writer-jill commented on code in PR #13718:
URL: https://github.com/apache/druid/pull/13718#discussion_r1090533694
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examples/quickstart/jupyter-notebooks/nested-columns-tutorial.ipynb:
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@@ -0,0 +1,517 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "ad4e60b6",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Working with nested columns\n",
+ "\n",
+ "<!--\n",
+ " ~ Licensed to the Apache Software Foundation (ASF) under one\n",
+ " ~ or more contributor license agreements. See the NOTICE file\n",
+ " ~ distributed with this work for additional information\n",
+ " ~ regarding copyright ownership. The ASF licenses this file\n",
+ " ~ to you under the Apache License, Version 2.0 (the\n",
+ " ~ \"License\"); you may not use this file except in compliance\n",
+ " ~ with the License. You may obtain a copy of the License at\n",
+ " ~\n",
+ " ~ http://www.apache.org/licenses/LICENSE-2.0\n";,
+ " ~\n",
+ " ~ Unless required by applicable law or agreed to in writing,\n",
+ " ~ software distributed under the License is distributed on an\n",
+ " ~ \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ " ~ KIND, either express or implied. See the License for the\n",
+ " ~ specific language governing permissions and limitations\n",
+ " ~ under the License.\n",
+ " -->\n",
+ "\n",
+ "This tutorial demonstrates how to work with [nested
columns](https://druid.apache.org/docs/latest/querying/nested-columns.html) in
Apache Druid.\n",
+ "\n",
+ "Druid stores nested data structures in `COMPLEX<json>` columns. In this
tutorial you perform the following tasks:\n",
+ "\n",
+ "- Ingest nested JSON data using SQL-based ingestion.\n",
+ "- Transform nested data during ingestion using SQL JSON functions.\n",
+ "- Perform queries to display, filter, and aggregate nested data.\n",
+ "\n",
+ "Druid supports directly ingesting nested data with the following formats:
JSON, Parquet, Avro, ORC, Protobuf.\n",
+ "\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "- [Prerequisites](#Prerequisites)\n",
+ "- [Ingest data](#Ingest-data)\n",
+ "- [Transform data](#Transform-data)\n",
+ "- [Query nested data](#Query-nested-data)\n",
+ "- [Learn more](#Learn-more)\n",
+ "\n",
+ "For the best experience, use Jupyter Lab so that you can always access
the table of contents."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e4638f98",
+ "metadata": {},
+ "source": [
+ "## Prerequisites\n",
+ "\n",
+ "You'll need install the Requests library for Python before you start. For
example:\n",
+ "\n",
+ "```bash\n",
+ "pip3 install requests\n",
+ "```\n",
+ "\n",
+ "Next, you'll need a Druid cluster. This tutorial uses the
`micro-quickstart` config described in the [Druid
quickstart](https://druid.apache.org/docs/latest/tutorials/index.html). So
download that and start it if you haven't already. In the root of the Druid
folder, run the following command to start Druid:\n",
+ "\n",
+ "```bash\n",
+ "./bin/start-druid\n",
+ "```\n",
+ "\n",
+ "Finally, you'll need either Jupyter lab (recommended) or Jupyter
notebook. Both the quickstart Druid cluster and Jupyter notebook are deployed
at `localhost:8888` by default, so you'll need to change the port for Jupyter.
To do so, stop Jupyter and start it again with the `port` parameter included.
For example, you can use the following command to start Jupyter on port
`3001`:\n",
+ "\n",
+ "```bash\n",
+ "# If you're using Jupyter lab\n",
+ "jupyter lab --port 3001\n",
+ "# If you're using Jupyter notebook\n",
+ "jupyter notebook --port 3001 \n",
+ "```\n",
+ "\n",
+ "To start this tutorial, run the next cell. It imports the Python packages
you'll need and defines a variable for the the Druid host the tutorial uses.
The quickstart deployment configures Druid to listen on port `8888` by default,
so you'll be making API calls against `http://localhost:8888`.";
Review Comment:
Updated.
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