We’re excited to announce the beta release of Dynatrace Hadoop monitoring! Hadoop server monitoring provides a high-level overview of main Hadoop components within your cluster. Enhanced insights are available for HDFS and MapReduce. Hadoop-specific metrics are presented alongside all infrastructure measurements, providing you with in-depth Hadoop performance analysis of both current and historical data.

To analyze your Hadoop components 

  1. Click Technologies in the menu.
  2. Click the Hadoop tile.
  3. Click an individual Hadoop component in the Process group list to view metrics and a timeline chart specific to that component. 

Enhanced insights for HDFS

To view NameNode metrics

  1. Follow the steps outlined above. Be sure to select a NameNode process group.
  2. Click the Process group details button. 
  3. On the Process group details page, select the Technology-specific metrics tab to view relevant cluster charts and metrics. Hadoop NameNode pages provide details about your HDFS capacity, usage, blocks, cache, files, and data-node health.
  4. Further down the page, you’ll find a number of cluster-specific charts.

NameNode metrics

Total Raw capacity of DataNodes in bytes
Used Used capacity across all DataNodes in bytes
Remaining Remaining capacity in bytes
Total load The number of connections
 Total The number of allocated blocks in the system
Pending deletion The number of blocks pending deletion
Files total Total number of files
Pending replication The number of blocks pending to be replicated
Under replicated The number of under-replicated blocks
Scheduled replication The number of blocks scheduled for replication
Live The number of live DataNodes
Dead The number of dead DataNodes
Decommission Live The number of decommissioning live DataNodes
Decommission Dead The number of decommissioning dead DataNodes
Usage – Volume failures total Total volume failures
Estimated capacity lost total Estimated capacity lost in bytes
Decommission Decommissioning The number of decommissioning data DataNodes
Stale The number of stale DataNodes
Blocks missing and corrupt – Missing The number of missing blocks
Capacity Cache capacity in bytes
Used Cache used in bytes
Blocks missing and corrupt – Corrupt The number of corrupt blocks
Capacity in bytes – Used, non-DFS Capacity used, non-DFS in bytes
Appended The number of files appended
Created The number of files and directories created by create or mkdir operations
Deleted The number of files and directories deleted by delete or rename operations
Renamed The number of rename operations

To view DataNode metrics

  1. To view DataNode metrics, expand the Details section of a DataNode process group.
  2. Click the Process group details button. 
  3. On the Process group details page, click the Technology-specific metrics tab and select the DataNode.
  4. Select the Hadoop HDFS metrics tab

DataNode metrics

Live The number of live DataNodes
Dead The number of dead DataNodes
Decommission Live The number of decommissioning live DataNodes
Decommission Dead The number of decommissioning dead DataNodes
Decommission Decommissioning The number of decommissioning data DataNodes
Stale The number of stale DataNodes
Capacity Cache capacity in bytes
Used Cache used in bytes
Capacity Disk capacity in bytes
DfsUsed Disk usage in bytes
Cached The number of blocks cached
Failed to cache The number of blocks that failed to cache
Failed to uncache The number of blocks that failed to remove from cache
Number of failed volumes The number of volume failures occurred
Capacity in bytes – Remaining The remaining disk space left in bytes
Blocks The number of blocks read from DataNode
Removed The number of blocks removed
Replicated The number of blocks replicated
Verified The number of blocks verified
Blocks The number of blocks written to DataNode
Bytes The number of bytes read from DataNode
Bytes The number of bytes written to DataNode

Enhanced insights  for MapReduce

To view ResourceManager metrics

  1. To view ResourceManager metrics, expand the Details section of the ResourceManager process group.
  2. Click the Process group details button. 
  3. On the Process group details page, select the Technology-specific metrics tab to view relevant cluster charts and metrics. Hadoop ResourceManager metrics pages provide information about your nodes, applications, memory, cores, and containers.
  4. Further down the page, you’ll find a number of ResourceManager-specific charts.

ResourceManager metrics

Active Number of active NodeManagers
Decommissioned Number of decommissioned NodeManagers
Lost Number of lost NodeManagers – no heartbeats
Rebooted Number of rebooted NodeManagers
Unhealthy Number of unhealthy NodeManagers
Allocated Number of allocated containers
Allocated Allocated memory in bytes
Allocated Number of allocated CPU in virtual cores
Completed Number of successfully completed applications
Failed Number of failed applications
Killed Number of killed applications
Pending Number of pending applications
Running Number of running applications
Submitted Number of submitted applications
Available Amount of available memory in bytes
Available Numberof available CPU in virtual cores
Pending Amount of pending memory resource requests in bytes that are not yet fulfilled by the scheduler
Pending Pending CPU allocation requests in virtual cores that are not yet fulfilled by the scheduler
Reserved Amount of reserved memory in bytes.
Reserved Number of reserved CPU in virtual cores

To view MRAppMaster metrics

  1. To view MRAppMaster metrics, expand the Details section of an MRAppMaster process group.
  2. Click the Process group details button. 
  3. On the Process group details page, click the Technology-specific metrics tab and select the MRAppMaster process.
  4. Click the Hadoop MapReduce
    tab.

MRAppMaster metrics

Jobs finished – Completed The number of successfully completed jobs
Jobs finished – Failed The number of failed jobs
Jobs finished – Killed The number of killed jobs
Jobs – Preparing The number of preparing jobs
Jobs – Running The number of running jobs
Maps finished – Completed The number of successfully completed maps
Maps finished – Failed The number of failed maps
Maps finished – Killed The number of killed maps
Maps – Running The number of running maps
Maps – Waiting The number of waiting maps
Reduces finished – Completed The number of successfully completed reduces
Reduces finished – Failed The number of failed reduces
Reduces finished – Killed The number of killed reduces
Reduces – Running The number of running reduces
Reduces – Waiting The number of waiting reduces

To view NodeManager metrics

  1. To view NodeManager metrics, expand the Details section of the NodeManager manager process group.
  2. Click the Process group details button. 
  3. On the Process group details page, click the Technology-specific metrics tab and select a NodeManager process.
  4. Click the Hadoop MapReduce tab.

NodeManager metrics

GB Available Current available memory in GB
GB Allocated Current allocated memory in GB
Completed Total number of successfully completed containers
Running Current number of running containers
Launched Total number of launched containers
Initing Current number of initializing containers
Allocated Current number of allocated containers
Failed Total number of failed containers
Killed Total number of killed containers
Connections Number of current connections
Output Bytes output in bytes
Outputs Failed Number of failed outputs
Outputs OK Number of succeeded outputs

Prerequisites

  • For full Hadoop visibility, OneAgent must be installed on all machines running the following Hadoop processess:
    NameNode, ResourceManager, NodeManager, DataNode, and MRAppMaster
  • Linux OS
  • OneAgent 1.103+
  • Hadoop version 2.4.1+

Enable Hadoop monitoring globally

With Hadoop monitoring enabled globally, Dynatrace automatically collects Hadoop metrics whenever a new host running Hadoop is detected in your environment.

  1. Go to Settings > Monitoring > Monitored technologies.
  2. Set the Hadoop switch to On.

Want to read more?

Visit our dedicated Hadoop monitoring webpage to learn more about big data monitoring and how Dynatrace supports Hadoop.

Have feedback?

Your feedback about Dynatrace Hadoop monitoring is most welcome! Let us know what you think of the new Hadoop plugin by adding a comment below. Or post your questions and feedback to Dynatrace Answers.