Working with hadoop and map-reduce framework, i was thinking that the reduce tasks must be fine-grained so that the different nodes that processes them can do it separately.
I think that the number of keys can influence in the granularity of the tasks. So, is the number of keys or the variety of them a significant factor in efficiency?
For example, if i had only one key or two, that would be a problem?
All the same keys should end in the same reducer, then, if you have only one key, you will really use only one reducer not matter if you have set 10 reducers. The remaining reducers won't have any output (but they will be instantiated).
This is a big issue named "skew data" and you require to redefine (and redistribute) your keys to be able to run the process in parallel.
Ideally the data should be distributed in sets with the same amount of records, this means that all reducers will have the same load of work.
I have a general question to the MAP/Reduce Framework.
I have a task, which can be separated into several partitions. For each partition, I need to run a computation intensive algorithm.
Then, according to the MAP/Reduce Framework, it seems that I have two choices:
Run the algorithm in the Map stage, so that in the reduce stage, there is no work needed to be done, except collect the results of each partition from the Map stage and do summarization
In the Map stage, just divide and send the partitions (with data) to the reduce stage. In the reduce stage, run the algorithm first, and then collect and summarize the results from each partitions.
Correct me if I misunderstand.
I am a beginner. I may not understand the MAP/Reduce very well. I only have basic parallel computing concept.
You're actually really confused. In a broad and general sense, the map portion takes the task and divides it among some n many nodes or so. Those n nodes that receive a fraction of the whole task do something with their piece. When finished computing some steps on their data, the reduce operation reassembles the data.
The REAL power of map-reduce is how scalable it is.
Given a dataset D running on a map-reduce cluster m with n nodes under it, each node is mapped 1/D pieces of the task. Then the cluster m with n nodes reduces those pieces into a single element. Now, take a node q to be a cluster n with p nodes under it. If m assigns q 1/D, q can map 1/D to (1/D)/p with respect to n. Then n's nodes can reduce the data back to q where q can supply its data to its neighbors for m.
Make sense?
In MapReduce, you have a Mapper and a Reducer. You also have a Partitioner and a Combiner.
Hadoop is a distributed file system that partitions(or splits, you might say) the file into blocks of BLOCK SIZE. These partitioned blocks are places on different nodes. So, when a job is submitted to the MapReduce Framework, it divides that job such that there is a Mapper for every input split(for now lets say it is the partitioned block). Since, these blocks are distributed onto different nodes, these Mappers also run on different nodes.
In the Map stage,
The file is divided into records by the RecordReader, the definition of record is controlled by InputFormat that we choose. Every record is a key-value pair.
The map() of our Mapper is run for every such record. The output of this step is again in key-value pairs
The output of our Mapper is partitioned using the Partitioner that we provide, or the default HashPartitioner. Here in this step, by partitioning, I mean deciding which key and its corresponding values go to which Reducer(if there is only one Reducer, its of no use anyway)
Optionally, you can also combine/minimize the output that is being sent to the reducer. You can use a Combiner to do that. Note that, the framework does not guarantee the number of times a Combiner will be called. It is only part of optimization.
This is where your algorithm on the data is usually written. Since these tasks run in parallel, it makes a good candidate for computation intensive tasks.
After all the Mappers complete running on all nodes, the intermediate data i.e the data at end of Map stage is copied to their corresponding reducer.
In the Reduce stage, the reduce() of our Reducer is run on each record of data from the Mappers. Here the record comprises of a key and its corresponding values, not necessarily just one value. This is where you generally run your summarization/aggregation logic.
When you write your MapReduce job you usually think about what can be done on each record of data in both the Mapper and Reducer. A MapReduce program can just contain a Mapper with map() implemented and a Reducer with reduce() implemented. This way you can focus more on what you want to do with the data and not bother about parallelizing. You don't have to worry about how the job is split, the framework does that for you. However, you will have to learn about it sooner or later.
I would suggest you to go through Apache's MapReduce tutorial or Yahoo's Hadoop tutorial for a good overview. I personally like yahoo's explanation of Hadoop but Apache's details are good and their explanation using word count program is very nice and intuitive.
Also, for
I have a task, which can be separated into several partitions. For
each partition, I need to run a computing intensive algorithm.
Hadoop distributed file system has data split onto multiple nodes and map reduce framework assigns a task to every every split. So, in hadoop, the process goes and executes where the data resides. You cannot define the number of map tasks to run, data does. You can however, specify/control the number of reduce tasks.
I hope I have comprehensively answered your question.
In Map Reduce programming the reduce phase has shuffling, sorting and reduce as its sub-parts. Sorting is a costly affair.
What is the purpose of shuffling and sorting phase in the reducer in Map Reduce Programming?
First of all shuffling is the process of transfering data from the mappers to the reducers, so I think it is obvious that it is necessary for the reducers, since otherwise, they wouldn't be able to have any input (or input from every mapper). Shuffling can start even before the map phase has finished, to save some time. That's why you can see a reduce status greater than 0% (but less than 33%) when the map status is not yet 100%.
