You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@druid.apache.org by GitBox <gi...@apache.org> on 2022/12/02 01:31:58 UTC
[GitHub] [druid] somu-imply commented on a diff in pull request #13479: docs: documentation for unnest datasource
somu-imply commented on code in PR #13479:
URL: https://github.com/apache/druid/pull/13479#discussion_r1037709658
##########
docs/tutorials/tutorial-unnest-datasource.md:
##########
@@ -0,0 +1,558 @@
+---
+id: tutorial-unnest-datasource
+sidebar_label: "Using the unnest datasource"
+title: "Tutorial: Unnest data in a column"
+---
+
+<!--
+ ~ Licensed to the Apache Software Foundation (ASF) under one
+ ~ or more contributor license agreements. See the NOTICE file
+ ~ distributed with this work for additional information
+ ~ regarding copyright ownership. The ASF licenses this file
+ ~ to you under the Apache License, Version 2.0 (the
+ ~ "License"); you may not use this file except in compliance
+ ~ with the License. You may obtain a copy of the License at
+ ~
+ ~ http://www.apache.org/licenses/LICENSE-2.0
+ ~
+ ~ Unless required by applicable law or agreed to in writing,
+ ~ software distributed under the License is distributed on an
+ ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ~ KIND, either express or implied. See the License for the
+ ~ specific language governing permissions and limitations
+ ~ under the License.
+ -->
+
+> If you're looking for information about how to unnest `COMPLEX<json>` columns, see [Nested columns](../querying/nested-columns.md).
+
+This tutorial demonstrates how to use the unnest datasource to unnest a column that has data stored in arrays. For example, if you have a column named `dim3` with values like `[a,b]` or `[c,d,f]`, the unnest datasource can output the data to a new column with individual rows that contain single values like `a` and `b`. When doing this though, be mindful of the following:
+
+- Unnesting data can dramatically increase the total number of rows.
+- You cannot unnest an array within an array.
+
+You can use the Druid console or API to unnest data. To start though, you may want to use the Druid console so that viewing the nested and unnested data is easier.
+
+## Prerequisites
+
+You need a Druid cluster, such as the [micro-quickstart](./index.md). The cluster does not need any existing datasources. You'll load a basic one as part of this tutorial.
+
+## Load data with nested values
+
+You can load this data by running a query for SQL-based ingestion or submitting a JSON-based ingestion spec. The example loads data into a table named `nested_data`
+
+<!--DOCUSAURUS_CODE_TABS-->
+
+<!--SQL-based ingestion-->
+
+```sql
+REPLACE INTO nested_data OVERWRITE ALL
+SELECT
+ TIME_PARSE("t") as __time,
+ dim1,
+ dim2,
+ dim3,
+ m1,
+ m2
+FROM TABLE(
+ EXTERN(
+ '{"type":"inline","data":"{\"t\":\"2000-01-01\",\"m1\":\"1.0\",\"m2\":\"1.0\",\"dim1\":\"\",\"dim2\":[\"a\"],\"dim3\":[\"a\",\"b\"]},\n{\"t\":\"2000-01-02\",\"m1\":\"2.0\",\"m2\":\"2.0\",\"dim1\":\"10.1\",\"dim2\":[],\"dim3\":[\"c\",\"d\"]},\n{\"t\":\"2000-01-03\",\"m1\":\"3.0\",\"m2\":\"3.0\",\"dim1\":\"2\",\"dim2\":[\"\"],\"dim3\":[\"e\",\"f\"]},\n{\"t\":\"2001-01-01\",\"m1\":\"4.0\",\"m2\":\"4.0\",\"dim1\":\"1\",\"dim2\":[\"a\"],\"dim3\":[\"g\",\"h\"]},\n{\"t\":\"2001-01-02\",\"m1\":\"5.0\",\"m2\":\"5.0\",\"dim1\":\"def\",\"dim2\":[\"abc\"],\"dim3\":[\"i\",\"j\"]},\n{\"t\":\"2001-01-03\",\"m1\":\"6.0\",\"m2\":\"6.0\",\"dim1\":\"abc\",\"dim2\":[\"a\"],\"dim3\":[\"k\",\"l\"]},\n{\"t\":\"2001-01-02\",\"m1\":\"5.0\",\"m2\":\"5.0\",\"dim1\":\"def\",\"dim2\":[\"abc\"],\"dim3\":[\"m\",\"n\"]}"}',
