techdocsmith commented on code in PR #16953:
URL: https://github.com/apache/druid/pull/16953#discussion_r1761958474
##########
docs/tutorials/tutorial-sketches-theta.md:
##########
@@ -95,103 +97,37 @@ date,uid,show,episode
## Ingest data using Theta sketches
-1. Navigate to the **Load data** wizard in the web console.
-2. Select `Paste data` as the data source and paste the given data:
-
-
-
-3. Leave the source type as `inline` and click **Apply** and **Next: Parse
data**.
-4. Parse the data as CSV, with included headers:
-
-
-
-5. Accept the default values in the **Parse time**, **Transform**, and
**Filter** stages.
-6. In the **Configure schema** stage, enable rollup and confirm your choice in
the dialog. Then set the query granularity to `day`.
-
-
-
-7. Add the Theta sketch during this stage. Select **Add metric**.
-8. Define the new metric as a Theta sketch with the following details:
- * **Name**: `theta_uid`
- * **Type**: `thetaSketch`
- * **Field name**: `uid`
- * **Size**: Accept the default value, `16384`.
- * **Is input theta sketch**: Accept the default value, `False`.
-
-
-
-9. Click **Apply** to add the new metric to the data model.
-
-
-10. 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 trashcan icon on the right:
-
-
-
-11. For the remaining stages of the **Load data** wizard, set the following
options:
- * **Partition**: Set **Segment granularity** to `day`.
- * **Tune**: Leave the default options.
- * **Publish**: Set the datasource name to `ts_tutorial`.
-
-On the **Edit spec** page, your final input spec should match the following:
-
-```json
-{
- "type": "index_parallel",
- "spec": {
- "ioConfig": {
- "type": "index_parallel",
- "inputSource": {
- "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"
- },
- "inputFormat": {
- "type": "csv",
- "findColumnsFromHeader": true
- }
- },
- "tuningConfig": {
- "type": "index_parallel",
- "partitionsSpec": {
- "type": "hashed"
- },
- "forceGuaranteedRollup": true
- },
- "dataSchema": {
- "dataSource": "ts_tutorial",
- "timestampSpec": {
- "column": "date",
- "format": "auto"
- },
- "dimensionsSpec": {
- "dimensions": [
- "show",
- "episode"
- ]
- },
- "granularitySpec": {
- "queryGranularity": "day",
- "rollup": true,
- "segmentGranularity": "day"
- },
- "metricsSpec": [
- {
- "name": "count",
- "type": "count"
- },
- {
- "type": "thetaSketch",
- "name": "theta_uid",
- "fieldName": "uid"
- }
- ]
- }
- }
-}
+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:
+
+
+```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
```
-Notice the `theta_uid` object in the `metricsSpec` list, that defines the
`thetaSketch` aggregator on the `uid` column during ingestion.
+Notice that there is no `uid` in the `SELECT` statement.
Review Comment:
```suggestion
Notice the `theta_uid` column in the `SELECT` statement. It defines the
`thetaSketch` aggregator on the `uid` column during ingestion.
```
##########
docs/tutorials/tutorial-sketches-theta.md:
##########
@@ -209,36 +145,22 @@ Let's first see what the data looks like in Druid. Run
the following SQL stateme
SELECT * FROM ts_tutorial
```
-
+
Review Comment:
nit/not necessary for this pr: In general, I think tables represent query
results better than screen shots. And you can also wrap the results in a `view
results` expander/details section to make the overall document smaller.
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