mapreduce - How does Hadoop decide how many nodes will perform the Map and Reduce tasks?

Question

I'm new to hadoop and I'm trying to understand it. Im talking about hadoop 2. When I have an input file which I wanto to do a MapReduce, in the MapReduce programm I say the parameter of the Split, so it will make as many map tasks as splits,right?

The resource manager knows where the files are and will send the tasks to the nodes who have the data, but who says how many nodes will do the tasks? After the maps are donde there is the shuffle, which node will do a reduce task is decided by the partitioner who does a hash map,right? How many nodes will do reduce tasks? Will nodes who have done maps will do too reduce tasks?

Thank you.

TLDR: If I have a cluster and I run a MapReduce job, how does Hadoop decides how many nodes will do map tasks and then which nodes will do the reduce tasks?

Answer 1

How Many Maps?

The number of maps is usually driven by the total size of the inputs, that is, the total number of blocks of the input files.

The right level of parallelism for maps seems to be around 10-100 maps per-node, although it has been set up to 300 maps for very cpu-light map tasks. Task setup takes a while, so it is best if the maps take at least a minute to execute.

If you havve 10TB of input data and a blocksize of 128MB, you’ll end up with 82,000 maps, unless Configuration.set(MRJobConfig.NUM_MAPS, int) (which only provides a hint to the framework) is used to set it even higher.

How Many Reduces?

The right number of reduces seems to be 0.95 or 1.75 multiplied by ( < no. of nodes > * < no. of maximum containers per node > ).

With 0.95 all of the reduces can launch immediately and start transferring map outputs as the maps finish. With 1.75 the faster nodes will finish their first round of reduces and launch a second wave of reduces doing a much better job of load balancing.

Increasing the number of reduces increases the framework overhead, but increases load balancing and lowers the cost of failures.

Reducer NONE

It is legal to set the number of reduce-tasks to zero if no reduction is desired

Which nodes for Reduce tasks?

You can configure number of mappers and number of reducers per node as per Configuration parameters like mapreduce.tasktracker.reduce.tasks.maximum

if you set this parameter as zero, that node won't be considered for Reduce tasks. Otherwise, all nodes in the cluster are eligible for Reduce tasks.

Source : Map Reduce Tutorial from Apache.

Note: For a given Job, you can set mapreduce.job.maps & mapreduce.job.reduces. But it may not be effective. We should leave the decisions to Map Reduce Framework to decide on number of Map & Reduce tasks

EDIT:

How to decide which Reducer node?

Assume that you have equal reduce slots available on two nodes N1 and N2 and current load on N1 > N2, then , Reduce task will be assigned to N2. If both load and number of slots are same, whoever sends first heartbeat to resource manager will get the task. This is the code block for reduce assignment:http://grepcode.com/file/repository.cloudera.com/content/repositories/releases/com.cloudera.hadoop/hadoop-core/0.20.2-320/org/apache/hadoop/mapred/JobQueueTaskScheduler.java#207