---
title: Interpolation
description: >-
  Fill missing metric values using linear, last value, zero, or null
  interpolation methods in timeseries data.
breadcrumbs: Docs > Dashboards > Functions > Interpolation
---

# Interpolation

## Fill{% #fill %}

| Function | Description                                       | Example                                    |
| -------- | ------------------------------------------------- | ------------------------------------------ |
| `fill()` | Interpolate missing metric values for the metric. | `<METRIC_NAME>{*}.fill(<METHOD>, <LIMIT>)` |

The `fill()` function has two parameters:

- **`METHOD`**: The function to use as an interpolation method; choose from:

  - **linear**: Gives you a linear interpolation between the beginning and the end of the gap.
  - **last**: Fills the gap with the last value of the gap.
  - **zero**: Fills the gap with a zero value.
  - **null**: Deactivates the interpolation.

- `LIMIT` [*optional*, *default*=**300**, *maximum*=**600**]: The interpolation limit (in seconds) that represents the maximum size of a gap you want to interpolate.

Read [Interpolation and the Fill Modifier](https://docs.datadoghq.com/metrics/guide/interpolation-the-fill-modifier-explained/) for a detailed explanation of the `.fill()` function and its impact on interpolation.

## Default zero{% #default-zero %}

| Function         | Description                             | Example                          |
| ---------------- | --------------------------------------- | -------------------------------- |
| `default_zero()` | Adds a default value to sparse metrics. | `default_zero(system.load.1{*})` |

The `default_zero()` function fills empty time intervals using the value 0 or, if interpolation is enabled, with interpolation. **Note**: Interpolation is enabled by default for `GAUGE` type metrics. Like most functions, `default_zero()` is applied **after** [time and space aggregation](https://docs.datadoghq.com/dashboards/functions/#add-a-function).

### Use cases{% #use-cases %}

The `default_zero()` function is intended to address the following use cases (though it may also work for other use cases):

- Aligning gauges as 0 when performing arithmetic on sparse metrics (note: `COUNT` or `RATE` type metrics queried `as_count()` or `as_rate()` are *always* aligned as 0, so using `default_zero()` does not change how they are aligned; it only affects `GAUGE` type metrics).
- Resolving monitors before they enter a no-data condition. This works for both simple and multi alerts, but the value 0 must not cause the monitor to trigger. For example, this would not work for a monitor with the query `avg(last_10m):avg:system.cpu.idle{*} < 10` because this monitor triggers (instead of resolving) when it evaluates to 0. Avoid using this function for error rate monitors with `as_count()` queries. See the [as_count() in Monitor Evaluations guide](https://docs.datadoghq.com/monitors/guide/as-count-in-monitor-evaluations/) for more details.
- Filling in empty intervals in sparse (but nonempty) series for visual reasons or to affect the min/max/average of a timeseries in a monitor evaluation. If the evaluation window doesn't contain any points of data, `default_zero()` has no effect.
- Showing the value 0 on the timeseries widget when there is no data.

### Example{% #example %}

To demonstrate how the `default_zero()` function works, consider this single point created for a custom metric [using DogStatsD](https://docs.datadoghq.com/metrics/):

```text
$ echo -n "custom_metric:1|g" | nc -4u -w0 127.0.0.1 8125
```

When this metric is queried over the past 30 minutes, there is a single timestamp, because only one of the query's rollup intervals has a point:

```text
avg:custom_metric{*}

+---------------------+---------------+
| Timestamp           | custom_metric |
+---------------------+---------------+
| ---------           | ---------     |
| 2019-04-17 17:45:00 | 1             |
+---------------------+---------------+
```

The `default_zero()` function interpolates this point five minutes forward in time (the default interpolation limit for gauges), then fills the remaining empty intervals with zeros:

```text
default_zero(avg:custom_metric{*})

+---------------------+-----------------------------+
| Timestamp           | default_zero(custom_metric) |
+---------------------+-----------------------------+
| ---------           | ---------                   |
| 2019-04-17 17:30:00 | 0                           |
| 2019-04-17 17:31:00 | 0                           |
...
| 2019-04-17 17:44:00 | 0                           |
| 2019-04-17 17:45:00 | 1                           |
| 2019-04-17 17:46:00 | 1                           |
| 2019-04-17 17:47:00 | 1                           |
| 2019-04-17 17:48:00 | 1                           |
| 2019-04-17 17:49:00 | 1                           |
| 2019-04-17 17:50:00 | 1                           |
| 2019-04-17 17:51:00 | 0                           |
| 2019-04-17 17:52:00 | 0                           |
...
+---------------------+-----------------------------+
```

## Further reading{% #further-reading %}

- [Other function types](https://docs.datadoghq.com/dashboards/functions/)
- [Interpolation and the Fill Modifier](https://docs.datadoghq.com/metrics/guide/interpolation-the-fill-modifier-explained/)