The Shuffle process is the core of MapReduce, also known as where the magic happens. To understand MapReduce, Shuffle must be understood. I have read a lot of relevant materials, but every time I read it, I am confused and confused. It is difficult to sort out the general logic. Some time ago, I was working on performance tuning of MapReduce jobs, and I needed to go deep into the code to study the operation mechanism of MapReduce. Considering that I was very annoyed when I read the relevant information and couldn't understand it, so here I try to make Shuffle as clear as possible, so that every friend who wants to understand its principles can gain something. If you have any questions or suggestions about this article, please leave a message to the back, thank you!
The normal meaning of shuffle is to shuffle or shuffle. Maybe you are more familiar with the Collections.shuffle(List) method in the Java API, which randomly shuffles the order of elements in the parameter list. If you don't know what Shuffle is in MapReduce, look at this picture: This is the official description of the Shuffle process. But what I am sure of is that it is basically impossible for you to understand the process of Shuffle from this picture alone, because it is quite different from the truth, and the details are confusing. I will describe the actual situation of Shuffle in detail later, so here you only need to know the approximate scope of Shuffle - how to effectively transmit the output of the map task to the reduce side. It can also be understood in this way, Shuffle describes the process of data output from the map task to the input of the reduce task. 
In a cluster environment like Hadoop, most map tasks and reduce tasks are executed on different nodes. Of course, in many cases, when Reduce is executed, it is necessary to pull map task results on other nodes across nodes. If there are many jobs running in the cluster, the normal execution of tasks will consume a lot of network resources inside the cluster. This kind of network consumption is normal, and we cannot limit it. What we can do is to minimize unnecessary consumption. Also within the node, compared to memory, the impact of disk IO on job completion time is also considerable. From the most basic requirements, our expectations for the Shuffle process can be:
- Completely pull data from the map task side to the reduce side.
- Minimize unnecessary consumption of bandwidth when pulling data across nodes.
- Reduce the impact of disk IO on task execution.
OK, when you see this, you can stop and think about what your design goals are if you design this Shuffle process yourself. I think the optimizations are mainly to reduce the amount of data pulled and try to use memory instead of disk.
My analysis is based on the source code of Hadoop0.21.0. If there is a difference with the Shuffle process you know, please point out. I'll take WordCount as an example and assume it has 8 map tasks and 3 reduce tasks. As can be seen from the above figure, the Shuffle process spans both ends of map and reduce, so I will also expand it in two parts below.
First look at the situation on the map side, as shown in the following figure: The above figure may be the operation of a map task. Comparing it with the left half of the official map reveals a lot of inconsistencies. The official map does not clearly explain the stage in which partition, sort and combiner work. I drew this picture, hoping to let everyone clearly understand the whole process from the input of map data to the preparation of all data on the map side. The whole process is divided into four steps. To put it simply, each map task has a memory buffer, which stores the output results of the map. When the buffer is almost full, the data in the buffer needs to be stored in a temporary file to the disk. After the task ends, merge all the temporary files generated by the map task on the disk to generate the final official output file, and then wait for the reduce task to pull the data. Of course, each step here may contain multiple steps and details. Let me explain the details one by one: 1 
. 在map task执行时，它的输入数据来源于HDFS的block，当然在MapReduce概念中，map task只读取split。Split与block的对应关系可能是多对一，默认是一对一。在WordCount例子里，假设map的输入数据都是像“aaa”这样的字符串。
