[GitHub] [incubator-pinot] harleyjj commented on a change in pull request #4738: [TE] frontend - harleyjj/alert-details - fix more preview comparison …

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harleyjj commented on a change in pull request #4738: [TE] frontend - harleyjj/alert-details - fix more preview comparison …
URL: https://github.com/apache/incubator-pinot/pull/4738#discussion_r338228716
 
 

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 File path: thirdeye/thirdeye-frontend/app/utils/yaml-tools.js
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 @@ -0,0 +1,352 @@
+import yamljs from 'yamljs';
+import jsyaml from 'js-yaml';
+
+export const defaultDetectionYaml = `# Below is a sample template. You may refer the documentation for more examples and update the fields accordingly.
+# Give a name for this anomaly detection pipeline (should be unique).
+detectionName: name_of_the_detection
+# Tell the alert recipients what it means if this alert is fired.
+description: If this alert fires then it means so-and-so and check so-and-so for irregularities
+# The metric you want to do anomaly detection on. You may type a few characters and look ahead (ctrl + space) to auto-fill.
+metric: metric_name
+# The dataset or UMP table name to which the metric belongs. Look ahead should auto populate this field.
+dataset: dataset_name
+rules:                            # Can configure multiple rules with "OR" relationship.
+- detection:
+    - name: detection_rule_1
+      type: ALGORITHM             # Configure the detection type here. See doc for more details.
+      params:                     # The parameters for this rule. Different rules have different params.
+        configuration:
+          bucketPeriod: P1D       # Use PT1H for hourly and PT5M for minute level (ingraph metrics) data.
+          pValueThreshold: 0.05   # Higher value means more sensitive to small changes.
+          mlConfig: true          # The machine learning auto config to select and maintain the configuration with the best performance.
+  filter:                         # Filter out anomalies detected by rules to reduce noise.
+    - name: filter_rule_1
+      type: PERCENTAGE_CHANGE_FILTER
+      params:
+        pattern: UP_OR_DOWN       # Other patterns: "UP","DOWN".
+        threshold: 0.05           # Filter out all changes less than 5% compared to baseline.
+`;
+
+export const defaultSubscriptionYaml = `# Below is a sample subscription group template. You may refer the documentation and update accordingly.
+# The name of the subscription group. You may choose an existing or a provide a new subscription group name
+subscriptionGroupName: test_subscription_group
+# Every alert in ThirdEye is attached to an application. Please specify the registered application name here. You may request for a new application by dropping an email to ask_thirdeye
+application: thirdeye-internal
+# The default notification type. See additional settings for details and exploring other notification types like dimension alerter.
+type: DEFAULT_ALERTER_PIPELINE
+# List of detection names that you want to subscribe. Copy-paste the detection name from the above anomaly detection config here.
+subscribedDetections:
+  - name_of_the_detection_above
+# Configure how you want to be alerted. You can receive the standard ThirdEye email alert (recommended)
+# or for advanced critical use-cases setup Iris alert by referring to the documentation
+alertSchemes:
+- type: EMAIL
+recipients:
+ to:
+  - "me@company.com"          # Specify alert recipient email address here
+  - "me@company.com"
+ cc:
+  - "cc_email@company.com"
+fromAddress: thirdeye-dev@linkedin.com
+# The frequency at which you want to be notified. Typically you want to be notified immediately after
+# an anomaly is detected. The below cron runs every 5 minutes. Use online cronmaker to compute this.
+cron: "0 0/5 * 1/1 * ? *"
 
 Review comment:
   Removed.

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