Struct aws_sdk_computeoptimizer::model::instance_recommendation::Builder

#[non_exhaustive]
pub struct Builder { /* fields omitted */ }

A builder for InstanceRecommendation

Implementations

impl Builder

pub fn instance_arn(self, input: impl Into<String>) -> Self

The Amazon Resource Name (ARN) of the current instance.

pub fn set_instance_arn(self, input: Option<String>) -> Self

The Amazon Resource Name (ARN) of the current instance.

pub fn account_id(self, input: impl Into<String>) -> Self

The Amazon Web Services account ID of the instance.

pub fn set_account_id(self, input: Option<String>) -> Self

The Amazon Web Services account ID of the instance.

pub fn instance_name(self, input: impl Into<String>) -> Self

The name of the current instance.

pub fn set_instance_name(self, input: Option<String>) -> Self

The name of the current instance.

pub fn current_instance_type(self, input: impl Into<String>) -> Self

The instance type of the current instance.

pub fn set_current_instance_type(self, input: Option<String>) -> Self

The instance type of the current instance.

pub fn finding(self, input: Finding) -> Self

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.

pub fn set_finding(self, input: Option<Finding>) -> Self

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.

pub fn finding_reason_codes(
    self,
    input: impl Into<InstanceRecommendationFindingReasonCode>
) -> Self

Appends an item to finding_reason_codes.

To override the contents of this collection use set_finding_reason_codes.

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.

  Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.

• 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 VolumeReadOps and VolumeWriteOps 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 VolumeReadOps and VolumeWriteOps 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 VolumeReadBytes and VolumeWriteBytes 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 VolumeReadBytes and VolumeWriteBytes 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.

For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.

pub fn set_finding_reason_codes(
    self,
    input: Option<Vec<InstanceRecommendationFindingReasonCode>>
) -> Self

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.

  Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.

• 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 VolumeReadOps and VolumeWriteOps 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 VolumeReadOps and VolumeWriteOps 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 VolumeReadBytes and VolumeWriteBytes 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 VolumeReadBytes and VolumeWriteBytes 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.

For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.

pub fn utilization_metrics(self, input: impl Into<UtilizationMetric>) -> Self

Appends an item to utilization_metrics.

To override the contents of this collection use set_utilization_metrics.

An array of objects that describe the utilization metrics of the instance.

pub fn set_utilization_metrics(
    self,
    input: Option<Vec<UtilizationMetric>>
) -> Self

An array of objects that describe the utilization metrics of the instance.

pub fn look_back_period_in_days(self, input: f64) -> Self

The number of days for which utilization metrics were analyzed for the instance.

pub fn set_look_back_period_in_days(self, input: Option<f64>) -> Self

The number of days for which utilization metrics were analyzed for the instance.

pub fn recommendation_options(
    self,
    input: impl Into<InstanceRecommendationOption>
) -> Self

Appends an item to recommendation_options.

To override the contents of this collection use set_recommendation_options.

An array of objects that describe the recommendation options for the instance.

pub fn set_recommendation_options(
    self,
    input: Option<Vec<InstanceRecommendationOption>>
) -> Self

An array of objects that describe the recommendation options for the instance.

pub fn recommendation_sources(
    self,
    input: impl Into<RecommendationSource>
) -> Self

Appends an item to recommendation_sources.

To override the contents of this collection use set_recommendation_sources.

An array of objects that describe the source resource of the recommendation.

pub fn set_recommendation_sources(
    self,
    input: Option<Vec<RecommendationSource>>
) -> Self

An array of objects that describe the source resource of the recommendation.

pub fn last_refresh_timestamp(self, input: DateTime) -> Self

The timestamp of when the instance recommendation was last refreshed.

pub fn set_last_refresh_timestamp(self, input: Option<DateTime>) -> Self

The timestamp of when the instance recommendation was last refreshed.

pub fn build(self) -> InstanceRecommendation

Consumes the builder and constructs a InstanceRecommendation

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Builder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

fn eq(&self, other: &Builder) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Builder) -> bool

This method tests for !=.

impl StructuralPartialEq for Builder