Grafana Tutorial: A Beginner’s Guide

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Okay, here’s a comprehensive article on Grafana, designed as a beginner’s guide, totaling approximately 5000 words:

Grafana Tutorial: A Beginner’s Guide

Introduction: The Power of Visualization

In today’s data-driven world, the ability to understand and interpret vast amounts of information is crucial. Raw data, however, is often overwhelming and difficult to glean insights from. This is where data visualization comes in. By transforming raw data into charts, graphs, and other visual representations, we can quickly identify trends, patterns, anomalies, and key performance indicators (KPIs). This, in turn, allows for more informed decision-making, faster problem-solving, and a deeper understanding of complex systems.

Grafana is a leading open-source platform for data visualization, monitoring, and analysis. It excels at connecting to a wide variety of data sources, providing a flexible and powerful interface for creating interactive dashboards, and offering robust alerting capabilities. This tutorial will guide you through the fundamentals of Grafana, from installation to creating your first dashboard, setting up alerts, and exploring advanced features. No prior experience with Grafana is required, but a basic understanding of databases, APIs, and the concept of time-series data will be helpful.

Table of Contents

  1. What is Grafana?

    • Key Features and Benefits
    • Use Cases: When to Use Grafana
    • Grafana vs. Other Visualization Tools (brief comparison)
    • Open Source vs. Grafana Cloud vs. Grafana Enterprise

  2. Installation and Setup

    • System Requirements
    • Installation Methods:
      • Docker (Recommended for Beginners)
      • Linux (Debian/Ubuntu, CentOS/RHEL)
      • Windows
      • macOS
      • From Source (Advanced)
    • Initial Configuration and Access
    • Understanding the Grafana UI (Initial Login)

  3. Connecting to Data Sources

    • What are Data Sources?
    • Supported Data Sources (Overview and Examples)
      • Time-Series Databases (Prometheus, InfluxDB, Graphite)
      • SQL Databases (MySQL, PostgreSQL, Microsoft SQL Server)
      • Cloud Monitoring Services (AWS CloudWatch, Google Cloud Monitoring, Azure Monitor)
      • Other Data Sources (Elasticsearch, Loki, SimpleJson, Testdata)
    • Adding a Data Source (Step-by-Step Example with Prometheus)
    • Testing the Data Source Connection
    • Managing Data Sources

  4. Building Your First Dashboard

    • What is a Dashboard?
    • Creating a New Dashboard
    • Understanding Panels
    • Adding a Panel (Step-by-Step Example with a Time Series Graph)
    • Panel Configuration Options:
      • General Settings (Title, Description, Transparency)
      • Query Editor (Selecting Metrics, Applying Functions, Using Variables)
      • Visualization Settings (Graph Types, Axes, Legends, Colors)
      • Alerting (Basic Introduction, covered in detail later)
    • Saving and Organizing Dashboards
    • Dashboard Variables (Introduction)

  5. Exploring Panel Types

    • Time Series (Graphs)
    • Singlestat (Single Value Displays)
    • Gauge
    • Bar Gauge
    • Table
    • Heatmap
    • Logs
    • Stat
    • Pie Chart
    • Text (Markdown and HTML)
    • Alert List
    • Dashboard List
    • Plugins (Extending Functionality)

  6. Mastering the Query Editor

    • Data Source Specific Query Languages (PromQL, InfluxQL, SQL, etc.)
    • Common Query Editor Features:
      • Metric Selection
      • Label Filters
      • Functions (Aggregation, Rate, Transformations)
      • Time Range Selection
      • Auto-Refresh
    • Example Queries for Different Data Sources (Prometheus, InfluxDB, MySQL)

  7. Alerting: Staying Informed

    • Why Use Alerting?
    • Alerting Concepts:
      • Alert Rules
      • Notification Channels
      • Alert States (OK, Alerting, No Data, Error)
    • Creating an Alert Rule (Step-by-Step Example)
      • Defining Alert Conditions (Thresholds, Time Periods)
      • Configuring Notification Channels (Email, Slack, PagerDuty, etc.)
      • Testing Alert Rules
    • Managing Alert Rules and Notifications
    • Alerting Best Practices

