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Rightsizing Recommendations for AWS EC2 Instances using AWS Compute Optimizer
This task utilizes AWS Compute Optimizer to fetch rightsizing recommendations for AWS EC2 instances, aiming to optimize instance sizes based on actual usage. It assesses whether instances are under-utilized or over-utilized, suggesting adjustments to enhance performance and reduce costs. By querying across specified or all regions, it allows for a comprehensive optimization strategy, ensuring resources are efficiently allocated and maximizes cost-effectiveness and performance across your AWS environment.
- 1O5FtOq5CNw0pQ0RzZs9iFetch Rightsizing Recommendations for AWS EC2 Instances
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Fetch Rightsizing Recommendations for AWS EC2 Instances
There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.This task retrieves AWS EC2 instance rightsizing recommendations using AWS Compute Optimizer, identifying cost-saving and performance-enhancing opportunities by analyzing usage patterns. It suggests optimal instance types or sizes, ensuring efficient resource utilization.
inputsoutputsimport json import boto3 from botocore.exceptions import BotoCoreError, ClientError from datetime import datetime creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] # Initialize boto3 clients #compute_optimizer_client = boto3.client('compute-optimizer', region_name='us-west-2') pricing_client = boto3.client('pricing',aws_access_key_id=access_key,aws_secret_access_key=secret_key, region_name='us-east-1') def datetime_converter(o): if isinstance(o, datetime): return o.__str__() def get_price_for_instance(instance_type, region): # Mapping AWS region to the Pricing API format region_name_map = { "us-east-1": "US East (N. Virginia)", "us-east-2": "US East (Ohio)", "us-west-1": "US West (N. California)", "us-west-2": "US West (Oregon)", "af-south-1": "Africa (Cape Town)", "ap-east-1": "Asia Pacific (Hong Kong)", "ap-south-1": "Asia Pacific (Mumbai)", "ap-northeast-3": "Asia Pacific (Osaka)", "ap-northeast-2": "Asia Pacific (Seoul)", "ap-southeast-1": "Asia Pacific (Singapore)", "ap-southeast-2": "Asia Pacific (Sydney)", "ap-northeast-1": "Asia Pacific (Tokyo)", "ca-central-1": "Canada (Central)", "eu-central-1": "EU (Frankfurt)", "eu-west-1": "EU (Ireland)", "eu-west-2": "EU (London)", "eu-south-1": "EU (Milan)", "eu-west-3": "EU (Paris)", "eu-north-1": "EU (Stockholm)", "me-south-1": "Middle East (Bahrain)", "sa-east-1": "South America (São Paulo)"} region_name = region_name_map.get(region, region) # Default to using the region code if no mapping found try: response = pricing_client.get_products( ServiceCode='AmazonEC2', Filters=[ {'Type': 'TERM_MATCH', 'Field': 'instanceType', 'Value': instance_type}, {'Type': 'TERM_MATCH', 'Field': 'location', 'Value': region_name}, {'Type': 'TERM_MATCH', 'Field': 'preInstalledSw', 'Value': 'NA'}, {'Type': 'TERM_MATCH', 'Field': 'operatingSystem', 'Value': 'Linux'}, {'Type': 'TERM_MATCH', 'Field': 'tenancy', 'Value': 'shared'}, {'Type': 'TERM_MATCH', 'Field': 'capacitystatus', 'Value': 'Used'}, ], MaxResults=1 ) price_info = json.loads(response['PriceList'][0]) price_dimensions = next(iter(price_info['terms']['OnDemand'].values()))['priceDimensions'] price_per_unit = next(iter(price_dimensions.values()))['pricePerUnit']['USD'] return float(price_per_unit) except Exception as e: print(f"Error fetching price for {instance_type} in {region}: {e}") return None def display_instance_recommendations(recommendations): # Initialize table with the desired structure and headers table = context.newtable() table.title = "EC2 Instance Rightsizing Recommendations" table.num_cols = 10 # Adjust the number of columns to match the number of attributes you want to display table.num_rows = 1 # Starts with one row for headers table.has_header_row = True # Define header names based on the new structure headers = [ "Instance ID", "Instance Name", "Current Instance Type", "Region", "Findings", "Recommended Instance Type", "Migration Effort", "Savings Opportunity (%)", "Estimated Monthly Savings ($)", "Price