ektravel commented on code in PR #16762:
URL: https://github.com/apache/druid/pull/16762#discussion_r1691847557
##########
docs/tutorials/tutorial-rollup.md:
##########
@@ -49,150 +52,98 @@ For this tutorial, we'll use a small sample of network
flow event data, represen
{"timestamp":"2018-01-02T21:35:45Z","srcIP":"7.7.7.7",
"dstIP":"8.8.8.8","packets":12,"bytes":2818}
```
-A file containing this sample input data is located at
`quickstart/tutorial/rollup-data.json`.
-
-We'll ingest this data using the following ingestion task spec, located at
`quickstart/tutorial/rollup-index.json`.
-
-```json
-{
- "type" : "index_parallel",
- "spec" : {
- "dataSchema" : {
- "dataSource" : "rollup-tutorial",
- "dimensionsSpec" : {
- "dimensions" : [
- "srcIP",
- "dstIP"
- ]
- },
- "timestampSpec": {
- "column": "timestamp",
- "format": "iso"
- },
- "metricsSpec" : [
- { "type" : "count", "name" : "count" },
- { "type" : "longSum", "name" : "packets", "fieldName" : "packets" },
- { "type" : "longSum", "name" : "bytes", "fieldName" : "bytes" }
- ],
- "granularitySpec" : {
- "type" : "uniform",
- "segmentGranularity" : "week",
- "queryGranularity" : "minute",
- "intervals" : ["2018-01-01/2018-01-03"],
- "rollup" : true
- }
- },
- "ioConfig" : {
- "type" : "index_parallel",
- "inputSource" : {
- "type" : "local",
- "baseDir" : "quickstart/tutorial",
- "filter" : "rollup-data.json"
- },
- "inputFormat" : {
- "type" : "json"
- },
- "appendToExisting" : false
- },
- "tuningConfig" : {
- "type" : "index_parallel",
- "partitionsSpec": {
- "type": "dynamic"
- },
- "maxRowsInMemory" : 25000
- }
- }
-}
+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 data inline. In the [Druid web
console](../operations/web-console.md), go to the **Query** view and run the
following query:
+
+```sql
+INSERT INTO "rollup_tutorial"
+WITH "inline_data" AS (
+ SELECT *
+ FROM TABLE(EXTERN('{
+ "type":"inline",
+
"data":"{\"timestamp\":\"2018-01-01T01:01:35Z\",\"srcIP\":\"1.1.1.1\",\"dstIP\":\"2.2.2.2\",\"packets\":20,\"bytes\":9024}\n{\"timestamp\":\"2018-01-01T01:02:14Z\",\"srcIP\":\"1.1.1.1\",\"dstIP\":\"2.2.2.2\",\"packets\":38,\"bytes\":6289}\n{\"timestamp\":\"2018-01-01T01:01:59Z\",\"srcIP\":\"1.1.1.1\",\"dstIP\":\"2.2.2.2\",\"packets\":11,\"bytes\":5780}\n{\"timestamp\":\"2018-01-01T01:01:51Z\",\"srcIP\":\"1.1.1.1\",\"dstIP\":\"2.2.2.2\",\"packets\":255,\"bytes\":21133}\n{\"timestamp\":\"2018-01-01T01:02:29Z\",\"srcIP\":\"1.1.1.1\",\"dstIP\":\"2.2.2.2\",\"packets\":377,\"bytes\":359971}\n{\"timestamp\":\"2018-01-01T01:03:29Z\",\"srcIP\":\"1.1.1.1\",\"dstIP\":\"2.2.2.2\",\"packets\":49,\"bytes\":10204}\n{\"timestamp\":\"2018-01-02T21:33:14Z\",\"srcIP\":\"7.7.7.7\",\"dstIP\":\"8.8.8.8\",\"packets\":38,\"bytes\":6289}\n{\"timestamp\":\"2018-01-02T21:33:45Z\",\"srcIP\":\"7.7.7.7\",\"dstIP\":\"8.8.8.8\",\"packets\":123,\"bytes\":93999}\n{\"timestamp\":\"2018-01-02T21:35:45Z\",\"srcIP\"
:\"7.7.7.7\",\"dstIP\":\"8.8.8.8\",\"packets\":12,\"bytes\":2818}"}',
+ '{"type":"json"}'))
+ EXTEND ("timestamp" VARCHAR, "srcIP" VARCHAR, "dstIP" VARCHAR, "packets"
BIGINT, "bytes" BIGINT)
+)
+SELECT
+ FLOOR(TIME_PARSE("timestamp") TO MINUTE) AS __time,
+ "srcIP",
+ "dstIP",
+ SUM("bytes") AS "bytes",
+ SUM("packets") AS "packets",
+ COUNT(*) AS "count"
+FROM "inline_data"
+GROUP BY 1, 2, 3
+PARTITIONED BY DAY
```
-Rollup has been enabled by setting `"rollup" : true` in the `granularitySpec`.
-
-Note that we have `srcIP` and `dstIP` defined as dimensions, a longSum metric
is defined for the `packets` and `bytes` columns, and the `queryGranularity`
has been defined as `minute`.
+In the query, you group by dimensions, `timestamp`, `srcIP`, and `dstIP`. Note
that the query uses the `FLOOR` function to bucket rows based on MINUTE
granularity.
+For the metrics, you apply aggregations to sum the `bytes` and `packets`
columns and add a column that counts the number of rows that get rolled up.
Review Comment:
```suggestion
For the metrics, you apply aggregations to sum the `bytes` and `packets`
columns and add a column that counts the number of rolled-up rows.
```
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