  8. Dashboard Variables: Dynamic Dashboards

    • What are Dashboard Variables?
    • Types of Variables:
      • Query
      • Custom
      • Constant
      • Data Source
      • Interval
      • Text Box
      • Ad hoc filters
    • Creating and Using Variables (Step-by-Step Examples)
    • Using Variables in Queries and Panel Titles
    • Templating for Dynamic Dashboards

  9. User Management and Permissions

    • Users and Organizations
    • Roles (Admin, Editor, Viewer)
    • Creating and Managing Users
    • Assigning Roles and Permissions
    • Authentication Methods (Local, LDAP, OAuth)

  10. Advanced Features and Best Practices

    • Annotations: Adding Context to Your Data
    • Provisioning: Automating Grafana Setup
    • Explore: Ad-Hoc Querying and Data Exploration
    • Grafana CLI: Command-Line Interface
    • API Access: Integrating with Other Systems
    • Performance Optimization
    • Security Best Practices
    • Backup and Restore

  11. Troubleshooting Common Issues

    • Data Source Connection Errors
    • Panel Display Issues
    • Alerting Problems
    • Performance Problems
    • Finding Help and Resources

  12. Conclusion: Your Journey with Grafana

1. What is Grafana?

Grafana is an open-source, platform-agnostic, and highly extensible data visualization and monitoring tool. It allows you to query, visualize, alert on, and understand your metrics no matter where they are stored. Think of it as a central hub for all your operational data, providing a single pane of glass view into the health and performance of your systems.

  • Key Features and Benefits:

    • Data Source Agnostic: Connects to a vast array of data sources, including time-series databases, SQL databases, cloud monitoring services, and more.
    • Interactive Dashboards: Create dynamic and customizable dashboards with a wide variety of panel types.
    • Powerful Query Editor: Craft complex queries using data source-specific languages (like PromQL for Prometheus) and built-in functions.
    • Robust Alerting: Set up alerts based on metric thresholds and receive notifications through various channels (email, Slack, PagerDuty, etc.).
    • User and Permission Management: Control access to dashboards and data sources with granular permissions.
    • Extensible via Plugins: Expand Grafana’s functionality with a rich ecosystem of community and official plugins.
    • Open Source and Community Driven: Benefit from a large and active community, constant development, and transparent codebase.
    • Templating and Variables: Create dynamic dashboards that adapt to different environments and data sets.
    • Annotations: Add contextual information to your graphs, highlighting events or deployments.
    • Explore View: Perform ad-hoc querying and data exploration outside of dashboards.
    • Provisioning: Grafana can be provisioned, meaning you can automate the creation of data sources, dashboards, and users through configuration files.

  • Use Cases: When to Use Grafana:

    • System Monitoring: Track server metrics (CPU, memory, disk usage), application performance (response times, error rates), and network health.
    • Application Performance Monitoring (APM): Monitor the performance of your applications, identify bottlenecks, and troubleshoot issues.
    • Business Intelligence (BI): Visualize key business metrics, track sales, marketing performance, and customer behavior.
    • IoT Monitoring: Collect and visualize data from IoT devices, such as sensors and actuators.
    • Security Monitoring: Analyze security logs, detect suspicious activity, and monitor security infrastructure.
    • Process Monitoring: Track the performance of industrial processes, manufacturing lines, and other operational workflows.
    • Home Automation: Monitor your smart home devices and energy consumption.

  • Grafana vs. Other Visualization Tools (brief comparison):

    • Kibana: Primarily focused on visualizing data from Elasticsearch, while Grafana supports a wider range of data sources.
    • Tableau/Power BI: More focused on business intelligence and data analysis, often requiring paid licenses. Grafana’s core functionality is open source.
    • Prometheus (with its built-in expression browser): Prometheus’s built-in visualization capabilities are limited compared to Grafana’s rich dashboarding features. Grafana is often used with Prometheus.
    • Chronograf: Part of the TICK stack (Telegraf, InfluxDB, Chronograf, Kapacitor), primarily focused on InfluxDB data.

