2024/03/28 - AWS Compute Optimizer - 5 updated api methods
Changes This release enables AWS Compute Optimizer to analyze and generate recommendations with a new customization preference, Memory Utilization.
{'autoScalingGroupRecommendations': {'effectiveRecommendationPreferences': {'utilizationPreferences': {'metricName': {'MemoryUtilization'},
'metricParameters': {'headroom': {'PERCENT_10'}}}}}}
Returns Auto Scaling group recommendations.
Compute Optimizer generates recommendations for Amazon EC2 Auto Scaling groups that meet a specific set of requirements. For more information, see the Supported resources and requirements in the Compute Optimizer User Guide.
See also: AWS API Documentation
Request Syntax
client.get_auto_scaling_group_recommendations(
accountIds=[
'string',
],
autoScalingGroupArns=[
'string',
],
nextToken='string',
maxResults=123,
filters=[
{
'name': 'Finding'|'FindingReasonCodes'|'RecommendationSourceType'|'InferredWorkloadTypes',
'values': [
'string',
]
},
],
recommendationPreferences={
'cpuVendorArchitectures': [
'AWS_ARM64'|'CURRENT',
]
}
)
list
The ID of the Amazon Web Services account for which to return Auto Scaling group recommendations.
If your account is the management account of an organization, use this parameter to specify the member account for which you want to return Auto Scaling group recommendations.
Only one account ID can be specified per request.
(string) --
list
The Amazon Resource Name (ARN) of the Auto Scaling groups for which to return recommendations.
(string) --
string
The token to advance to the next page of Auto Scaling group recommendations.
integer
The maximum number of Auto Scaling group recommendations to return with a single request.
To retrieve the remaining results, make another request with the returned nextToken value.
list
An array of objects to specify a filter that returns a more specific list of Auto Scaling group recommendations.
(dict) --
Describes a filter that returns a more specific list of recommendations. Use this filter with the GetAutoScalingGroupRecommendations and GetEC2InstanceRecommendations actions.
You can use EBSFilter with the GetEBSVolumeRecommendations action, LambdaFunctionRecommendationFilter with the GetLambdaFunctionRecommendations action, and JobFilter with the DescribeRecommendationExportJobs action.
name (string) --
The name of the filter.
Specify Finding to return recommendations with a specific finding classification. For example, Underprovisioned.
Specify RecommendationSourceType to return recommendations of a specific resource type. For example, Ec2Instance.
Specify FindingReasonCodes to return recommendations with a specific finding reason code. For example, CPUUnderprovisioned.
Specify InferredWorkloadTypes to return recommendations of a specific inferred workload. For example, Redis.
You can filter your EC2 instance recommendations by tag:key and tag-key tags.
A tag:key is a key and value combination of a tag assigned to your recommendations. Use the tag key in the filter name and the tag value as the filter value. For example, to find all recommendations that have a tag with the key of Owner and the value of TeamA, specify tag:Owner for the filter name and TeamA for the filter value.
A tag-key is the key of a tag assigned to your recommendations. Use this filter to find all of your recommendations that have a tag with a specific key. This doesn’t consider the tag value. For example, you can find your recommendations with a tag key value of Owner or without any tag keys assigned.
values (list) --
The value of the filter.
The valid values for this parameter are as follows, depending on what you specify for the name parameter and the resource type that you wish to filter results for:
Specify Optimized or NotOptimized if you specify the name parameter as Finding and you want to filter results for Auto Scaling groups.
Specify Underprovisioned, Overprovisioned, or Optimized if you specify the name parameter as Finding and you want to filter results for EC2 instances.
Specify Ec2Instance or AutoScalingGroup if you specify the name parameter as RecommendationSourceType.
Specify one of the following options if you specify the name parameter as FindingReasonCodes:
CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload.
CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance.
MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload.
MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance.
EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload.
EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance.
EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload.
EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance.
NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload.
NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload.
NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance.
DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload.
DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance.
DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload.
DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance.
(string) --
dict
An object to specify the preferences for the Auto Scaling group recommendations to return in the response.
cpuVendorArchitectures (list) --
Specifies the CPU vendor and architecture for Amazon EC2 instance and Auto Scaling group recommendations.
For example, when you specify AWS_ARM64 with:
A GetEC2InstanceRecommendations or GetAutoScalingGroupRecommendations request, Compute Optimizer returns recommendations that consist of Graviton2 instance types only.
A GetEC2RecommendationProjectedMetrics request, Compute Optimizer returns projected utilization metrics for Graviton2 instance type recommendations only.
A ExportEC2InstanceRecommendations or ExportAutoScalingGroupRecommendations request, Compute Optimizer exports recommendations that consist of Graviton2 instance types only.
(string) --
dict
Response Syntax
{
'nextToken': 'string',
'autoScalingGroupRecommendations': [
{
'accountId': 'string',
'autoScalingGroupArn': 'string',
'autoScalingGroupName': 'string',
'finding': 'Underprovisioned'|'Overprovisioned'|'Optimized'|'NotOptimized',
'utilizationMetrics': [
{
'name': 'Cpu'|'Memory'|'EBS_READ_OPS_PER_SECOND'|'EBS_WRITE_OPS_PER_SECOND'|'EBS_READ_BYTES_PER_SECOND'|'EBS_WRITE_BYTES_PER_SECOND'|'DISK_READ_OPS_PER_SECOND'|'DISK_WRITE_OPS_PER_SECOND'|'DISK_READ_BYTES_PER_SECOND'|'DISK_WRITE_BYTES_PER_SECOND'|'NETWORK_IN_BYTES_PER_SECOND'|'NETWORK_OUT_BYTES_PER_SECOND'|'NETWORK_PACKETS_IN_PER_SECOND'|'NETWORK_PACKETS_OUT_PER_SECOND'|'GPU_PERCENTAGE'|'GPU_MEMORY_PERCENTAGE',
'statistic': 'Maximum'|'Average',
'value': 123.0
},
],
'lookBackPeriodInDays': 123.0,
'currentConfiguration': {
'desiredCapacity': 123,
'minSize': 123,
'maxSize': 123,
'instanceType': 'string'
},
'recommendationOptions': [
{
'configuration': {
'desiredCapacity': 123,
'minSize': 123,
'maxSize': 123,
'instanceType': 'string'
},
'projectedUtilizationMetrics': [
{
'name': 'Cpu'|'Memory'|'EBS_READ_OPS_PER_SECOND'|'EBS_WRITE_OPS_PER_SECOND'|'EBS_READ_BYTES_PER_SECOND'|'EBS_WRITE_BYTES_PER_SECOND'|'DISK_READ_OPS_PER_SECOND'|'DISK_WRITE_OPS_PER_SECOND'|'DISK_READ_BYTES_PER_SECOND'|'DISK_WRITE_BYTES_PER_SECOND'|'NETWORK_IN_BYTES_PER_SECOND'|'NETWORK_OUT_BYTES_PER_SECOND'|'NETWORK_PACKETS_IN_PER_SECOND'|'NETWORK_PACKETS_OUT_PER_SECOND'|'GPU_PERCENTAGE'|'GPU_MEMORY_PERCENTAGE',
'statistic': 'Maximum'|'Average',
'value': 123.0
},
],
'performanceRisk': 123.0,
'rank': 123,
'savingsOpportunity': {
'savingsOpportunityPercentage': 123.0,
'estimatedMonthlySavings': {
'currency': 'USD'|'CNY',
'value': 123.0
}
},
'migrationEffort': 'VeryLow'|'Low'|'Medium'|'High',
'instanceGpuInfo': {
'gpus': [
{
'gpuCount': 123,
'gpuMemorySizeInMiB': 123
},
]
},
'savingsOpportunityAfterDiscounts': {
'savingsOpportunityPercentage': 123.0,
'estimatedMonthlySavings': {
'currency': 'USD'|'CNY',
'value': 123.0
}
}
},
],
'lastRefreshTimestamp': datetime(2015, 1, 1),
'currentPerformanceRisk': 'VeryLow'|'Low'|'Medium'|'High',
'effectiveRecommendationPreferences': {
'cpuVendorArchitectures': [
'AWS_ARM64'|'CURRENT',
],
'enhancedInfrastructureMetrics': 'Active'|'Inactive',
'inferredWorkloadTypes': 'Active'|'Inactive',
'externalMetricsPreference': {
'source': 'Datadog'|'Dynatrace'|'NewRelic'|'Instana'
},
'lookBackPeriod': 'DAYS_14'|'DAYS_32'|'DAYS_93',
'utilizationPreferences': [
{
'metricName': 'CpuUtilization'|'MemoryUtilization',
'metricParameters': {
'threshold': 'P90'|'P95'|'P99_5',
'headroom': 'PERCENT_30'|'PERCENT_20'|'PERCENT_10'|'PERCENT_0'
}
},
],
'preferredResources': [
{
'name': 'Ec2InstanceTypes',
'includeList': [
'string',
],
'effectiveIncludeList': [
'string',
],
'excludeList': [
'string',
]
},
],
'savingsEstimationMode': {
'source': 'PublicPricing'|'CostExplorerRightsizing'|'CostOptimizationHub'
}
},
'inferredWorkloadTypes': [
'AmazonEmr'|'ApacheCassandra'|'ApacheHadoop'|'Memcached'|'Nginx'|'PostgreSql'|'Redis'|'Kafka'|'SQLServer',
],
'currentInstanceGpuInfo': {
'gpus': [
{
'gpuCount': 123,
'gpuMemorySizeInMiB': 123
},
]
}
},
],
'errors': [
{
'identifier': 'string',
'code': 'string',
'message': 'string'
},
]
}
Response Structure
(dict) --
nextToken (string) --
The token to use to advance to the next page of Auto Scaling group recommendations.
