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Apache Ambari

Apache Ambari

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OVERVIEW

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.

What Ambari Does

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:

  • Simplified Installation, Configuration and Management. Easily and efficiently create, manage and monitor clusters at scale. Takes the guesswork out of configuration with Smart Configs and Cluster Recommendations.  Enables repeatable, automated cluster creation with Ambari Blueprints.
  • Centralized Security Setup. Reduce the complexity to administer and configure cluster security across the entire platform. Helps automate the setup and configuration of advanced cluster security capabilities such as Kerberos and Apache Ranger.
  • Full Visibility into Cluster Health. Ensure your cluster is healthy and available with a holistic approach to monitoring. Configures predefined alerts — based on operational best practices — for cluster monitoring. Captures and visualizes critical operational metrics — using Grafana — for analysis and troubleshooting. Integrated with Hortonworks SmartSense for proactive issue prevention and resolution.
  • Highly Extensible and Customizable. Fit Hadoop seamlessly into your enterprise environment. Highly extensible with Ambari Stacks for bringing custom services under management, and with Ambari Views for customizing the Ambari Web UI.

Collaboration and Focus

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.

  • OpenDeliver a complete set of features for Hadoop operations, in public and with the community, by defining the operational framework and lifecycle.
  • IntegratedEnsure that Hadoop operations can be integrated with existing IT tools, behind a single pane of glass, by providing REST APIs and multiple views of the cluster.
  • IntuitiveMake Hadoop’s most complex operational challenges easy to manage with more insight and visibility into cluster performance.

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.

Recent Improvements

Apache Ambari 2.4 which is part of the Hortonworks Data Platform 2.5 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 2.4 has many new features in this release which include some of the following:

Role-Based Access Control

Improve operational efficiency, flexibility and security. In this release, Ambari includes additional cluster operational roles provide more granular division of control for cluster operations.

  • Flexible role based access control. Provide different users with a controlled set of functional access.
  • Grant different levels of cluster operational privileges to users and groups. Easily control what user has access to different privileges for managing, adding and removing services within the cluster.

Ambari 2.4 image1

Log Search (Technical Preview)

Reduce time to troubleshoot problems. Ambari automatically configures the collection of cluster operational metrics to aid with analysis and troubleshooting. In addition to operational data, Apache Hadoop components create a lot of log data. Accessing that log data to understand what the component is telling you, especially when issues arise, is critical. Apache Ambari includes a new Log Search service that provides agents for log collection and a delivers a custom UI for searching those logs. This is essential to providing a streamlined approach to search for stack traces, exceptions, block ID’s, and other information that needs to be seen across all nodes in the cluster.

  • Rapid Search of Cluster Component Logs. When an issue arises, operators will be able to quickly find issues across all HDP components.
  • Search across time ranges, log levels, and for keywords. Makes it easier to troubleshoot and resolve the issue quickly with all the information at hand.

Ambari 2.4 image3

Customizable Cluster Alert Checks

Tailor HDP to fit with your enterprise monitoring environment. Having full visibility into the service health and availability of your cluster is critical for any enterprise. Ambari configures a set of predefined alerts — based on operational best practices — for cluster monitoring that can seamlessly integrate with your existing enterprise monitoring tools. These predefined alerts provide full visibility into the cluster health. New customizable alert capabilities include:

  • Tune Alert Checks. Customize the number of times an alert is checked before dispatching a notification. Checks can be set globally for all alerts or overridden for a specific alerts. This control prevents dispatching alert notifications in case of a transient environment issues.
  • Customize Alert Thresholds. Easily customize the various alert thresholds via the Ambari Web UI. For example, a threshold can be set to trigger the amount of disk space used if it goes above a specific threshold value. This allows you to tune alerts to best fit with your environment.

Ambari 2.4 image2

Ambari User Views

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. .

User View Description
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.

Recent Ambari Releases

Download Ambari 2.4 and Learn More

For additional details about this release review the following resources:

Ambari Version Notable Enhancements
2.4
  • Role-Based Access Control
  • New and Customizable Alerts
  • Log Search (Technical Preview)
2.2
  • Advanced Metrics Visualization and Dashboards
  • HDP Express Upgrade for fast cluster upgrades
  • Ranger Guided Configurations for Simplified Security Setup
  • Improved capabilities for accessing cluster metrics
  • SmartSense Automatic Installation
2.1
  • Guided Configurations
  • Customizable Dashboards
  • RHEL/CentOS 7 Support
  • JDK 1.8 Support
2.0
  • Automated Rolling Upgrades for HDP
  • Simplified Kerberos Setup
  • Ranger and Spark support
  • Ambari Alerts & Ambari Metrics
  • New Tez View
1.7.0
  • Configuration versioning and history
  • Introduced the Ambari Stacks “Stack Advisor” for configuration validation
  • Introduced Ambari Views Framework for customizable user interfaces
1.6.0
  • Introduced Ambari Blueprints for automating cluster installs
  • Improved usability guardrails with more host and environment checks
  • Support for PostgreSQL database

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Ambari in our Blog

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