Difference ($/hr)" ] # Set headers in the first row for col_num, header in enumerate(headers): table.setval(0, col_num, header) # Populate the table with data from recommendations for row_num, recommendation in enumerate(recommendations, start=1): instance_id = recommendation['instanceArn'].split('/')[-1] instance_name = recommendation.get('instanceName', 'N/A') current_instance_type = recommendation.get('currentInstanceType', 'N/A') region = recommendation['instanceArn'].split(':')[3] findings = recommendation.get('finding', 'N/A') migration_effort = "N/A" savings_opportunity_percentage = "N/A" estimated_monthly_savings_value = "N/A" price_difference = "N/A" if recommendation.get('recommendationOptions'): option = recommendation['recommendationOptions'][0] recommended_instance_type = option.get('instanceType') migration_effort = option.get('migrationEffort', 'N/A') savings_opportunity_percentage = option.get('savingsOpportunity', {}).get('savingsOpportunityPercentage', 'N/A') estimated_monthly_savings_value = option.get('savingsOpportunity', {}).get('estimatedMonthlySavings', {}).get('value', 'N/A') current_price = get_price_for_instance(current_instance_type, region) recommended_price = get_price_for_instance(recommended_instance_type, region) price_difference = "N/A" if current_price is None or recommended_price is None else f"{current_price - recommended_price:.4f}" values = [ instance_id, instance_name, current_instance_type, region, findings, recommended_instance_type, migration_effort, f"{savings_opportunity_percentage}%", f"${estimated_monthly_savings_value}", f"${price_difference}/hr" ] table.num_rows += 1 # Add a row for each recommendation for col_num, value in enumerate(values): table.setval(row_num, col_num, value) def get_ec2_rightsizing_recommendations(region_name_to_search_recommendations=None): regions_to_search = [] if region_name_to_search_recommendations: regions_to_search.append(region_name_to_search_recommendations) else: # Fetch all regions if none specified ec2_client = boto3.client('ec2', aws_access_key_id=access_key, aws_secret_access_key=secret_key, region_name='us-east-1') all_regions_response = ec2_client.describe_regions() regions_to_search = [region['RegionName'] for region in all_regions_response['Regions']] all_recommendations = [] for region in regions_to_search: try: # Initialize compute-optimizer client with the proper region local_compute_optimizer_client = boto3.client('compute-optimizer', aws_access_key_id=access_key, aws_secret_access_key=secret_key, region_name=region) next_token = None #page_counter = 1 # To count the number of pages fetched while True: if next_token: response = local_compute_optimizer_client.get_ec2_instance_recommendations(NextToken=next_token) else: response = local_compute_optimizer_client.get_ec2_instance_recommendations() recommendations = response.get('instanceRecommendations', []) if recommendations: all_recommendations.extend(recommendations) #print(f"Fetched {len(recommendations)} recommendations for page {page_counter}.") # Pagination - Check if there's a next page of recommendations next_token = response.get('NextToken') if not next_token: break # Exit loop if there's no more data to fetch #page_counter += 1 except ClientError as error: print(f"Client error in region {region}: {error}") except BotoCoreError as error: print(f"BotoCore error in region {region}: {error}") return all_recommendations def process_recommendations(region_name_to_search_recommendations=None): # Fetch recommendations once, using the provided region or searching all regions. recommendations = get_ec2_rightsizing_recommendations(region_name_to_search_recommendations) display_instance_recommendations(recommendations) # table printing line # If no recommendations were found after searching, exit the function. if not recommendations: print("No recommendations found. Please check if the region is correct or if there are any permissions issues.") return data_for_plotting = [] # Iterate through the fetched recommendations for processing. for recommendation in recommendations: # Extract details from each recommendation as