This value is null when there are no more pages of Auto Scaling group recommendations to return.
autoScalingGroupRecommendations (list) --
An array of objects that describe Auto Scaling group recommendations.
(dict) --
Describes an Auto Scaling group recommendation.
accountId (string) --
The Amazon Web Services account ID of the Auto Scaling group.
autoScalingGroupArn (string) --
The Amazon Resource Name (ARN) of the Auto Scaling group.
autoScalingGroupName (string) --
The name of the Auto Scaling group.
finding (string) --
The finding classification of the Auto Scaling group.
Findings for Auto Scaling groups include:
NotOptimized —An Auto Scaling group is considered not optimized when Compute Optimizer identifies a recommendation that can provide better performance for your workload.
Optimized —An Auto Scaling group is considered optimized when Compute Optimizer determines that the group is correctly provisioned to run your workload based on the chosen instance type. For optimized resources, Compute Optimizer might recommend a new generation instance type.
utilizationMetrics (list) --
An array of objects that describe the utilization metrics of the Auto Scaling group.
(dict) --
Describes a utilization metric of a resource, such as an Amazon EC2 instance.
Compare the utilization metric data of your resource against its projected utilization metric data to determine the performance difference between your current resource and the recommended option.
name (string) --
The name of the utilization metric.
The following utilization metrics are available:
Cpu - The percentage of allocated EC2 compute units that are currently in use on the instance. This metric identifies the processing power required to run an application on the instance. Depending on the instance type, tools in your operating system can show a lower percentage than CloudWatch when the instance is not allocated a full processor core. Units: Percent
Memory - The percentage of memory that is currently in use on the instance. This metric identifies the amount of memory required to run an application on the instance. Units: Percent
GPU - The percentage of allocated GPUs that currently run on the instance.
GPU_MEMORY - The percentage of total GPU memory that currently runs on the instance.
EBS_READ_OPS_PER_SECOND - The completed read operations from all EBS volumes attached to the instance in a specified period of time. Unit: Count
EBS_WRITE_OPS_PER_SECOND - The completed write operations to all EBS volumes attached to the instance in a specified period of time. Unit: Count
EBS_READ_BYTES_PER_SECOND - The bytes read from all EBS volumes attached to the instance in a specified period of time. Unit: Bytes
EBS_WRITE_BYTES_PER_SECOND - The bytes written to all EBS volumes attached to the instance in a specified period of time. Unit: Bytes
DISK_READ_OPS_PER_SECOND - The completed read operations from all instance store volumes available to the instance in a specified period of time. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_WRITE_OPS_PER_SECOND - The completed write operations from all instance store volumes available to the instance in a specified period of time. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_READ_BYTES_PER_SECOND - The bytes read from all instance store volumes available to the instance. This metric is used to determine the volume of the data the application reads from the disk of the instance. This can be used to determine the speed of the application. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_WRITE_BYTES_PER_SECOND - The bytes written to all instance store volumes available to the instance. This metric is used to determine the volume of the data the application writes onto the disk of the instance. This can be used to determine the speed of the application. If there are no instance store volumes, either the value is 0 or the metric is not reported.
NETWORK_IN_BYTES_PER_SECOND - The number of bytes received by the instance on all network interfaces. This metric identifies the volume of incoming network traffic to a single instance.
NETWORK_OUT_BYTES_PER_SECOND - The number of bytes sent out by the instance on all network interfaces. This metric identifies the volume of outgoing network traffic from a single instance.
NETWORK_PACKETS_IN_PER_SECOND - The number of packets received by the instance on all network interfaces. This metric identifies the volume of incoming traffic in terms of the number of packets on a single instance.
NETWORK_PACKETS_OUT_PER_SECOND - The number of packets sent out by the instance on all network interfaces. This metric identifies the volume of outgoing traffic in terms of the number of packets on a single instance.
statistic (string) --
The statistic of the utilization metric.
The Compute Optimizer API, Command Line Interface (CLI), and SDKs return utilization metrics using only the Maximum statistic, which is the highest value observed during the specified period.
The Compute Optimizer console displays graphs for some utilization metrics using the Average statistic, which is the value of Sum / SampleCount during the specified period. For more information, see Viewing resource recommendations in the Compute Optimizer User Guide. You can also get averaged utilization metric data for your resources using Amazon CloudWatch. For more information, see the Amazon CloudWatch User Guide.
value (float) --
The value of the utilization metric.
lookBackPeriodInDays (float) --
The number of days for which utilization metrics were analyzed for the Auto Scaling group.
currentConfiguration (dict) --
An array of objects that describe the current configuration of the Auto Scaling group.
desiredCapacity (integer) --
The desired capacity, or number of instances, for the Auto Scaling group.
minSize (integer) --
The minimum size, or minimum number of instances, for the Auto Scaling group.
maxSize (integer) --
The maximum size, or maximum number of instances, for the Auto Scaling group.
instanceType (string) --
The instance type for the Auto Scaling group.
recommendationOptions (list) --
An array of objects that describe the recommendation options for the Auto Scaling group.
(dict) --
Describes a recommendation option for an Auto Scaling group.
configuration (dict) --
An array of objects that describe an Auto Scaling group configuration.
desiredCapacity (integer) --
The desired capacity, or number of instances, for the Auto Scaling group.
minSize (integer) --
The minimum size, or minimum number of instances, for the Auto Scaling group.
maxSize (integer) --
The maximum size, or maximum number of instances, for the Auto Scaling group.
instanceType (string) --
The instance type for the Auto Scaling group.
projectedUtilizationMetrics (list) --
An array of objects that describe the projected utilization metrics of the Auto Scaling group recommendation option.
(dict) --
Describes a utilization metric of a resource, such as an Amazon EC2 instance.
Compare the utilization metric data of your resource against its projected utilization metric data to determine the performance difference between your current resource and the recommended option.
name (string) --
The name of the utilization metric.
The following utilization metrics are available:
Cpu - The percentage of allocated EC2 compute units that are currently in use on the instance. This metric identifies the processing power required to run an application on the instance. Depending on the instance type, tools in your operating system can show a lower percentage than CloudWatch when the instance is not allocated a full processor core. Units: Percent
Memory - The percentage of memory that is currently in use on the instance. This metric identifies the amount of memory required to run an application on the instance. Units: Percent
GPU - The percentage of allocated GPUs that currently run on the instance.
GPU_MEMORY - The percentage of total GPU memory that currently runs on the instance.
EBS_READ_OPS_PER_SECOND - The completed read operations from all EBS volumes attached to the instance in a specified period of time. Unit: Count
EBS_WRITE_OPS_PER_SECOND - The completed write operations to all EBS volumes attached to the instance in a specified period of time. Unit: Count
EBS_READ_BYTES_PER_SECOND - The bytes read from all EBS volumes attached to the instance in a specified period of time. Unit: Bytes
EBS_WRITE_BYTES_PER_SECOND - The bytes written to all EBS volumes attached to the instance in a specified period of time. Unit: Bytes
DISK_READ_OPS_PER_SECOND - The completed read operations from all instance store volumes available to the instance in a specified period of time. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_WRITE_OPS_PER_SECOND - The completed write operations from all instance store volumes available to the instance in a specified period of time. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_READ_BYTES_PER_SECOND - The bytes read from all instance store volumes available to the instance. This metric is used to determine the volume of the data the application reads from the disk of the instance. This can be used to determine the speed of the application. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_WRITE_BYTES_PER_SECOND - The bytes written to all instance store volumes available to the instance. This metric is used to determine the volume of the data the application writes onto the disk of the instance. This can be used to determine the speed of the application. If there are no instance store volumes, either the value is 0 or the metric is not reported.
NETWORK_IN_BYTES_PER_SECOND - The number of bytes received by the instance on all network interfaces. This metric identifies the volume of incoming network traffic to a single instance.
NETWORK_OUT_BYTES_PER_SECOND - The number of bytes sent out by the instance on all network interfaces. This metric identifies the volume of outgoing network traffic from a single instance.
NETWORK_PACKETS_IN_PER_SECOND - The number of packets received by the instance on all network interfaces. This metric identifies the volume of incoming traffic in terms of the number of packets on a single instance.
NETWORK_PACKETS_OUT_PER_SECOND - The number of packets sent out by the instance on all network interfaces. This metric identifies the volume of outgoing traffic in terms of the number of packets on a single instance.
statistic (string) --
The statistic of the utilization metric.
The Compute Optimizer API, Command Line Interface (CLI), and SDKs return utilization metrics using only the Maximum statistic, which is the highest value observed during the specified period.
The Compute Optimizer console displays graphs for some utilization metrics using the Average statistic, which is the value of Sum / SampleCount during the specified period. For more information, see Viewing resource recommendations in the Compute Optimizer User Guide. You can also get averaged utilization metric data for your resources using Amazon CloudWatch. For more information, see the Amazon CloudWatch User Guide.
value (float) --
The value of the utilization metric.
performanceRisk (float) --
The performance risk of the Auto Scaling group configuration recommendation.
Performance risk indicates the likelihood of the recommended instance type not meeting the resource needs of your workload. Compute Optimizer calculates an individual performance risk score for each specification of the recommended instance, including CPU, memory, EBS throughput, EBS IOPS, disk throughput, disk IOPS, network throughput, and network PPS. The performance risk of the recommended instance is calculated as the maximum performance risk score across the analyzed resource specifications.