  • Open Source vs. Grafana Cloud vs. Grafana Enterprise:

    • Grafana (Open Source): The core Grafana platform, free to use and self-hosted. Requires you to manage the infrastructure.
    • Grafana Cloud: A fully managed Grafana service hosted by Grafana Labs. Offers a free tier and paid plans with additional features and support. Simplifies setup and maintenance.
    • Grafana Enterprise: A commercial version of Grafana with additional features, support, and plugins, designed for large organizations with specific needs (e.g., advanced authentication, auditing, usage insights).

2. Installation and Setup

This section covers the installation process for Grafana. We’ll focus on Docker as the recommended method for beginners due to its simplicity and portability. We’ll also briefly cover other installation methods.

  • System Requirements:

    • Grafana has modest resource requirements. A small instance can run on a system with 1 CPU core and 256MB of RAM. However, for production use, especially with many dashboards and users, more resources are recommended.
    • A modern web browser (Chrome, Firefox, Safari, Edge) is required to access the Grafana UI.

  • Installation Methods:

    • Docker (Recommended for Beginners):

      Docker provides a containerized environment, making installation and management very easy. You’ll need Docker and Docker Compose installed on your system.

      1. Create a docker-compose.yml file:

        “`yaml
        version: ‘3’

        services:
        grafana:
        image: grafana/grafana:latest
        ports:
        – “3000:3000”
        volumes:
        – grafana-storage:/var/lib/grafana

        volumes:
        grafana-storage:
        “`

      2. Run Docker Compose:

        bash
        docker-compose up -d

        This command will download the latest Grafana image, create a container, and start it. The -d flag runs the container in detached mode (in the background). The volumes section creates a persistent volume to store Grafana’s data, ensuring it survives container restarts.

      3. Access Grafana: Open your web browser and go to http://localhost:3000 (or the IP address of your Docker host).

    • Linux (Debian/Ubuntu, CentOS/RHEL):

      Installation on Linux typically involves downloading the appropriate package (.deb for Debian/Ubuntu, .rpm for CentOS/RHEL) and using the package manager to install it.

      Debian/Ubuntu:

      bash
      sudo apt-get install -y apt-transport-https software-properties-common wget
      wget -q -O - https://packages.grafana.com/gpg.key | sudo apt-key add -
      echo "deb https://packages.grafana.com/oss/deb stable main" | sudo tee -a /etc/apt/sources.list.d/grafana.list
      sudo apt-get update
      sudo apt-get install grafana
      sudo systemctl start grafana-server
      sudo systemctl enable grafana-server

      CentOS/RHEL:

      bash
      sudo yum install -y https://dl.grafana.com/oss/release/grafana-latest.x86_64.rpm
      sudo systemctl start grafana-server
      sudo systemctl enable grafana-server

    • Windows:

      Download the Windows installer from the Grafana website (grafana.com/downloads) and follow the installation instructions.

    • macOS:

      You can use Homebrew to install Grafana on macOS:

      bash
      brew update
      brew install grafana
      brew services start grafana

    • From Source (Advanced):

      This method involves cloning the Grafana GitHub repository and building the software from source. This is generally only recommended for developers or those who need to customize the Grafana codebase. Refer to the official Grafana documentation for detailed instructions.

  • Initial Configuration and Access:

    After installation, you may need to adjust some configuration settings. The main configuration file is usually located at:

    • Linux: /etc/grafana/grafana.ini
    • Docker: You can modify the configuration by mounting a custom grafana.ini file into the container.
    • Windows: C:\Program Files\GrafanaLabs\grafana\conf\defaults.ini (and custom.ini for overrides)
    • macOS: /usr/local/etc/grafana/grafana.ini

    Key configuration options include:

    • http_port: The port Grafana listens on (default: 3000).
    • domain: The domain name or IP address of your Grafana instance.
    • root_url: The full URL used to access Grafana.
    • admin_user: The initial administrator username (default: admin).
    • admin_password: The initial administrator password (default: admin). Change this immediately!

