---
title: Fill gaps in data
description: Use date_bin_gapfill with interpolate or locf to fill gaps of time where no data is returned.
url: https://docs.influxdata.com/influxdb3/cloud-serverless/query-data/sql/fill-gaps/
estimated_tokens: 2555
product: InfluxDB Cloud Serverless
version: cloud-serverless
---

# Fill gaps in data

Use [`date_bin_gapfill`](/influxdb3/cloud-serverless/reference/sql/functions/time-and-date/#date_bin_gapfill) with [`interpolate`](/influxdb3/cloud-serverless/reference/sql/functions/misc/#interpolate) or [`locf`](/influxdb3/cloud-serverless/reference/sql/functions/misc/#locf) to fill gaps of time where no data is returned. Gap-filling SQL queries handle missing data in time series data by filling in gaps with interpolated values or by carrying forward the last available observation.

**To fill gaps in data:**

1. Use the `date_bin_gapfill` function to window your data into time-based groups and apply an [aggregate function](/influxdb3/cloud-serverless/reference/sql/functions/aggregate/) to each window. If no data exists in a window, `date_bin_gapfill` inserts a new row with the starting timestamp of the window, all columns in the `GROUP BY` clause populated, and null values for the queried fields.
    
2. Use either `interpolate` or `locf` to fill the inserted null values in the specified column.
    
    -   **interpolate**: fills null values by interpolating values between non-null values.
    -   **locf**: fills null values by carrying the last observed value forward.
    
    The expression passed to `interpolate` or `locf` must use an [aggregate function](/influxdb3/cloud-serverless/reference/sql/functions/aggregate/).
    
3. Include a `WHERE` clause that sets upper and lower time bounds. For example:
    

\`\`\`sql WHERE time >= '2022-01-01T08:00:00Z' AND time <= '2022-01-01T10:00:00Z' \`\`\`

## Example of filling gaps in data

The following examples use the sample data set provided in [Get started with InfluxDB tutorial](/influxdb3/cloud-serverless/get-started/write/#construct-line-protocol) to show how to use `date_bin_gapfill` and the different results of `interplate` and `locf`.

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**interpolate:**

```sql
SELECT
  date_bin_gapfill(INTERVAL '30 minutes', time) as _time,
  room,
  interpolate(avg(temp))
FROM home
WHERE
    time >= '2022-01-01T08:00:00Z'
    AND time <= '2022-01-01T10:00:00Z'
GROUP BY _time, room
```

| _time | room | AVG(home.temp) |
| --- | --- | --- |
| 2022-01-01T08:00:00Z | Kitchen | 21 |
| 2022-01-01T08:30:00Z | Kitchen | 22 |
| 2022-01-01T09:00:00Z | Kitchen | 23 |
| 2022-01-01T09:30:00Z | Kitchen | 22.85 |
| 2022-01-01T10:00:00Z | Kitchen | 22.7 |
| 2022-01-01T08:00:00Z | Living Room | 21.1 |
| 2022-01-01T08:30:00Z | Living Room | 21.25 |
| 2022-01-01T09:00:00Z | Living Room | 21.4 |
| 2022-01-01T09:30:00Z | Living Room | 21.6 |
| 2022-01-01T10:00:00Z | Living Room | 21.8 |

**locf:**

```sql
SELECT
  date_bin_gapfill(INTERVAL '30 minutes', time) as _time,
  room,
  locf(avg(temp))
FROM home
WHERE
    time >= '2022-01-01T08:00:00Z'
    AND time <= '2022-01-01T10:00:00Z'
GROUP BY _time, room
```

| _time | room | AVG(home.temp) |
| --- | --- | --- |
| 2022-01-01T08:00:00Z | Kitchen | 21 |
| 2022-01-01T08:30:00Z | Kitchen | 21 |
| 2022-01-01T09:00:00Z | Kitchen | 23 |
| 2022-01-01T09:30:00Z | Kitchen | 23 |
| 2022-01-01T10:00:00Z | Kitchen | 22.7 |
| 2022-01-01T08:00:00Z | Living Room | 21.1 |
| 2022-01-01T08:30:00Z | Living Room | 21.1 |
| 2022-01-01T09:00:00Z | Living Room | 21.4 |
| 2022-01-01T09:30:00Z | Living Room | 21.4 |
| 2022-01-01T10:00:00Z | Living Room | 21.8 |

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#### Related

-   [SQL reference documentation](/influxdb3/cloud-serverless/reference/sql/)