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|
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "de3b5d4c", |
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"metadata": {}, |
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"source": [ |
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"# 🧠 Grafana Dashboard Summarizer\n", |
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"Simulate reading a Grafana dashboard JSON and summarize its panels using GPT or plain logic." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "0abf3aaf", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import os\n", |
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"from dotenv import load_dotenv\n", |
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"from IPython.display import Markdown, display\n", |
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"from openai import OpenAI\n", |
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"import json\n", |
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"import pandas as pd" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ad82ca65", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"\n", |
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"\n", |
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"with open(\"mock_grafana_dashboard.json\", \"r\") as f:\n", |
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" data = json.load(f)\n", |
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"\n", |
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"dashboard = data[\"dashboard\"]\n", |
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"panels = dashboard[\"panels\"]\n", |
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"print(f\"Dashboard Title: {dashboard['title']}\")\n", |
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"print(f\"Total Panels: {len(panels)}\\n\")\n", |
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"for p in panels:\n", |
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" print(f\"- {p['title']} ({p['type']})\")\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "1bf45c0f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Extracting panel data\n", |
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"\n", |
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"panel_data = []\n", |
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"for p in panels:\n", |
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" thresholds = p.get(\"fieldConfig\", {}).get(\"defaults\", {}).get(\"thresholds\", {}).get(\"steps\", [])\n", |
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" panel_data.append({\n", |
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" \"Title\": p[\"title\"],\n", |
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" \"Type\": p[\"type\"],\n", |
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" \"Unit\": p.get(\"fieldConfig\", {}).get(\"defaults\", {}).get(\"unit\", \"N/A\"),\n", |
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" \"Thresholds\": thresholds\n", |
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" })\n", |
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"\n", |
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"df = pd.DataFrame(panel_data)\n", |
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"df\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "90b67133", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"\n", |
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"summary_prompt = f\"\"\"\n", |
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"You are a helpful assistant summarizing a system monitoring dashboard.\n", |
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"\n", |
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"Dashboard: {dashboard['title']}\n", |
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"Panels:\n", |
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"\"\"\"\n", |
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"for idx, row in df.iterrows():\n", |
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" summary_prompt += f\"- {row['Title']} [{row['Type']}] - Unit: {row['Unit']}, Thresholds: {row['Thresholds']}\\n\"\n", |
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"\n", |
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"print(summary_prompt)\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "69a4208c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"\n", |
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"\n", |
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"load_dotenv(override=True)\n", |
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"api_key = os.getenv('OPENAI_API_KEY')\n", |
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"# Check the key\n", |
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"\n", |
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"if not api_key:\n", |
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
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"elif not api_key.startswith(\"sk-proj-\"):\n", |
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" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
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"elif api_key.strip() != api_key:\n", |
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" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
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"else:\n", |
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" print(\"API key found and looks good so far!\")\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "2eee5a32", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"openai = OpenAI()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "660eedb7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def summarize():\n", |
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" response = openai.chat.completions.create(\n", |
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" model=\"gpt-4o-mini\",\n", |
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" messages=[\n", |
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" {\"role\": \"system\", \"content\": \"You are a Grafana dashboard summarizer.\"},\n", |
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" {\"role\": \"user\", \"content\": summary_prompt}\n", |
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" ]\n", |
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")\n", |
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" return response.choices[0].message.content" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "55f57d56", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"\n", |
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"summary = summarize()\n", |
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"display(Markdown(summary))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "10dbfd6c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "arunllms", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.11" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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} |
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{ |
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"dashboard": { |
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"id": null, |
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"uid": "mock-sys-metrics", |
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"title": "Mock System Metrics Dashboard", |
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"timezone": "browser", |
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"panels": [ |
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{ |
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"id": 1, |
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"title": "CPU Usage (%)", |
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"type": "stat", |
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"datasource": "MockData", |
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"targets": [], |
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"fieldConfig": { |
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"defaults": { |
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"unit": "percent", |
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"thresholds": { |
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"mode": "percentage", |
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"steps": [ |
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{ "color": "green", "value": null }, |
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{ "color": "orange", "value": 70 }, |
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{ "color": "red", "value": 90 } |
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] |
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} |
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} |
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}, |
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"options": { |
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"reduceOptions": { |
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"calcs": ["mean"] |
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} |
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} |
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}, |
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{ |
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"id": 2, |
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"title": "Memory Usage", |
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"type": "gauge", |
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"datasource": "MockData", |
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"fieldConfig": { |
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"defaults": { |
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"unit": "bytes", |
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"max": 16e9, |
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"thresholds": { |
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"steps": [ |
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{ "color": "green", "value": null }, |
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{ "color": "orange", "value": 12e9 }, |
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{ "color": "red", "value": 14e9 } |
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] |
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} |
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} |
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} |
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}, |
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{ |
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"id": 3, |
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"title": "Disk Read Errors", |
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"type": "stat", |
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"datasource": "MockData", |
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"fieldConfig": { |
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"defaults": { |
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"unit": "short", |
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"thresholds": { |
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"steps": [ |
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{ "color": "green", "value": null }, |
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{ "color": "orange", "value": 5 }, |
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{ "color": "red", "value": 10 } |
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] |
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} |
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} |
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} |
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}, |
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{ |
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"id": 4, |
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"title": "GPU Usage (%)", |
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"type": "gauge", |
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"datasource": "MockData", |
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"fieldConfig": { |
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"defaults": { |
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"unit": "percent", |
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"thresholds": { |
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"steps": [ |
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{ "color": "green", "value": null }, |
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{ "color": "orange", "value": 75 }, |
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{ "color": "red", "value": 90 } |
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] |
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} |
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} |
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} |
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} |
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], |
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"schemaVersion": 30, |
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"version": 1, |
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"refresh": "30s" |
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}, |
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"overwrite": true |
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} |
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|
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