feat: add get_cost_report.py script

bin/get_cost_report.py

@@ -0,0 +1,88 @@
+import argparse
+from typing import Any, Dict, List
+
+import boto3
+import pandas as pd
+from tabulate import tabulate
+
+
+def get_combined_cost_data(start_time: str, end_time: str) -> pd.DataFrame:
+    """
+    Fetches AWS cost data for a specified time range and combines it into a DataFrame.
+
+    :param start_time: Start time in ISO8601 format (e.g., '2023-10-20T09:00:00Z').
+    :param end_time: End time in ISO8601 format (e.g., '2023-10-20T11:00:00Z').
+    :return: A DataFrame containing cost data for the specified time range.
+    """
+    print("Getting cost data for {} to {}".format(start_time, end_time))
+    session = boto3.Session(profile_name="default", region_name="us-west-2")  # Replace with your AWS profile name
+
+    # AWS Cost Explorer client
+    ce = session.client("ce")
+
+    # Initialize an empty list to store data for all hours
+    combined_data = []
+
+    # Calculate the number of hours between start and end times
+    num_hours = (pd.to_datetime(end_time) - pd.to_datetime(start_time)).total_seconds() / 3600
+
+    # Iterate through each hour and fetch data
+    for hour in range(int(num_hours) + 1):
+        hour_start = pd.to_datetime(start_time) + pd.DateOffset(hours=hour)
+        hour_end = hour_start + pd.DateOffset(hours=1)
+
+        # Get an AWS cost and usage report with the service filter for the current hour
+        response = ce.get_cost_and_usage(
+            TimePeriod={"Start": hour_start.strftime("%Y-%m-%dT%H:%M:%SZ"), "End": hour_end.strftime("%Y-%m-%dT%H:%M:%SZ")},
+            Granularity="HOURLY",
+            Metrics=["UnblendedCost"],
+            GroupBy=[{"Type": "DIMENSION", "Key": "SERVICE"}],
+        )
+
+        # Append the data for the current hour to the combined data list
+        combined_data += response["ResultsByTime"]
+
+    # Create a DataFrame from the combined data
+    df_list: List[Dict[str, Any]] = []
+
+    # Initialize total cost
+    total_cost = 0.0
+
+    for period in combined_data:
+        for group in period.get("Groups", []):
+            service_name = group["Keys"][0]
+            cost_amount = float(group["Metrics"]["UnblendedCost"]["Amount"])
+            df_list.append({"Service": service_name, "Cost": cost_amount, "TimePeriod": period["TimePeriod"]["Start"]})
+            total_cost += cost_amount  # Accumulate the total cost
+
+    # Add a row for total cost
+    df_list.append({"Service": "Total", "Cost": total_cost, "TimePeriod": "Total"})
+
+    df = pd.DataFrame(df_list)
+    return df
+
+
+def main():
+    """
+    Fetches AWS cost data for a specified time range and combines it into a DataFrame for display.
+    :return:
+    """
+    parser = argparse.ArgumentParser(description="Fetch AWS cost data for a specified time range.")
+    parser.add_argument("start_time", help="Start time in ISO8601 format (e.g., 2023-10-20T09:00:00Z)")
+    parser.add_argument("end_time", help="End time in ISO8601 format (e.g., 2023-10-20T11:00:00Z)")
+    args = parser.parse_args()
+
+    print("Please ensure that the cost explorer has been updated for the request time window!")
+
+    # Get combined cost data for the specified time range
+    df = get_combined_cost_data(args.start_time, args.end_time)
+
+    # Create a pretty table for cost breakdown
+    table = tabulate(df, headers="keys", tablefmt="fancy_grid")
+
+    # Display the pretty table
+    print(table)
+
+
+if __name__ == "__main__":
+    main()