In today’s fast-paced digital world, data analysis is no longer a luxury—it’s a necessity. Organizations are increasingly relying on real-time analytics to drive decisions, monitor systems, and understand customer behavior. OpenSearch Dashboards, part of the OpenSearch project, offers a powerful, open-source tool to visualize, explore, and analyze your data in real-time. Whether you’re tracking logs, monitoring application performance, or analyzing large volumes of customer data, OpenSearch Dashboards is an excellent choice.
In this blog post, we’ll walk you through the process of building real-time analytics with OpenSearch Dashboards, from setting up your environment to creating interactive dashboards that help you make data-driven decisions in real time.
What is OpenSearch Dashboards?
OpenSearch Dashboards is an open-source data visualization and exploration tool that integrates seamlessly with OpenSearch. It provides a web interface for querying and visualizing data stored in OpenSearch indices. With real-time capabilities, you can display live data and create dynamic dashboards that update instantly as new information flows into your system.
Think of OpenSearch Dashboards as the UI layer for OpenSearch, similar to how Kibana works for Elasticsearch. It is perfect for visualizing large amounts of data, making complex datasets more understandable, and helping organizations react quickly to changing data.
Step 1: Setting Up OpenSearch and OpenSearch Dashboards
Before diving into building real-time analytics, you need to set up both OpenSearch (the data store) and OpenSearch Dashboards (the visualization layer).
1.1 Install OpenSearch
First, you need to have OpenSearch running. You can either run OpenSearch locally, on a server, or in a cloud environment. For simplicity, let’s assume you’re running OpenSearch in a Docker container:
docker pull opensearchproject/opensearch:2.10.0
docker run -d –name opensearch -p 9200:9200 opensearchproject/opensearch:2.10.0
This will pull the OpenSearch Docker image and run it on port 9200.
1.2 Install OpenSearch Dashboards
Next, you need to install OpenSearch Dashboards. You can either download and install it manually or run it using Docker.
To install it using Docker:
docker pull opensearchproject/opensearch-dashboards:2.10.0
docker run -d –name opensearch-dashboards -p 5601:5601 –link opensearch:opensearch opensearchproject/opensearch-dashboards:2.10.0
This will pull and run OpenSearch Dashboards on port 5601. Once installed, you can access it by visiting http://localhost:5601 in your browser.
Step 2: Index Your Data into OpenSearch
Before building real-time analytics, you need to ingest data into OpenSearch. OpenSearch supports a wide range of data sources, including logs, metrics, documents, and more. For this example, let’s assume you’re working with log data.
2.1 Ingesting Log Data with Filebeat
One of the most common ways to send logs to OpenSearch is by using Filebeat, an open-source shipper that forwards log files to OpenSearch.
- Install Filebeat on your server or local machine:
- wget https://artifacts.elastic.co/downloads/beats/filebeat/filebeat-8.5.3-x86_64.rpm
- sudo rpm -vi filebeat-8.5.3-x86_64.rpm
- Configure Filebeat to send logs to OpenSearch by editing the filebeat.yml file:
- output.elasticsearch:
- hosts: [“http://localhost:9200”]
- Start Filebeat:
- sudo service filebeat start
Now, your log data will be continuously ingested into OpenSearch, and you can access it through OpenSearch Dashboards.
Step 3: Creating Real-Time Dashboards in OpenSearch Dashboards
Now that OpenSearch is running and data is flowing into it, you can begin building your real-time dashboards. OpenSearch Dashboards provides an intuitive user interface for creating visualizations and dashboards from your data.
3.1 Access OpenSearch Dashboards
Go to http://localhost:5601 in your browser to access OpenSearch Dashboards. You should see the main dashboard screen, where you can explore your data.
3.2 Configure Index Patterns
To begin using your data in OpenSearch Dashboards, you need to create an index pattern that points to the data you’ve ingested.
- In OpenSearch Dashboards, go to Management > Index Patterns.
- Click Create index pattern.
- Enter the index pattern that matches your log data (e.g., filebeat-*).
- Select the appropriate time field (e.g., @timestamp).
- Click Create index pattern.
This index pattern allows OpenSearch Dashboards to query your log data and use it in visualizations.
3.3 Building Visualizations
Now that your data is indexed, you can create visualizations. Let’s walk through how to create a simple real-time line graph to track log events over time.
- In OpenSearch Dashboards, go to Visualize > Create Visualization.
- Choose a visualization type (e.g., Line Chart).
- Select the index pattern you created earlier (filebeat-*).
- Choose the metric (e.g., Count) to represent the number of log events.
- Configure the time field (e.g., @timestamp) for the X-axis.
- Optionally, group by other fields such as log level or host to break down the data further.
Once your visualization is created, you can see the number of log events over time.
3.4 Building a Real-Time Dashboard
After creating your visualizations, you can combine them into a comprehensive real-time dashboard.
- Go to Dashboard > Create new dashboard.
- Click Add to add the visualizations you created (e.g., line chart, pie chart, table).
- Arrange the visualizations on the dashboard as needed.
- Set the refresh interval to update the dashboard in real-time (e.g., every 10 seconds).
Your dashboard will now update dynamically, showing real-time data as new log events are ingested into OpenSearch.
Step 4: Adding Alerts for Real-Time Monitoring
In a real-time analytics system, alerts are crucial for proactively identifying issues. OpenSearch Dashboards provides alerting capabilities to notify you when certain conditions are met (e.g., error rates spike or a particular log message appears).
4.1 Setting Up Alerts
- In OpenSearch Dashboards, go to Alerting > Create alert.
- Choose the Monitor (e.g., “Log Error Monitor”).
- Set the Condition (e.g., when the error log count exceeds a threshold).
- Define the Trigger (e.g., when the count is above 100 errors in 5 minutes).
- Set up a Notification (e.g., email or Slack notification).
Now, you’ll receive alerts whenever the log data meets the defined condition, helping you react in real-time to any issues.
Step 5: Performance and Scaling Considerations
While OpenSearch Dashboards is powerful, real-time analytics with large datasets can become resource-intensive. Here are some tips to ensure good performance:
- Sharding and Indexing: OpenSearch provides ways to optimize data storage using sharding. For large datasets, consider optimizing your index settings to handle high volumes of data efficiently.
- Caching: OpenSearch Dashboards caches the most recent queries and visualizations. You can optimize the refresh intervals and limit data to a manageable window to prevent slowdowns.
- Cluster Sizing: As your data grows, ensure your OpenSearch cluster has enough resources (memory, CPU, storage) to handle the load.
- Use of Data Streams: For time-series data like logs, using data streams in OpenSearch provides better performance and easier management of large data volumes.
Conclusion
Building real-time analytics with OpenSearch Dashboards is an efficient and flexible way to analyze and visualize your data. By following the steps outlined above, you can set up real-time dashboards to track your logs, monitor application performance, and gain valuable insights from your data.
Whether you’re troubleshooting system issues, analyzing user behavior, or monitoring metrics, OpenSearch Dashboards provides a powerful, open-source solution for real-time analytics. With its intuitive interface and integration with OpenSearch, it enables businesses and developers to make data-driven decisions faster and more effectively.
Happy analyzing!