Ambari vs Ganglia: What are the differences?

Introduction Ambari and Ganglia are both monitoring and management tools used in big data environments. However, they have several key differences that set them apart from each other.

1. Scalability and Flexibility: Ambari is designed to handle large clusters and provides the ability to scale resources dynamically. It supports the management of various Hadoop components and allows for customization and integration with other services. On the other hand, Ganglia is primarily focused on monitoring and is best suited for smaller clusters. It lacks the flexibility and extensibility provided by Ambari.

2. User Interface: Ambari offers a more user-friendly and intuitive web-based interface, making it easier for administrators to manage and monitor their clusters. It provides detailed metrics, visualizations, and configuration options, making it suitable for both beginners and advanced users. Ganglia, on the other hand, has a simpler interface with basic visualizations and limited configuration options. It may require additional setup and configuration for a more user-friendly experience.

3. Compatibility and Integration: Ambari is tightly integrated with the Hadoop ecosystem, providing seamless compatibility and easy integration with various Hadoop components and services. It supports the management of Hadoop distributions like Apache Hadoop, Hortonworks, and others. Ganglia, on the other hand, is a standalone monitoring system and may require custom configurations or plugins to monitor specific components or services outside the Hadoop ecosystem.

4. Alerting and Notifications: Ambari provides advanced alerting and notification features, allowing administrators to set up thresholds and receive alerts when certain metrics exceed predefined limits. It supports email alerts, SNMP notifications, and integration with external alerting systems. In contrast, Ganglia has limited alerting capabilities and may require additional tools or configurations to achieve similar functionality.

5. Community and Support: Ambari has a strong community and active development, with regular updates and bug fixes. It is supported by major Hadoop distributions and has extensive documentation and resources available. Ganglia also has a community and support, but it may not be as actively maintained or widely adopted as Ambari. Users may find less support and resources available for troubleshooting or expanding its functionality.

6. Architecture and Monitoring Approach: Ambari follows a centralized architecture, where a single Ambari server manages and monitors the entire cluster. It collects metrics and configurations from various agents running on cluster nodes. Ganglia, on the other hand, follows a distributed architecture with each node having its own monitoring daemon. It relies on multicast or unicast communication to aggregate data from individual nodes. This difference in architecture affects the scalability, performance, and ease of setup and maintenance.

In summary, Ambari provides scalable and flexible management and monitoring capabilities with a user-friendly interface and extensive compatibility, while Ganglia is more suited for smaller clusters with limited customization and integration options. Ambari also offers advanced alerting and notification features and has a larger community and support, making it a popular choice for managing Hadoop clusters.