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Co-authored-by: Victoria Lim <[email protected]>
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edgar2020 and vtlim authored Jul 26, 2024
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Expand Up @@ -82,6 +82,9 @@ Note the following aspects of the ingestion statement:
* You create the `bytes` and `packets` metrics, which are summed from their respective input fields.
* You also create the `count` metric that records the number of rows that get rolled-up per each row in the datasource.
With rollup, Druid combines rows with identical timestamp and dimension values after the timestamp truncation.
Druid computes and stores the metric values using the specified aggregation function over each set of rolled-up rows.
After the ingestion completes, you can query the data.
## Query the example data
Expand All @@ -102,7 +105,7 @@ Returns the following:
| `2018-01-02T21:33:00.000Z` | `7.7.7.7` | `8.8.8.8` | `100,288` | `2` | `161` |
| `2018-01-02T21:35:00.000Z` | `7.7.7.7` | `8.8.8.8` | `2,818` | `1` | `12` |
Notice there are only six rows as opposed to the nine rows in the example data. The next section covers how ingestion with rollup accomplishes this.
Notice there are only six rows as opposed to the nine rows in the example data. In the next section, you explore the components of the rolled-up rows.
## View rollup in action
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