Hadoop MapReduce is not showing CPU time. It has a problem called Speculative execution. This means that several maps and reduces are running on the same slave simultaneously. This causes overlap in computation and I/O. The slaves then tell the master when they have empty task slots and the scheduler assigns them to the slaves.
Speculative execution in MapReduce
Speculative execution is a technique used in MapReduce in Hadoop to deal with poor performance on individual machines. A large cluster may have some machines that are not performing well, causing the overall system performance to suffer. This technique allows MapReduce tasks to be run multiple times on the same machine.
You might be wondering why your Hadoop MapReduce CPU time is not showing the time that you’re spending on speculation. The reason is that the MapReduce algorithm divides a job into n separate tasks and routes them in parallel. Occasionally, one task will slow down and a new task may be launched. This can happen for several reasons, and Hadoop will take action to speed things up by launching a copy of the task on another node. This process is known as speculative execution.
When you run a map-reduce job, you may notice that your CPU usage does not reflect your progress. This is caused by the fact that Hadoop runs several maps and reduces at the same time, and the computation and I/O is often overlapped. The scheduler assigns tasks to each slave according to its task-slot availability.
If your R2 Hadoop MapReduce cpu time is not showing, the problem could be due to a map task. The map task may be crashing deterministically on some inputs. This can be caused by bugs in the map function or in a third party library. In such cases, you should increase the number of reduces to increase load balancing and decrease the cost of failure. The optimum number of reduces is between 0.95 and 1.75. These numbers are lower than the whole number because the scale factor reserves slots for failed or speculative tasks.
RDMA over Mellanox ConnectX-3 adapters
OneFS 9.2 introduces Remote Direct Memory Access support to improve throughput and reduce client and cluster CPU usage. It is available for PowerScale servers equipped with Mellanox ConnectX network adapters and 25, 40, or 100 Gig Ethernet connectivity. You can determine which network adapters are in your cluster by issuing the ‘isi network interfaces list’ command.
Hive mapreduce cpu time not showing
If you’re running Hive and your mapreduce jobs are taking a long time to finish, you might be wondering why the CPU time is not showing up. Hive has a few different components that you can check to see which one is taking a long time. First, you should check the’map phase’, which consists of record reader, combiner, partitioner, and reduce. This time is actually the total time of both the map phase and the reduce phase, so it will be the sum of the time of all the nodes in your cluster.