Update: Add new features

compose.yml

@@ -3,7 +3,7 @@ services:
 
   my-app:
     image: containers-monitoring-app
-    container_name: my-python-container
+    container_name: main-runner
     restart: always
     environment:
       - DOCKER_HOST=tcp://host.docker.internal:2375
@@ -16,7 +16,7 @@ services:
     ports:
       - "9090:9090"
     volumes:
-      - D:/ContainerMonitoringProgram/container-monitoring/prometheus/prometheus.yml:/etc/prometheus/prometheus.yml
+      - D:/own_projects/Containers-monitoring-application/prometheus/prometheus.yml:/etc/prometheus/prometheus.yml
     command:
       - "--config.file=/etc/prometheus/prometheus.yml"
     restart: always
@@ -48,7 +48,7 @@ services:
     volumes:
       - grafana-data:/var/lib/grafana
     env_file:
-      - D:/ContainerMonitoringProgram/container-monitoring/.env
+      - D:/own_projects/Containers-monitoring-application/.env
     restart: always
 
 volumes:

LoggerPack/log_container.py → logger_setup/logger.py

@@ -1,16 +1,24 @@
+"""
+The module is responsible for creating logs for monitoring execution
+"""
+
+import sys
 import logging
+
 from logging.handlers import RotatingFileHandler
-import sys
+
 
 logger = logging.getLogger("ContainerMonitor")
 logger.setLevel(logging.DEBUG)
 
-formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+formatter = logging.Formatter('%(asctime)s - %(name)s - \
+                              %(levelname)s - %(message)s')
 
 console_handler = logging.StreamHandler(sys.stdout)
 console_handler.setFormatter(formatter)
 logger.addHandler(console_handler)
 
-file_handler = RotatingFileHandler("../container_monitor.log", maxBytes=1024 * 1024, backupCount=5)
+file_handler = RotatingFileHandler("../container_monitor.log",
+                                   maxBytes=1024 * 1024, backupCount=5)
 file_handler.setFormatter(formatter)
 logger.addHandler(file_handler)

main.py

@@ -1,15 +1,25 @@
-import LoggerPack.log_container as lc
-from TelegramBotAlertsPack import alerts
+import logger_setup.logger as lc
+from telegram_bot_alerts import setup_alerts
 
-def main_block():
+
+def main() -> int:
+    """
+    Main entry point of the application.
+
+    This function initializes the logger, starts the Telegram bot for alerts,
+    and returns 0 upon successful execution.
+
+    Steps:
+    1. Logs that the application has started.
+    2. Starts the Telegram bot using `setup_alerts.start_telegram_bot()`.
+    3. Returns 0 when execution completes successfully.
+    """
     lc.logger.info("The app has started!")
-    alerts.start_telegram_bot()
+    setup_alerts.start_telegram_bot()
     return 0
 
+
 if __name__ == "__main__":
-    ending_code = main_block()
-    if ending_code == 0:
+    END = main()
+    if END == 0:
         lc.logger.info("The app is complete!")
-
-
-

MonitorPack/monitor.py → monitoring/setup_monitoring.py

@@ -1,7 +1,9 @@
-import LoggerPack.log_container as lc
+import sys
 import docker
 from docker import errors
-import sys
+from typing import Optional
+
+import logger_setup.logger as lc
 
 if __name__ == "__main__":
     lc.logger.error("This file cannot be run as main!")
@@ -11,10 +13,26 @@
 try:
     client = docker.from_env()
 except errors.DockerException:
+    client = None
     lc.logger.error("Docker is not working right now!")
 
-def get_container_metrics():
 
+def get_container_metrics() -> list[str]:
+    """
+    Retrieve metrics for all running Docker containers.
+
+    For each container, this function collects:
+        - container name
+        - container status
+        - parent image
+        - creation time
+        - CPU usage percentage
+        - RAM usage in MB
+
+    Returns:
+        list[str]: A list of formatted strings containing metrics
+        for each container.
+    """
     containers = client.containers.list()
     containers_stats = []
     for container in containers:
@@ -33,31 +51,53 @@ def get_container_metrics():
                                 f"~  RAM use: {mem_usage_mb:.2f} MB\n")
     return containers_stats
 
-def calculate_cpu_percent(stats):
-    cpu_delta = stats["cpu_stats"]["cpu_usage"]["total_usage"] - stats["precpu_stats"]["cpu_usage"]["total_usage"]
-    system_delta = stats["cpu_stats"]["system_cpu_usage"] - stats["precpu_stats"]["system_cpu_usage"]
+
+def calculate_cpu_percent(stats) -> float:
+    """
+    Calculate CPU usage percentage for a Docker container.
+
+    Args:
+        stats (dict): The container statistics dictionary
+            obtained from `container.stats(stream=False)`.
+
+    Returns:
+        float: The CPU usage percentage.
+    """
+    cpu_delta = stats["cpu_stats"]["cpu_usage"]["total_usage"] - \
+        stats["precpu_stats"]["cpu_usage"]["total_usage"]
+    system_delta = stats["cpu_stats"]["system_cpu_usage"] - \
+        stats["precpu_stats"]["system_cpu_usage"]
 
     if system_delta > 0:
-        return (cpu_delta / system_delta) * len(stats["cpu_stats"]["cpu_usage"]["percpu_usage"]) * 100.0
+        return (cpu_delta / system_delta) * \
+            len(stats["cpu_stats"]["cpu_usage"]["percpu_usage"]) * 100.0
     return 0.0
 
-def get_container_status_real_time():
+
+def get_container_status_real_time() -> Optional[str]:
+    """
+    Check running containers for high resource usage in real time.
+
+    This function monitors all containers and reports if:
+        - CPU usage exceeds 50%
+        - Memory usage exceeds 500 MB
+
+    Returns:
+        str or int: Warning message string if any container exceeds
+        thresholds; otherwise, returns None.
+    """
     containers = client.containers.list()
 
     for container in containers:
         stats = container.stats(stream=False)
         cpu_percent = calculate_cpu_percent(stats)
 
         if float(cpu_percent) > 50.00:
-            return f"The container {container.name} uses more than 50% of the available processor resources on the host"
+            return f"The container {container.name} uses more than 50% \
+                of the available processor resources on the host"
 
         mem_usage_mb = stats["memory_stats"]["usage"] / (1024 * 1024)
         if float(mem_usage_mb) > 500.00:
-            return f"The container {container.name} uses 500 MB of the available RAM resources on the host"
-
-    return 0
-
-
-
-
-
+            return f"The container {container.name} uses \
+                500 MB of the available RAM resources on the host"
+    return None

requirements.txt

@@ -1,3 +1,7 @@
 docker
 pyTelegramBotAPI
 python-dotenv
+black
+pylint
+flake8
+pytest