#[non_exhaustive]pub struct Builder { /* private fields */ }
Expand description
A builder for InstanceRecommendation
Implementations
sourceimpl Builder
impl Builder
sourcepub fn instance_arn(self, input: impl Into<String>) -> Self
pub fn instance_arn(self, input: impl Into<String>) -> Self
The Amazon Resource Name (ARN) of the current instance.
sourcepub fn set_instance_arn(self, input: Option<String>) -> Self
pub fn set_instance_arn(self, input: Option<String>) -> Self
The Amazon Resource Name (ARN) of the current instance.
sourcepub fn account_id(self, input: impl Into<String>) -> Self
pub fn account_id(self, input: impl Into<String>) -> Self
The Amazon Web Services account ID of the instance.
sourcepub fn set_account_id(self, input: Option<String>) -> Self
pub fn set_account_id(self, input: Option<String>) -> Self
The Amazon Web Services account ID of the instance.
sourcepub fn instance_name(self, input: impl Into<String>) -> Self
pub fn instance_name(self, input: impl Into<String>) -> Self
The name of the current instance.
sourcepub fn set_instance_name(self, input: Option<String>) -> Self
pub fn set_instance_name(self, input: Option<String>) -> Self
The name of the current instance.
sourcepub fn current_instance_type(self, input: impl Into<String>) -> Self
pub fn current_instance_type(self, input: impl Into<String>) -> Self
The instance type of the current instance.
sourcepub fn set_current_instance_type(self, input: Option<String>) -> Self
pub fn set_current_instance_type(self, input: Option<String>) -> Self
The instance type of the current instance.
sourcepub fn finding(self, input: Finding) -> Self
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.
sourcepub fn set_finding(self, input: Option<Finding>) -> Self
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.
sourcepub fn finding_reason_codes(
self,
input: InstanceRecommendationFindingReasonCode
) -> Self
pub fn finding_reason_codes(
self,
input: 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 theCPUUtilization
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 theCPUUtilization
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 theCWAgent
namespace, or the legacyMemoryUtilization
metric in theSystem/Linux
namespace. On Windows instances, Compute Optimizer analyses theMemory % Committed Bytes In Use
metric in theCWAgent
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 theVolumeReadOps
andVolumeWriteOps
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 theVolumeReadOps
andVolumeWriteOps
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 theVolumeReadBytes
andVolumeWriteBytes
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 theVolumeReadBytes
andVolumeWriteBytes
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 theNetworkIn
andNetworkOut
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 theNetworkIn
andNetworkOut
metrics of the current instance during the look-back period. This finding reason happens when theNetworkIn
orNetworkOut
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 theNetworkPacketsIn
andNetworkPacketsIn
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 theNetworkPacketsIn
andNetworkPacketsIn
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 theDiskReadOps
andDiskWriteOps
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 theDiskReadOps
andDiskWriteOps
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 theDiskReadBytes
andDiskWriteBytes
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 theDiskReadBytes
andDiskWriteBytes
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.
sourcepub fn set_finding_reason_codes(
self,
input: Option<Vec<InstanceRecommendationFindingReasonCode>>
) -> Self
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 theCPUUtilization
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 theCPUUtilization
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 theCWAgent
namespace, or the legacyMemoryUtilization
metric in theSystem/Linux
namespace. On Windows instances, Compute Optimizer analyses theMemory % Committed Bytes In Use
metric in theCWAgent
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 theVolumeReadOps
andVolumeWriteOps
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 theVolumeReadOps
andVolumeWriteOps
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 theVolumeReadBytes
andVolumeWriteBytes
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 theVolumeReadBytes
andVolumeWriteBytes
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 theNetworkIn
andNetworkOut
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 theNetworkIn
andNetworkOut
metrics of the current instance during the look-back period. This finding reason happens when theNetworkIn
orNetworkOut
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 theNetworkPacketsIn
andNetworkPacketsIn
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 theNetworkPacketsIn
andNetworkPacketsIn
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 theDiskReadOps
andDiskWriteOps
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 theDiskReadOps
andDiskWriteOps
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 theDiskReadBytes
andDiskWriteBytes
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 theDiskReadBytes
andDiskWriteBytes
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.