The value ranges from 0 - 4, with 0 meaning that the recommended resource is predicted to always provide enough hardware capability. The higher the performance risk is, the more likely you should validate whether the recommendation will meet the performance requirements of your workload before migrating your resource.
rank (integer) --
The rank of the Auto Scaling group recommendation option.
The top recommendation option is ranked as 1.
savingsOpportunity (dict) --
An object that describes the savings opportunity for the Auto Scaling group recommendation option. Savings opportunity includes the estimated monthly savings amount and percentage.
savingsOpportunityPercentage (float) --
The estimated monthly savings possible as a percentage of monthly cost by adopting Compute Optimizer recommendations for a given resource.
estimatedMonthlySavings (dict) --
An object that describes the estimated monthly savings amount possible by adopting Compute Optimizer recommendations for a given resource. This is based on the On-Demand instance pricing..
currency (string) --
The currency of the estimated monthly savings.
value (float) --
The value of the estimated monthly savings.
migrationEffort (string) --
The level of effort required to migrate from the current instance type to the recommended instance type.
For example, the migration effort is Low if Amazon EMR is the inferred workload type and an Amazon Web Services Graviton instance type is recommended. The migration effort is Medium if a workload type couldn't be inferred but an Amazon Web Services Graviton instance type is recommended. The migration effort is VeryLow if both the current and recommended instance types are of the same CPU architecture.
instanceGpuInfo (dict) --
Describes the GPU accelerator settings for the recommended instance type of the Auto Scaling group.
gpus (list) --
Describes the GPU accelerators for the instance type.
(dict) --
Describes the GPU accelerators for the instance type.
gpuCount (integer) --
The number of GPUs for the instance type.
gpuMemorySizeInMiB (integer) --
The total size of the memory for the GPU accelerators for the instance type, in MiB.
savingsOpportunityAfterDiscounts (dict) --
An object that describes the savings opportunity for the Auto Scaling group recommendation option that includes Savings Plans and Reserved Instances discounts. Savings opportunity includes the estimated monthly savings and percentage.
savingsOpportunityPercentage (float) --
The estimated monthly savings possible as a percentage of monthly cost after applying the Savings Plans and Reserved Instances discounts. This saving can be achieved by adopting Compute Optimizer’s Auto Scaling group recommendations.
estimatedMonthlySavings (dict) --
An object that describes the estimated monthly savings possible by adopting Compute Optimizer’s Auto Scaling group recommendations. This is based on the Savings Plans and Reserved Instances pricing discounts.
currency (string) --
The currency of the estimated monthly savings.
value (float) --
The value of the estimated monthly savings.
lastRefreshTimestamp (datetime) --
The timestamp of when the Auto Scaling group recommendation was last generated.
currentPerformanceRisk (string) --
The risk of the current Auto Scaling group not meeting the performance needs of its workloads. The higher the risk, the more likely the current Auto Scaling group configuration has insufficient capacity and cannot meet workload requirements.
effectiveRecommendationPreferences (dict) --
An object that describes the effective recommendation preferences for the Auto Scaling group.
cpuVendorArchitectures (list) --
Describes the CPU vendor and architecture for an instance or Auto Scaling group recommendations.
For example, when you specify AWS_ARM64 with:
A GetEC2InstanceRecommendations or GetAutoScalingGroupRecommendations request, Compute Optimizer returns recommendations that consist of Graviton2 instance types only.
A GetEC2RecommendationProjectedMetrics request, Compute Optimizer returns projected utilization metrics for Graviton2 instance type recommendations only.
A ExportEC2InstanceRecommendations or ExportAutoScalingGroupRecommendations request, Compute Optimizer exports recommendations that consist of Graviton2 instance types only.
(string) --
enhancedInfrastructureMetrics (string) --
Describes the activation status of the enhanced infrastructure metrics preference.
A status of Active confirms that the preference is applied in the latest recommendation refresh, and a status of Inactive confirms that it's not yet applied to recommendations.
For more information, see Enhanced infrastructure metrics in the Compute Optimizer User Guide.
inferredWorkloadTypes (string) --
Describes the activation status of the inferred workload types preference.
A status of Active confirms that the preference is applied in the latest recommendation refresh. A status of Inactive confirms that it's not yet applied to recommendations.
externalMetricsPreference (dict) --
An object that describes the external metrics recommendation preference.
If the preference is applied in the latest recommendation refresh, an object with a valid source value appears in the response. If the preference isn't applied to the recommendations already, then this object doesn't appear in the response.
source (string) --
Contains the source options for external metrics preferences.
lookBackPeriod (string) --
The number of days the utilization metrics of the Amazon Web Services resource are analyzed.
utilizationPreferences (list) --
The resource’s CPU and memory utilization preferences, such as threshold and headroom, that are used to generate rightsizing recommendations.
(dict) --
The preference to control the resource’s CPU utilization thresholds - threshold and headroom.
metricName (string) --
The name of the resource utilization metric name to customize.
metricParameters (dict) --
The parameters to set when customizing the resource utilization thresholds.
threshold (string) --
The threshold value used for the specified metric parameter.
headroom (string) --
The headroom value in percentage used for the specified metric parameter.
The following lists the valid values for CPU and memory utilization.
CPU utilization: PERCENT_30 | PERCENT_20 | PERCENT_0
Memory utilization: PERCENT_30 | PERCENT_20 | PERCENT_10
preferredResources (list) --
The resource type values that are considered as candidates when generating rightsizing recommendations.
(dict) --
Describes the effective preferred resources that Compute Optimizer considers as rightsizing recommendation candidates.
name (string) --
The name of the preferred resource list.
includeList (list) --
The list of preferred resource values that you want considered as rightsizing recommendation candidates.
(string) --
effectiveIncludeList (list) --
The expanded version of your preferred resource's include list.
(string) --
excludeList (list) --
The list of preferred resources values that you want excluded from rightsizing recommendation candidates.
(string) --
savingsEstimationMode (dict) --
Describes the savings estimation mode applied for calculating savings opportunity for a resource.
source (string) --
Describes the source for calculating the savings opportunity for Amazon EC2 instances.
inferredWorkloadTypes (list) --
The applications that might be running on the instances in the Auto Scaling group as inferred by Compute Optimizer.
Compute Optimizer can infer if one of the following applications might be running on the instances:
AmazonEmr - Infers that Amazon EMR might be running on the instances.
ApacheCassandra - Infers that Apache Cassandra might be running on the instances.
ApacheHadoop - Infers that Apache Hadoop might be running on the instances.
Memcached - Infers that Memcached might be running on the instances.
NGINX - Infers that NGINX might be running on the instances.
PostgreSql - Infers that PostgreSQL might be running on the instances.
Redis - Infers that Redis might be running on the instances.
Kafka - Infers that Kafka might be running on the instance.
SQLServer - Infers that SQLServer might be running on the instance.
(string) --
currentInstanceGpuInfo (dict) --
Describes the GPU accelerator settings for the current instance type of the Auto Scaling group.
gpus (list) --
Describes the GPU accelerators for the instance type.
(dict) --
Describes the GPU accelerators for the instance type.
gpuCount (integer) --
The number of GPUs for the instance type.
gpuMemorySizeInMiB (integer) --
The total size of the memory for the GPU accelerators for the instance type, in MiB.
errors (list) --
An array of objects that describe errors of the request.
For example, an error is returned if you request recommendations for an unsupported Auto Scaling group.
(dict) --
Describes an error experienced when getting recommendations.
For example, an error is returned if you request recommendations for an unsupported Auto Scaling group, or if you request recommendations for an instance of an unsupported instance family.
identifier (string) --
The ID of the error.
code (string) --
The error code.
message (string) --
The message, or reason, for the error.
{'instanceRecommendations': {'effectiveRecommendationPreferences': {'utilizationPreferences': {'metricName': {'MemoryUtilization'},
'metricParameters': {'headroom': {'PERCENT_10'}}}}}}
Returns Amazon EC2 instance recommendations.
Compute Optimizer generates recommendations for Amazon Elastic Compute Cloud (Amazon EC2) instances that meet a specific set of requirements. For more information, see the Supported resources and requirements in the Compute Optimizer User Guide.
See also: AWS API Documentation
Request Syntax
client.get_ec2_instance_recommendations(
instanceArns=[
'string',
],
nextToken='string',
maxResults=123,
filters=[
{
'name': 'Finding'|'FindingReasonCodes'|'RecommendationSourceType'|'InferredWorkloadTypes',
'values': [
'string',
]
},
],
accountIds=[
'string',
],
recommendationPreferences={
'cpuVendorArchitectures': [
'AWS_ARM64'|'CURRENT',
]
}
)
list
The Amazon Resource Name (ARN) of the instances for which to return recommendations.
(string) --
string
The token to advance to the next page of instance recommendations.
integer
The maximum number of instance recommendations to return with a single request.
To retrieve the remaining results, make another request with the returned nextToken value.
list
An array of objects to specify a filter that returns a more specific list of instance recommendations.
(dict) --
Describes a filter that returns a more specific list of recommendations. Use this filter with the GetAutoScalingGroupRecommendations and GetEC2InstanceRecommendations actions.
You can use EBSFilter with the GetEBSVolumeRecommendations action, LambdaFunctionRecommendationFilter with the GetLambdaFunctionRecommendations action, and JobFilter with the DescribeRecommendationExportJobs action.
name (string) --
The name of the filter.
Specify Finding to return recommendations with a specific finding classification. For example, Underprovisioned.
Specify RecommendationSourceType to return recommendations of a specific resource type. For example, Ec2Instance.
Specify FindingReasonCodes to return recommendations with a specific finding reason code. For example, CPUUnderprovisioned.
