Struct CreateLogAnomalyDetectorFluentBuilder

pub struct CreateLogAnomalyDetectorFluentBuilder { /* private fields */ }

Fluent builder constructing a request to CreateLogAnomalyDetector.

Creates an anomaly detector that regularly scans one or more log groups and look for patterns and anomalies in the logs.

An anomaly detector can help surface issues by automatically discovering anomalies in your log event traffic. An anomaly detector uses machine learning algorithms to scan log events and find patterns. A pattern is a shared text structure that recurs among your log fields. Patterns provide a useful tool for analyzing large sets of logs because a large number of log events can often be compressed into a few patterns.

The anomaly detector uses pattern recognition to find anomalies, which are unusual log events. It uses the evaluationFrequency to compare current log events and patterns with trained baselines.

Fields within a pattern are called tokens. Fields that vary within a pattern, such as a request ID or timestamp, are referred to as dynamic tokens and represented by <*>.

The following is an example of a pattern:

\[INFO\] Request time: <*> ms

This pattern represents log events like \[INFO\] Request time: 327 ms and other similar log events that differ only by the number, in this csse 327. When the pattern is displayed, the different numbers are replaced by <*>

Any parts of log events that are masked as sensitive data are not scanned for anomalies. For more information about masking sensitive data, see Help protect sensitive log data with masking.

Implementations

impl CreateLogAnomalyDetectorFluentBuilder

pub fn as_input(&self) -> &CreateLogAnomalyDetectorInputBuilder

Access the CreateLogAnomalyDetector as a reference.

pub async fn send( self, ) -> Result<CreateLogAnomalyDetectorOutput, SdkError<CreateLogAnomalyDetectorError, HttpResponse>>

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 customize( self, ) -> CustomizableOperation<CreateLogAnomalyDetectorOutput, CreateLogAnomalyDetectorError, Self>

Consumes this builder, creating a customizable operation that can be modified before being sent.

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

Appends an item to logGroupArnList.

To override the contents of this collection use set_log_group_arn_list.

An array containing the ARN of the log group that this anomaly detector will watch. You can specify only one log group ARN.

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

An array containing the ARN of the log group that this anomaly detector will watch. You can specify only one log group ARN.

pub fn get_log_group_arn_list(&self) -> &Option<Vec<String>>

An array containing the ARN of the log group that this anomaly detector will watch. You can specify only one log group ARN.

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

A name for this anomaly detector.

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

A name for this anomaly detector.

pub fn get_detector_name(&self) -> &Option<String>

A name for this anomaly detector.

pub fn evaluation_frequency(self, input: EvaluationFrequency) -> Self

Specifies how often the anomaly detector is to run and look for anomalies. Set this value according to the frequency that the log group receives new logs. For example, if the log group receives new log events every 10 minutes, then 15 minutes might be a good setting for evaluationFrequency .

pub fn set_evaluation_frequency( self, input: Option<EvaluationFrequency>, ) -> Self

Specifies how often the anomaly detector is to run and look for anomalies. Set this value according to the frequency that the log group receives new logs. For example, if the log group receives new log events every 10 minutes, then 15 minutes might be a good setting for evaluationFrequency .

pub fn get_evaluation_frequency(&self) -> &Option<EvaluationFrequency>

Specifies how often the anomaly detector is to run and look for anomalies. Set this value according to the frequency that the log group receives new logs. For example, if the log group receives new log events every 10 minutes, then 15 minutes might be a good setting for evaluationFrequency .

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

You can use this parameter to limit the anomaly detection model to examine only log events that match the pattern you specify here. For more information, see Filter and Pattern Syntax.

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

You can use this parameter to limit the anomaly detection model to examine only log events that match the pattern you specify here. For more information, see Filter and Pattern Syntax.

pub fn get_filter_pattern(&self) -> &Option<String>

You can use this parameter to limit the anomaly detection model to examine only log events that match the pattern you specify here. For more information, see Filter and Pattern Syntax.

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

Optionally assigns a KMS key to secure this anomaly detector and its findings. If a key is assigned, the anomalies found and the model used by this detector are encrypted at rest with the key. If a key is assigned to an anomaly detector, a user must have permissions for both this key and for the anomaly detector to retrieve information about the anomalies that it finds.

For more information about using a KMS key and to see the required IAM policy, see Use a KMS key with an anomaly detector.

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

Optionally assigns a KMS key to secure this anomaly detector and its findings. If a key is assigned, the anomalies found and the model used by this detector are encrypted at rest with the key. If a key is assigned to an anomaly detector, a user must have permissions for both this key and for the anomaly detector to retrieve information about the anomalies that it finds.

For more information about using a KMS key and to see the required IAM policy, see Use a KMS key with an anomaly detector.

pub fn get_kms_key_id(&self) -> &Option<String>

Optionally assigns a KMS key to secure this anomaly detector and its findings. If a key is assigned, the anomalies found and the model used by this detector are encrypted at rest with the key. If a key is assigned to an anomaly detector, a user must have permissions for both this key and for the anomaly detector to retrieve information about the anomalies that it finds.

For more information about using a KMS key and to see the required IAM policy, see Use a KMS key with an anomaly detector.

pub fn anomaly_visibility_time(self, input: i64) -> Self

The number of days to have visibility on an anomaly. After this time period has elapsed for an anomaly, it will be automatically baselined and the anomaly detector will treat new occurrences of a similar anomaly as normal. Therefore, if you do not correct the cause of an anomaly during the time period specified in anomalyVisibilityTime, it will be considered normal going forward and will not be detected as an anomaly.

pub fn set_anomaly_visibility_time(self, input: Option<i64>) -> Self

The number of days to have visibility on an anomaly. After this time period has elapsed for an anomaly, it will be automatically baselined and the anomaly detector will treat new occurrences of a similar anomaly as normal. Therefore, if you do not correct the cause of an anomaly during the time period specified in anomalyVisibilityTime, it will be considered normal going forward and will not be detected as an anomaly.

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

The number of days to have visibility on an anomaly. After this time period has elapsed for an anomaly, it will be automatically baselined and the anomaly detector will treat new occurrences of a similar anomaly as normal. Therefore, if you do not correct the cause of an anomaly during the time period specified in anomalyVisibilityTime, it will be considered normal going forward and will not be detected as an anomaly.

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.

An optional list of key-value pairs to associate with the resource.

For more information about tagging, see Tagging Amazon Web Services resources

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

An optional list of key-value pairs to associate with the resource.

For more information about tagging, see Tagging Amazon Web Services resources

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

An optional list of key-value pairs to associate with the resource.

For more information about tagging, see Tagging Amazon Web Services resources

Trait Implementations

impl Clone for CreateLogAnomalyDetectorFluentBuilder

fn clone(&self) -> CreateLogAnomalyDetectorFluentBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateLogAnomalyDetectorFluentBuilder

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

Formats the value using the given formatter.