> ## 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.

# TimeIndexFiller

Fills missing time data points. Used to ensure data fetched with SQL is a correct timeseries for downstream nodes. The SQL language is not timeseries oriented. The `GROUP BY timeColumn` clause does not create a point if there is no data for a time bucket.

## Inputs

A table with a time index to fill.

## Outputs

The table with the time index filled. No naming constraint for `outputKey` and `outputName`.

## Parameters

| name                              | description                                                                       | default value         |
| --------------------------------- | --------------------------------------------------------------------------------- | --------------------- |
| `component.timestamp`             | The name of the time index column.                                                | `timestamp`           |
| `component.monitoringGranularity` | Granularity of the time index. In ISO-8601 format.   Eg `P1D`.                    |                       |
| `component.minTimeInference`      | Inference strategy for the minimum time constraint.                               | `FROM_DATA`           |
| `component.maxTimeInference`      | Inference strategy for the maximum time constraint.                               | `FROM_DETECTION_TIME` |
| `component.lookback`              | Offset to use for a time inference with lookback. In ISO-8601 format.   Eg `P7D`. |                       |
| `component.fillNullMethod`        | Method to use to fill null values in the metric column.                           | `FILL_WITH_ZEROES`    |

<Info>
  **Automatic configuration with macros.**

  `minTimeInference`, `maxTimeInference` and  `lookback` are **not required** if the `__timeFilter`  macro was used to get the input data.\
  `monitoringGranularity` is **not required** if the `__timeGroup` macro was used to get the input data.
</Info>

### Available inference strategies for `minTimeInference` and `maxTimeInference`

The `TimeIndexFiller` needs a start and an end to fill the time index. These boundaries can be computed with different strategies.

| name                                | description                                                                                                                |
| ----------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| `FROM_DATA`                         | The minimum (resp maximum) time for which to fill the index is the minimum (resp maximum) time observed in the input data. |
| `FROM_DETECTION_TIME`               | The minimum (resp maximum) time corresponds to the minimum (resp maximum) time used by the detection pipeline run.         |
| `FROM_DETECTION_TIME_WITH_LOOKBACK` | Like above, use the detection pipeline boundaries, but apply an offset.   Eg: minTime - lookback.                          |

### Available methods for `fillNullMethod`

When a missing time point is added, the metric value is unknown. This value can be filled with one the following methods.

| name               | description                                                                             |
| ------------------ | --------------------------------------------------------------------------------------- |
| `FILL_WITH_ZEROES` | Replace null values with zeroes.                                                        |
| `KEEP_NULL`        | Keep the null values.                                                                   |
| `INTERPOLATE`      | *Available soon.* Interpolate a value based on the preceding and next available values. |

Note that most of the detectors are not compatible with null values for the moment.

## Examples

### Without macros

```json theme={null}
    {
      "name": "baselineMissingDataManager",
      "type": "TimeIndexFiller",
      "params": {
        "component.monitoringGranularity": "P1D",
        "component.timestamp": "ts",
        "component.minTimeInference": "FROM_DETECTION_TIME_WITH_LOOKBACK",
        "component.maxTimeInference": "FROM_DETECTION_TIME_WITH_LOOKBACK",
        "component.lookback": "P7D"
      },
      "inputs": [
        {
          "sourcePlanNode": "baselineDataFetcher",
          "sourceProperty": "baselineOutput"
        }
      ],
      "outputs": [
        {
          "outputKey": "filler",
          "outputName": "baselineOutput"
        }
      ]
    }
```

### With macros

If `__timeFilter` and `__timeGroup` macros are used in the upstream DataFetcher node, and if the metric is named `metric`:

```json theme={null}
  {
    "name": "missingDataManager",
    "type": "TimeIndexFiller",
    "params": {
        "component.timestamp": "ts"
      },
    "inputs": [
      {
        "sourcePlanNode": "baselineDataFetcher",
        "sourceProperty": "baselineOutput"
      }
    ],
    "outputs": [
      {
        "outputKey": "filler",
        "outputName": "baselineOutput"
      }
    ]
  }
```