Specify InferredWorkloadTypes to return recommendations of a specific inferred workload. For example, Redis.
You can filter your EC2 instance recommendations by tag:key and tag-key tags.
A tag:key is a key and value combination of a tag assigned to your recommendations. Use the tag key in the filter name and the tag value as the filter value. For example, to find all recommendations that have a tag with the key of Owner and the value of TeamA, specify tag:Owner for the filter name and TeamA for the filter value.
A tag-key is the key of a tag assigned to your recommendations. Use this filter to find all of your recommendations that have a tag with a specific key. This doesn’t consider the tag value. For example, you can find your recommendations with a tag key value of Owner or without any tag keys assigned.
values (list) --
The value of the filter.
The valid values for this parameter are as follows, depending on what you specify for the name parameter and the resource type that you wish to filter results for:
Specify Optimized or NotOptimized if you specify the name parameter as Finding and you want to filter results for Auto Scaling groups.
Specify Underprovisioned, Overprovisioned, or Optimized if you specify the name parameter as Finding and you want to filter results for EC2 instances.
Specify Ec2Instance or AutoScalingGroup if you specify the name parameter as RecommendationSourceType.
Specify one of the following options if you specify the name parameter as FindingReasonCodes:
CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload.
CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance.
MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload.
MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance.
EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload.
EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance.
EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload.
EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance.
NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload.
NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload.
NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance.
DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload.
DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance.
DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload.
DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance.
(string) --
list
The ID of the Amazon Web Services account for which to return instance recommendations.
If your account is the management account of an organization, use this parameter to specify the member account for which you want to return instance recommendations.
Only one account ID can be specified per request.
(string) --
dict
An object to specify the preferences for the Amazon EC2 instance recommendations to return in the response.
cpuVendorArchitectures (list) --
Specifies the CPU vendor and architecture for Amazon EC2 instance and Auto Scaling group recommendations.
For example, when you specify AWS_ARM64 with:
A GetEC2InstanceRecommendations or GetAutoScalingGroupRecommendations request, Compute Optimizer returns recommendations that consist of Graviton2 instance types only.
A GetEC2RecommendationProjectedMetrics request, Compute Optimizer returns projected utilization metrics for Graviton2 instance type recommendations only.
A ExportEC2InstanceRecommendations or ExportAutoScalingGroupRecommendations request, Compute Optimizer exports recommendations that consist of Graviton2 instance types only.
(string) --
dict
Response Syntax
{
'nextToken': 'string',
'instanceRecommendations': [
{
'instanceArn': 'string',
'accountId': 'string',
'instanceName': 'string',
'currentInstanceType': 'string',
'finding': 'Underprovisioned'|'Overprovisioned'|'Optimized'|'NotOptimized',
'findingReasonCodes': [
'CPUOverprovisioned'|'CPUUnderprovisioned'|'MemoryOverprovisioned'|'MemoryUnderprovisioned'|'EBSThroughputOverprovisioned'|'EBSThroughputUnderprovisioned'|'EBSIOPSOverprovisioned'|'EBSIOPSUnderprovisioned'|'NetworkBandwidthOverprovisioned'|'NetworkBandwidthUnderprovisioned'|'NetworkPPSOverprovisioned'|'NetworkPPSUnderprovisioned'|'DiskIOPSOverprovisioned'|'DiskIOPSUnderprovisioned'|'DiskThroughputOverprovisioned'|'DiskThroughputUnderprovisioned'|'GPUUnderprovisioned'|'GPUOverprovisioned'|'GPUMemoryUnderprovisioned'|'GPUMemoryOverprovisioned',
],
'utilizationMetrics': [
{
'name': 'Cpu'|'Memory'|'EBS_READ_OPS_PER_SECOND'|'EBS_WRITE_OPS_PER_SECOND'|'EBS_READ_BYTES_PER_SECOND'|'EBS_WRITE_BYTES_PER_SECOND'|'DISK_READ_OPS_PER_SECOND'|'DISK_WRITE_OPS_PER_SECOND'|'DISK_READ_BYTES_PER_SECOND'|'DISK_WRITE_BYTES_PER_SECOND'|'NETWORK_IN_BYTES_PER_SECOND'|'NETWORK_OUT_BYTES_PER_SECOND'|'NETWORK_PACKETS_IN_PER_SECOND'|'NETWORK_PACKETS_OUT_PER_SECOND'|'GPU_PERCENTAGE'|'GPU_MEMORY_PERCENTAGE',
'statistic': 'Maximum'|'Average',
'value': 123.0
},
],
'lookBackPeriodInDays': 123.0,
'recommendationOptions': [
{
'instanceType': 'string',
'projectedUtilizationMetrics': [
{
'name': 'Cpu'|'Memory'|'EBS_READ_OPS_PER_SECOND'|'EBS_WRITE_OPS_PER_SECOND'|'EBS_READ_BYTES_PER_SECOND'|'EBS_WRITE_BYTES_PER_SECOND'|'DISK_READ_OPS_PER_SECOND'|'DISK_WRITE_OPS_PER_SECOND'|'DISK_READ_BYTES_PER_SECOND'|'DISK_WRITE_BYTES_PER_SECOND'|'NETWORK_IN_BYTES_PER_SECOND'|'NETWORK_OUT_BYTES_PER_SECOND'|'NETWORK_PACKETS_IN_PER_SECOND'|'NETWORK_PACKETS_OUT_PER_SECOND'|'GPU_PERCENTAGE'|'GPU_MEMORY_PERCENTAGE',
'statistic': 'Maximum'|'Average',
'value': 123.0
},
],
'platformDifferences': [
'Hypervisor'|'NetworkInterface'|'StorageInterface'|'InstanceStoreAvailability'|'VirtualizationType'|'Architecture',
],
'performanceRisk': 123.0,
'rank': 123,
'savingsOpportunity': {
'savingsOpportunityPercentage': 123.0,
'estimatedMonthlySavings': {
'currency': 'USD'|'CNY',
'value': 123.0
}
},
'migrationEffort': 'VeryLow'|'Low'|'Medium'|'High',
'instanceGpuInfo': {
'gpus': [
{
'gpuCount': 123,
'gpuMemorySizeInMiB': 123
},
]
},
'savingsOpportunityAfterDiscounts': {
'savingsOpportunityPercentage': 123.0,
'estimatedMonthlySavings': {
'currency': 'USD'|'CNY',
'value': 123.0
}
}
},
],
'recommendationSources': [
{
'recommendationSourceArn': 'string',
'recommendationSourceType': 'Ec2Instance'|'AutoScalingGroup'|'EbsVolume'|'LambdaFunction'|'EcsService'|'License'
},
],
'lastRefreshTimestamp': datetime(2015, 1, 1),
'currentPerformanceRisk': 'VeryLow'|'Low'|'Medium'|'High',
'effectiveRecommendationPreferences': {
'cpuVendorArchitectures': [
'AWS_ARM64'|'CURRENT',
],
'enhancedInfrastructureMetrics': 'Active'|'Inactive',
'inferredWorkloadTypes': 'Active'|'Inactive',
'externalMetricsPreference': {
'source': 'Datadog'|'Dynatrace'|'NewRelic'|'Instana'
},
'lookBackPeriod': 'DAYS_14'|'DAYS_32'|'DAYS_93',
'utilizationPreferences': [
{
'metricName': 'CpuUtilization'|'MemoryUtilization',
'metricParameters': {
'threshold': 'P90'|'P95'|'P99_5',
'headroom': 'PERCENT_30'|'PERCENT_20'|'PERCENT_10'|'PERCENT_0'
}
},
],
'preferredResources': [
{
'name': 'Ec2InstanceTypes',
'includeList': [
'string',
],
'effectiveIncludeList': [
'string',
],
'excludeList': [
'string',
]
},
],
'savingsEstimationMode': {
'source': 'PublicPricing'|'CostExplorerRightsizing'|'CostOptimizationHub'
}
},
'inferredWorkloadTypes': [
'AmazonEmr'|'ApacheCassandra'|'ApacheHadoop'|'Memcached'|'Nginx'|'PostgreSql'|'Redis'|'Kafka'|'SQLServer',
],
'instanceState': 'pending'|'running'|'shutting-down'|'terminated'|'stopping'|'stopped',
'tags': [
{
'key': 'string',
'value': 'string'
},
],
'externalMetricStatus': {
'statusCode': 'NO_EXTERNAL_METRIC_SET'|'INTEGRATION_SUCCESS'|'DATADOG_INTEGRATION_ERROR'|'DYNATRACE_INTEGRATION_ERROR'|'NEWRELIC_INTEGRATION_ERROR'|'INSTANA_INTEGRATION_ERROR'|'INSUFFICIENT_DATADOG_METRICS'|'INSUFFICIENT_DYNATRACE_METRICS'|'INSUFFICIENT_NEWRELIC_METRICS'|'INSUFFICIENT_INSTANA_METRICS',
'statusReason': 'string'
},
'currentInstanceGpuInfo': {
'gpus': [
{
'gpuCount': 123,
'gpuMemorySizeInMiB': 123
},
]
},
'idle': 'True'|'False'
},
],
'errors': [
{
'identifier': 'string',
'code': 'string',
'message': 'string'
},
]
}
Response Structure
(dict) --
nextToken (string) --
The token to use to advance to the next page of instance recommendations.
This value is null when there are no more pages of instance recommendations to return.
instanceRecommendations (list) --
An array of objects that describe instance recommendations.
