Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. This way we don't depend on cluster specific memory and max map and reduce tasks on that cluster. The text was updated successfully, but these errors were encountered:. Skip to content. Star New issue.
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Now live: A fully responsive profile. Linked 6. Related 0. Hot Network Questions. Question feed. Stack Overflow works best with JavaScript enabled. The total time taken in running all the map tasks in ms, including speculative tasks. No ideal optimal value as it depends on mapper logic. Optimize the mapper code as much as possible by profiling it for memory leaks and following best coding standards. Total time spent by all reduces in occupied slots ms. This value indicates wall clock time for the reduce tasks.
Total time spent by all reduces waiting after reserving slots ms. Total time spent by all reduce tasks ms. The total time taken in running all the reduce tasks in ms, including speculative tasks.
No ideal optimal value as it depends on reducer logic. Optimize the reducer code as much as possible by profiling it for memory leaks and following best coding standards. Total vcore-seconds taken by all map tasks. Vcore stands for virtual core in CPU of a computer. This counter measures the cpu resources used by all the mappers. It is the aggregated number of vcores that each mapper had been allocated times the number of seconds that mapper had run. The number of vcores allocated to a mapper is controlled by property.
And in yarn, it is controlled by. It gives the value of cpu resources that were blocked by mappers. The ideal optimal value is as much low value for this counter as possible. To reduce value to optimize the value of this counter, either reduce the number of vcore allocated to each mapper or the time taken to execute a mapper instance.
In other words, mapper should be made optimized and less resource-intensive as much possible. Total vcore-seconds taken by all reduce tasks. This counter measures the cpu resources used by all the reducers. It is the aggregated number of vcores that each reducer had been allocated times the number of seconds that reducer had run. The number of vcores allocated to a reducer is controlled by property.
It gives the value of cpu resources that were blocked by reducers. To reduce value to optimize the value of this counter, either reduce the number of vcore allocated to each reducer or the time taken to execute a mapper instance. Total megabyte-seconds taken by all map tasks. This counter measures the memory resources used by all the mappers.
It is the aggregated amount of memory in megabytes that each mapper had been allocated times the number of seconds that mapper had run. The amount of memory MB allocated to a mapper is controlled by property.
It gives the value of memory resources that were blocked by mappers. To reduce value to optimize the value of this counter, either reduce the number of memory-mb allocated to each mapper or the time taken to execute a mapper instance. Total megabyte-seconds taken by all reduce tasks. This counter measures the memory resources used by all the reducers.
It is the aggregated amount of memory in megabytes that each reducer had been allocated times the number of seconds that reducer had run. The amount of memory MB allocated to a reducer is controlled by property. It gives the value of memory resources that were blocked by reducers. To reduce value to optimize the value of this counter, either reduce the number of memory-mb allocated to each reducer or the time taken to execute a reducer instance.
Data-local map tasks. Total number of map tasks run on local data blocks data locality. It should be as much near to total maps as possible.
Optimally, all the map tasks will execute on local data to exploit locality of reference. The only solution is to ensure maximum data locality. The way to ensure it can be one of the following:.
Overall, load on a particular node will decrease and probability of map task getting data locally will increase. Rack-local map tasks. Total number of map tasks for which data blocks are on same rack as node on which map task is executing. Ideally should be zero. Other local map tasks tasks. Total number of map tasks for which data blocks are on some other rack as node on which map task is executing.
Launched map tasks. It defines how many map tasks were launched for the job, including failed tasks, killed tasks and tasks that were started speculatively. Optimally, this number is the same as the number of splits for the job. In case of value not maching number of input splits, verify the InputFormat used.
Launched reduce tasks. It defines how many reduce tasks were launched for the job, including failed tasks, killed tasks and tasks that were started speculatively. Optimally, should be equal to the number of reducers configured for that mapreduce job default is 2 reducers.
Failed map tasks. Failed reduce tasks. Killed map tasks. The reason of killing could be speculative execution in which slower task is killed or failure of any node on which task is running. A high number indicates too many tasks getting killed due to speculative execution or node failures. The way to reduce the instances of speculative execution can be one of the following:.
Killed reduce tasks. Input split bytes. The total number of bytes of input-split object consumed read by maps. These objects represent the split metadata offset and length within a file rather than split data itself.
No ideal optimal value. Map input bytes. The number of bytes of uncompressed input read by all the maps in the job. Must be equal to number of bytes in input data.
Map input records. The number of input records consumed by all the maps in the job. Records that the map tasks failed to read are not included in these counters. Must be equal to number of records in input data. In case of non-matching value, check RecordReader logic.
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