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This project contains several diagrams describing Apache Hadoop internals (2.3.0 or later). Even if these diagrams are NOT specified in any formal or unambiguous language (e.g., UML), they should be reasonably understandable and useful for any person who want to grasp the main ideas behind Hadoop. Unfortunately, not all the internal details are covered by these diagrams. You are free to help :)

Actors	Tasks	Model of computation	Extra
Job Submitter Node Manager Resource Manager Application Master	Map Task Reduce Task Merger Input	Job Task Task Attempt Application Container	Async Dispatcher Localized Resource Container Allocator [AM] Container Launcher [AM] Containers Launcher [NM]

Parameter	File	Default	Diagram(s)
mapreduce.task.io.sort.mb	mapred-site.xml	100	MapTask > Shuffle
		MapTask > Execution
mapreduce.map.sort.spill.percent	mapred-site.xml	0.80	MapTask > Shuffle
		MapTask > Execution
mapreduce.task.io.sort.factor	mapred-site.xml	100	MapTask > Shuffle
		Merge
		ReduceTask > Shuffle
mapreduce.map.combine.minspills	mapred-site.xml	3	MapTask > Shuffle
mapreduce.job.reduces	mapred-site.xml	1	MapTask > Shuffle
mapreduce.cluster.local.dir	mapred-site.xml	${hadoop.tmp.dir}/mapred/local	MapTask > Shuffle
mapreduce.reduce.merge.memtomem.enabled	mapred-site.xml	False	Reduce Task > Shuffle
mapreduce.framework.name	mapred-site.xml	yarn/local	Reduce Task > Shuffle
mapreduce.reduce.shuffle.parallelcopies	mapred-site.xml	5	Reduce Task > Shuffle
mapreduce.reduce.memory.totalbytes	mapred-site.xml	min(Int.MAX_VALUE, Runtime.getRuntime().maxMemory()`),	Reduce Task > Fetcher
mapreduce.reduce.shuffle.memory.limit.percent	mapred-site.xml	0.25	Reduce Task > Fetcher