(dict) --
Describes an Amazon EC2 instance recommendation.
instanceArn (string) --
The Amazon Resource Name (ARN) of the current instance.
accountId (string) --
The Amazon Web Services account ID of the instance.
instanceName (string) --
The name of the current instance.
currentInstanceType (string) --
The instance type of the current instance.
finding (string) --
The finding classification of the instance.
Findings for instances include:
Underprovisioned —An instance is considered under-provisioned when at least one specification of your instance, such as CPU, memory, or network, does not meet the performance requirements of your workload. Under-provisioned instances may lead to poor application performance.
Overprovisioned —An instance is considered over-provisioned when at least one specification of your instance, such as CPU, memory, or network, can be sized down while still meeting the performance requirements of your workload, and no specification is under-provisioned. Over-provisioned instances may lead to unnecessary infrastructure cost.
Optimized —An instance is considered optimized when all specifications of your instance, such as CPU, memory, and network, meet the performance requirements of your workload and is not over provisioned. For optimized resources, Compute Optimizer might recommend a new generation instance type.
findingReasonCodes (list) --
The reason for the finding classification of the instance.
Finding reason codes for instances include:
CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
(string) --
utilizationMetrics (list) --
An array of objects that describe the utilization metrics of the instance.
(dict) --
Describes a utilization metric of a resource, such as an Amazon EC2 instance.
Compare the utilization metric data of your resource against its projected utilization metric data to determine the performance difference between your current resource and the recommended option.
name (string) --
The name of the utilization metric.
The following utilization metrics are available:
Cpu - The percentage of allocated EC2 compute units that are currently in use on the instance. This metric identifies the processing power required to run an application on the instance. Depending on the instance type, tools in your operating system can show a lower percentage than CloudWatch when the instance is not allocated a full processor core. Units: Percent
Memory - The percentage of memory that is currently in use on the instance. This metric identifies the amount of memory required to run an application on the instance. Units: Percent
GPU - The percentage of allocated GPUs that currently run on the instance.
GPU_MEMORY - The percentage of total GPU memory that currently runs on the instance.
EBS_READ_OPS_PER_SECOND - The completed read operations from all EBS volumes attached to the instance in a specified period of time. Unit: Count
EBS_WRITE_OPS_PER_SECOND - The completed write operations to all EBS volumes attached to the instance in a specified period of time. Unit: Count
EBS_READ_BYTES_PER_SECOND - The bytes read from all EBS volumes attached to the instance in a specified period of time. Unit: Bytes
EBS_WRITE_BYTES_PER_SECOND - The bytes written to all EBS volumes attached to the instance in a specified period of time. Unit: Bytes
DISK_READ_OPS_PER_SECOND - The completed read operations from all instance store volumes available to the instance in a specified period of time. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_WRITE_OPS_PER_SECOND - The completed write operations from all instance store volumes available to the instance in a specified period of time. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_READ_BYTES_PER_SECOND - The bytes read from all instance store volumes available to the instance. This metric is used to determine the volume of the data the application reads from the disk of the instance. This can be used to determine the speed of the application. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_WRITE_BYTES_PER_SECOND - The bytes written to all instance store volumes available to the instance. This metric is used to determine the volume of the data the application writes onto the disk of the instance. This can be used to determine the speed of the application. If there are no instance store volumes, either the value is 0 or the metric is not reported.
NETWORK_IN_BYTES_PER_SECOND - The number of bytes received by the instance on all network interfaces. This metric identifies the volume of incoming network traffic to a single instance.
NETWORK_OUT_BYTES_PER_SECOND - The number of bytes sent out by the instance on all network interfaces. This metric identifies the volume of outgoing network traffic from a single instance.
NETWORK_PACKETS_IN_PER_SECOND - The number of packets received by the instance on all network interfaces. This metric identifies the volume of incoming traffic in terms of the number of packets on a single instance.
NETWORK_PACKETS_OUT_PER_SECOND - The number of packets sent out by the instance on all network interfaces. This metric identifies the volume of outgoing traffic in terms of the number of packets on a single instance.
statistic (string) --
The statistic of the utilization metric.
The Compute Optimizer API, Command Line Interface (CLI), and SDKs return utilization metrics using only the Maximum statistic, which is the highest value observed during the specified period.
The Compute Optimizer console displays graphs for some utilization metrics using the Average statistic, which is the value of Sum / SampleCount during the specified period. For more information, see Viewing resource recommendations in the Compute Optimizer User Guide. You can also get averaged utilization metric data for your resources using Amazon CloudWatch. For more information, see the Amazon CloudWatch User Guide.
value (float) --
The value of the utilization metric.
lookBackPeriodInDays (float) --
The number of days for which utilization metrics were analyzed for the instance.
recommendationOptions (list) --
An array of objects that describe the recommendation options for the instance.
(dict) --
Describes a recommendation option for an Amazon EC2 instance.
instanceType (string) --
The instance type of the instance recommendation.
projectedUtilizationMetrics (list) --
An array of objects that describe the projected utilization metrics of the instance recommendation option.
(dict) --
Describes a utilization metric of a resource, such as an Amazon EC2 instance.
Compare the utilization metric data of your resource against its projected utilization metric data to determine the performance difference between your current resource and the recommended option.
name (string) --
The name of the utilization metric.
The following utilization metrics are available:
Cpu - The percentage of allocated EC2 compute units that are currently in use on the instance. This metric identifies the processing power required to run an application on the instance. Depending on the instance type, tools in your operating system can show a lower percentage than CloudWatch when the instance is not allocated a full processor core. Units: Percent
Memory - The percentage of memory that is currently in use on the instance. This metric identifies the amount of memory required to run an application on the instance. Units: Percent
GPU - The percentage of allocated GPUs that currently run on the instance.
GPU_MEMORY - The percentage of total GPU memory that currently runs on the instance.
EBS_READ_OPS_PER_SECOND - The completed read operations from all EBS volumes attached to the instance in a specified period of time. Unit: Count
EBS_WRITE_OPS_PER_SECOND - The completed write operations to all EBS volumes attached to the instance in a specified period of time. Unit: Count
EBS_READ_BYTES_PER_SECOND - The bytes read from all EBS volumes attached to the instance in a specified period of time. Unit: Bytes
EBS_WRITE_BYTES_PER_SECOND - The bytes written to all EBS volumes attached to the instance in a specified period of time. Unit: Bytes
DISK_READ_OPS_PER_SECOND - The completed read operations from all instance store volumes available to the instance in a specified period of time. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_WRITE_OPS_PER_SECOND - The completed write operations from all instance store volumes available to the instance in a specified period of time. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_READ_BYTES_PER_SECOND - The bytes read from all instance store volumes available to the instance. This metric is used to determine the volume of the data the application reads from the disk of the instance. This can be used to determine the speed of the application. If there are no instance store volumes, either the value is 0 or the metric is not reported.
DISK_WRITE_BYTES_PER_SECOND - The bytes written to all instance store volumes available to the instance. This metric is used to determine the volume of the data the application writes onto the disk of the instance. This can be used to determine the speed of the application. If there are no instance store volumes, either the value is 0 or the metric is not reported.
NETWORK_IN_BYTES_PER_SECOND - The number of bytes received by the instance on all network interfaces. This metric identifies the volume of incoming network traffic to a single instance.
NETWORK_OUT_BYTES_PER_SECOND - The number of bytes sent out by the instance on all network interfaces. This metric identifies the volume of outgoing network traffic from a single instance.
NETWORK_PACKETS_IN_PER_SECOND - The number of packets received by the instance on all network interfaces. This metric identifies the volume of incoming traffic in terms of the number of packets on a single instance.
NETWORK_PACKETS_OUT_PER_SECOND - The number of packets sent out by the instance on all network interfaces. This metric identifies the volume of outgoing traffic in terms of the number of packets on a single instance.
statistic (string) --
The statistic of the utilization metric.
The Compute Optimizer API, Command Line Interface (CLI), and SDKs return utilization metrics using only the Maximum statistic, which is the highest value observed during the specified period.
The Compute Optimizer console displays graphs for some utilization metrics using the Average statistic, which is the value of Sum / SampleCount during the specified period. For more information, see Viewing resource recommendations in the Compute Optimizer User Guide. You can also get averaged utilization metric data for your resources using Amazon CloudWatch. For more information, see the Amazon CloudWatch User Guide.
value (float) --
The value of the utilization metric.
platformDifferences (list) --
Describes the configuration differences between the current instance and the recommended instance type. You should consider the configuration differences before migrating your workloads from the current instance to the recommended instance type. The Change the instance type guide for Linux and Change the instance type guide for Windows provide general guidance for getting started with an instance migration.
Platform differences include:
Hypervisor — The hypervisor of the recommended instance type is different than that of the current instance. For example, the recommended instance type uses a Nitro hypervisor and the current instance uses a Xen hypervisor. The differences that you should consider between these hypervisors are covered in the Nitro Hypervisor section of the Amazon EC2 frequently asked questions. For more information, see Instances built on the Nitro System in the Amazon EC2 User Guide for Linux, or Instances built on the Nitro System in the Amazon EC2 User Guide for Windows.
NetworkInterface — The network interface of the recommended instance type is different than that of the current instance. For example, the recommended instance type supports enhanced networking and the current instance might not. To enable enhanced networking for the recommended instance type, you must install the Elastic Network Adapter (ENA) driver or the Intel 82599 Virtual Function driver. For more information, see Networking and storage features and Enhanced networking on Linux in the Amazon EC2 User Guide for Linux, or Networking and storage features and Enhanced networking on Windows in the Amazon EC2 User Guide for Windows.
