Introduction

Let's face it, building a monitoring dashboard is crucial for keeping track of your application's performance and infrastructure health to deliver a seamless user experience. But, we've all been there—staring at a sea of metrics, struggling to make sense of it all. That's where effective monitoring dashboards come in.

A well-designed dashboard can help you identify potential issues before they become major problems, optimize resource allocation, and make data-driven decisions. But, what makes a dashboard truly effective? And how do you build a monitoring dashboard that provides actionable insights?

OpenObserve: Simplifying Monitoring

Our open-source observability platform makes monitoring simple and intuitive, with:

• Easy setup
• Powerful dashboards
• Intuitive user interface

Follow along with our simple guide below and check out our detailed YouTube tutorial!

What to Expect?

In this guide, we'll explore how to build monitoring dashboards in OpenObserve, we'll dive into the key features, best practices, and step-by-step instructions for creating customizable dashboards that meet your specific needs.

Key Features of OpenObserve Dashboards

• Real-time data visualizations: Automatically refresh your dashboards to monitor live streams with accuracy.
• Easy Dashboard Creation: Intuitive UI and extensive panel customization.
• 18+ Chart Types: Visualize data with a variety of chart options, including heatmaps, tables, and time-series graphs.
• Customizable: Personalize dashboards with filters, variables, and templating for tailored views.
• Alerting and Notification: Set up custom alerts and notifications to stay informed of performance issues.
• Scalable: Support for large-scale deployments with high-performance rendering.

Now that you know the highlights, let's move on to creating your first dashboard in OpenObserve!

Getting Started: Setting Up OpenObserve

Step 1: Installing OpenObserve

Using Docker (Recommended for Quick Setup):

If you have Docker installed on your machine, setting up OpenObserve is straightforward. Run the following command in your terminal or command prompt:

Docker images are available at https://gallery.ecr.aws/zinclabs/openobserve

docker run -v $PWD/data:/data -e ZO_DATA_DIR="/data" -p 5080:5080 \
 -e ZO_ROOT_USER_EMAIL="root@example.com" -e ZO_ROOT_USER_PASSWORD="Complexpass#123" \public.ecr.aws/zinclabs/openobserve:latest

Replace root@example.com and Complexpass#123 with your desired credentials.

Once the container is up and running, navigate to http://localhost:5080 and login in your web browser to access the OpenObserve UI.

For more installation options and instructions, refer to our Quickstart Documentation.

Step 2: Loading Sample Data

Let's start by loading sample data into OpenObserve. This will allow you to quickly experience all the Dashboard features without needing to set up custom data sources, making it easy to dive into building and visualizing dashboards.

Ensure you have curl installed on your system before running these commands.

Use the following command to download real-life log data:

curl -L https://zinc-public-data.s3.us-west-2.amazonaws.com/zinc-enl/sample-k8s-logs/k8slog_json.json.zip -o k8slog_json.json.zip

Unzip the downloaded file:

unzip k8slog_json.json.zip

To ingest this data into OpenObserve, use:

curl http://localhost:5080/api/default/default/_json -i -u "root@example.com:Complexpass#123"  -d "@k8slog_json.json"

Note: The process is similar for cloud service, simply replace the URL and credentials accordingly.

Explore Sample Data in OpenObserve

1. Access OpenObserve UI: Cloud | Self-Hosted
2. Login with your credentials.
3. Visit Logs page and select default index.

Tip: Use the search bar to quickly find specific log entries or patterns.

Building Your First Monitoring Dashboard with OpenObserve

Creating a New Dashboard

1. Navigate to the Dashboards section in OpenObserve's left-side menu.
2. Click New Dashboard and provide a name, description, and choose a folder.

Tip: Use descriptive names for your dashboards to easily identify them later.

3. Click Save to create an empty dashboard.

Adding and Configuring Panels

1. Click Add Panel on the empty dashboard.
2. Select the stream type to Logs and select a data stream.

3. Choose a field (e.g. stream_name, body).

Panel 1:

4. Assign fields to axes:

• X-axis: timestamp (default)
• Y-axis: body (count)

Chart Type: Line Chart (for time-series data)

5. Click Apply to preview and save changes.

Panel 2:

1. Add a New Panel: Click on + Add Panel and select a chart-type to visualize

2. Repeat the panel configuration steps as needed.
3. Click Apply and preview.

• Note: OpenObserve offers 18 different chart types, each suited for a specific purpose. To create an effective dashboard, select the chart type that best fits your use case. For instance, use an Area Chart to show trends over time or a Bar Chart to compare categories.

