1 1 | // Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
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2 - | pub use crate::operation::create_log_anomaly_detector::_create_log_anomaly_detector_output::CreateLogAnomalyDetectorOutputBuilder;
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3 - |
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4 2 | pub use crate::operation::create_log_anomaly_detector::_create_log_anomaly_detector_input::CreateLogAnomalyDetectorInputBuilder;
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5 3 |
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4 + | pub use crate::operation::create_log_anomaly_detector::_create_log_anomaly_detector_output::CreateLogAnomalyDetectorOutputBuilder;
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5 + |
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6 6 | impl crate::operation::create_log_anomaly_detector::builders::CreateLogAnomalyDetectorInputBuilder {
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7 7 | /// Sends a request with this input using the given client.
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8 8 | pub async fn send_with(
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9 9 | self,
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10 10 | client: &crate::Client,
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11 11 | ) -> ::std::result::Result<
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12 12 | crate::operation::create_log_anomaly_detector::CreateLogAnomalyDetectorOutput,
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13 13 | ::aws_smithy_runtime_api::client::result::SdkError<
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14 14 | crate::operation::create_log_anomaly_detector::CreateLogAnomalyDetectorError,
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15 15 | ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
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16 16 | >,
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17 17 | > {
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18 18 | let mut fluent_builder = client.create_log_anomaly_detector();
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19 19 | fluent_builder.inner = self;
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20 20 | fluent_builder.send().await
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21 21 | }
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22 22 | }
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23 23 | /// Fluent builder constructing a request to `CreateLogAnomalyDetector`.
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24 24 | ///
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25 25 | /// <p>Creates an <i>anomaly detector</i> that regularly scans one or more log groups and look for patterns and anomalies in the logs.</p>
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26 26 | /// <p>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 <i>patterns</i>. 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.</p>
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27 27 | /// <p>The anomaly detector uses pattern recognition to find <code>anomalies</code>, which are unusual log events. It uses the <code>evaluationFrequency</code> to compare current log events and patterns with trained baselines.</p>
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28 28 | /// <p>Fields within a pattern are called <i>tokens</i>. Fields that vary within a pattern, such as a request ID or timestamp, are referred to as <i>dynamic tokens</i> and represented by <code><*></code>.</p>
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29 29 | /// <p>The following is an example of a pattern:</p>
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30 30 | /// <p><code>\[INFO\] Request time: <*> ms</code></p>
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31 31 | /// <p>This pattern represents log events like <code>\[INFO\] Request time: 327 ms</code> 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 <code><*></code></p><note>
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32 32 | /// <p>Any parts of log events that are masked as sensitive data are not scanned for anomalies. For more information about masking sensitive data, see <a href="https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/mask-sensitive-log-data.html">Help protect sensitive log data with masking</a>.</p>
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33 33 | /// </note>
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34 34 | #[derive(::std::clone::Clone, ::std::fmt::Debug)]
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35 35 | pub struct CreateLogAnomalyDetectorFluentBuilder {
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