before... instance_id = recommendation['instanceArn'].split('/')[-1] instance_name = recommendation.get('instanceName', 'N/A') findings = recommendation.get('finding', 'N/A') finding_reasons = ", ".join(recommendation.get('findingReasonCodes', [])) instance_state = recommendation.get('instanceState', 'N/A') current_instance_type = recommendation.get('currentInstanceType', 'N/A') tags = json.dumps(recommendation.get('tags', []), default=datetime_converter) account_id = recommendation['instanceArn'].split(':')[4] region = recommendation['instanceArn'].split(':')[3] # Print details for each recommendation... print(f"Instance ID: {instance_id}") print(f"Instance Name: {instance_name}") print(f"Findings: {findings}") print(f"Finding Reasons: {finding_reasons}") print(f"Instance State: {instance_state}") print(f"Current Instance Type: {current_instance_type}") print(f"Tags: {tags}") print(f"Account ID: {account_id}") print(f"Region: {region}") print("-" * 50) for option in recommendation['recommendationOptions']: recommended_instance_type = option.get('instanceType') migration_effort = option.get('migrationEffort', 'N/A') savings_opportunity_percentage = option.get('savingsOpportunity', {}).get('savingsOpportunityPercentage', 'N/A') estimated_monthly_savings_value = option.get('savingsOpportunity', {}).get('estimatedMonthlySavings', {}).get('value', 'N/A') current_price = get_price_for_instance(current_instance_type, region) recommended_price = get_price_for_instance(recommended_instance_type, region) price_difference = "N/A" if current_price is None or recommended_price is None else current_price - recommended_price data_for_plotting.append({ "instance_id": instance_id, "instance_name": instance_name, "estimated_monthly_savings_value": estimated_monthly_savings_value }) print(f"\tRecommended Instance Type: {recommended_instance_type}") print(f"\tMigration Effort: {migration_effort}") print(f"\tSavings Opportunity (%): {savings_opportunity_percentage}") print(f"\tEstimated Monthly Savings: USD {estimated_monthly_savings_value}") print(f"\tCurrent Price: {current_price if current_price is not None else 'N/A'} USD per hour") print(f"\tRecommended Price: {recommended_price if recommended_price is not None else 'N/A'} USD per hour") print(f"\tPrice Difference: {price_difference} USD per hour") print("-" * 25) return data_for_plotting #region_name_to_search_recommendations = None data_for_plotting = process_recommendations(region_name_to_search_recommendations)copied1 - 2yL3S7RVMgqHLjI2r0KezPlot Savings based on AWS EC2 Rightsizing Recommendations
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Plot Savings based on AWS EC2 Rightsizing Recommendations
There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.This task generates a bar chart visualizing AWS EC2 rightsizing savings, with instance names on the X-axis and different recommendations distinguished by instance ID and rank in the legend.
inputsoutputs# print(json.dumps(data_for_plotting,indent=4)) # Aggregate savings values for each instance, keeping track of both instance ID and name savings_by_instance = {} for entry in data_for_plotting: instance_id = entry["instance_id"] instance_name = entry["instance_name"] # Keep instance name for labeling purposes savings_value = entry["estimated_monthly_savings_value"] # Check if the instance ID is already a key in the dictionary if instance_id not in savings_by_instance: savings_by_instance[instance_id] = {'name': instance_name, 'savings': [savings_value]} else: savings_by_instance[instance_id]['savings'].append(savings_value) # Plotting context.plot.xlabel = "Instance Name" context.plot.ylabel = "Estimated Monthly Savings ($)" context.plot.title = "Estimated Monthly Savings by Instance" # Add a trace for each instance's savings values for instance_id, info in savings_by_instance.items(): instance_name = info['name'] # Retrieve instance name for labeling savings_values = info['savings'] for i, savings_value in enumerate(savings_values): trace_name = f"({instance_id})-Rec{i+1}" context.plot.add_trace(name=trace_name, xpts=[instance_name], ypts=[savings_value], tracetype='bar')copied2