2. 在经过mapper的运行后，我们得知mapper的输出是这样一个key/value对： key是“aaa”， value是数值1。因为当前map端只做加1的操作，在reduce task里才去合并结果集。前面我们知道这个job有3个reduce task，到底当前的“aaa”应该交由哪个reduce去做呢，是需要现在决定的。
MapReduce提供Partitioner接口，它的作用就是根据key或value及reduce的数量来决定当前的这对输出数据最终应该交由哪个reduce task处理。默认对key hash后再以reduce task数量取模。默认的取模方式只是为了平均reduce的处理能力，如果用户自己对Partitioner有需求，可以订制并设置到job上。
在我们的例子中，“aaa”经过Partitioner后返回0，也就是这对值应当交由第一个reducer来处理。接下来，需要将数据写入内存缓冲区中，缓冲区的作用是批量收集map结果，减少磁盘IO的影响。我们的key/value对以及Partition的结果都会被写入缓冲区。当然写入之前，key与value值都会被序列化成字节数组。
整个内存缓冲区就是一个字节数组，它的字节索引及key/value存储结构我没有研究过。如果有朋友对它有研究，那么请大致描述下它的细节吧。
3. 这个内存缓冲区是有大小限制的，默认是100MB。当map task的输出结果很多时，就可能会撑爆内存，所以需要在一定条件下将缓冲区中的数据临时写入磁盘，然后重新利用这块缓冲区。这个从内存往磁盘写数据的过程被称为Spill，中文可译为溢写，字面意思很直观。这个溢写是由单独线程来完成，不影响往缓冲区写map结果的线程。溢写线程启动时不应该阻止map的结果输出，所以整个缓冲区有个溢写的比例spill.percent。这个比例默认是0.8，也就是当缓冲区的数据已经达到阈值（buffer size * spill percent = 100MB * 0.8 = 80MB），溢写线程启动，锁定这80MB的内存，执行溢写过程。Map task的输出结果还可以往剩下的20MB内存中写，互不影响。
当溢写线程启动后，需要对这80MB空间内的key做排序(Sort)。排序是MapReduce模型默认的行为，这里的排序也是对序列化的字节做的排序。
在这里我们可以想想，因为map task的输出是需要发送到不同的reduce端去，而内存缓冲区没有对将发送到相同reduce端的数据做合并，那么这种合并应该是体现是磁盘文件中的。从官方图上也可以看到写到磁盘中的溢写文件是对不同的reduce端的数值做过合并。所以溢写过程一个很重要的细节在于，如果有很多个key/value对需要发送到某个reduce端去，那么需要将这些key/value值拼接到一块，减少与partition相关的索引记录。
在针对每个reduce端而合并数据时，有些数据可能像这样：“aaa”/1， “aaa”/1。对于WordCount例子，就是简单地统计单词出现的次数，如果在同一个map task的结果中有很多个像“aaa”一样出现多次的key，我们就应该把它们的值合并到一块，这个过程叫reduce也叫combine。但MapReduce的术语中，reduce只指reduce端执行从多个map task取数据做计算的过程。除reduce外，非正式地合并数据只能算做combine了。其实大家知道的，MapReduce中将Combiner等同于Reducer。
如果client设置过Combiner，那么现在就是使用Combiner的时候了。将有相同key的key/value对的value加起来，减少溢写到磁盘的数据量。Combiner会优化MapReduce的中间结果，所以它在整个模型中会多次使用。那哪些场景才能使用Combiner呢？从这里分析，Combiner的输出是Reducer的输入，Combiner绝不能改变最终的计算结果。所以从我的想法来看，Combiner只应该用于那种Reduce的输入key/value与输出key/value类型完全一致，且不影响最终结果的场景。比如累加，最大值等。Combiner的使用一定得慎重，如果用好，它对job执行效率有帮助，反之会影响reduce的最终结果。
4. 每次溢写会在磁盘上生成一个溢写文件，如果map的输出结果真的很大，有多次这样的溢写发生，磁盘上相应的就会有多个溢写文件存在。当map task真正完成时，内存缓冲区中的数据也全部溢写到磁盘中形成一个溢写文件。最终磁盘中会至少有一个这样的溢写文件存在(如果map的输出结果很少，当map执行完成时，只会产生一个溢写文件)，因为最终的文件只有一个，所以需要将这些溢写文件归并到一起，这个过程就叫做Merge。Merge是怎样的？如前面的例子，“aaa”从某个map task读取过来时值是5，从另外一个map 读取时值是8，因为它们有相同的key，所以得merge成group。什么是group。对于“aaa”就是像这样的：{“aaa”, [5, 8, 2, …]}，数组中的值就是从不同溢写文件中读取出来的，然后再把这些值加起来。请注意，因为merge是将多个溢写文件合并到一个文件，所以可能也有相同的key存在，在这个过程中如果client设置过Combiner，也会使用Combiner来合并相同的key。
至此，map端的所有工作都已结束，最终生成的这个文件也存放在TaskTracker够得着的某个本地目录内。每个reduce task不断地通过RPC从JobTracker那里获取map task是否完成的信息，如果reduce task得到通知，获知某台TaskTracker上的map task执行完成，Shuffle的后半段过程开始启动。
简单地说，reduce task在执行之前的工作就是不断地拉取当前job里每个map task的最终结果，然后对从不同地方拉取过来的数据不断地做merge，也最终形成一个文件作为reduce task的输入文件。见下图： 
如map 端的细节图，Shuffle在reduce端的过程也能用图上标明的三点来概括。当前reduce copy数据的前提是它要从JobTracker获得有哪些map task已执行结束，这段过程不表，有兴趣的朋友可以关注下。Reducer真正运行之前，所有的时间都是在拉取数据，做merge，且不断重复地在做。如前面的方式一样，下面我也分段地描述reduce 端的Shuffle细节：
1. Copy过程，简单地拉取数据。Reduce进程启动一些数据copy线程(Fetcher)，通过HTTP方式请求map task所在的TaskTracker获取map task的输出文件。因为map task早已结束，这些文件就归TaskTracker管理在本地磁盘中。
2. Merge阶段。这里的merge如map端的merge动作，只是数组中存放的是不同map端copy来的数值。Copy过来的数据会先放入内存缓冲区中，这里的缓冲区大小要比map端的更为灵活，它基于JVM的heap size设置，因为Shuffle阶段Reducer不运行，所以应该把绝大部分的内存都给Shuffle用。这里需要强调的是，merge有三种形式：1)内存到内存 2)内存到磁盘 3)磁盘到磁盘。默认情况下第一种形式不启用，让人比较困惑，是吧。当内存中的数据量到达一定阈值，就启动内存到磁盘的merge。与map 端类似，这也是溢写的过程，这个过程中如果你设置有Combiner，也是会启用的，然后在磁盘中生成了众多的溢写文件。第二种merge方式一直在运行，直到没有map端的数据时才结束，然后启动第三种磁盘到磁盘的merge方式生成最终的那个文件。
3. Reducer's input file. After continuous merge, a "final file" is finally generated. Why the quotes? Because this file may exist on disk or in memory. For us, of course, we want it to be in memory, directly as input to the Reducer, but by default, this file is stored on disk. As for how to make this file appear in memory, I will talk about it later in the performance optimization article . When the input file of the Reducer has been determined, the entire Shuffle finally ends. Then the Reducer executes and puts the result on HDFS.
The above is the entire Shuffle process. There's a lot of detail, and I've skipped a lot of it, just trying to get the gist out of the way. Of course, I may also have a lot of problems in understanding or expression, and I don't hesitate to give pointers. I hope to continuously improve and revise this article to make it popular and easy to understand, and you can know all aspects of Shuffle after reading it. As for the specific implementation principle, if you are interested, you can explore it yourself. If it is inconvenient, leave a message to me, and I will study and give feedback.