Filter out AWS RDS Instances with Low CPU Utilization

This task identifies Amazon RDS instances that are underperforming or underutilized in terms of CPU usage. By utilizing Amazon CloudWatch metrics, users can monitor and assess the CPU performance of their RDS instances over a specified period. By setting a CPU utilization threshold, they can filter out instances that consistently operate below this limit, indicating potential over-provisioning or underuse. Highlighting these low-utilization instances aids organizations in optimizing their AWS resource allocation, ensuring cost efficiency and facilitating informed decisions about scaling or decommissioning certain database resources.

import boto3 from datetime import datetime, timedelta from botocore.exceptions import BotoCoreError, ClientError creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] # Constants # Define the threshold for CPU utilization(Defined in input parameters). # Instances below this will be considered low utilization. LOW_CPU_THRESHOLD=20 # Hardcoded for One time Filter Result LOOKBACK_PERIOD_HOURS=24 # Hardcoded for One time Filter Result LOOKBACK_PERIOD = 3600 * int(LOOKBACK_PERIOD_HOURS) # Define the period to check. Here, it's set to 24 hours. def get_low_cpu_rds_instances(instances): low_cpu_instances = [] # List to store the IDs of RDS instances with low CPU utilization # Group instances by region instances_by_region = {} for instance_info in instances: region = instance_info['region'] if region not in instances_by_region: instances_by_region[region] = [] instances_by_region[region].append(instance_info['instance']) # Check each region for region, instance_list in instances_by_region.items(): cloudwatch = boto3.client('cloudwatch', aws_access_key_id=access_key,aws_secret_access_key=secret_key,region_name=region) # Loop through each RDS instance for instance_id in instance_list: try: end_time = datetime.utcnow() start_time = end_time - timedelta(seconds=LOOKBACK_PERIOD) metrics = cloudwatch.get_metric_data( MetricDataQueries=[ { 'Id': 'cpuUtilization', 'MetricStat': { 'Metric': { 'Namespace': 'AWS/RDS', 'MetricName': 'CPUUtilization', 'Dimensions': [{ 'Name': 'DBInstanceIdentifier', 'Value': instance_id }] }, 'Period': LOOKBACK_PERIOD, 'Stat': 'Average' }, 'ReturnData': True, }, ], StartTime=start_time, EndTime=end_time ) if metrics['MetricDataResults'][0]['Values']: cpu_utilization = metrics['MetricDataResults'][0]['Values'][0] if cpu_utilization < int(LOW_CPU_THRESHOLD): low_cpu_instances.append({ 'InstanceID': instance_id, 'AverageCPU': cpu_utilization, 'Region': region }) except Exception as e: print(f"Error fetching CloudWatch metrics for RDS instance {instance_id} in {region}: {e}") return low_cpu_instances def display_low_cpu_instances(data): # Initialize table with the desired structure and headers table = context.newtable() table.title = "Low Usage RDS Instances Overview" table.num_cols = 3 # Number of columns for Instance ID, Average CPU, and Region table.num_rows = 1 # Starts with one row for headers table.has_header_row = True # Define header names based on the structure of low_cpu_instances data headers = ["Instance ID", "Average CPU (%)", "Region"] # Set headers in the first row for col_num, header in enumerate(headers): table.setval(0, col_num, header) # Populate the table with instance data for row_num, instance in enumerate(data, start=1): # Starting from the second row table.num_rows += 1 # Add a row for each entry values = [ instance["InstanceID"], f"{instance['AverageCPU']:.2f}", # Format average CPU as a float with 2 decimal places instance["Region"] ] for col_num, value in enumerate(values): table.setval(row_num, col_num, value) low_cpu_rds = get_low_cpu_rds_instances(instances) # Print the results if low_cpu_rds: display_low_cpu_instances(low_cpu_rds) ''' print("RDS instances with low CPU utilization:") for instance_info in low_cpu_rds: print(f"Instance ID: {instance_info['InstanceID']}, Average CPU: {instance_info['AverageCPU']}% in Region: {instance_info['Region']}")''' else: print("No RDS instances with low CPU utilization found.") context.skip_sub_tasks=True

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AWS RDS

1flcqDvNamXsOTVclwnPaDelete AWS RDS Instance
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Delete AWS RDS Instance
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This task terminates the AWS RDS database and deletes all associated data. Before deletion, users often create a final snapshot to preserve the database's current state, enabling future restoration if necessary. It's essential to ensure that the "Deletion Protection" feature, designed to prevent accidental deletions, is disabled before proceeding. Once deleted, the RDS instance is no longer operational, and associated costs cease. However, any retained backups or snapshots will persist and may incur storage charges until they too are deleted.
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import boto3 creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] def delete_rds_instance(instance_info): """ Delete an RDS instance after taking necessary precautions like disabling deletion protection and creating a final snapshot. Parameters: - instance_info (dict): Dictionary containing InstanceID and Region. """ instance_id = instance_info['InstanceID'] region = instance_info['Region'] # Initialize the boto3 client for the Amazon Relational Database Service (RDS) in the specified region rds = boto3.client('rds', aws_access_key_id=access_key,aws_secret_access_key=secret_key,region_name=region) try: instance_details = rds.describe_db_instances(DBInstanceIdentifier=instance_id) if instance_details['DBInstances'][0].get('DeletionProtection', False): rds.modify_db_instance(DBInstanceIdentifier=instance_id, DeletionProtection=False) print(f"Deletion protection disabled for {instance_id}") except rds.exceptions.DBInstanceNotFoundFault: print(f"RDS instance {instance_id} not found.") return except Exception as e: print(f"Error modifying RDS instance {instance_id}: {e}") return try: snapshot_name = f"final-snapshot-{instance_id}" rds.create_db_snapshot(DBInstanceIdentifier=instance_id, DBSnapshotIdentifier=snapshot_name) print(f"Final snapshot creation initiated for {instance_id}") waiter = rds.get_waiter('db_snapshot_completed') waiter.wait(DBSnapshotIdentifier=snapshot_name) print(f"Final snapshot {snapshot_name} created for {instance_id}") except rds.exceptions.SnapshotQuotaExceededFault: print(f"Snapshot quota exceeded for {instance_id}.") return except rds.exceptions.DBInstanceNotFoundFault: print(f"RDS instance {instance_id} not found.") return except Exception as e: print(f"Error creating snapshot for RDS instance {instance_id}: {e}") return try: rds.delete_db_instance(DBInstanceIdentifier=instance_id, SkipFinalSnapshot=True) print(f"RDS instance {instance_id} deletion initiated") except rds.exceptions.DBInstanceNotFoundFault: print(f"RDS instance {instance_id} not found.") except Exception as e: print(f"Error deleting RDS instance {instance_id}: {e}") rds_instances_to_delete = low_cpu_rds # Make sure low_cpu_rds is a list of dictionaries with 'InstanceID' and 'Region' keys # Check if the list is empty if not rds_instances_to_delete: print("No RDS instances provided for deletion.") else: # Loop through each RDS instance in the list and call the delete function for instance_info in rds_instances_to_delete: delete_rds_instance(instance_info)
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AWS RDS
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Filter out AWS RDS Instances with Low CPU Utilization

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