  • Understanding the Grafana UI (Initial Login):

    Once Grafana is running, access it through your web browser using the configured URL (e.g., http://localhost:3000). You’ll be presented with the login screen. Use the default credentials (admin/admin) and change the password immediately after logging in.

    The main Grafana UI elements are:

    • Sidebar: Provides navigation to different sections (Dashboards, Explore, Alerting, Configuration, etc.).
    • Top Navigation Bar: Contains the dashboard search, time range selector, refresh control, and user menu.
    • Dashboard Area: The main area where dashboards and panels are displayed.
    • Panel Editor: (Appears when adding or editing a panel) Allows you to configure the panel’s data source, query, visualization settings, and alerting.

3. Connecting to Data Sources

  • What are Data Sources?

    Data sources are the backends that store the data you want to visualize in Grafana. Grafana doesn’t store the data itself; it acts as a visualization layer on top of your existing data stores.

  • Supported Data Sources (Overview and Examples):

    Grafana supports a wide range of data sources, including:

    • Time-Series Databases:

      • Prometheus: A popular open-source monitoring and alerting system with a powerful query language (PromQL). Excellent for collecting metrics from applications and infrastructure.
      • InfluxDB: A time-series database designed for high-performance ingestion and querying of time-stamped data. Often used for IoT and sensor data.
      • Graphite: A mature time-series database that uses a hierarchical naming scheme for metrics.
      • OpenTSDB: A scalable time-series database built on top of HBase.
      • TimescaleDB: A time-series database built on top of PostgreSQL.

    • SQL Databases:

      • MySQL: A widely used open-source relational database.
      • PostgreSQL: Another popular open-source relational database known for its extensibility.
      • Microsoft SQL Server: A commercial relational database from Microsoft.

    • Cloud Monitoring Services:

      • AWS CloudWatch: Amazon’s monitoring service for AWS resources and applications.
      • Google Cloud Monitoring (formerly Stackdriver): Google’s monitoring service for Google Cloud Platform resources and applications.
      • Azure Monitor: Microsoft’s monitoring service for Azure resources and applications.

    • Other Data Sources:

      • Elasticsearch: A search and analytics engine often used for log data.
      • Loki: A log aggregation system inspired by Prometheus, designed for efficiency and scalability.
      • SimpleJson: A simple HTTP-based data source for custom applications.
      • Testdata: A built-in data source for generating sample data for testing and experimentation.
      • Splunk: A data platform to search, analyze, and visualize machine-generated data.

  • Adding a Data Source (Step-by-Step Example with Prometheus):

    Let’s add a Prometheus data source as an example. Assume you have a Prometheus server running and collecting metrics.

    1. Go to Configuration -> Data Sources: In the Grafana sidebar, click the gear icon (Configuration) and then select “Data Sources.”
    2. Click “Add data source”: This will bring up a list of all supported data sources.
    3. Select “Prometheus”: Click on the Prometheus data source type.
    4. Configure the Data Source:
      • Name: Give your data source a descriptive name (e.g., “My Prometheus”).
      • URL: Enter the URL of your Prometheus server (e.g., http://localhost:9090).
      • Access: Choose “Server (default)” for direct access from the Grafana server. “Browser” access is used if Grafana needs to access the data source through the user’s browser (less common).
      • HTTP Method: Usually GET
      • Auth: If your Prometheus server requires authentication, configure the appropriate settings (Basic Auth, API Key, etc.).
      • Scrape interval: Set a custom scrape interval if you need to override the default
      • Query timeout: Configure how long Grafana should wait for a query before timing out.
      • Other Settings: You can customize various other settings, such as HTTP headers, TLS configuration, and more. Refer to the Grafana documentation for details.
    5. Click “Save & Test”: Grafana will attempt to connect to your Prometheus server and verify the settings. If successful, you’ll see a green “Data source is working” message.

  • Testing the Data Source Connection:

    The “Save & Test” button is crucial for verifying that Grafana can successfully communicate with your data source. If the test fails, double-check the URL, authentication settings, and network connectivity.

  • Managing Data Sources:

    The Data Sources page allows you to:

    • Edit existing data sources: Modify the settings of a data source.
    • Delete data sources: Remove a data source from Grafana.
    • Set a default data source: Choose the data source that will be selected by default when creating new panels.

