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create_action_plan.py
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create_action_plan.py
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import pandas as pd
from kubernetes import client, config
import csv
from get_details_for_kuber_cluster import convert_memory_to_ki, convert_cpu_to_millicore
import pandas as pd
from datetime import datetime
import logging
import argparse
import os
def create_action_plan(k8s_view_file, timestamp):
# Read the CSV file into a pandas DataFrame
data = pd.read_csv(k8s_view_file)
# Check if the DataFrame is empty
if data.empty:
logging.debug("No data found in the file. Exiting...")
return None, None
total_node_allocated_cpu = 0
total_node_allocated_memory = 0
total_remaining_node_request_cpu = 0
total_remaining_node_request_memory = 0
total_remaining_node_limit_cpu = 0
total_remaining_node_limit_memory = 0
k8s_descending_data = []
for index, row in data.iterrows():
if row['view'] == 'NODE_VIEW':
total_node_allocated_cpu += convert_cpu_to_millicore(row['allocated_cpu'])
total_node_allocated_memory += convert_memory_to_ki(row['allocated_memory'])
total_remaining_node_request_cpu += convert_cpu_to_millicore(row['remaining_cpu_request'])
total_remaining_node_request_memory += convert_memory_to_ki(row['remaining_memory_request'])
total_remaining_node_limit_cpu += convert_cpu_to_millicore(row['remaining_cpu_limit'])
total_remaining_node_limit_memory += convert_memory_to_ki(row['remaining_memory_limit'])
row['remaining_cpu_request_percentage'] = round(float(float(convert_cpu_to_millicore(row['remaining_cpu_request'])) / float(convert_cpu_to_millicore(row['allocated_cpu']))) * 100)
row['remaining_memory_request_percentage'] = round(float(float(convert_memory_to_ki(row['remaining_memory_request'])) / float(convert_memory_to_ki(row['allocated_memory']))) * 100)
row['remaining_cpu_limit_percentage'] = round(float(float(convert_cpu_to_millicore(row['remaining_cpu_limit'])) / float(convert_cpu_to_millicore(row['allocated_cpu']))) * 100)
row['remaining_memory_limit_percentage'] = round(float(float(convert_memory_to_ki(row['remaining_memory_limit'])) / float(convert_memory_to_ki(row['allocated_memory']))) * 100)
k8s_descending_data.append(row)
sorted_k8s_data = sorted(k8s_descending_data, key=lambda x: (
-x['remaining_cpu_request_percentage'],
-x['remaining_memory_request_percentage'],
-x['remaining_cpu_limit_percentage'],
-x['remaining_memory_limit_percentage']
))
logging.debug("\nSorted:", sorted_k8s_data)
output_folder = 'action_plan'
if not os.path.exists(output_folder):
os.makedirs(output_folder)
csv_file_path = os.path.join(output_folder, f'k8s_action_plan_{timestamp}.csv')
df = pd.DataFrame(sorted_k8s_data)
df.to_csv(csv_file_path, index=False)
logging.debug(f"Total remaining node allocated cpu: {total_node_allocated_cpu}")
logging.debug(f"Total remaining node allocated memory: {total_node_allocated_memory}")
logging.debug(f"Total remaining node request cpu: {total_remaining_node_request_cpu}")
logging.debug(f"Total remaining node request memory: {total_remaining_node_request_memory}")
logging.debug(f"Total remaining node limit cpu: {total_remaining_node_limit_cpu}")
logging.debug(f"Total remaining node limit memory: {total_remaining_node_limit_memory}")
return sorted_k8s_data[0]['node_name'], csv_file_path
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--file_name', help='Specify the k8s_output file name', required=True)
args = parser.parse_args()
return args.file_name
if __name__ == "__main__":
file_name = parse_arguments()
now = datetime.now()
timestamp = now.strftime("%d_%m_%Y_%H_%M_%S")
create_action_plan(file_name, timestamp)