sourcepub fn utilization_metrics(self, input: UtilizationMetric) -> Self
pub fn utilization_metrics(self, input: 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.
sourcepub fn set_utilization_metrics(
self,
input: Option<Vec<UtilizationMetric>>
) -> Self
pub fn set_utilization_metrics(
self,
input: Option<Vec<UtilizationMetric>>
) -> Self
An array of objects that describe the utilization metrics of the instance.
sourcepub fn look_back_period_in_days(self, input: f64) -> Self
pub fn look_back_period_in_days(self, input: f64) -> Self
The number of days for which utilization metrics were analyzed for the instance.
sourcepub fn set_look_back_period_in_days(self, input: Option<f64>) -> Self
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.
sourcepub fn recommendation_options(self, input: InstanceRecommendationOption) -> Self
pub fn recommendation_options(self, input: 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.
sourcepub fn set_recommendation_options(
self,
input: Option<Vec<InstanceRecommendationOption>>
) -> Self
pub fn set_recommendation_options(
self,
input: Option<Vec<InstanceRecommendationOption>>
) -> Self
An array of objects that describe the recommendation options for the instance.
sourcepub fn recommendation_sources(self, input: RecommendationSource) -> Self
pub fn recommendation_sources(self, input: 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.
sourcepub fn set_recommendation_sources(
self,
input: Option<Vec<RecommendationSource>>
) -> Self
pub fn set_recommendation_sources(
self,
input: Option<Vec<RecommendationSource>>
) -> Self
An array of objects that describe the source resource of the recommendation.
sourcepub fn last_refresh_timestamp(self, input: DateTime) -> Self
pub fn last_refresh_timestamp(self, input: DateTime) -> Self
The timestamp of when the instance recommendation was last generated.
sourcepub fn set_last_refresh_timestamp(self, input: Option<DateTime>) -> Self
pub fn set_last_refresh_timestamp(self, input: Option<DateTime>) -> Self
The timestamp of when the instance recommendation was last generated.
sourcepub fn current_performance_risk(self, input: CurrentPerformanceRisk) -> Self
pub fn current_performance_risk(self, input: CurrentPerformanceRisk) -> Self
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.
sourcepub fn set_current_performance_risk(
self,
input: Option<CurrentPerformanceRisk>
) -> Self
pub fn set_current_performance_risk(
self,
input: Option<CurrentPerformanceRisk>
) -> Self
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.
sourcepub fn effective_recommendation_preferences(
self,
input: EffectiveRecommendationPreferences
) -> Self
pub fn effective_recommendation_preferences(
self,
input: EffectiveRecommendationPreferences
) -> Self
An object that describes the effective recommendation preferences for the instance.
sourcepub fn set_effective_recommendation_preferences(
self,
input: Option<EffectiveRecommendationPreferences>
) -> Self
pub fn set_effective_recommendation_preferences(
self,
input: Option<EffectiveRecommendationPreferences>
) -> Self
An object that describes the effective recommendation preferences for the instance.
sourcepub fn inferred_workload_types(self, input: InferredWorkloadType) -> Self
pub fn inferred_workload_types(self, input: InferredWorkloadType) -> Self
Appends an item to inferred_workload_types
.
To override the contents of this collection use set_inferred_workload_types
.
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.
sourcepub fn set_inferred_workload_types(
self,
input: Option<Vec<InferredWorkloadType>>
) -> Self
pub fn set_inferred_workload_types(
self,
input: Option<Vec<InferredWorkloadType>>
) -> Self
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.
sourcepub fn build(self) -> InstanceRecommendation
pub fn build(self) -> InstanceRecommendation
Consumes the builder and constructs a InstanceRecommendation
Trait Implementations
impl StructuralPartialEq for Builder
Auto Trait Implementations
impl RefUnwindSafe for Builder
impl Send for Builder
impl Sync for Builder
impl Unpin for Builder
impl UnwindSafe for Builder
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcepub fn borrow_mut(&mut self) -> &mut T
pub fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcepub fn to_owned(&self) -> T
pub fn to_owned(&self) -> T
Creates owned data from borrowed data, usually by cloning. Read more
sourcepub fn clone_into(&self, target: &mut T)
pub fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more