4. Building Your First Dashboard

  • What is a Dashboard?

    A dashboard is a collection of panels that visualize data from one or more data sources. Dashboards provide a high-level overview of your systems, applications, or processes.

  • Creating a New Dashboard:

    1. Click the “+” icon in the sidebar: Select “Dashboard” -> “New Dashboard”
    2. Choose “Add new panel”: This will start with an empty dashboard. You can also choose to import a dashboard from a JSON file or a Grafana.com dashboard.

  • Understanding Panels:

    Panels are the building blocks of dashboards. Each panel displays data from a specific query and uses a particular visualization type (graph, singlestat, table, etc.).

  • Adding a Panel (Step-by-Step Example with a Time Series Graph):

    1. Click “Add new panel” (or the “+” icon in an existing dashboard).
    2. Select a Data Source: Choose the data source you want to use for this panel (e.g., the Prometheus data source you added earlier).

    3. Write a Query: In the Query Editor, write a query to retrieve the data you want to visualize. For Prometheus, this will be a PromQL query. For example, to display the total CPU usage, you might use a query like:

      promql
      100 - (avg by (instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)
      This query calculates the percentage of CPU time not spent in idle mode, averaged over the last 5 minutes, grouped by instance. The irate function calculates the per-second rate of increase.
      4. Choose “Time series” Visualization: The default visualization should be Time series, represented by a graph icon.

    4. Configure Visualization Settings:

      • General: Give your panel a title (e.g., “CPU Usage”).
      • Axes: Configure the units for the Y-axis (e.g., percent).
      • Legend: Show or hide the legend, and customize its position and format.
      • Display: Choose line styles, fill opacity, point visibility, etc.
      • Time Range: Select the time range to display (e.g., last 1 hour, last 6 hours, etc.). This can also be controlled globally for the entire dashboard.

  • Panel Configuration Options:

    • General Settings:

      • Title: The name of the panel.
      • Description: An optional description of the panel.
      • Transparency: Make a panel background transparent.
      • Repeat Panel: Repeat a panel for each value of a variable (more on variables later).

    • Query Editor:

      • Data Source Selection: Choose the data source for the panel.
      • Query Input: Write the query to retrieve data (using the appropriate query language for the data source).
      • Metric Selection: (If applicable) Browse and select metrics from the data source.
      • Label Filters: (If applicable) Filter data based on labels.
      • Functions: Apply functions to transform or aggregate the data.
      • Time Range: Control the time range for the query (overrides the dashboard-level time range).
      • Instant Queries: Get the current value of a metric, rather than a time series.

    • Visualization Settings:

      • Graph Types: Select the type of graph (line, bar, stacked, etc.).
      • Axes: Configure the units, labels, and scales for the X and Y axes.
      • Legend: Customize the appearance and position of the legend.
      • Colors: Choose colors for lines, bars, and other elements.
      • Display: Control line styles, fill opacity, point visibility, stacking, and other visual aspects.
      • Thresholds: Define thresholds to visually highlight values above or below certain levels.

    • Alerting (Basic Introduction):

      • The “Alert” tab allows you to create alert rules based on the panel’s query. We’ll cover alerting in more detail later.

  • Saving and Organizing Dashboards:

    • Click the “Save” icon (disk) in the top navigation bar.
    • Give your dashboard a name.
    • Optionally, add a description.
    • Choose a folder to save the dashboard in. Folders help you organize your dashboards.

  • Dashboard Variables (Introduction):

    Dashboard variables allow you to create dynamic dashboards. For example, you could create a variable to select different instances or environments, and the panels on the dashboard would update automatically based on the selected variable. We’ll cover variables in more detail later.

