Struct aws_sdk_evidently::operation::create_experiment::CreateExperimentInput

#[non_exhaustive]
pub struct CreateExperimentInput {
    pub project: Option<String>,
    pub name: Option<String>,
    pub description: Option<String>,
    pub treatments: Option<Vec<TreatmentConfig>>,
    pub metric_goals: Option<Vec<MetricGoalConfig>>,
    pub randomization_salt: Option<String>,
    pub sampling_rate: Option<i64>,
    pub online_ab_config: Option<OnlineAbConfig>,
    pub segment: Option<String>,
    pub tags: Option<HashMap<String, String>>,
}

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.

project: Option<String>

The name or ARN of the project that you want to create the new experiment in.

name: Option<String>

A name for the new experiment.

description: Option<String>

An optional description of the experiment.

treatments: Option<Vec<TreatmentConfig>>

An array of structures that describe the configuration of each feature variation used in the experiment.

metric_goals: Option<Vec<MetricGoalConfig>>

An array of structures that defines the metrics used for the experiment, and whether a higher or lower value for each metric is the goal.

randomization_salt: Option<String>

When Evidently assigns a particular user session to an experiment, it must use a randomization ID to determine which variation the user session is served. This randomization ID is a combination of the entity ID and randomizationSalt. If you omit randomizationSalt, Evidently uses the experiment name as the randomizationSalt.

sampling_rate: Option<i64>

The portion of the available audience that you want to allocate to this experiment, in thousandths of a percent. The available audience is the total audience minus the audience that you have allocated to overrides or current launches of this feature.

This is represented in thousandths of a percent. For example, specify 10,000 to allocate 10% of the available audience.

online_ab_config: Option<OnlineAbConfig>

A structure that contains the configuration of which variation to use as the "control" version. tThe "control" version is used for comparison with other variations. This structure also specifies how much experiment traffic is allocated to each variation.

segment: Option<String>

Specifies an audience segment to use in the experiment. When a segment is used in an experiment, only user sessions that match the segment pattern are used in the experiment.

tags: Option<HashMap<String, String>>

Assigns one or more tags (key-value pairs) to the experiment.

Tags can help you organize and categorize your resources. You can also use them to scope user permissions by granting a user permission to access or change only resources with certain tag values.

Tags don't have any semantic meaning to Amazon Web Services and are interpreted strictly as strings of characters.

You can associate as many as 50 tags with an experiment.

For more information, see Tagging Amazon Web Services resources.

Implementations

impl CreateExperimentInput

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

The name or ARN of the project that you want to create the new experiment in.

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

A name for the new experiment.

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

An optional description of the experiment.

pub fn treatments(&self) -> &[TreatmentConfig]

An array of structures that describe the configuration of each feature variation used in the experiment.

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

pub fn metric_goals(&self) -> &[MetricGoalConfig]

An array of structures that defines the metrics used for the experiment, and whether a higher or lower value for each metric is the goal.

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

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

When Evidently assigns a particular user session to an experiment, it must use a randomization ID to determine which variation the user session is served. This randomization ID is a combination of the entity ID and randomizationSalt. If you omit randomizationSalt, Evidently uses the experiment name as the randomizationSalt.

pub fn sampling_rate(&self) -> Option<i64>

The portion of the available audience that you want to allocate to this experiment, in thousandths of a percent. The available audience is the total audience minus the audience that you have allocated to overrides or current launches of this feature.

This is represented in thousandths of a percent. For example, specify 10,000 to allocate 10% of the available audience.

pub fn online_ab_config(&self) -> Option<&OnlineAbConfig>

A structure that contains the configuration of which variation to use as the "control" version. tThe "control" version is used for comparison with other variations. This structure also specifies how much experiment traffic is allocated to each variation.

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

Specifies an audience segment to use in the experiment. When a segment is used in an experiment, only user sessions that match the segment pattern are used in the experiment.

pub fn tags(&self) -> Option<&HashMap<String, String>>

Assigns one or more tags (key-value pairs) to the experiment.

Tags can help you organize and categorize your resources. You can also use them to scope user permissions by granting a user permission to access or change only resources with certain tag values.

Tags don't have any semantic meaning to Amazon Web Services and are interpreted strictly as strings of characters.

You can associate as many as 50 tags with an experiment.

For more information, see Tagging Amazon Web Services resources.

impl CreateExperimentInput

pub fn builder() -> CreateExperimentInputBuilder

Creates a new builder-style object to manufacture CreateExperimentInput.

Trait Implementations

impl Clone for CreateExperimentInput

fn clone(&self) -> CreateExperimentInput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateExperimentInput

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

Formats the value using the given formatter.

impl PartialEq for CreateExperimentInput

fn eq(&self, other: &CreateExperimentInput) -> 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 CreateExperimentInput