> ## Documentation Index
> Fetch the complete documentation index at: https://docs.startree.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Multiple Inputs Alerts

As explained in the [alert configuration documentation](/thirdeye/alert-configuration), you can create detection pipelines that form DAGs with execution dependencies. In this example, we use the [percentage rule detector](/thirdeye/concepts-data-alerts-notifications/concepts-anomaly-detection-algorithms#percentage-change), which requires 2 inputs: `baseline` and `current`.

The pipeline looks like this:

<img src="https://mintcdn.com/startree/zMiJ17Ik7Xto2JoM/img/thirdeye/_multi_inputs_percentage_rule.png?fit=max&auto=format&n=zMiJ17Ik7Xto2JoM&q=85&s=6ce6bcd210ce7cfc3ad445f0edd8ba4d" width="60%" data-path="img/thirdeye/_multi_inputs_percentage_rule.png" />

Here is the corresponding configuration:

```json theme={null}
{
  "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

```json theme={null}
{
  "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.