5. Exploring Panel Types

Grafana offers a variety of panel types to visualize your data in different ways. Here’s an overview of some of the most common panel types:

  • Time Series (Graphs): The most common panel type, used to display data over time. Supports various graph styles (line, bar, stacked, etc.). Excellent for visualizing trends, patterns, and anomalies.
  • Singlestat (Single Value Displays): Displays a single numerical value, often with a unit and optional thresholds. Useful for showing key metrics at a glance.
  • Gauge: Displays a single value on a gauge, with customizable ranges and thresholds. Visually appealing for representing values within a specific range.
  • Bar Gauge: Similar to a gauge, but displays the value as a horizontal or vertical bar. Good for showing progress or utilization.
  • Table: Displays data in a tabular format. Useful for showing detailed information or lists of items.
  • Heatmap: Visualizes data using a color scale, where each cell represents a value. Effective for identifying patterns and outliers in large datasets.
  • Logs: Displays log data from data sources like Loki or Elasticsearch. Allows you to view, filter, and search logs directly within Grafana.
  • Stat: A more modern version of Singlestat, offering more flexibility and visualization options.
  • Pie Chart: Displays data as a pie chart, showing the proportions of different categories.
  • Text (Markdown and HTML): Displays text, formatted using Markdown or HTML. Useful for adding instructions, explanations, or links to your dashboards.
  • Alert List: Displays a list of active alerts.
  • Dashboard List: Displays a list of other dashboards, allowing you to create navigation dashboards.

  • Plugins (Extending Functionality): Grafana’s functionality can be extended through plugins. There are many community-developed and official plugins available, adding new data sources, panel types, and features. Examples include:

    • Worldmap Panel: Displays data on a geographical map.
    • Clock Panel: Displays the current time.
    • Status Panel: Shows the status of different services or components.

6. Mastering the Query Editor

The Query Editor is where you define how Grafana retrieves data from your data sources. Each data source has its own query language. Understanding the basics of these languages is essential for creating effective visualizations.

  • Data Source Specific Query Languages:

    • Prometheus (PromQL): A powerful and flexible query language designed for time-series data. Key concepts include:

      • Metric Names: Identify the metric you want to query (e.g., node_cpu_seconds_total).
      • Labels: Key-value pairs that provide additional dimensions to metrics (e.g., mode="idle", instance="server1").
      • Selectors: Used to filter metrics based on labels (e.g., {mode="idle"}).
      • Functions: Used to aggregate, calculate rates, and transform data (e.g., avg(), irate(), sum()).
      • Time Ranges: Specify the time period for the query (e.g., [5m], [1h]).
      • Instant Vectors: Return the latest value for each time series.
      • Range Vectors: Return a range of values over time for each time series.

    • InfluxDB (InfluxQL): A SQL-like query language for InfluxDB. Key concepts include:

      • SELECT: Specifies the fields to retrieve.
      • FROM: Specifies the measurement (table) to query.
      • WHERE: Filters data based on tags and time.
      • GROUP BY: Groups data by tags or time intervals.
      • Functions: Used for aggregation and transformations.

    • MySQL/PostgreSQL/Microsoft SQL Server (SQL): The standard query language for relational databases. Key concepts include:

      • SELECT: Specifies the columns to retrieve.
      • FROM: Specifies the table to query.
      • WHERE: Filters data based on conditions.
      • GROUP BY: Groups data by columns.
      • ORDER BY: Sorts the results.
      • JOIN: Combines data from multiple tables.
      • Functions: Used for calculations and data manipulation. Grafana has special macros like $__timeFilter() to help with time series data in SQL.

    • Elasticsearch (Elasticsearch Query DSL): A JSON-based query language for Elasticsearch.

    • Loki (LogQL): A query language inspired by PromQL, designed for querying logs in Loki.

  • Common Query Editor Features:

    • Metric Selection: Many data sources offer a metric browser or autocomplete to make selecting metrics easier.
    • Label Filters: Use label filters to narrow down the data you’re querying, selecting specific instances, environments, or other dimensions.

    • Functions: Apply various functions to your data to perform calculations, aggregations, and transformations. Examples include:

      • avg(): Calculates the average.
      • sum(): Calculates the sum.
      • min(): Finds the minimum value.
      • max(): Finds the maximum value.
      • rate(): Calculates the per-second rate of increase (useful for counters).
      • irate(): Calculates the per-second rate of increase, more sensitive to short-term changes than rate().
      • count(): Counts the number of time series.
      • topk(): Returns the top K time series.
      • bottomk(): Returns the bottom K time series.

