In map-reduce v.1 mapreduce.tasktracker.map.tasks.maximum and mapreduce.tasktracker.reduce.tasks.maximum are used to configure number of map slots and reduce slots accordingly in mapred-site.xml.

starting from map-reduce v.2 (YARN), containers is a more generic term is used instead of slots, containers represents the max number of tasks that can run in parallel under the node regardless being Map task, Reduce task or application master task (in YARN).

Suppose you have a TaskTracker with 32 GB of memory, 16 map slots, and 8 reduce slots. If all task JVMs use 1 GB of memory and all slots are filled, you have 24 Java processes with 1 GB each, for a total of 24 GB. Because you have 32 GB of physical memory, there is probably enough memory for all 24 processes. On the other hand, if your average map and reduce tasks need 2 GB of memory and all slots are full, the 24 tasks could need up to 48 GB of memory, more than is available. To avoid over-committing TaskTracker node memory, reduce the number of slots.

Question : I have 10 node cluster and each node has a quad core processor
so in total there are 80 slots (8×10). It is said that one map task is alloted
per slot(map slot). Suppose usable slot is 60 (say other slots are busy handling other)
daemons). If I have a file that has 1000 blocks, and then say 40 slots are
for map task and 20 for reduce task, how the blocks are gonna process?
as the number of map task starts on number of blocks available (1000 here)
but the available map slots are 40.

Answer : The number of map tasks for a given job is driven by the number of input splits and not by the mapred.map.tasks parameter. For each input split a map task is spawned. So, over the lifetime of a mapreduce job the number of map tasks is equal to the number of input splits. mapred.map.tasks is just a hint to the InputFormat for the number of maps.

Assume your hadoop input file size is 2 GB and you set block size as 64 MB so 32 Mappers tasks are set to run while each mapper will process 64 MB block to complete the Mapper Job of your Hadoop Job.

==> Number of mappers set to run are completely dependent on 1) File Size and 2) Block Size

Assume you have running hadoop on a cluster size of 4: Assume you set mapred.map.tasks and mapred.reduce.tasks parameters in your conf file to the nodes as follows:

Node 1: mapred.map.tasks = 4 and mapred.reduce.tasks = 4
Node 2: mapred.map.tasks = 2 and mapred.reduce.tasks = 2
Node 3: mapred.map.tasks = 4 and mapred.reduce.tasks = 4
Node 4: mapred.map.tasks = 1 and mapred.reduce.tasks = 1

Assume you set the above paramters for 4 of your nodes in this cluster. If you notice Node 2 has set only 2 and 2 respectively because the processing resources of the Node 2 might be less e.g(2 Processors, 2 Cores) and Node 4 is even set lower to just 1 and 1 respectively might be due to processing resources on that node is 1 processor, 2 cores so can’t run more than 1 mapper and 1 reducer task.

So when you run the job Node 1, Node 2, Node 3, Node 4 are configured to run a max. total of (4+2+4+1)11 mapper tasks simultaneously out of 42 mapper tasks that needs to be completed by the Job. After each Node completes its map tasks it will take the remaining mapper tasks left in 42 mapper tasks.

Processor is the entire chipset including all the cores. Cores are like 2 (or more like 4 core, 6 core) parts of the processor that does parallel processing (processing two different data simultaneously in different units) which helps in multitasking without causing much strain on the processor. Each core itself is a processor technically. But the chipset is manufactured in such a way that the different cores work with coordination and not individually. An anlogy is dividing a large hall into several “identical” bedrooms so that there is no overcrowding. Each bedroom is like a core that does the same function of keeping the guests but are different physically.