Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am working on sorting WordCount example by frequency of words. I looked for some posts and found out that I can not items where year is 2011 sorting by value in a MapReduce. So I decided to make two map reduce jobs separately. Thus the first one is original wordcount and the second one reads the output from the first MapReduce and sorting words by their frequency.
The input file the second MapReduce uses is like the following which is the output of the first MapReduce. And here is the code for the second mapreduce code. I made a Pair class which I think unnecessary here and used it as keys. I just start to learn hadoop and it is the first one I made on my own. Please let me know how to solve this error and I would be appreciated if you give me some comments nnmx sex filmi izle the overall code.
Your SortingMapper. A Text object can not be cast to IntWritableit's why you get:. Learn more. Asked 6 years, 3 months ago. Active 6 years, 3 months ago. Viewed 3k times. The input file the second MapReduce uses is like the following which is the output of the first MapReduce 1 apple 2 ball 1 cartoon 4 day And here is the code for the second mapreduce code. DataInput; import java.022 What is Key Value Pair
DataOutput; import java. IOException; import java.
Comparator; abati andrea da prato org. Configuration; import org. Path; import org.
IntWritable; import org. LongWritable; import org. NullWritable; import org. Text; import org. WritableComparable; import org.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Can any one explain me how secondary sorting works in hadoop? Why must one use GroupingComparator and how does it work in hadoop? I was going through the link given below and got doubt on how groupcomapator works.
Can any one explain me how grouping comparator works? Once the data reaches a reducer, all data is grouped by key. Since we have a composite key, we need to make sure records are grouped solely by the natural key.
This is accomplished by writing a custom GroupPartitioner. We have a Comparator object only considering the yearMonth field of the TemperaturePair class for the purposes of grouping the records together. Also, we have been able to take a deeper look at the inner workings of Hadoop by working with custom partitioners and group partitioners.
Refer this link also. What is the use of grouping comparator in hadoop map reduce. I find it easy to understand certain concepts with help of diagrams and this is certainly one of them.
Lets assume that our secondary sorting is on a composite key made out of Last Name and First Name. The partitioner and the group comparator use only natural keythe partitioner uses it to channel all records with the same natural key to a single reducer.
This partitioning happens in the Map Phase, data from various Map tasks are received by reducers where they are grouped and then sent to the reduce method. This grouping is where the group comparator comes into picture, if we would not have specified a custom group comparator then Hadoop would have used the default implementation which would have considered the entire composite key, which would have lead to incorrect results. Here is an example for grouping. Consider a composite key a, b and its value v.
And let's assume that after sorting you end up, among others, with the following group of key, value pairs:. With the default group comparator the framework will call the reduce function 3 times with respective key, value pairs, since all keys are different.
However, if you provide your own custom group comparator, and define it so that it depends only on aignoring bthen the framework concludes that all keys in this group are equal and calls the reduce function only once using the following key and the list of values:. Note that only the first composite key is used, and that b12 and b13 are "lost", i. In the well known example from the "Hadoop" book computing the max temperature by year, a is the year, and b 's are temperatures sorted in descending order, thus b11 is the desired max temperature and you don't care about other b 's.WordCount example reads text files and counts how often words occur.
The input is text files and the output is text files, each line of which contains a word and the count of how often it occured, separated by a tab. Each mapper takes a line as input and breaks it into words. As an optimization, the reducer is also used as a combiner on the map outputs. This reduces the amount of data sent across the network by combining each word into a single record.
All of the files in the input directory called in-dir in the command line above are read and the counts of words in the input are written to the output directory called out-dir above. Word count supports generic options : see DevelopmentCommandLineOptions. Evaluate Confluence today. Pages Blog. Page tree. Browse pages.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. My aim is to have the result in a descending order regarding the amount of appearences. Unfortunately the program sorts the output lexicographically by the key. I want a natural order of the integer value. So I added a custom comparator with job. But this doesn't work as expected. I'm getting the following exception:.
I've listed the whole program below as there may be a reason for the exception which I obviously don't know. As you can see I am using the new mapreduce api org. The comparator step occurs between the Mapper and Reducerwhich wont work for you as you swap the key and value around in the Reducer itself. The default WritableComparator would normally handle your numerical ordering if the key was IntWritableexcept it's getting a Text key thus resulting in lexicographical ordering. As to why exactly the output at the end isn't sorted by your written out IntWritable key, I'm unsure.
Perhaps it has something to do with the way TextOutputFormat works? You might have to dig deeper into TextOutputFormat source code for clues on that, but in short, setting the sort comparator probably won't help you here I'm afraid.
As quetzalcoatl said Your comparator is not useful, Since it is used between Map and reduce phase and not after Reduce phase. So to accomplish this you need to either sort in cleanup of Reducer or write another program to sort output of reducer. Basically, you need sort by value. There are 2 ways to achieve this. But in short you need 2 map-reduce, i. After completing normal map-reduce do one more map reduce where you take output of first map reduce as input to second map reduce.