    • Time Range Selection: Control the time range for the query. You can use relative time ranges (e.g., “now-1h”, “now-7d”) or absolute time ranges.

    • Auto-Refresh: Configure the query to automatically refresh at a specified interval.

  • Example Queries for Different Data Sources:

    • Prometheus (PromQL):

      • Total CPU usage (percentage):

        promql
        100 - (avg by (instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

      • Memory usage (bytes):

        promql
        node_memory_MemTotal_bytes - node_memory_MemFree_bytes - node_memory_Buffers_bytes - node_memory_Cached_bytes

      • HTTP request rate:

        promql
        rate(http_requests_total[5m])

    • InfluxDB (InfluxQL):

      • CPU usage (percentage) from a measurement called ‘cpu’:

        influxql
        SELECT 100 - mean("usage_idle") FROM "cpu" WHERE $timeFilter GROUP BY time($__interval) fill(null)
        $__interval and $timeFilter are Grafana macros that get replaced with appropriate values.

      • Average temperature from a measurement called ‘sensors’:

        influxql
        SELECT mean("temperature") FROM "sensors" WHERE $timeFilter GROUP BY time($__interval) fill(null)

    • MySQL (SQL):

      • Number of users registered in the last hour:

        sql
        SELECT count(*) FROM users WHERE created_at > NOW() - INTERVAL 1 HOUR

      • Average order value:
        sql
        SELECT $__time(order_date), AVG(order_value) as "Average Order Value"
        FROM orders
        WHERE $__timeFilter(order_date)
        GROUP BY 1
        ORDER BY 1
        The $__time and $__timeFilter Grafana macros simplify time-series queries.

7. Alerting: Staying Informed

  • Why Use Alerting?

    Alerting is a crucial part of monitoring. It allows you to be notified when something goes wrong, enabling you to respond quickly to issues and minimize downtime. Grafana’s alerting system is powerful and flexible, allowing you to create sophisticated alert rules based on your metrics.

  • Alerting Concepts:

    • Alert Rules: Define the conditions that trigger an alert. An alert rule is associated with a specific panel and query.
    • Notification Channels: Specify how you want to be notified when an alert is triggered (e.g., email, Slack, PagerDuty, webhook).
    • Alert States:
      • OK: The alert condition is not met.
      • Alerting: The alert condition is met, and a notification has been sent.
      • No Data: The query returned no data.
      • Error: An error occurred while evaluating the alert rule.
      • Pending: (Optional) An alert state between OK and Alerting, giving time for the condition to persist before actually alerting.

  • Creating an Alert Rule (Step-by-Step Example):

    1. Edit the Panel: Go to the panel you want to create an alert for and click the “Edit” button.
    2. Go to the “Alert” Tab: Click the “Alert” tab in the panel editor.
    3. Click “Create Alert”.

    4. Configure the Alert Rule:

      • Name: Give your alert rule a descriptive name (e.g., “High CPU Usage”).
      • Evaluate every: Specify how often Grafana should evaluate the alert rule (e.g., “1m” for every minute).
      • For: Specify how long the condition must be true before the alert triggers (e.g. “5m”). This helps prevent alerts from firing on short-lived spikes.
      • Conditions: Define the conditions that trigger the alert. This usually involves comparing the result of a query to a threshold. For example:
        • WHEN: Choose a reducer function, such as avg(), min(), max(), sum(), last(). This reduces the time series returned by your query to a single value.
        • OF: Select the query (usually ‘A’ if you have only one query in the panel).
        • IS ABOVE: Choose a comparison operator (IS ABOVE, IS BELOW, IS OUTSIDE RANGE, IS WITHIN RANGE, HAS NO VALUE).
        • Value: Enter the threshold value. For example, if you want to be alerted when the average CPU usage is above 80%, you would choose avg(), IS ABOVE, and enter 80.
      • No Data & Error Handling: Configure what happens if the query returns No Data or results in an Error.

    5. Configure Notifications:

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