StorageInterface — The storage interface of the recommended instance type is different than that of the current instance. For example, the recommended instance type uses an NVMe storage interface and the current instance does not. To access NVMe volumes for the recommended instance type, you will need to install or upgrade the NVMe driver. For more information, see Networking and storage features and Amazon EBS and NVMe on Linux instances in the Amazon EC2 User Guide for Linux, or Networking and storage features and Amazon EBS and NVMe on Windows instances in the Amazon EC2 User Guide for Windows.
InstanceStoreAvailability — The recommended instance type does not support instance store volumes and the current instance does. Before migrating, you might need to back up the data on your instance store volumes if you want to preserve them. For more information, see How do I back up an instance store volume on my Amazon EC2 instance to Amazon EBS? in the Amazon Web Services Premium Support Knowledge Base. For more information, see Networking and storage features and Amazon EC2 instance store in the Amazon EC2 User Guide for Linux, or see Networking and storage features and Amazon EC2 instance store in the Amazon EC2 User Guide for Windows.
VirtualizationType — The recommended instance type uses the hardware virtual machine (HVM) virtualization type and the current instance uses the paravirtual (PV) virtualization type. For more information about the differences between these virtualization types, see Linux AMI virtualization types in the Amazon EC2 User Guide for Linux, or Windows AMI virtualization types in the Amazon EC2 User Guide for Windows.
Architecture — The CPU architecture between the recommended instance type and the current instance is different. For example, the recommended instance type might use an Arm CPU architecture and the current instance type might use a different one, such as x86. Before migrating, you should consider recompiling the software on your instance for the new architecture. Alternatively, you might switch to an Amazon Machine Image (AMI) that supports the new architecture. For more information about the CPU architecture for each instance type, see Amazon EC2 Instance Types.
(string) --
performanceRisk (float) --
The performance risk of the instance recommendation option.
Performance risk indicates the likelihood of the recommended instance type not meeting the resource needs of your workload. Compute Optimizer calculates an individual performance risk score for each specification of the recommended instance, including CPU, memory, EBS throughput, EBS IOPS, disk throughput, disk IOPS, network throughput, and network PPS. The performance risk of the recommended instance is calculated as the maximum performance risk score across the analyzed resource specifications.
The value ranges from 0 - 4, with 0 meaning that the recommended resource is predicted to always provide enough hardware capability. The higher the performance risk is, the more likely you should validate whether the recommendation will meet the performance requirements of your workload before migrating your resource.
rank (integer) --
The rank of the instance recommendation option.
The top recommendation option is ranked as 1.
savingsOpportunity (dict) --
An object that describes the savings opportunity for the instance recommendation option. Savings opportunity includes the estimated monthly savings amount and percentage.
savingsOpportunityPercentage (float) --
The estimated monthly savings possible as a percentage of monthly cost by adopting Compute Optimizer recommendations for a given resource.
estimatedMonthlySavings (dict) --
An object that describes the estimated monthly savings amount possible by adopting Compute Optimizer recommendations for a given resource. This is based on the On-Demand instance pricing..
currency (string) --
The currency of the estimated monthly savings.
value (float) --
The value of the estimated monthly savings.
migrationEffort (string) --
The level of effort required to migrate from the current instance type to the recommended instance type.
For example, the migration effort is Low if Amazon EMR is the inferred workload type and an Amazon Web Services Graviton instance type is recommended. The migration effort is Medium if a workload type couldn't be inferred but an Amazon Web Services Graviton instance type is recommended. The migration effort is VeryLow if both the current and recommended instance types are of the same CPU architecture.
instanceGpuInfo (dict) --
Describes the GPU accelerator settings for the recommended instance type.
gpus (list) --
Describes the GPU accelerators for the instance type.
(dict) --
Describes the GPU accelerators for the instance type.
gpuCount (integer) --
The number of GPUs for the instance type.
gpuMemorySizeInMiB (integer) --
The total size of the memory for the GPU accelerators for the instance type, in MiB.
savingsOpportunityAfterDiscounts (dict) --
An object that describes the savings opportunity for the instance recommendation option that includes Savings Plans and Reserved Instances discounts. Savings opportunity includes the estimated monthly savings and percentage.
savingsOpportunityPercentage (float) --
The estimated monthly savings possible as a percentage of monthly cost after applying the Savings Plans and Reserved Instances discounts. This saving can be achieved by adopting Compute Optimizer’s EC2 instance recommendations.
estimatedMonthlySavings (dict) --
An object that describes the estimated monthly savings possible by adopting Compute Optimizer’s Amazon EC2 instance recommendations. This is based on pricing after applying the Savings Plans and Reserved Instances discounts.
currency (string) --
The currency of the estimated monthly savings.
value (float) --
The value of the estimated monthly savings.
recommendationSources (list) --
An array of objects that describe the source resource of the recommendation.
(dict) --
Describes the source of a recommendation, such as an Amazon EC2 instance or Auto Scaling group.
recommendationSourceArn (string) --
The Amazon Resource Name (ARN) of the recommendation source.
recommendationSourceType (string) --
The resource type of the recommendation source.
lastRefreshTimestamp (datetime) --
The timestamp of when the instance recommendation was last generated.
currentPerformanceRisk (string) --
The risk of the current instance not meeting the performance needs of its workloads. The higher the risk, the more likely the current instance cannot meet the performance requirements of its workload.
effectiveRecommendationPreferences (dict) --
An object that describes the effective recommendation preferences for the instance.
cpuVendorArchitectures (list) --
Describes the CPU vendor and architecture for an instance or Auto Scaling group recommendations.
For example, when you specify AWS_ARM64 with:
A GetEC2InstanceRecommendations or GetAutoScalingGroupRecommendations request, Compute Optimizer returns recommendations that consist of Graviton2 instance types only.
A GetEC2RecommendationProjectedMetrics request, Compute Optimizer returns projected utilization metrics for Graviton2 instance type recommendations only.
A ExportEC2InstanceRecommendations or ExportAutoScalingGroupRecommendations request, Compute Optimizer exports recommendations that consist of Graviton2 instance types only.
(string) --
enhancedInfrastructureMetrics (string) --
Describes the activation status of the enhanced infrastructure metrics preference.
A status of Active confirms that the preference is applied in the latest recommendation refresh, and a status of Inactive confirms that it's not yet applied to recommendations.
For more information, see Enhanced infrastructure metrics in the Compute Optimizer User Guide.
inferredWorkloadTypes (string) --
Describes the activation status of the inferred workload types preference.
A status of Active confirms that the preference is applied in the latest recommendation refresh. A status of Inactive confirms that it's not yet applied to recommendations.
externalMetricsPreference (dict) --
An object that describes the external metrics recommendation preference.
If the preference is applied in the latest recommendation refresh, an object with a valid source value appears in the response. If the preference isn't applied to the recommendations already, then this object doesn't appear in the response.
source (string) --
Contains the source options for external metrics preferences.
lookBackPeriod (string) --
The number of days the utilization metrics of the Amazon Web Services resource are analyzed.
utilizationPreferences (list) --
The resource’s CPU and memory utilization preferences, such as threshold and headroom, that are used to generate rightsizing recommendations.
(dict) --
The preference to control the resource’s CPU utilization thresholds - threshold and headroom.
metricName (string) --
The name of the resource utilization metric name to customize.
metricParameters (dict) --
The parameters to set when customizing the resource utilization thresholds.
threshold (string) --
The threshold value used for the specified metric parameter.
headroom (string) --
The headroom value in percentage used for the specified metric parameter.
The following lists the valid values for CPU and memory utilization.
CPU utilization: PERCENT_30 | PERCENT_20 | PERCENT_0
Memory utilization: PERCENT_30 | PERCENT_20 | PERCENT_10
preferredResources (list) --
The resource type values that are considered as candidates when generating rightsizing recommendations.
(dict) --
Describes the effective preferred resources that Compute Optimizer considers as rightsizing recommendation candidates.
name (string) --
The name of the preferred resource list.
includeList (list) --
The list of preferred resource values that you want considered as rightsizing recommendation candidates.
(string) --
effectiveIncludeList (list) --
The expanded version of your preferred resource's include list.
(string) --
excludeList (list) --
The list of preferred resources values that you want excluded from rightsizing recommendation candidates.
(string) --
savingsEstimationMode (dict) --
Describes the savings estimation mode applied for calculating savings opportunity for a resource.
source (string) --
Describes the source for calculating the savings opportunity for Amazon EC2 instances.
inferredWorkloadTypes (list) --
The applications that might be running on the instance as inferred by Compute Optimizer.
Compute Optimizer can infer if one of the following applications might be running on the instance:
AmazonEmr - Infers that Amazon EMR might be running on the instance.
ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
Memcached - Infers that Memcached might be running on the instance.
NGINX - Infers that NGINX might be running on the instance.
PostgreSql - Infers that PostgreSQL might be running on the instance.
Redis - Infers that Redis might be running on the instance.
Kafka - Infers that Kafka might be running on the instance.
SQLServer - Infers that SQLServer might be running on the instance.
(string) --
instanceState (string) --
The state of the instance when the recommendation was generated.
tags (list) --
A list of tags assigned to your Amazon EC2 instance recommendations.
(dict) --
A list of tag key and value pairs that you define.
key (string) --
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
value (string) --
One part of a key-value pair that make up a tag. A value acts as a descriptor within a tag category (key). The value can be empty or null.
externalMetricStatus (dict) --
An object that describes Compute Optimizer's integration status with your external metrics provider.
statusCode (string) --
The status code for Compute Optimizer's integration with an external metrics provider.
statusReason (string) --
The reason for Compute Optimizer's integration status with your external metric provider.
currentInstanceGpuInfo (dict) --
Describes the GPU accelerator settings for the current instance type.
gpus (list) --
Describes the GPU accelerators for the instance type.
