestamp <= parsed_timestamp and en_timestamp>=parsed_timestamp:
+ if metric_name in self.output_map:
+ if ip in self.output_map[metric_name]:
+ self.output_map[metric_name][ip].append(python_obj)
+ logging.info("::Appended a record to existing time series data::")
+ else:
+ self.output_map[metric_name][ip] = list()
+ self.output_map[metric_name][ip].append(python_obj)
+ logging.info("::Appended a recorded to existing time series data with a new ip::")
+ else:
+ self.output_map[metric_name] = dict()
+ self.output_map[metric_name][ip] = list()
+ self.output_map[metric_name][ip].append(python_obj)
+ logging.info("::Inserted the first record to a new time series::")
+
+ logging.info("The size of the o/p map :: {}".format(len(self.output_map[metric_name][ip])))
+ if len(self.output_map[metric_name][ip]) == self.no_of_recs_wanted:
+ logging.info("Size of the q {}-{} exceeded ".format(metric_name, ip))
+ logging.info("Poping out the record: {}".format(self.output_map[metric_name][ip].pop(0)))
+
+
+ def executeQuery(self, metric_name, ip):
+ if metric_name in self.output_map:
+ if ip in self.output_map[metric_name]:
+ return self.output_map[metric_name][ip]
+
+
+ def consume(self):
+ self.consumer.subscribe([self.topic_name])
+ while True:
+ msg = self.consumer.poll(1.0)
+ if msg is None:
+ logging.info('Looking for message on topic:: {}'.format(self.topic_name))
+ continue
+ if msg.error():
+ print("Consumer error: {}".format(msg.error()))
+ continue
+ # print("msg type:: {} and msg:: {}".format(type(msg), msg))
+ # print('Received message key from producer: {}'.format(msg.key().decode('utf-8')))
+ # print('Received message val from producer: {}'.format(msg.value().decode('utf-8')))
+ # print("mes-key-type:: {}".format(type(msg.key().decode('utf-8'))))
+ # print("msg-value-type:: {}".format(type(msg.value().decode('utf-8'))))
+
+ self.processMessage(msg.key(), msg.value())
+ self.consumer.close()
\ No newline at end of file
--- /dev/null
+#!/usr/bin/env python3
+
+# from .consumer.CustomKafkaConsumer import CustomKafkaConsumer
+# from .producer.CustomKafkaProducer import CustomKafkaProducer
+
+import sys
+import os, threading
+import traceback
+import json
+import concurrent.futures
+import logging
+
+from consumer import CustomKafkaConsumer
+from producer import CustomKafkaProducer
+
+logging.basicConfig(format='%(asctime)s::%(process)d::%(levelname)s::%(message)s', level=logging.INFO, datefmt='%d-%b-%y %H:%M:%S')
+
+def main():
+ #Begin: Sample producer based on file
+ customKafkaProducer = CustomKafkaProducer.CustomKafkaProducer()
+ with open("./multithreading-metrics.json") as input_file:
+ for each_line in input_file:
+ python_obj = json.loads(each_line)
+ # print(python_obj["labels"]["__name__"])
+ customKafkaProducer.produce(each_line, python_obj["labels"]["__name__"])
+ #END: Sample producer based on file
+
+ customKafkaConsumer = CustomKafkaConsumer.CustomKafkaConsumer()
+
+ #Form a data structure for query formation
+ queries = []
+ queries.append({"metric_name" : "go_gc_duration_seconds_count", "ip": "10.42.1.93:8686"})
+ queries.append({"metric_name" : 'go_gc_duration_seconds_count', "ip": "10.42.1.92:8686"})
+
+ executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
+ executor.submit(customKafkaConsumer.consume)
+
+ while(True):
+ for each_record in queries:
+ list_of_records = customKafkaConsumer.executeQuery(each_record["metric_name"], each_record["ip"])
+ logging.info("The records collected :: {}".format(list_of_records))
+ logging.info("The length of records collected: {}".format(len(list_of_records)))
+ print("The records :: {}".format(list_of_records))
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
--- /dev/null
+import logging
+from confluent_kafka import Producer
+import traceback
+
+logging.basicConfig(format='%(asctime)s::%(process)d::%(levelname)s::%(message)s', level=logging.INFO, datefmt='%d-%b-%y %H:%M:%S')
+
+
+class CustomKafkaProducer:
+ def __init__(self):
+ self.topic_name = "metrics3"
+ #self.topic_name = "adatopic1"
+ conf = {'bootstrap.servers': 'kafka-cluster-kafka-bootstrap:9092'
+ }
+ self.producer = Producer(**conf)
+
+
+ def produce(self, kafka_msg, kafka_key):
+ try:
+ self.producer.produce(topic=self.topic_name,
+ value=kafka_msg,
+ key=kafka_key,
+ callback=lambda err, msg: self.on_delivery(err, msg)
+ )
+ self.producer.flush()
+
+ except Exception as e:
+ #print("Error during producing to kafka topic. Stacktrace is %s",e)
+ logging.error("Error during producing to kafka topic.")
+ traceback.print_exc()
+
+
+ def on_delivery(self, err, msg):
+ if err:
+ print("Message failed delivery, error: %s", err)
+ logging.error('%s raised an error', err)
+ else:
+ logging.info("Message delivered to %s on partition %s",
+ msg.topic(), msg.partition())
\ No newline at end of file
Code Review / demo.git / commitdiff