+ '{"type":"json"}',
+ '[{"name":"t","type":"string"},{"name":"dim1","type":"string"},{"name":"dim2","type":"string"},{"name":"dim3","type":"string"},{"name":"m1","type":"float"},{"name":"m2","type":"double"}]'
+ )
+)
+PARTITIONED BY YEAR
+```
+
+<!--Ingestion spec-->
+
+```json
+{
+ "type": "index_parallel",
+ "spec": {
+ "ioConfig": {
+ "type": "index_parallel",
+ "inputSource": {
+ "type": "inline",
+ "data": "{\"t\":\"2000-01-01\",\"m1\":\"1.0\",\"m2\":\"1.0\",\"dim1\":\"\",\"dim2\":[\"a\"],\"dim3\":[\"a\",\"b\"]},\n{\"t\":\"2000-01-02\",\"m1\":\"2.0\",\"m2\":\"2.0\",\"dim1\":\"10.1\",\"dim2\":[],\"dim3\":[\"c\",\"d\"]},\n{\"t\":\"2000-01-03\",\"m1\":\"3.0\",\"m2\":\"3.0\",\"dim1\":\"2\",\"dim2\":[\"\"],\"dim3\":[\"e\",\"f\"]},\n{\"t\":\"2001-01-01\",\"m1\":\"4.0\",\"m2\":\"4.0\",\"dim1\":\"1\",\"dim2\":[\"a\"],\"dim3\":[\"g\",\"h\"]},\n{\"t\":\"2001-01-02\",\"m1\":\"5.0\",\"m2\":\"5.0\",\"dim1\":\"def\",\"dim2\":[\"abc\"],\"dim3\":[\"i\",\"j\"]},\n{\"t\":\"2001-01-03\",\"m1\":\"6.0\",\"m2\":\"6.0\",\"dim1\":\"abc\",\"dim2\":[\"a\"],\"dim3\":[\"k\",\"l\"]},\n{\"t\":\"2001-01-02\",\"m1\":\"5.0\",\"m2\":\"5.0\",\"dim1\":\"def\",\"dim2\":[\"abc\"],\"dim3\":[\"m\",\"n\"]}"
+ },
+ "inputFormat": {
+ "type": "json"
+ }
+ },
+ "tuningConfig": {
+ "type": "index_parallel",
+ "partitionsSpec": {
+ "type": "dynamic"
+ }
+ },
+ "dataSchema": {
+ "dataSource": "nested_data",
+ "granularitySpec": {
+ "type": "uniform",
+ "queryGranularity": "NONE",
+ "rollup": false,
+ "segmentGranularity": "YEAR"
+ },
+ "timestampSpec": {
+ "column": "t",
+ "format": "auto"
+ },
+ "dimensionsSpec": {
+ "dimensions": [
+ "dim1",
+ "dim2",
+ "dim3"
+ ]
+ },
+ "metricsSpec": [
+ {
+ "name": "m1",
+ "type": "floatSum",
+ "fieldName": "m1"
+ },
+ {
+ "name": "m2",
+ "type": "doubleSum",
+ "fieldName": "m2"
+ }
+ ]
+ }
+ }
+}
+```
+<!--END_DOCUSAURUS_CODE_TABS-->
+
+## View the data
+
+Now that the data is loaded, run the following query:
+
+```sql
+select * FROM nested_data
+```
+
+In the results, notice that the column named `dim3` has nested values like `["a","b"]`. The example queries that follow unnest `dim3` and run queries, such as scan.
+
+## Unnest a single column
+
+The following section shows examples of how you can use the unnest datasource in queries. They all use the `nested_data` table you created earlier in the tutorial.
+
+### Scan query
+
+The following native Scan query also unnests the values in the `dim3` column by using the `unnest` datasource type:
+
+<details><summary>Show the query</summary>
+
+```json
+{
+ "queryType": "scan",
+ "dataSource": {
+ "type": "unnest",
+ "base": {
+ "type": "table",
+ "name": "nested_data"
+ },
+ "column": "dim3",
+ "dedup": false,
Review Comment:
We are not using dedup anymore
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: commits-unsubscribe@druid.apache.org
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@druid.apache.org
For additional commands, e-mail: commits-help@druid.apache.org