Struct aws_sdk_evidently::client::fluent_builders::CreateExperiment

pub struct CreateExperiment { /* private fields */ }

Fluent builder constructing a request to CreateExperiment.

Creates an Evidently experiment. Before you create an experiment, you must create the feature to use for the experiment.

An experiment helps you make feature design decisions based on evidence and data. An experiment can test as many as five variations at once. Evidently collects experiment data and analyzes it by statistical methods, and provides clear recommendations about which variations perform better.

Don't use this operation to update an existing experiment. Instead, use UpdateExperiment.

Implementations

impl CreateExperiment

pub async fn send(
    self
) -> Result<CreateExperimentOutput, SdkError<CreateExperimentError>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

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

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

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

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

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

A name for the new experiment.

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

A name for the new experiment.

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

An optional description of the experiment.

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

An optional description of the experiment.

pub fn treatments(self, input: TreatmentConfig) -> Self

Appends an item to treatments.

To override the contents of this collection use set_treatments.

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

pub fn set_treatments(self, input: Option<Vec<TreatmentConfig>>) -> Self

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

pub fn metric_goals(self, input: MetricGoalConfig) -> Self

Appends an item to metricGoals.

To override the contents of this collection use set_metric_goals.

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.

pub fn set_metric_goals(self, input: Option<Vec<MetricGoalConfig>>) -> Self

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.

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

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 set_randomization_salt(self, input: Option<String>) -> Self

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, input: i64) -> Self

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 set_sampling_rate(self, input: Option<i64>) -> Self

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, input: OnlineAbConfig) -> Self

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 set_online_ab_config(self, input: Option<OnlineAbConfig>) -> Self

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 tags(self, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to tags.

To override the contents of this collection use set_tags.

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.

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

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.

Trait Implementations

impl Clone for CreateExperiment

fn clone(&self) -> CreateExperiment

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateExperiment

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

Formats the value using the given formatter.