sergioferragut commented on code in PR #14742:
URL: https://github.com/apache/druid/pull/14742#discussion_r1289222461
##########
examples/quickstart/jupyter-notebooks/notebooks/01-introduction/02-datagen-intro.ipynb:
##########
@@ -0,0 +1,618 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9e07b3f5-d919-4179-91a1-0f6b66c42757",
+ "metadata": {},
+ "source": [
+ "# Data Generator Server\n",
+ "The default Docker Compose deployment includes a data generation service
created from the published Docker image at `imply/datagen:latest`. \n",
+ "This image is built by the project
https://github.com/implydata/druid-datagenerator. \n",
+ "\n",
+ "This notebook shows you how to use of the data generation service
included in the Docker Compose deployment. It explains how to use pre-defined
data generator configurations as well as how to build a custom data generator.
You will also learn how to create sample data files for batch ingestion and how
to generate live streaming data for streaming ingestion.\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "* [Initialization](#Initialization)\n",
+ "* [List Available Configurations](#List-available-configurations)\n",
+ "* [Generate a data file for backfilling
history](#Generate-a-data-file-for-backfilling-history)\n",
+ "* [Batch Ingestion of Generated
Files](#Batch-Ingestion-of-Generated-Files)\n",
+ "* [Generate custom data](#Generate-custom-data)\n",
+ "* [Stream generated data](#Stream-generated-data)\n",
+ "* [Ingest data from a stream](#Ingest-data-from-a-stream)\n",
+ "* [Cleanup](#Cleanup)\n",
+ "\n",
+ "\n",
+ "## Initialization\n",
+ "\n",
+ "To interact with the data generation service, use the REST client
provided in the [`druidapi` Python
package](https://druid.apache.org/docs/latest/tutorials/tutorial-jupyter-index.html#python-api-for-druid)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f84766c7-c6a5-4496-91a3-abdb8ddd2375",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import druidapi\n",
+ "import os\n",
+ "\n",
+ "# Datagen client \n",
+ "datagen = druidapi.rest.DruidRestClient(\"http://datagen:9999\";)\n",
+ "\n",
+ "if (os.environ['DRUID_HOST'] == None):\n",
+ " druid_host=f\"http://router:8888\"\n";,
+ "else:\n",
+ " druid_host=f\"http://{os.environ['DRUID_HOST']}:8888\"\n",
+ "\n",
+ "# Druid client\n",
+ "druid = druidapi.jupyter_client(druid_host)\n",
+ "\n",
+ "\n",
+ "\n",
+ "# these imports and constants are used by multiple cells\n",
+ "from datetime import datetime, timedelta\n",
+ "import json\n",
+ "\n",
+ "headers = {\n",
+ " 'Content-Type': 'application/json'\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c54af617-0998-4010-90c3-9b5a38a09a5f",
+ "metadata": {},
+ "source": [
+ "### List available configurations\n",
+ "Use `/list` API endpoint to get the data generator's available
configuration values with pre-defined data generator schemas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1ba6a80a-c49b-4abf-943b-9dad82f2ae13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(datagen.get(f\"/list\", require_ok=False).json())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae88a3b7-60da-405d-bcf4-fb4affcfe973",
+ "metadata": {},
+ "source": [
+ "### Generate a data file for backfilling history\n",
+ "When generating a file for backfill purposes, you can select the start
time and the duration of the simulation.\n",
+ "This example shows how to configure the data generator request:\n",
+ "* `name`: an arbitrary name you assign to the job. Refer to the job name
to get the job status or to stop the job.\n",
+ "* `target.type`: \"file\" to generate a data file\n",
+ "* `target.path`: identifies the name of the file to generate, it will
ignore any path specified.\n",
+ "* `time_type`,`time`: The data generator simulates the time range you
specify with a start timestamp in the `\"time_type\"` property and a duration
in the `\"time\"` property with `h` suffix for hours, `m` for minutes or `s`
for seconds.\n",
+ "- `\"concurrency\"` indicates the maximum number of entities used
concurrently to generate events. Each entity is a separate state machine that
simulates things like user sessions, IoT devices, or other concurrent sources
of event data. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "811ff58f-75af-4092-a08d-5e07a51592ff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Configure the start time to one hour prior to the current time. \n",
+ "startDateTime = (datetime.now() - timedelta(hours =
1)).strftime('%Y-%m-%dT%H:%M:%S.001')\n",
+ "print(f\"Starting to generate history at {startDateTime}.\")\n",
+ "\n",
+ "# give the datagen job a name for use in subsequent API calls\n",
Review Comment:
done
--
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: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
