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manticoresoftware/kibana-demo: Demo to compare the performance of Manticore and Elastic when working with Kibana

This repository provides a ready-to-use setup to demonstrate the integration of Manticore Search with Kibana. It also enables a performance comparison between two Kibana instances connected to Elasticsearch and Manticore, respectively.

Here are our test results with approximately 40 million documents, showing that the same dashboard renders in 7 seconds with Manticore Search, compared to 18 seconds with Elasticsearch.

The demo utilizes a set of Kibana visualizations inspired by this popular blog post, which is based on data from an Nginx access log file. The kscarlett-generator tool is used to generate fake data for the log file.

Before getting started, ensure you have the following installed:

• Git
• Docker Compose
• Zip and Unzip tools

Command to install these packages in Ubuntu / Debian:

apt -y update && apt -y install git docker-compose zip unzip

git clone https://github.com/manticoresoftware/kibana-demo.git
cd kibana-demo

Grant permissions for the Elasticsearch data folder:

Edit the .env file to set how many log entries to generate. By default, this is 50 million entries. Note that loading a large number of entries into Elasticsearch and Manticore can take time.

Set the following in .env:

You can also make it add new log entries after the main volume is uploaded to mimic real-time logging. Use the UPDATE_RATE option to set how many log entries are added per second. To turn off this feature, set UPDATE_RATE to 0.

You can configure custom ports for the Elasticsearch and Manticore Kibana instances by setting these options in .env:

ES_KIBANA_PORT=5612
MANTICORE_KIBANA_PORT=5613

Run the startup script:

Wait a few minutes for log generation and index loading to begin.

Verify the services are running:

• Elasticsearch Kibana: http://localhost:5612
• Manticore Kibana : http://localhost:5613

Choose Explore on my own on the Kibana start screen

Import the pre-built visualizations into Kibana to use for the generated data.

For the Elasticsearch Kibana instance:

• Go to Management > Saved objects in the Kibana menu
• Click Import and select the kibana_objects.ndjson file from the root of this repository.
• Refresh the Kibana window to apply the changes.

For the Manticore Kibana instance:

• Go to Discover in the Kibana menu
• Set test as the value for the Index pattern input field
• Click Next step
• Select @timestamp in the Time Filter select field
• Click Create index pattern

Once the setup is complete, explore the imported dashboards and visualizations based on the indexed log data. Navigate to Dashboard or Visualize in the Kibana menu.

After all log entries are loaded, you'll notice differences in dashboard load times between Manticore and Elasticsearch. Here are our test results with about 40 million documents:

To stop and remove all containers, run:

• Currently, Kibana version 7.6.0 is tested and recommended. Other 7.x versions may work but could cause issues. Versions 8.x are not supported.
• The following Elasticsearch-specific field types are not supported:
  • Spatial data types
  • Structured data types
  • Document ranking types
  • Text search types (except for plain 'text')
  • Relational data types
• Metric aggregation functions are limited to those supported by Manticore.
• The following Kibana tools are not supported:
  • Canvas – A visualization and presentation tool for combining data with colors and images.
  • Elastic Maps – A tool for analyzing geographical data.
  • Metrics – An app for monitoring infrastructure metrics.
  • Logs – A console-like display for exploring logs from common services.
  • Monitoring:
    • Uptime – Monitors the status of network endpoints via HTTP/S, TCP, and ICMP.
    • APM (Application Performance Monitoring) – Collects in-depth performance metrics from applications.
    • SIEM (Security Information and Event Management) – An interactive workspace for security teams to triage events and conduct initial investigations.
    • ILM (Index lifecycle management) - Automatically manage indices according to performance, resiliency, and retention requirements.
    • Stack Monitoring – Provides visualizations of monitoring data across the Elastic Stack.
  • Elasticsearch Management – A UI for managing Elastic Stack objects, including ILM (Index Lifecycle Management), etc.

Contributions are welcome! Please open an issue or submit a pull request if you’d like to improve this project.

This project is licensed under the MIT License.

For questions or issues, please visit the Manticore Search community forums or open an issue in this repository.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

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