Config Panel

For those who love customization, the Config Panel is your best friend! This powerful configuration tool ensures that every panel displays the data exactly how you need it.

• Adjust legends to enhance readability.
• Set units and decimals to control data precision.
• Configure axis properties to match your exact requirements.

Auto Mode for Panel Creation

Auto Mode is the easiest way to create panels. It allows you to:

• Drag-and-Drop Fields: Quickly add fields to your visualizations.
• Apply Filters: Easily filter your data using available values.
• 

Auto Mode is perfect for beginners or when you need quick insights without complex queries.

Custom Query Mode

Use Custom Queries for more control and flexibility. Here are some scenarios where Custom Query Mode is useful:

• 
• Advanced Aggregations: If you need specialized aggregations beyond basic counts or averages.
• Complex Filters: For advanced conditions like AND/OR combinations.
• SQL Functions: Use SQL functions for custom data manipulation. See the documentation for a list of available functions.

PromQL Queries for Metrics

For metrics, OpenObserve supports PromQL. PromQL is a powerful tool for querying time-series data. Here are some examples:

• 
• Instant Queries: Get current values, e.g., up{job="node-exporter"}.
• Range Queries: Retrieve data over a specific time range, e.g., rate(http_requests_total{job="web server"}[5m]).
• Aggregations and Filtering: Perform operations like sum or filter metrics, e.g., sum(cpu_usage{instance="example-server"}).
• Metric Operations: Apply calculations, e.g., rate(http_requests_total{job="web server"}[5m]) * 100.

PromQL is ideal for detailed metrics analysis, giving you full control over your data.

Tip: Auto Mode works well for most use cases, while Custom Queries and PromQL provide more advanced options when needed.

Advanced Dashboard Features

Dynamic Filtering with Variables

When you build monitoring dashboards, dynamic filtering with variables can streamline your monitoring setup, reducing the need for multiple dashboards and improving load times.

Practical Use Case: Multiple Kubernetes Namespaces

When monitoring multiple Kubernetes namespaces, creating separate dashboards can be inefficient. Instead of creating a separate dashboard for each namespace, you can create a variable called namespace. This variable can then be used across all panels with a filter, such as $namespace .

How It Works:

• Add the namespace variable to your panels.
• 
• Use it in your queries to filter the displayed data dynamically.
• 
• With just one dashboard, you can select any Kubernetes namespace and instantly load its relevant data.
• 

Save and Export

Once you've arranged all the panels to your satisfaction, click Save Dashboard.

Export: Click Export to save the dashboard as a JSON file for sharing or backup purposes.

Share: Copy and share the link with colleagues.

Note: Create tabs for CPU, Memory if you want to add additional related metrics without cluttering the main view.

Prebuilt Dashboards for OpenObserve

If you're short on time, you can take advantage of prebuilt monitoring dashboards available for OpenObserve. These templates provide a quick way to build a monitoring dashboard tailored to specific needs, and you can customize them further to match your infrastructure

Follow these easy steps to import a prebuilt dashboard into OpenObserve:

1. Download the Dashboard:
   • Visit the OpenObserve Dashboards Repository.
   • Download the JSON file for the dashboard that matches your needs.
2. Import the Dashboard:
   • In OpenObserve, navigate to the Dashboards section.
   • Click on the Import button.
   • Select the downloaded JSON file.
   • Click Import again to create a new dashboard using the prebuilt configuration.
   • • 

Tip: Prebuilt dashboards are an excellent starting point, but don't hesitate to customize them. Tailor them to match your infrastructure and monitoring needs by adding specific metrics, changing chart types, or configuring alerts.

Next Steps

• Add more filters, and use drill-downs to catch issues early.
• Add More Metrics: Add panels for Network Traffic, Disk I/O for a complete view of your cluster.
• Set Up Alerts: Configure custom alerts for notifications via email, Slack, or other channels.
• Join the Community: Connect with OpenObserve users and contribute your own dashboards to the community repository to help fellow users!

Need More Help?

• OpenObserve Documentation: Official Documentation
• Build Dashboards in openobserve: Youtube Video

Community Forums:

• OpenObserve Slack Community
• Github Discussions

Dive Into Dashboards with OpenObserve

Start your observability journey today with OpenObserve and transform how you monitor your systems!

• Sign up for a free trial of OpenObserve on our website.
• Want to self-host or contribute? Check out our GitHub repository to explore self-hosting options and help grow the community.

Thank you for reading! We hope you found this guide helpful. If you have any questions, feedback, or suggestions, we’d love to hear from you.

Happy Monitoring! 🚀