(dict) --
Describes the GPU accelerators for the instance type.
gpuCount (integer) --
The number of GPUs for the instance type.
gpuMemorySizeInMiB (integer) --
The total size of the memory for the GPU accelerators for the instance type, in MiB.
idle (string) --
Describes if an Amazon EC2 instance is idle.
errors (list) --
An array of objects that describe errors of the request.
For example, an error is returned if you request recommendations for an instance of an unsupported instance family.
(dict) --
Describes an error experienced when getting recommendations.
For example, an error is returned if you request recommendations for an unsupported Auto Scaling group, or if you request recommendations for an instance of an unsupported instance family.
identifier (string) --
The ID of the error.
code (string) --
The error code.
message (string) --
The message, or reason, for the error.
{'utilizationPreferences': {'metricName': {'MemoryUtilization'},
'metricParameters': {'headroom': {'PERCENT_10'}}}}
Returns the recommendation preferences that are in effect for a given resource, such as enhanced infrastructure metrics. Considers all applicable preferences that you might have set at the resource, account, and organization level.
When you create a recommendation preference, you can set its status to Active or Inactive. Use this action to view the recommendation preferences that are in effect, or Active.
See also: AWS API Documentation
Request Syntax
client.get_effective_recommendation_preferences(
resourceArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the resource for which to confirm effective recommendation preferences. Only EC2 instance and Auto Scaling group ARNs are currently supported.
dict
Response Syntax
{
'enhancedInfrastructureMetrics': 'Active'|'Inactive',
'externalMetricsPreference': {
'source': 'Datadog'|'Dynatrace'|'NewRelic'|'Instana'
},
'lookBackPeriod': 'DAYS_14'|'DAYS_32'|'DAYS_93',
'utilizationPreferences': [
{
'metricName': 'CpuUtilization'|'MemoryUtilization',
'metricParameters': {
'threshold': 'P90'|'P95'|'P99_5',
'headroom': 'PERCENT_30'|'PERCENT_20'|'PERCENT_10'|'PERCENT_0'
}
},
],
'preferredResources': [
{
'name': 'Ec2InstanceTypes',
'includeList': [
'string',
],
'effectiveIncludeList': [
'string',
],
'excludeList': [
'string',
]
},
]
}
Response Structure
(dict) --
enhancedInfrastructureMetrics (string) --
The status of the enhanced infrastructure metrics recommendation preference. Considers all applicable preferences that you might have set at the resource, account, and organization level.
A status of Active confirms that the preference is applied in the latest recommendation refresh, and a status of Inactive confirms that it's not yet applied to recommendations.
To validate whether the preference is applied to your last generated set of recommendations, review the effectiveRecommendationPreferences value in the response of the GetAutoScalingGroupRecommendations and GetEC2InstanceRecommendations actions.
For more information, see Enhanced infrastructure metrics in the Compute Optimizer User Guide.
externalMetricsPreference (dict) --
The provider of the external metrics recommendation preference. Considers all applicable preferences that you might have set at the account and organization level.
If the preference is applied in the latest recommendation refresh, an object with a valid source value appears in the response. If the preference isn't applied to the recommendations already, then this object doesn't appear in the response.
To validate whether the preference is applied to your last generated set of recommendations, review the effectiveRecommendationPreferences value in the response of the GetEC2InstanceRecommendations actions.
For more information, see Enhanced infrastructure metrics in the Compute Optimizer User Guide.
source (string) --
Contains the source options for external metrics preferences.
lookBackPeriod (string) --
The number of days the utilization metrics of the Amazon Web Services resource are analyzed.
To validate that the preference is applied to your last generated set of recommendations, review the effectiveRecommendationPreferences value in the response of the GetAutoScalingGroupRecommendations or GetEC2InstanceRecommendations actions.
utilizationPreferences (list) --
The resource’s CPU and memory utilization preferences, such as threshold and headroom, that were used to generate rightsizing recommendations. It considers all applicable preferences that you set at the resource, account, and organization level.
To validate that the preference is applied to your last generated set of recommendations, review the effectiveRecommendationPreferences value in the response of the GetAutoScalingGroupRecommendations or GetEC2InstanceRecommendations actions.
(dict) --
The preference to control the resource’s CPU utilization thresholds - threshold and headroom.
metricName (string) --
The name of the resource utilization metric name to customize.
metricParameters (dict) --
The parameters to set when customizing the resource utilization thresholds.
threshold (string) --
The threshold value used for the specified metric parameter.
headroom (string) --
The headroom value in percentage used for the specified metric parameter.
The following lists the valid values for CPU and memory utilization.
CPU utilization: PERCENT_30 | PERCENT_20 | PERCENT_0
Memory utilization: PERCENT_30 | PERCENT_20 | PERCENT_10
preferredResources (list) --
The resource type values that are considered as candidates when generating rightsizing recommendations. This object resolves any wildcard expressions and returns the effective list of candidate resource type values. It also considers all applicable preferences that you set at the resource, account, and organization level.
To validate that the preference is applied to your last generated set of recommendations, review the effectiveRecommendationPreferences value in the response of the GetAutoScalingGroupRecommendations or GetEC2InstanceRecommendations actions.
(dict) --
Describes the effective preferred resources that Compute Optimizer considers as rightsizing recommendation candidates.
name (string) --
The name of the preferred resource list.
includeList (list) --
The list of preferred resource values that you want considered as rightsizing recommendation candidates.
(string) --
effectiveIncludeList (list) --
The expanded version of your preferred resource's include list.
(string) --
excludeList (list) --
The list of preferred resources values that you want excluded from rightsizing recommendation candidates.
(string) --
{'recommendationPreferencesDetails': {'utilizationPreferences': {'metricName': {'MemoryUtilization'},
'metricParameters': {'headroom': {'PERCENT_10'}}}}}
Returns existing recommendation preferences, such as enhanced infrastructure metrics.
Use the scope parameter to specify which preferences to return. You can specify to return preferences for an organization, a specific account ID, or a specific EC2 instance or Auto Scaling group Amazon Resource Name (ARN).
For more information, see Activating enhanced infrastructure metrics in the Compute Optimizer User Guide.
See also: AWS API Documentation
Request Syntax
client.get_recommendation_preferences(
resourceType='Ec2Instance'|'AutoScalingGroup'|'EbsVolume'|'LambdaFunction'|'NotApplicable'|'EcsService'|'License',
scope={
'name': 'Organization'|'AccountId'|'ResourceArn',
'value': 'string'
},
nextToken='string',
maxResults=123
)
string
[REQUIRED]
The target resource type of the recommendation preference for which to return preferences.
The Ec2Instance option encompasses standalone instances and instances that are part of Auto Scaling groups. The AutoScalingGroup option encompasses only instances that are part of an Auto Scaling group.
dict
An object that describes the scope of the recommendation preference to return.
You can return recommendation preferences that are created at the organization level (for management accounts of an organization only), account level, and resource level. For more information, see Activating enhanced infrastructure metrics in the Compute Optimizer User Guide.
name (string) --
The name of the scope.
The following scopes are possible:
Organization - Specifies that the recommendation preference applies at the organization level, for all member accounts of an organization.
AccountId - Specifies that the recommendation preference applies at the account level, for all resources of a given resource type in an account.
ResourceArn - Specifies that the recommendation preference applies at the individual resource level.
value (string) --
The value of the scope.
If you specified the name of the scope as:
Organization - The value must be ALL_ACCOUNTS.
AccountId - The value must be a 12-digit Amazon Web Services account ID.
ResourceArn - The value must be the Amazon Resource Name (ARN) of an EC2 instance or an Auto Scaling group.
Only EC2 instance and Auto Scaling group ARNs are currently supported.
string
The token to advance to the next page of recommendation preferences.
integer
The maximum number of recommendation preferences to return with a single request.
To retrieve the remaining results, make another request with the returned nextToken value.
dict
Response Syntax
{
'nextToken': 'string',
'recommendationPreferencesDetails': [
{
'scope': {
'name': 'Organization'|'AccountId'|'ResourceArn',
'value': 'string'
},
'resourceType': 'Ec2Instance'|'AutoScalingGroup'|'EbsVolume'|'LambdaFunction'|'NotApplicable'|'EcsService'|'License',
'enhancedInfrastructureMetrics': 'Active'|'Inactive',
'inferredWorkloadTypes': 'Active'|'Inactive',
'externalMetricsPreference': {
'source': 'Datadog'|'Dynatrace'|'NewRelic'|'Instana'
},
'lookBackPeriod': 'DAYS_14'|'DAYS_32'|'DAYS_93',
'utilizationPreferences': [
{
'metricName': 'CpuUtilization'|'MemoryUtilization',
'metricParameters': {
'threshold': 'P90'|'P95'|'P99_5',
'headroom': 'PERCENT_30'|'PERCENT_20'|'PERCENT_10'|'PERCENT_0'
}
},
],
'preferredResources': [
{
'name': 'Ec2InstanceTypes',
'includeList': [
'string',
],
'effectiveIncludeList': [
'string',
],
'excludeList': [
'string',
]
},
],
'savingsEstimationMode': 'AfterDiscounts'|'BeforeDiscounts'
},
]
}
Response Structure
(dict) --
nextToken (string) --
The token to use to advance to the next page of recommendation preferences.
This value is null when there are no more pages of recommendation preferences to return.
recommendationPreferencesDetails (list) --
An array of objects that describe recommendation preferences.