Sorting saves time for the reducer, helping it easily distinguish when a new reduce task should start. It simply starts a new reduce task, when the next key in the sorted input data is different than the previous, to put it simply. Each reduce task takes a list of key-value pairs, but it has to call the reduce() method which takes a key-list(value) input, so it has to group values by key. It's easy to do so, if input data is pre-sorted (locally) in the map phase and simply merge-sorted in the reduce phase (since the reducers get data from many mappers).
Partitioning, that you mentioned in one of the answers, is a different process. It determines in which reducer a (key, value) pair, output of the map phase, will be sent. The default Partitioner uses a hashing on the keys to distribute them to the reduce tasks, but you can override it and use your own custom Partitioner.
A great source of information for these steps is this Yahoo tutorial (archived).
A nice graphical representation of this is the following (shuffle is called "copy" in this figure):
Note that shuffling and sorting are not performed at all if you specify zero reducers (setNumReduceTasks(0)). Then, the MapReduce job stops at the map phase, and the map phase does not include any kind of sorting (so even the map phase is faster).
UPDATE: Since you are looking for something more official, you can also read Tom White's book "Hadoop: The Definitive Guide". Here is the interesting part for your question.
Tom White has been an Apache Hadoop committer since February 2007, and is a member of the Apache Software Foundation, so I guess it is pretty credible and official...
Let's revisit key phases of Mapreduce program.
The map phase is done by mappers. Mappers run on unsorted input key/values pairs. Each mapper emits zero, one, or multiple output key/value pairs for each input key/value pairs.
The combine phase is done by combiners. The combiner should combine key/value pairs with the same key. Each combiner may run zero, once, or multiple times.
The shuffle and sort phase is done by the framework. Data from all mappers are grouped by the key, split among reducers and sorted by the key. Each reducer obtains all values associated with the same key. The programmer may supply custom compare functions for sorting and a partitioner for data split.
The partitioner decides which reducer will get a particular key value pair.
The reducer obtains sorted key/[values list] pairs, sorted by the key. The value list contains all values with the same key produced by mappers. Each reducer emits zero, one or multiple output key/value pairs for each input key/value pair.
Have a look at this javacodegeeks article by Maria Jurcovicova and mssqltips article by Datta for a better understanding
Below is the image from safaribooksonline article
I thought of just adding some points missing in above answers. This diagram taken from here clearly states the what's really going on.
If I state again the real purpose of
Split: Improves the parallel processing by distributing the processing load across different nodes (Mappers), which would save the overall processing time.
Combine: Shrinks the output of each Mapper. It would save the time spending for moving the data from one node to another.
Sort (Shuffle & Sort): Makes it easy for the run-time to schedule (spawn/start) new reducers, where while going through the sorted item list, whenever the current key is different from the previous, it can spawn a new reducer.
Some of the data processing requirements doesn't need sort at all. Syncsort had made the sorting in Hadoop pluggable. Here is a nice blog from them on sorting. The process of moving the data from the mappers to the reducers is called shuffling, check this article for more information on the same.
I've always assumed this was necessary as the output from the mapper is the input for the reducer, so it was sorted based on the keyspace and then split into buckets for each reducer input. You want to ensure all the same values of a Key end up in the same bucket going to the reducer so they are reduced together. There is no point sending K1,V2 and K1,V4 to different reducers as they need to be together in order to be reduced.
Tried explaining it as simply as possible
Shuffling is the process by which intermediate data from mappers are transferred to 0,1 or more reducers. Each reducer receives 1 or more keys and its associated values depending on the number of reducers (for a balanced load). Further the values associated with each key are locally sorted.
Because of its size, a distributed dataset is usually stored in partitions, with each partition holding a group of rows. This also improves parallelism for operations like a map or filter. A shuffle is any operation over a dataset that requires redistributing data across its partitions. Examples include sorting and grouping by key.
A common method for shuffling a large dataset is to split the execution into a map and a reduce phase. The data is then shuffled between the map and reduce tasks. For example, suppose we want to sort a dataset with 4 partitions, where each partition is a group of 4 blocks.The goal is to produce another dataset with 4 partitions, but this time sorted by key.
In a sort operation, for example, each square is a sorted subpartition with keys in a distinct range. Each reduce task then merge-sorts subpartitions of the same shade.
The above diagram shows this process. Initially, the unsorted dataset is grouped by color (blue, purple, green, orange). The goal of the shuffle is to regroup the blocks by shade (light to dark). This regrouping requires an all-to-all communication: each map task (a colored circle) produces one intermediate output (a square) for each shade, and these intermediate outputs are shuffled to their respective reduce task (a gray circle).
The text and image was largely taken from here.
There only two things that MapReduce does NATIVELY: Sort and (implemented by sort) scalable GroupBy.
