Struct aws_sdk_pi::types::Insight

#[non_exhaustive]
pub struct Insight {
    pub insight_id: 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>>,
}

Retrieves the list of performance issues which are identified.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

insight_id: 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

impl Insight

pub fn insight_id(&self) -> &str

The unique identifier for the insight. For example, insight-12345678901234567.

pub fn insight_type(&self) -> Option<&str>

The type of insight. For example, HighDBLoad, HighCPU, or DominatingSQLs.

pub fn context(&self) -> Option<&ContextType>

Indicates if the insight is causal or correlated insight.

pub fn start_time(&self) -> Option<&DateTime>

The start time of the insight. For example, 2018-10-30T00:00:00Z.

pub fn end_time(&self) -> Option<&DateTime>

The end time of the insight. For example, 2018-10-30T00:00:00Z.

pub fn severity(&self) -> Option<&Severity>

The severity of the insight. The values are: Low, Medium, or High.

pub fn supporting_insights(&self) -> &[Insight]

List of supporting insights that provide additional factors for the insight.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .supporting_insights.is_none().

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.

pub fn recommendations(&self) -> &[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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .recommendations.is_none().

pub fn insight_data(&self) -> &[Data]

List of data objects containing metrics and references from the time range while generating the insight.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .insight_data.is_none().

pub fn baseline_data(&self) -> &[Data]

Metric names and values from the timeframe used as baseline to generate the insight.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .baseline_data.is_none().

impl Insight

pub fn builder() -> InsightBuilder

Creates a new builder-style object to manufacture Insight.

Trait Implementations

impl Clone for Insight

fn clone(&self) -> Insight

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Insight

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

Formats the value using the given formatter.

impl PartialEq for Insight

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

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

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

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for Insight