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Community 1.0.0 release riding an elephant was never so easy…. Agenda. Differentiator Ultra light Deployment Features – Description Strategic Users & Positioning. Differentiator. What Jumbune solves?.

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slide1

Community 1.0.0 release

riding an elephant was never so easy…

agenda
Agenda
  • Differentiator
  • Ultra light Deployment
  • Features – Description
  • Strategic Users & Positioning
what jumbune solves
What Jumbune solves?
  • Detects Job Code & data Inaccuracies: Hadoop MapReduce analytics output is not as expected
  • Analyses Job Profiling: MapReduce Jobs have some performance bottlenecks
  • On demand Cluster Monitoring: Whole Cluster can’t be monitored/unmonitored at will
  • Non intrusive operation: Don’t want intrusive deployments on monitoring or analyzing daemons
key differentiating features
Key Differentiating Features
  • Cluster Monitoring can be turned-on on demand
  • MapReduce Flow drill down (World’s only)
  • Decoupled installation from Hadoop
  • MapReduce Phase wise statistics (time vs. data flow ratevs. resource)
features users
Features & Users
  • Hadoop Cluster Monitoring
  • MR Job Profiling
  • HDFS Data Validator
  • MR Flow Debugger
analytic solution costs solutions
Analytic Solution Costs & Solutions
  • MapReduce Solution Development Costs:
    • Fault prone - Development and Data Staging
    • Days to resolve on real data (because of Volume)
    • performance bottlenecks may be present - MapReduce Jobs
  • Hadoop Cluster Monitoring Costs:
    • Administrator – analyzes each node separately
    • On Each node – install & run monitoring daemons
    • Cluster resources – running daemons will consume them
hadoop cluster monitoring
Hadoop Cluster Monitoring
  • Data Centre & Rack aware nodes view
  • Dynamic Interval based monitoring
  • Hadoop JMX, Node Resource Statistics
  • Network Latency across Hadoop nodes
  • Per file, node wise replica Placement (which nodes have replicas of a given file ?)
  • HDFS data placement view (HDFS balanced ?)
  • HDFS Health statistics (HDFS corrupted ?)
mr job profiling
MR Job Profiling
  • Per Job Phase wise
    • performance for each JVM
    • data flow rate
    • Resource usage
  • Per Job Heap sites for Mapper & Reducer
  • Per Job CPU cycles for Mapper & Reducer
hdfs data validator
HDFS Data Validator
  • Validates inconsistencies in HDFS data in the form of :

- Null checks

- Data type checks

- Regular expression checks

mapreduce flow debugger
MapReduce Flow Debugger
  • Verifies the flow of input records in user’s map reduce implementation
  • Drill down visualization helps developer to quickly identify the problem.
  • Only tool to assist developers to figure out MapReduce implementation faults without any extra coding