A completely open source management platform for provisioning, managing, monitoring and securing Apache Hadoop clusters. Apache Ambari takes the guesswork out of operating Hadoop.
Apache Ambari, as part of the Hortonworks Data Platform, allows enterprises to plan, install and securely configure HDP making it easier to provide ongoing cluster maintenance and management, no matter the size of the cluster.
Ambari makes Hadoop management simpler by providing a consistent, secure platform for operational control. Ambari provides an intuitive Web UI as well as a robust REST API, which is particularly useful for automating cluster operations. With Ambari, Hadoop operators get the following core benefits:
Hortonworks is focused on going to market with a 100% open source solution. This focus allows us to collectively provide the product management guidance for Enterprise Grade Hadoop to mainstream enterprises and our partner ecosystem, and further innovate the core of Hadoop.
The community will continue to innovate Ambari so that its operational capabilities keep pace with Hadoop’s ever-expanding functionality for data management, data access, governance and security.
It is exciting to see Ambari come together and we are very interested in hearing feedback as these contributions mature. Therefore, we have made the Ambari Operations and User Views available within the Hortonworks Sandbox to make it easier for you to try them out. For questions and feedback on Ambari operations please post to the Ambari Forum. If you have questions or feedback on the User Views please post them to the Ambari User View Forum.
Apache Ambari 2.2, which is part of the Hortonworks Data Platform (HDP) 2.4 release, serves as the management system for enterprises looking to easily and securely adopt HDP. Ambari simplifies the experience of provisioning, managing, monitoring, securing and troubleshooting Apache Hadoop deployments. Ambari removes the manual — often error prone — tasks associated with operating Hadoop. It also provides the necessary customization “hooks” to fit seamlessly into the enterprise, and enables the IT Operator to focus on delivering world-class service and support for their consumers of the Hortonworks Data Platform.
The Ambari Metrics system provides a scalable low-latency storage system to capture the metrics data emitted from Hadoop. Understanding which specific metrics to look at for each of the Hadoop components takes experience and understanding of how the components work themselves and with each other.
To help simplify the process of reviewing metrics, and to be more prescriptive about which metrics to look at, Ambari 2.2 now including Grafana with Ambari Metrics for an advanced metrics visualization & dashboarding experience. Grafana is the leading graph and dashboard builder for visualizing time series metrics, and the integration of Grafana with Ambari Metrics brings the most important and valuable cluster metrics front-and-center. This makes is easier for the Hadoop Operator to troubleshoot cluster operations. With this new feature, the Hadoop Operator can:
Example of the HDFS NameNode Dashboard highlights file system activity.
As enterprises everywhere adopt Hadoop, they deploy more and more mission-critical analytic applications. This makes upgrading a system as diverse as Hadoop can be a daunting task. But with Ambari, Hadoop operators can easily upgrade their cluster to the latest software release in an automated, repeatable fashion.
Ambari 2.2 introduces a new Express Upgrade option that provides a faster way to upgrade your cluster with less prerequisites than the traditional Rolling Upgrade option.
Having both Rolling Upgrade and Express Upgrade options provides the choice and flexibility to upgrade your cluster based on your specific deployments needs.
Ambari provides automated features to setup and configure the comprehensive security capabilities that are used to protect the cluster.
Ambari 2.2 introduces two new capabilities that serve to further simplify the experience of configuring and managing Ranger and Kerberos.
Having full visibility into the service health and availability of your cluster is critical for any enterprise. Ambari configures predefined alerts — based on operational best practices — for cluster monitoring that can seamlessly integrate with your existing enterprise monitoring tools. Ambari also automatically configures the collection of cluster operational metrics to aid with analysis and troubleshooting.
Ambari 2.2 has added new capabilities that make accessing the cluster operational metrics easier.
It’s time to put a new face on Hadoop using the Ambari Views framework.A “view” is a way of extending Ambari that allows 3rd parties to plug in new resource types along with the APIs, providers and UI to support them. Ambari is the only open source and open community effort designed to provide a compelling user experience for Hadoop while delivering consistent lifecycle management and security.
Most notably, there are the Ambari User Views contributions actively being worked in the community. Ambari User Views are designed to provide capabilities that assist with the operational aspects of data application development and workload management. .
|Tez||The Tez View helps you understand and optimize your cluster resource usage. Using the view, you can optimize and accelerate individual SQL queries or Pig jobs to get the best performance in a multi-tenant Hadoop environment.|
|Hive||Hive View allows the user to write & execute SQL queries on the cluster. It shows the history of all Hive queries executed on the cluster whether run from Hive view or another source such as JDBC/ODBC or CLI. It also provides graphical view of the query execution plan. This helps the user debug the query for correctness and for tuning the performance. It integrates Tez View that allows the user to debug any Tez job, including monitoring the progress of a job (whether from Hive or Pig) while it is running. This view contribution can be found here.|
|Pig||Pig View is similar to the Hive View. It allows writing and running a Pig script. It has support for saving scripts, and loading and using existing UDFs in scripts. This view contribution can be found here.|
|Capacity Scheduler||Capacity Scheduler View helps a Hadoop operator setup YARN workload management easily to enable multi-tenant and multi-workload processing. This view provisions cluster resources by creating and managing YARN queues. This view contribution can be found here.|
|Files||Files View allows the user to manage, browse and upload files and folders in HDFS. This view contribution can be found here.|
Beyond these out of the box User Views there is a growing ecosystem of Ambari User Views that are being developed by the community. You can find community User Views in the Hortonworks Gallery.
For additional details about this release review the following resources:
|Ambari Version||Notable Enhancements|