As explained in the alert configuration documentation, you can create detection pipelines that form DAGs with execution dependencies. In this example, we use the percentage rule detector, which requires 2 inputs: baseline and current. The pipeline looks like this: Here is the corresponding configuration: 
{
  "name": "pageviews",
  "description": "This is a sample detection",
  "cron": "0 */1 * * * ?",
  "template": {
    "nodes": [
      {
        "name": "root",
        "type": "AnomalyDetector",
        "params": {
          "type": "PERCENTAGE_CHANGE",
          "component.monitoringGranularity": "${monitoringGranularity}",
          "component.timestamp": "ts",
          "component.metric": "met",
          "component.percentageChange": "${percentageChange}",
          "component.pattern": "${pattern}",
          "anomaly.metric": "${metric}"
        },
        "inputs": [
          {
            "targetProperty": "baseline",
            "sourcePlanNode": "baselineDataFetcher",
            "sourceProperty": "baselineOutput"
          },
          {
            "targetProperty": "current",
            "sourcePlanNode": "currentDataFetcher",
            "sourceProperty": "currentOutput"
          }
        ],
        "outputs": []
      },
      {
        "name": "baselineDataFetcher",
        "type": "DataFetcher",
        "params": {
          "component.dataSource": "${dataSource}",
          "component.query": "SELECT __timeGroup(\"${timeColumn}\", '${timeColumnFormat}', '${monitoringGranularity}') as ts, ${metric} as met FROM ${dataset} WHERE __timeFilter(\"${timeColumn}\", '${timeColumnFormat}', ${lookback}, ${lookback}) GROUP BY ts ORDER BY ts LIMIT 1000"
        },
        "inputs": [],
        "outputs": [
          {
            "outputKey": "pinot",
            "outputName": "baselineOutput"
          }
        ]
      },
      {
        "name": "currentDataFetcher",
        "type": "DataFetcher",
        "params": {
          "component.dataSource": "${dataSource}",
          "component.query": "SELECT __timeGroup(\"${timeColumn}\", '${timeColumnFormat}', '${monitoringGranularity}') as ts, ${metric} as met FROM ${dataset} WHERE __timeFilter(\"${timeColumn}\", '${timeColumnFormat}') GROUP BY ts ORDER BY ts LIMIT 1000"
        },
        "inputs": [],
        "outputs": [
          {
            "outputKey": "pinot",
            "outputName": "currentOutput"
          }
        ]
      }
    ]
  },
  "templateProperties": {
    "dataSource": "pinotQuickStartLocal",
    "dataset": "pageviews",
    "metric": "sum(views)",
    "monitoringGranularity": "P1D",
    "timeColumn": "date",
    "timeColumnFormat": "yyyyMMdd",
    "percentageChange": "0.2",
    "offset": "P1W",
    "pattern": "up_or_down"
  }
}
percentage_rule_alert.json Notice the pipeline has two DataFetcher nodes:
  • baselineDataFetcher, which has an output called baselineOutput
  • currentDataFetcher, which has an output called currentOutput
These outputs are then fed to the anomaly detector:
  • baselineOutput goes into the baseline property of the detector
  • currentOutput goes into the current property of the detector
{
  "name": "root", 
  "type": "AnomalyDetector", 
  "params": {
    "type": "PERCENTAGE_CHANGE", ...},
    "inputs": [
      {
        "targetProperty": "baseline",
        "sourcePlanNode": "baselineDataFetcher",
        "sourceProperty": "baselineOutput"
      },
      {
        "targetProperty": "current",
        "sourcePlanNode": "currentDataFetcher",
        "sourceProperty": "currentOutput"
      }
    ]
  }
If baseline and current properties were not given, the detector would fail.

​
About the baseline

To ensure the baseline timeframe is correctly computed based on the analysis timeframe,we’ll look at the SQL queries:
  • currentDataFetcher uses the macro where __timeFilter(\"${timeColumn}\", '${timeColumnFormat}'): the macro automatically filters on the analysis timeframe.
  • baselineDataFetcher uses the same macro, with an offset: where __timeFilter(\"${timeColumn}\", '${timeColumnFormat}', ${lookback}, ${lookback}), with offset=P1W.
  • P1W means one week. This means the baseline is one week before the analysis timeframe.