edgar2020 commented on code in PR #16762:
URL: https://github.com/apache/druid/pull/16762#discussion_r1692084584
##########
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.
-We will see how these definitions are used after we load this data.
+After the ingestion completes, you can query the data.
-## Load the example data
+## Query the example data
-From the apache-druid-{{DRUIDVERSION}} package root, run the following command:
+In the web console, open a new tab in the **Query** view. Run the following
query to view the ingested data:
-```bash
-bin/post-index-task --file quickstart/tutorial/rollup-index.json --url
http://localhost:8081
+```sql
+SELECT * FROM "rollup_tutorial"
```
-After the script completes, we will query the data.
+Returns the following:
-## Query the example data
+| `__time` | `srcIP` | `dstIP` | `bytes` | `count` | `packets` |
+| -- | -- | -- | -- | -- | -- |
+| `2018-01-01T01:01:00.000Z` | `1.1.1.1` | `2.2.2.2` | `35,937` | `3` | `286` |
+| `2018-01-01T01:02:00.000Z` | `1.1.1.1` | `2.2.2.2` | `366,260` | `2` | `415`
|
+| `2018-01-01T01:03:00.000Z` | `1.1.1.1` | `2.2.2.2` | `10,204` | `1` | `49` |
+| `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` |
-Let's run `bin/dsql` and issue a `select * from "rollup-tutorial";` query to
see what data was ingested.
-
-```bash
-$ bin/dsql
-Welcome to dsql, the command-line client for Druid SQL.
-Type "\h" for help.
-dsql> select * from "rollup-tutorial";
-┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
-│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
-├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
-│ 2018-01-01T01:01:00.000Z │ 35937 │ 3 │ 2.2.2.2 │ 286 │ 1.1.1.1 │
-│ 2018-01-01T01:02:00.000Z │ 366260 │ 2 │ 2.2.2.2 │ 415 │ 1.1.1.1 │
-│ 2018-01-01T01:03:00.000Z │ 10204 │ 1 │ 2.2.2.2 │ 49 │ 1.1.1.1 │
-│ 2018-01-02T21:33:00.000Z │ 100288 │ 2 │ 8.8.8.8 │ 161 │ 7.7.7.7 │
-│ 2018-01-02T21:35:00.000Z │ 2818 │ 1 │ 8.8.8.8 │ 12 │ 7.7.7.7 │
-└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
-Retrieved 5 rows in 1.18s.
-
-dsql>
-```
+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.
+
+## View rollup in action
-Let's look at the three events in the original input data that occurred during
`2018-01-01T01:01`:
+Consider the three events in the original input data that occur over the
course of minute `2018-01-01T01:01`:
```json
{"timestamp":"2018-01-01T01:01:35Z","srcIP":"1.1.1.1",
"dstIP":"2.2.2.2","packets":20,"bytes":9024}
{"timestamp":"2018-01-01T01:01:51Z","srcIP":"1.1.1.1",
"dstIP":"2.2.2.2","packets":255,"bytes":21133}
{"timestamp":"2018-01-01T01:01:59Z","srcIP":"1.1.1.1",
"dstIP":"2.2.2.2","packets":11,"bytes":5780}
```
-These three rows have been "rolled up" into the following row:
+Druid combines the three rows into one during rollup:
-```bash
-┌──────────────────────────┬────────┬───────┬─────────┬─────────┬─────────┐
-│ __time │ bytes │ count │ dstIP │ packets │ srcIP │
-├──────────────────────────┼────────┼───────┼─────────┼─────────┼─────────┤
-│ 2018-01-01T01:01:00.000Z │ 35937 │ 3 │ 2.2.2.2 │ 286 │ 1.1.1.1 │
-└──────────────────────────┴────────┴───────┴─────────┴─────────┴─────────┘
-```
+| `__time` | `srcIP` | `dstIP` | `bytes` | `count` | `packets` |
+| -- | -- | -- | -- | -- | -- |
+| `2018-01-01T01:01:00.000Z` | `1.1.1.1` | `2.2.2.2` | `35,937` | `3` | `286` |
-The input rows have been grouped by the timestamp and dimension columns
`{timestamp, srcIP, dstIP}` with sum aggregations on the metric columns
`packets` and `bytes`.
+Before the grouping occurs, the `FLOOR(TIME_PARSE("timestamp") TO MINUTE)`
expression buckets (floors) the timestamp column of the original input by
minute.
-Before the grouping occurs, the timestamps of the original input data are
bucketed/floored by minute, due to the `"queryGranularity":"minute"` setting in
the ingestion spec.
+The input rows are grouped because they have the same values for their
dimension columns `{timestamp, srcIP, dstIP}`. The metric columns calculate the
sum aggregation of the grouped rows for `packets` and `bytes`. The `count`
metric shows how many rows from the original input data contributed to the
final "rolled up" row.
Review Comment:
"roll up" changed to rolled-up
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