Most of applications and Design Patterns over MapReduce are built over these two operations, which are provided by shuffle and sort.
This is a good reading. Hope it helps. In terms of sorting you are concerning, I think it is for the merge operation in last step of Map. When map operation is done, and need to write the result to local disk, a multi-merge will be operated on the splits generated from buffer. And for a merge operation, sorting each partition in advanced is helpful.
Well,
In Mapreduce there are two important phrases called Mapper and reducer both are too important, but Reducer is mandatory. In some programs reducers are optional. Now come to your question.
Shuffling and sorting are two important operations in Mapreduce. First Hadoop framework takes structured/unstructured data and separate the data into Key, Value.
Now Mapper program separate and arrange the data into keys and values to be processed. Generate Key 2 and value 2 values. This values should process and re arrange in proper order to get desired solution. Now this shuffle and sorting done in your local system (Framework take care it) and process in local system after process framework cleanup the data in local system.
Ok
Here we use combiner and partition also to optimize this shuffle and sort process. After proper arrangement, those key values passes to Reducer to get desired Client's output. Finally Reducer get desired output.
K1, V1 -> K2, V2 (we will write program Mapper), -> K2, V' (here shuffle and soft the data) -> K3, V3 Generate the output. K4,V4.
Please note all these steps are logical operation only, not change the original data.
Your question: What is the purpose of shuffling and sorting phase in the reducer in Map Reduce Programming?
Short answer: To process the data to get desired output. Shuffling is aggregate the data, reduce is get expected output.
One of the big benefits of Hadoop MapReduce is the fact that Map processes take place on the same machine that the data they operate upon resides (to the extent possible). But can this be or is this perhaps already true of the Reduce side? For example, in the extreme case of a Map-only job, all of the output data ends up on the same machine as the corresponding input data (right?). But in an intermediate case in which the output is somewhat correlated with the output, it seems reasonable to partition the output and to the extent possible keep it on same machine at it started on.
Is this possible? Does this already happen?
Inputs to the Reducers can reside on any node(local or remote) and not necessarily on the same machine where they are running. As Mappers complete their output gets written onto the local FS of the machine where they are running. Once this is done the intermediate output is needed by the machines that are about to run the reduce task. One thing to note here is that all the values corresponding to a particular key go the same reducer. So, it's not always possible that the input to Reducers is local, since different sets of key/value pairs are processed by different Mappers running on different machines.
Now, before the Mapper output is sent to Reducers for further processing, the data is partitioned based on keys and each partition goes to a Reducer and all the key/value pairs in that partition get processed by that Reducer. During the process a lot of data shuffling takes place. So it's not possible to maintain the data locality in case of Reducers.
Hope this answers the question.
If you know that the data for a particular reducer is already on the right node after the map phase, and the algorithm allows for it (see this blog post about it) you should insert your reducer as a combiner. Combiners are like miniature reducers that only get to see co-located data. Often you can dramatically improve performance because the combiner output can be orders of magnitude smaller than the map output, so what's left to shuffle is trivial.
Of course, if indeed the map phase leaves your data already correctly partitioned, why use a reducer at all? Why not create a second map job that simulates a reducer?
I'm trying to understand MapReduce model and I need advice because I'm not sure about the way how is sorted and partitioned file with intermediate results of map function. The most my knowledges about MapReduce I got from MapReduce papers of Jeffrey Dean & Sanjay Ghemawat and from Hadoop: The Definitive Guide.
The file with intermediate results of map function is compound of small sorted and partitioned files. These small files are divided into partitions corresponding to reduce workers. Then small files are merged into one file. I need to know how is partitioning of small files done. First I thought that every partition has some range of keys.
For example: if we've got keys as integer in range <1;100> and file is divided to three partitions then the first partition can consists of values with keys in range <1,33>, second partition with keys in range <34;66> and third partition <67;100>. The same partitioning is in merged file too.
But I'm not sure about it. Every partition is send to corresponding reduce worker. In our example, if we have two reduce workers then partitions with first two ranges of keys (<1,33> and <34;66>) can be sent to first worker and last partition to third worker. But if I'm wrong and the files are divided in another way (I mean that partitions hasn't got their own range of possible keys) then every reduce worker can has results for the same keys. So I need somehow merge results of these reduce workers, right? Can I send these results to master node and merge them there?
In short version: I need explain the way how files in map phase are divided (if my description is wrong) and explain how and where I can process results of reduce workers.
I hope I described my problem enough to understand. I can explain it more, of course.
Thanks a lot for your answers.
There is a Partitioner class that does this. Each key/value pair in the intermediate file is passed to the partitioner along with the total number of reducers (partitions) and the partitioner returns the partition number that should handle that specific key/value pair.
There is a default partitioner that does an OK job of partitioning, but if you want better control or if you have a specially formatted (e.g. complex) key then you can and should write your own partitioner.