Struct aws_sdk_pi::types::Insight
source · #[non_exhaustive]pub struct Insight {
pub insight_id: Option<String>,
pub insight_type: Option<String>,
pub context: Option<ContextType>,
pub start_time: Option<DateTime>,
pub end_time: Option<DateTime>,
pub severity: Option<Severity>,
pub supporting_insights: Option<Vec<Insight>>,
pub description: Option<String>,
pub recommendations: Option<Vec<Recommendation>>,
pub insight_data: Option<Vec<Data>>,
pub baseline_data: Option<Vec<Data>>,
}
Expand description
Retrieves the list of performance issues which are identified.
Fields (Non-exhaustive)§
This struct is marked as non-exhaustive
Struct { .. }
syntax; cannot be matched against without a wildcard ..
; and struct update syntax will not work.insight_id: Option<String>
The unique identifier for the insight. For example, insight-12345678901234567
.
insight_type: Option<String>
The type of insight. For example, HighDBLoad
, HighCPU
, or DominatingSQLs
.
context: Option<ContextType>
Indicates if the insight is causal or correlated insight.
start_time: Option<DateTime>
The start time of the insight. For example, 2018-10-30T00:00:00Z
.
end_time: Option<DateTime>
The end time of the insight. For example, 2018-10-30T00:00:00Z
.
severity: Option<Severity>
The severity of the insight. The values are: Low
, Medium
, or High
.
supporting_insights: Option<Vec<Insight>>
List of supporting insights that provide additional factors for the insight.
description: Option<String>
Description of the insight. For example: A high severity Insight found between 02:00 to 02:30, where there was an unusually high DB load 600x above baseline. Likely performance impact
.
recommendations: Option<Vec<Recommendation>>
List of recommendations for the insight. For example, Investigate the following SQLs that contributed to 100% of the total DBLoad during that time period: sql-id
.
insight_data: Option<Vec<Data>>
List of data objects containing metrics and references from the time range while generating the insight.
baseline_data: Option<Vec<Data>>
Metric names and values from the timeframe used as baseline to generate the insight.
Implementations§
source§impl Insight
impl Insight
sourcepub fn insight_id(&self) -> Option<&str>
pub fn insight_id(&self) -> Option<&str>
The unique identifier for the insight. For example, insight-12345678901234567
.
sourcepub fn insight_type(&self) -> Option<&str>
pub fn insight_type(&self) -> Option<&str>
The type of insight. For example, HighDBLoad
, HighCPU
, or DominatingSQLs
.
sourcepub fn context(&self) -> Option<&ContextType>
pub fn context(&self) -> Option<&ContextType>
Indicates if the insight is causal or correlated insight.
sourcepub fn start_time(&self) -> Option<&DateTime>
pub fn start_time(&self) -> Option<&DateTime>
The start time of the insight. For example, 2018-10-30T00:00:00Z
.
sourcepub fn end_time(&self) -> Option<&DateTime>
pub fn end_time(&self) -> Option<&DateTime>
The end time of the insight. For example, 2018-10-30T00:00:00Z
.
sourcepub fn severity(&self) -> Option<&Severity>
pub fn severity(&self) -> Option<&Severity>
The severity of the insight. The values are: Low
, Medium
, or High
.
sourcepub fn supporting_insights(&self) -> Option<&[Insight]>
pub fn supporting_insights(&self) -> Option<&[Insight]>
List of supporting insights that provide additional factors for the insight.
sourcepub fn description(&self) -> Option<&str>
pub fn description(&self) -> Option<&str>
Description of the insight. For example: A high severity Insight found between 02:00 to 02:30, where there was an unusually high DB load 600x above baseline. Likely performance impact
.
sourcepub fn recommendations(&self) -> Option<&[Recommendation]>
pub fn recommendations(&self) -> Option<&[Recommendation]>
List of recommendations for the insight. For example, Investigate the following SQLs that contributed to 100% of the total DBLoad during that time period: sql-id
.
sourcepub fn insight_data(&self) -> Option<&[Data]>
pub fn insight_data(&self) -> Option<&[Data]>
List of data objects containing metrics and references from the time range while generating the insight.
sourcepub fn baseline_data(&self) -> Option<&[Data]>
pub fn baseline_data(&self) -> Option<&[Data]>
Metric names and values from the timeframe used as baseline to generate the insight.