1. Tuning MapReduce
7/6/2012
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2. Tuning MapReduce
Agenda
• Tuning MapReduce
• ExpressLane™
• Label-Based Scheduling
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3. Tuning MapReduce
Objectives
At the end of this module you will be able to:
• Effectively tune your MapReduce jobs
• Explain how ExpressLane works and what jobs it applies to in your cluster
• Configure label-based scheduling
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4. Tuning MapReduce
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5. Important Parameters
 Number of task slots per node
 Number of task slots on the cluster
 Memory buffer size
 JVM size
 Speculative execution
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6. Number of Task Slots per Node
 Number of concurrent map and reduce tasks on a node
 In mapred-site.xml
– mapred.tasktracker.map.tasks.maximum
– mapred.tasktracker.reduce.tasks.maximum
 Recommendations:
– Map slots: 0.75 * # of cores (minimum 1)
– Reduce slots: 0.5 * # of cores (minimum 1)
 Decrease map and reduce slots on CLDB nodes
 Increase slots on nodes with more memory, disk, network
bandwidth
– E.g. reducers are bandwidth-intensive
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7. Number of Task Slots on the Cluster
 How many concurrent map and reduce tasks can run
 In mapred-site.xml
– mapred.map.tasks
– mapred.reduce.tasks
• Best parameter to tune
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8. Memory Buffer Size
 Memory used by map task for output during shuffle
– io.sort.mb
 Set to about 2x block size
– Use hadoop mfs to see block size
 If set too low, spills will result in lower performance
– Visible in MapR Metrics
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9. JVM Size
 Size of child JVM that runs a map or reduce task
– mapred.map.child.java.opts – set to about 2x io.sort.mb
– mapred.reduce.child.java.opts – leave at default setting
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10. Speculative Execution
 Set to true:
– mapred.map.tasks.speculative.execution
– mapred.reduce.tasks.speculative.execution
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11. ExpressLane™
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12. ExpressLane™
 Allow a small job to run when all slots are occupied
 Only applies when cluster is busy and if job meets criteria specified
in mapred-site.xml
 Check the documentation for ExpressLane criteria
– http://mapr.com/doc/display/MapR/ExpressLane
 Note: jobs that fit the small job definition, but are in fact larger
than anticipated are killed and re-queued for normal execution
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13. Label-Based Scheduling
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14. Label-Based Scheduling
 Restrict job execution to a set of nodes within the cluster
– By hardware config, department, etc.
 Admin applies label(s) to nodes
 User specifies label when submitting job
 Admin can specify default/override label per queue
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15. Label-Based Scheduling
 On a jobtracker node in mapred-site.xml
mapreduce.jobtracker.node.labels.file =
<path to node-label mapping file>
– Within the mapping file, each line uses the format
<node pattern/regex> <labels>
– Examples
hadoop-prod-0* qa
/hadoop-prod-1.*/ sales, product, 4_disks
hadoop-prod-2 12_disks, engineering
hadoop-prod-3 big_ram, support
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16. Label-Based Scheduling
 Specify a label when submitting a job in hadoop command line
mapred.job.label = <label>
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17. Label-Based Scheduling
 Default label per queue
– Examples
mapred.queue.<queue-name>.label = <label>
mapred.queue.<queue-name>.label.policy = <PREFER_QUEUE |
PREFER_JOB | AND | OR>
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18. Questions
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