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pSQEnVjE9egrrY2PCcg5Filter out Underutilized AWS Redshift Clusters
Filter out Underutilized AWS Redshift Clusters
There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.
This task pertains to identifying Amazon Redshift clusters that exhibit consistently low CPU utilization over a predefined time span. By leveraging Amazon CloudWatch metrics, organizations can detect underutilized Redshift clusters. Recognizing such clusters provides valuable insights, allowing teams to make informed decisions about potential downscaling, resource reallocation, or other optimization measures to ensure efficient cloud resource usage.
inputs
outputs
import boto3
from datetime import datetime, timedelta
creds = _get_creds(cred_label)['creds']
access_key = creds['username']
secret_key = creds['password']
# Constants
#LOW_CPU_THRESHOLD = 20 # Example Threshold for CPU utilization
#LOOKBACK_PERIOD_HOURS = 24 # Example lookback period in hours
LOOKBACK_PERIOD = 3600 * LOOKBACK_PERIOD_HOURS # Convert hours to seconds
def get_low_cpu_redshift_clusters(all_clusters):
"""
Identify and list Redshift clusters with average CPU utilization below a defined threshold
over a specific period.
Parameters:
- all_clusters (dict): Dictionary with region as keys and list of cluster info as values.
Returns:
- list: List of dictionaries containing Redshift cluster identifiers and their average CPU utilization.
"""
low_cpu_clusters = [] # List to store the cluster details with low CPU utilization
for region, clusters in all_clusters.items():
# Initialize boto3 client for CloudWatch in the specific region
cloudwatch = boto3.client('cloudwatch', aws_access_key_id=access_key,aws_secret_access_key=secret_key, region_name=region)
for cluster in clusters:
cluster_id = cluster['ClusterIdentifier']
try:
# Query CloudWatch to fetch the CPU utilization metric for the defined period
metrics = cloudwatch.get_metric_data(
MetricDataQueries=[
{
'Id': 'cpuUtilization',
'MetricStat': {
'Metric': {
'Namespace': 'AWS/Redshift',
'MetricName': 'CPUUtilization',
'Dimensions': [{'Name': 'ClusterIdentifier', 'Value': cluster_id}]
},
'Period': LOOKBACK_PERIOD,
'Stat': 'Average' # We're interested in the average CPU utilization
},
'ReturnData': True,
},
],
StartTime=datetime.utcnow() - timedelta(seconds=LOOKBACK_PERIOD),
EndTime=datetime.utcnow()
)
# Check if the cluster's CPU utilization falls below the threshold
if metrics['MetricDataResults'][0]['Values']:
cpu_utilization = metrics['MetricDataResults'][0]['Values'][0]
if cpu_utilization < LOW_CPU_THRESHOLD:
low_cpu_clusters.append({
'Region': region,
'ClusterID': cluster_id,
'AverageCPU': cpu_utilization
})
except Exception as e:
print(f"Error checking CPU utilization for cluster {cluster_id} in region {region}: {e}")
return low_cpu_clusters
# Example usage (assuming all_clusters is provided from an upstream task)
# all_clusters = {
# 'us-west-2': [{'ClusterIdentifier': 'cluster1'}, {'ClusterIdentifier': 'cluster2'}],
# 'us-east-1': [{'ClusterIdentifier': 'cluster3'}],
# }
clusters_info = get_low_cpu_redshift_clusters(all_clusters)
# Print the results
if clusters_info:
print("Redshift clusters with low CPU utilization:")
for cluster in clusters_info:
print(f"Region: {cluster['Region']}, Cluster ID: {cluster['ClusterID']}, Average CPU: {cluster['AverageCPU']:.2f}%")
else:
print("No Redshift clusters with low CPU utilization found.")
context.skip_sub_tasks=True
copied- 1qDood607VHbhjFv6yYZoDelete AWS Redshift Clusters
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There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.This task involves terminating specific Amazon Redshift clusters, effectively removing them from an AWS account. Deleting a Redshift cluster permanently erases all the data within the cluster and cannot be undone. This process might be undertaken to manage costs, decommission outdated data warehouses, or perform clean-up operations. It's crucial to ensure appropriate backups (snapshots) are in place before initiating a deletion to prevent accidental data loss.
inputsoutputsimport boto3 creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] def delete_redshift_cluster(cluster_id, region): """ Attempts to delete a specified Amazon Redshift cluster in a given region. Parameters: - cluster_id (str): The unique identifier of the Redshift cluster to be deleted. - region (str): The AWS region where the Redshift cluster is located. """ try: # Initialize the boto3 client for Amazon Redshift with the appropriate region redshift = boto3.client('redshift', aws_access_key_id=access_key, aws_secret_access_key=secret_key, region_name=region) # Initiate the deletion of the specified Redshift cluster. response = redshift.delete_cluster(ClusterIdentifier=cluster_id, SkipFinalClusterSnapshot=True) print(f"Redshift cluster {cluster_id} deletion initiated in region {region}.") except redshift.exceptions.ClusterNotFoundFault: print(f"Redshift cluster {cluster_id} not found in region {region}.") except redshift.exceptions.InvalidClusterStateFault: print(f"Redshift cluster {cluster_id} is in an invalid state for deletion in region {region}.") except Exception as e: print(f"Error deleting Redshift cluster {cluster_id} in region {region}: {e}") # Example usage #clusters_info = [{'Region': 'us-west-2', 'ClusterID': 'example-cluster-1'}, {'Region': 'us-east-1', 'ClusterID': 'example-cluster-2'}] clusters_to_delete = clusters_info # clusters_info passed down from parent task to delete said Redshift Clusters # Can replace clusters_info with a list of cluster_id to delete any Redshift Cluster using this task if clusters_to_delete: for cluster in clusters_to_delete: delete_redshift_cluster(cluster['ClusterID'], cluster['Region']) else: print("No Redshift Clusters provided for deletion")copied1