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using sql ingestion instead of batch-sql

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edgar2020 2024-08-26 14:02:41 -07:00
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docs/tutorials/tutorial-sketches-theta.md
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@ -97,51 +97,29 @@ date,uid,show,episode
## Ingest data using Theta sketches
1. Navigate to the **Load data > Batch-SQL** wizard in the web console.
2. Select `Paste data` as the data source and paste the [sample data](#sample-data):
Load the sample dataset using the [`INSERT INTO`](../multi-stage-query/reference.md/#insert) statement and the [`EXTERN`](../multi-stage-query/reference.md/#extern-function) function to ingest the sample data inline. In the [Druid web console](../operations/web-console.md), go to the **Query** view and run the following query:
![Load data view with pasted data](../assets/tutorial-theta-v2_01.png)
3. Select **Connect data**.
4. Keep the default values and select **Next** to parse the data as a CSV, with included headers:
```sql
INSERT INTO "ts_tutorial"
WITH "source" AS (SELECT * FROM TABLE(
EXTERN(
'{"type":"inline","data":"date,uid,show,episode\n2022-05-19,alice,Game of Thrones,S1E1\n2022-05-19,alice,Game of Thrones,S1E2\n2022-05-19,alice,Game of Thrones,S1E1\n2022-05-19,bob,Bridgerton,S1E1\n2022-05-20,alice,Game of Thrones,S1E1\n2022-05-20,carol,Bridgerton,S1E2\n2022-05-20,dan,Bridgerton,S1E1\n2022-05-21,alice,Game of Thrones,S1E1\n2022-05-21,carol,Bridgerton,S1E1\n2022-05-21,erin,Game of Thrones,S1E1\n2022-05-21,alice,Bridgerton,S1E1\n2022-05-22,bob,Game of Thrones,S1E1\n2022-05-22,bob,Bridgerton,S1E1\n2022-05-22,carol,Bridgerton,S1E2\n2022-05-22,bob,Bridgerton,S1E1\n2022-05-22,erin,Game of Thrones,S1E1\n2022-05-22,erin,Bridgerton,S1E2\n2022-05-23,erin,Game of Thrones,S1E1\n2022-05-23,alice,Game of Thrones,S1E1"}',
'{"type":"csv","findColumnsFromHeader":true}'
)
) EXTEND ("date" VARCHAR, "show" VARCHAR, "episode" VARCHAR, "uid" VARCHAR))
SELECT
TIME_FLOOR(TIME_PARSE("date"), 'P1D') AS "__time",
"show",
"episode",
COUNT(*) AS "count",
DS_THETA("uid") AS "theta_uid"
FROM "source"
GROUP BY 1, 2, 3
PARTITIONED BY DAY
```
![Parse raw data](../assets/tutorial-theta-v2_02.png)
5. Select **`Datasource:inline_data`** to open the Destination dialog.
![Open destination dialog to change table name](../assets/tutorial-theta-v2_03.png)
6. Navigate to the **New table** tab and replace the current name with `ts_tutorial`.
![Change table name](../assets/tutorial-theta-v2_04.png)
7. Select **Save**
8. Toggle **Rollup** and confirm your choice in the dialog box so that the adjacent label displays `on`.
![Configure schema for rollup](../assets/tutorial-theta-v2_05.png)
9. Select **Add column > Custom metric** to open up a dialog on the right hand side.
![Open dialog for new metrics](../assets/tutorial-theta-v2_06.png)
10. Define the new metric as a theta sketch with the following details:
* **Name**: `theta_uid`
* **SQL expression**: `DS_THETA(uid)`
![Add theta sketch metric](../assets/tutorial-theta-v2_07.png)
11. Click **Apply** to add the new metric to the data model.
12. You are not interested in individual user ID's, only the unique counts. Right now, `uid` is still in the data model. To remove it, click on the `uid` column in the data model and delete it using the red trashcan icon on the right:
![Delete uid column](../assets/tutorial-theta-v2_08.png)
13. Select **Start loading data** to begin the ingestion job.
14. When the ingestion job finishes, select **`Query:ts_tutorial`**.
![Begin querying with theta sketches](../assets/tutorial-theta-v2_09.png)
Notice how there is no `uid` in the `SELECT` statement. In this scenario you are not interested in individual user ID's, only the unique counts. Instead you use the `DS_THETA` aggregator function to create a Theta sketch on the values of `uid`. The [`DS_THETA`](../development/extensions-core/datasketches-theta.md#aggregator) function has an optional second parameter, `size`, which accepts a positive integer-power of 2 greater than 0. The `size` parameter refers to the maximum number of entries the Theta sketch object retains. Higher values of `size` result in higher accuracy, but require more space. The default value of `size` is 16384, and is recommended in most use cases.
## Query the Theta sketch column
@ -264,7 +242,7 @@ FROM ts_tutorial
- This allows us to use rollup and discard the individual values, just retaining statistical approximations in the sketches.
- With Theta sketch set operations, affinity analysis is easier, for example, to answer questions such as which segments correlate or overlap by how much.
## Further reading
## Learn more
See the following topics for more information:
* [Theta sketch](../development/extensions-core/datasketches-theta.md) for reference on ingestion and native queries on Theta sketches in Druid.