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 2.7 which is part of the Hortonworks Data Platform 3.0 release, serves as the management system for enterprises looking to easily and securely adopt Apache Hadoop. Ambari simplifies the experience of provisioning, managing, monitoring, securing and troubleshooting 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. Apache Ambari 2.7 includes many improvements in the following product areas:
Make Ambari easier to install and use, and provide a consistent look and feel across Hortonworks products
Ambari 2.7 has been redesigned using the Hortonworks Fluid Design Spec to enable a more intuitive cluster installation and management experience. We centralized the most commonly modified configurations (such as credentials, database choice, directories, and accounts) and created a more natural way to help you choose the right configuration, so your installation is successful.
Ambari 2.7 also makes it easier to generate and download the blueprint of a cluster, enabling a repeatable and consistent cluster deployment process.
Manage 5,000 node clusters with multiple concurrent admins
Large clusters have large administrative teams managing them, and we wanted to not only support the management of these large clusters but also give the right tools and experience to support large operational teams.
To reduce the load on the Ambari Server, Ambari 2.7 includes a redesigned agent/server communication pattern utilizing WebSockets. This allows more work to be pushed down to the individual agents, even giving the Ambari Agent autonomy over the state of the services it manages, which means services can be automatically started if the Ambari Server is not reachable.
To support more effective team communication, Ambari 2.7 makes it easy to identify which user restarted specific services, or is currently adding a host or service to the cluster.
To reduce the daily pain of large cluster operators, Ambari also improved support for bulk host actions, such as adding and removing components and hosts.
For those large deployments looking to take advantage of HDFS Federation in HDP 3.0, Ambari 2.7 supports the configuration and management of multiple HDFS namespaces.
Simplify SSO setup for DPS services, and integrate with FreeIPA
Securing distributed systems has always been challenging, due to the large surface area of attack they inherently have. Nevertheless, with the prevalence of cloud deployments and emerging regulations for data privacy, security in Hadoop clusters is becoming a ubiquitous requirement.
To make security more convenient to enable from the beginning, Ambari supports and automates Kerberos integration with Active Directory MIT KDC, and newly added in Ambari 2.7, FreeIPA.
In Ambari 2.7, it’s now easier to configure Single Sign-On with Knox for those services used with DataPlane.
Help teams discover and understand our API, and integrate with 3rd party file systems
Ambari has a powerful API that can be leveraged to automate many of the common configuration tasks via 3rd party tools. To make this API easier to use, Ambari 2.7 exposes a Swagger-based API reference portal where authenticated users can explore the API and try out actions directly from their browser.
Alongside supporting custom services via Management Packs, Ambari 2.7 includes native support for 3rd party file systems such as EMC Isilon OneFS. Using this new integration, it’s now easy to setup HDP services using OneFS as their default filesystem.
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||Améliorations notables|