In second map reduce's map phase you can use a custom class as key e. In WordCountVO you can keep both word and count but compare based on count only. Now when you receive key-value pairs in second reducer then your data will be all sorted by values.Thank you so much for sharing this worthwhile to spent time on. You are running a really awesome blog. Keep up this good work Hadoop training chennai velachery Hadoop training velachery Hadoop training institute in t nagar. Final Year Students Projects take a shot at them to improve their aptitudes.
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Thanks for sharing content like this once again. Keep sharing content like this. Software testing training in chennai Software testing training Software testing institute in chennai. This works fine as the input K, V are unique values, this gives problems with duplicate keys while grouping. Correct me if I am wrong. Thanks for providing this informative information you may also refer. Thanks for your valuable post It is amazing and wonderful to visit your site.
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Data Science training in Chennai Data science training in bangalore Data science training in pune Data science online training. Thanks for taking the time to discuss this, I feel strongly about it and love learning more on this topic. Greetings from Florida!A secondary sort problem relates to sorting values associated with a key in the reduce phase. Sometimes, it is called value-to-key conversion. The secondary sorting technique will enable us to sort the values in ascending or descending order passed to each reducer.
I will provide concrete examples of how to achieve secondary sorting in ascending or descending order. In software design and programming, a design pattern is a reusable algorithm that is used to solve a commonly occurring problem. Typically, a design pattern is not presented in a specific programming language but instead can be implemented by many programming languages.
The MapReduce framework automatically sorts the keys generated by mappers.
This means that, before starting reducers, all intermediate key-value pairs generated by mappers must be sorted by key and not by value. Values passed to each reducer are not sorted at all; they can be in any order. So, for those applications such as time series data in which you want to sort your reducer data, the Secondary Sort design pattern enables you to do so.
First, the map function receives a key-value pair input, key 1value 1. Then it outputs any number of key-value pairs, key 2value 2. Next, the reduce function receives as input another key-value pair, key 2list value 2and outputs any number of key 3value 3 pairs. Now consider the following key-value pair, key 2list value 2as an input for a reducer:. The goal of the Secondary Sort pattern is to give some ordering to the values received by a reducer.
So, once we apply the pattern to our MapReduce paradigm, then we will have:. Here is an example of a secondary sorting problem: consider the temperature data from a scientific experiment. A dump of the temperature data might look something like the following columns are yearmonthdayand daily temperaturerespectively :.
Suppose we want to output the temperature for every year-month with the values sorted in ascending order. Essentially, we want the reducer values iterator to be sorted. Therefore, we want to generate something like this output the first column is year-month and the second column is the sorted temperatures :. There are at least two possible approaches for sorting the reducer values. The first approach involves having the reducer read and buffer all of the values for a given key in an array data structure, for examplethen doing an in-reducer sort on the values.
This approach will not scale: since the reducer will be receiving all values for a given keythis approach might cause the reducer to run out of memory java. On the other hand, this approach can work well if the number of values is small enough that it will not cause an out-of-memory error.
The second approach involves using the MapReduce framework for sorting the reducer values this does not require in-reducer sorting of values passed to the reducer.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I found how to sort it by word using the command sortByKey.
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I was wondering how it could be possible to do the same thing for sorting by the value, that in this case in the number that a word occur in the document. The sorting usually should be done before collect is called since that returns the dataset to the driver program and also that is the way an hadoop map-reduce job would be programmed in java so that the final output you want is written typically to HDFS.
With the spark API this approach provides the flexibility of writing the output in "raw" form where you want, such as to a file where it could be used as input for further processing.
Using spark's scala API sorting before collect can be done following eliasah's suggestion and using Tuple2. Below is an example of how this is scripted in spark-shell:. In order to reverse the ordering of the sort use sortByKey false,1 since its first arg is the boolean value of ascending. Its second argument is the number of tasks equivilent to number of partitions which is set to 1 for testing with a small input file where only one output data file is desired; reduceByKey also takes this optional argument.
Using pyspark a python script very similar to the scala script shown above produces output that is effectively the same. Here is the pyspark version demonstrating sorting a collection by value:. In order to sortbyKey in descending order its first arg should be 0. The main difference in the output of the spark and python version of wordCount is that where spark outputs word,3 python outputs u'word', 3.
RDD for scala. Ordering argument such as Romeo Kienzler showed in a previous answer to this question. The reverse int Ordering already defined by Romeo Kienzler is:. Another common way to define this reverse Ordering is to reverse the order of a and b and drop the -1 on the right hand side of the compare definition:.
On the other hand if the values are already in the form that is suitable for your desired ordering, then:. I think you can use the generic sortBy transformation not an action, i.
Simplest way to sort the output by values. After the reduceByKey you can swap the output like key as value and value as key and then you can appply sortByKey method where false sorts in the Descending order.
By default it will sort in the ascending order. The second map then maps the now sorted second rdd back to the original format of. An implicit assumption here is that mapping does not change the order of the RDD rows otherwise the second map might mess with the sorting. Learn more. Spark get collection sorted by value Ask Question. Asked 6 years, 3 months ago. Active 7 months ago. Viewed 89k times. Casimir Crystal