(dict) --
Describes a recommendation preference.
scope (dict) --
An object that describes the scope of the recommendation preference.
Recommendation preferences can be created at the organization level (for management accounts of an organization only), account level, and resource level. For more information, see Activating enhanced infrastructure metrics in the Compute Optimizer User Guide.
name (string) --
The name of the scope.
The following scopes are possible:
Organization - Specifies that the recommendation preference applies at the organization level, for all member accounts of an organization.
AccountId - Specifies that the recommendation preference applies at the account level, for all resources of a given resource type in an account.
ResourceArn - Specifies that the recommendation preference applies at the individual resource level.
value (string) --
The value of the scope.
If you specified the name of the scope as:
Organization - The value must be ALL_ACCOUNTS.
AccountId - The value must be a 12-digit Amazon Web Services account ID.
ResourceArn - The value must be the Amazon Resource Name (ARN) of an EC2 instance or an Auto Scaling group.
Only EC2 instance and Auto Scaling group ARNs are currently supported.
resourceType (string) --
The target resource type of the recommendation preference to create.
The Ec2Instance option encompasses standalone instances and instances that are part of Auto Scaling groups. The AutoScalingGroup option encompasses only instances that are part of an Auto Scaling group.
enhancedInfrastructureMetrics (string) --
The status of the enhanced infrastructure metrics recommendation preference.
When the recommendations page is refreshed, a status of Active confirms that the preference is applied to the recommendations, and a status of Inactive confirms that the preference isn't yet applied to recommendations.
For more information, see Enhanced infrastructure metrics in the Compute Optimizer User Guide.
inferredWorkloadTypes (string) --
The status of the inferred workload types recommendation preference.
When the recommendations page is refreshed, a status of Active confirms that the preference is applied to the recommendations, and a status of Inactive confirms that the preference isn't yet applied to recommendations.
externalMetricsPreference (dict) --
An object that describes the external metrics recommendation preference.
If the preference is applied in the latest recommendation refresh, an object with a valid source value appears in the response. If the preference isn't applied to the recommendations already, then this object doesn't appear in the response.
source (string) --
Contains the source options for external metrics preferences.
lookBackPeriod (string) --
The preference to control the number of days the utilization metrics of the Amazon Web Services resource are analyzed. If the preference isn’t set, this object is null.
utilizationPreferences (list) --
The preference to control the resource’s CPU utilization threshold, CPU utilization headroom, and memory utilization headroom. If the preference isn’t set, this object is null.
(dict) --
The preference to control the resource’s CPU utilization thresholds - threshold and headroom.
metricName (string) --
The name of the resource utilization metric name to customize.
metricParameters (dict) --
The parameters to set when customizing the resource utilization thresholds.
threshold (string) --
The threshold value used for the specified metric parameter.
headroom (string) --
The headroom value in percentage used for the specified metric parameter.
The following lists the valid values for CPU and memory utilization.
CPU utilization: PERCENT_30 | PERCENT_20 | PERCENT_0
Memory utilization: PERCENT_30 | PERCENT_20 | PERCENT_10
preferredResources (list) --
The preference to control which resource type values are considered when generating rightsizing recommendations. This object resolves any wildcard expressions and returns the effective list of candidate resource type values. If the preference isn’t set, this object is null.
(dict) --
Describes the effective preferred resources that Compute Optimizer considers as rightsizing recommendation candidates.
name (string) --
The name of the preferred resource list.
includeList (list) --
The list of preferred resource values that you want considered as rightsizing recommendation candidates.
(string) --
effectiveIncludeList (list) --
The expanded version of your preferred resource's include list.
(string) --
excludeList (list) --
The list of preferred resources values that you want excluded from rightsizing recommendation candidates.
(string) --
savingsEstimationMode (string) --
Describes the savings estimation mode used for calculating savings opportunity.
Only the account manager or delegated administrator of your organization can activate this preference.
{'utilizationPreferences': {'metricName': {'MemoryUtilization'},
'metricParameters': {'headroom': {'PERCENT_10'}}}}
Creates a new recommendation preference or updates an existing recommendation preference, such as enhanced infrastructure metrics.
For more information, see Activating enhanced infrastructure metrics in the Compute Optimizer User Guide.
See also: AWS API Documentation
Request Syntax
client.put_recommendation_preferences(
resourceType='Ec2Instance'|'AutoScalingGroup'|'EbsVolume'|'LambdaFunction'|'NotApplicable'|'EcsService'|'License',
scope={
'name': 'Organization'|'AccountId'|'ResourceArn',
'value': 'string'
},
enhancedInfrastructureMetrics='Active'|'Inactive',
inferredWorkloadTypes='Active'|'Inactive',
externalMetricsPreference={
'source': 'Datadog'|'Dynatrace'|'NewRelic'|'Instana'
},
lookBackPeriod='DAYS_14'|'DAYS_32'|'DAYS_93',
utilizationPreferences=[
{
'metricName': 'CpuUtilization'|'MemoryUtilization',
'metricParameters': {
'threshold': 'P90'|'P95'|'P99_5',
'headroom': 'PERCENT_30'|'PERCENT_20'|'PERCENT_10'|'PERCENT_0'
}
},
],
preferredResources=[
{
'name': 'Ec2InstanceTypes',
'includeList': [
'string',
],
'excludeList': [
'string',
]
},
],
savingsEstimationMode='AfterDiscounts'|'BeforeDiscounts'
)
string
[REQUIRED]
The target resource type of the recommendation preference to create.
The Ec2Instance option encompasses standalone instances and instances that are part of Auto Scaling groups. The AutoScalingGroup option encompasses only instances that are part of an Auto Scaling group.
dict
An object that describes the scope of the recommendation preference to create.
You can create recommendation preferences at the organization level (for management accounts of an organization only), account level, and resource level. For more information, see Activating enhanced infrastructure metrics in the Compute Optimizer User Guide.
name (string) --
The name of the scope.
The following scopes are possible:
Organization - Specifies that the recommendation preference applies at the organization level, for all member accounts of an organization.
AccountId - Specifies that the recommendation preference applies at the account level, for all resources of a given resource type in an account.
ResourceArn - Specifies that the recommendation preference applies at the individual resource level.
value (string) --
The value of the scope.
If you specified the name of the scope as:
Organization - The value must be ALL_ACCOUNTS.
AccountId - The value must be a 12-digit Amazon Web Services account ID.
ResourceArn - The value must be the Amazon Resource Name (ARN) of an EC2 instance or an Auto Scaling group.
Only EC2 instance and Auto Scaling group ARNs are currently supported.
string
The status of the enhanced infrastructure metrics recommendation preference to create or update.
Specify the Active status to activate the preference, or specify Inactive to deactivate the preference.
For more information, see Enhanced infrastructure metrics in the Compute Optimizer User Guide.
string
The status of the inferred workload types recommendation preference to create or update.
Specify the Inactive status to deactivate the feature, or specify Active to activate it.
For more information, see Inferred workload types in the Compute Optimizer User Guide.
dict
The provider of the external metrics recommendation preference to create or update.
Specify a valid provider in the source field to activate the preference. To delete this preference, see the DeleteRecommendationPreferences action.
This preference can only be set for the Ec2Instance resource type.
For more information, see External metrics ingestion in the Compute Optimizer User Guide.
source (string) --
Contains the source options for external metrics preferences.
string
The preference to control the number of days the utilization metrics of the Amazon Web Services resource are analyzed. When this preference isn't specified, we use the default value DAYS_14.
list
The preference to control the resource’s CPU utilization threshold, CPU utilization headroom, and memory utilization headroom. When this preference isn't specified, we use the following default values.
CPU utilization:
P99_5 for threshold
PERCENT_20 for headroom
Memory utilization:
PERCENT_20 for headroom
(dict) --
The preference to control the resource’s CPU utilization thresholds - threshold and headroom.
metricName (string) --
The name of the resource utilization metric name to customize.
metricParameters (dict) --
The parameters to set when customizing the resource utilization thresholds.
threshold (string) --
The threshold value used for the specified metric parameter.
headroom (string) --
The headroom value in percentage used for the specified metric parameter.
The following lists the valid values for CPU and memory utilization.
CPU utilization: PERCENT_30 | PERCENT_20 | PERCENT_0
Memory utilization: PERCENT_30 | PERCENT_20 | PERCENT_10
list
The preference to control which resource type values are considered when generating rightsizing recommendations. You can specify this preference as a combination of include and exclude lists. You must specify either an includeList or excludeList. If the preference is an empty set of resource type values, an error occurs.
(dict) --
The preference to control which resource type values are considered when generating rightsizing recommendations. You can specify this preference as a combination of include and exclude lists. You must specify either an includeList or excludeList. If the preference is an empty set of resource type values, an error occurs. For more information, see Rightsizing recommendation preferences in the Compute Optimizer User Guide.
name (string) --
The type of preferred resource to customize.
includeList (list) --
The preferred resource type values to include in the recommendation candidates. You can specify the exact resource type value, such as m5.large, or use wild card expressions, such as m5. If this isn’t specified, all supported resources are included by default. You can specify up to 1000 values in this list.
(string) --
excludeList (list) --
The preferred resource type values to exclude from the recommendation candidates. If this isn’t specified, all supported resources are included by default. You can specify up to 1000 values in this list.
(string) --
string
The status of the savings estimation mode preference to create or update.
Specify the AfterDiscounts status to activate the preference, or specify BeforeDiscounts to deactivate the preference.
Only the account manager or delegated administrator of your organization can activate this preference.
For more information, see Savings estimation mode in the Compute Optimizer User Guide.
dict
Response Syntax
{}
Response Structure
(dict) --