Jumbune

Big Data APM & DevOps Tool

Proactive Cluster Monitoring

Identify & Address issues before they occur.

Improve Productivity and Efficieny of infrastructure team by quickly understand application behavior, service performance, cluster utilization, network bottlenecks, host or daemon malfunctioning.

Proactive monitoring and detail analysis of an Enterprise Hadoop cluster plays a very critical role in successful Hadoop Data Lakes. Monitoring teams, Architects, Devops, Administrators uses a set of monitoring tools and dashboards to ensure that analytical workloads executes as per expected SLAs and Hadoop as a platform is utilized optimally by them. Jumbune offers Cluster Analysis feature which suites your proactive monitoring, analysis requirements, gives differentiated & niche insights about Applications, Containers, and Cluster.

This module makes clusters reliable, easier to maintain and make them more efficiently utilized. DevOps and IT Administration teams in large enterprise companies need the necessary monitoring and analysis tools to makes sure that analytical applications run and scale seamlessly on resource sharing frameworks like YARN, Mesos. It provides a unified dashboard with all the relevant analysis, metrics, recommendations, alerts and graphs.

Highlighting features of Jumbune which embraces IT Teams for enabling proactive monitoring are,

Get Real time Recommendations - Jumbune analyzes resource usage patterns of applications, exposed yarn containers and queue utilization to understand usage behavior of the Hadoop cluster, containers, queues and application. With the usage analysis Jumbune produces diverse set of recommendations in real time like Optimal Analytical Workload Schedules, Resource utilization for analytical jobs, Optimal Configurations for cluster, daemons and queues level recommendations for Analytical workloads, etc.

Custom Escalation of Alerts - Alerts on Cluster Nodes, Daemons, File System, Yarn Containers, Jobs can be customized for configurable multiple level escalations to diverse set of recipients on Mail, SNMP and Ticketing Systems.

Capacity utilization - Describes the Job wise Map/Reduce memory utilization and suggests Quick recommendation of the Hadoop configuration parameters according to the job with Job wise Profiling stats. And also warns if the Mapper/Reducer have more than 50% resource utilization.

High resource utilizing jobs - Shows the job that are taking large resources on the basis of VCores and Memory utilization according to its configurable value and also captures the user that are executing these jobs.

Long Running Jobs - Tracks the jobs that overshoots the Service level agreements (SLA) and exceeds the defined threshold limits.

Deep Queue Analysis - Jumbune continuously observes and analyzes defined Line of Business (LOBs), workload queues for performing analysis of - how efficiently defined queues are being utilized by the job submissions happening on respective queues. Average Waiting Time is also shown which is an indicative help to effectively plan capacities offered by queues and migrate critical user/group to other queues where they will experience lesser waiting times.

Establish Charge back Model - Hadoop as a service platform is one of the prominent use case of creating Hadoop Based Data lake. Enterprise team exposes this platform to different Business Unit, Users and Groups to bring in their use cases of this platform. In such scenarios, the enterprise team may wish to establish a charge back metering model of the utilization of the exposed platform by various use case users. Jumbune helps you to publish a charge back model by analyzing and publishing platform utilization. Reports like High Resource Consumption Business Users & applications, Long Running Applications by Business Users & Applications, Queue utilization behaviors by Business Users are very helpful to derive these models.

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