Map Reduce Word Count
- MapReduce Word Count | Guide to MapReduce Word Count | Examples
- Map reduce word count your sheep
- MapReduce Tutorial–Learn to implement Hadoop WordCount Example
The Map class which extends the public class Mapper
MapReduce Word Count | Guide to MapReduce Word Count | Examples
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- Map reduce word count
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- Spark mapreduce word count
- Mapreduce word count python
- Hadoop Mapreduce word count example - execute Wordcount jar on single node cluster ~ DevinLine - full stack development
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- MapReduce Word Count | Guide to MapReduce Word Count | Examples
Let's look at the mapper Python code and a Reducer Python code and how to execute that using a streaming jar file. The API has a technical name for this task which is shuffle and sort phase. MapReduce has everything as a Key-Value pair, that is, the value of mapper input, output and value of reducer input, the output is in key values. Input: I am the best there is at what I do but what I do isn't very nice. considering this as the input, this is how input is taken using a map and reduce it. Output: We need to know the datatypes of mapper and reducer inputs and outputs. Lately, all the input is taken and is read one by one by the mapper and then passed the output to the reducer. In this case, first comes I and then am and so on. Importing a few files and other libraries for running the program will help you get done with the word count. Conclusion Imagine you have lots of documents, which is huge data. And you need to count the number of occurrences of each word throughout the documents.
waitForCompletion(true)? 0: 1);}} Required jars for Hadoop MapReduce code You will also need to add at least the following Hadoop jars so that your code can compile. You will find these jars inside the /share/hadoop directory of your Hadoop installation. With in /share/hadoop path look in hdfs, mapreduce and common directories for required jars. Creating jar of your wordcount MapReduce code Once you are able to compile your code you need to create jar file. In the eclipse IDE righ click on your Java program and select Export – Java – jar file. Running the MapReduce code You can use the following command to run the program. Assuming you are in your hadoop installation directory. bin/hadoop jar /netjs/MapReduce/ /user/process /user/out Explanation for the arguments passed is as follows- /netjs/MapReduce/ is the path to your jar file. is the fully qualified path to your Java program class. /user/process – path to input directory. /user/out – path to output directory. One your word count MapReduce program is succesfully executed you can verify the output file.
Map reduce word count your sheep
waitForCompletion(true)? 0: 1);}} To compile this program, use the below command hadoop Create the jar file which is named as jar cf WordCount* Run wordcount program, output will be automaically routed to hadoop jar WordCount /user_review_hdfs /usr/review_count_out Have a look at the output hadoop fs -cat /usr/review_count_out/part-r-00000 Part of the output We can take the output to a text file mkdir /home/hduser/Output/ hadoop fs -cat /usr/review_count_out/part-r-00000 >> /home/hduser/Output/
MapReduce Tutorial–Learn to implement Hadoop WordCount Example
This is a basic principle of using regular expressions and testing each string against the source string and emitting the found count for the result. In mapReduce terms, you want your "mapper" function to possibly emit multiple values for each "term" as a key, and for every array element present in each document. So you basically want a source array of regular expressions to process ( likely just a word list) to iterate and test and also iterate each array member. Basically something like this: pReduce( function() { var list = ["the", "quick", "brown"]; // words you want to count rEach(function(project) { (function(log) { rEach(function(word) { var res = (new RegExp("\\b" + word + "\\b", "ig")); if ( res! = null) emit(word, ); // returns number of matches for word});});});}, function(key, values) { return (values);}, { "out": { "inline": 1}}) So the loop processes the array elements in the document and then applies each word to look for with a regular expression to test. The () method will return an array of matches in the string or null if done was found.
Copying the files over is pretty simple: > hadoop dfs -copyFromLocal books /user/hduser/books You can verify they were copied successfully using the following command: > hadoop dfs -ls /user/hduser/ After the data has been copied over to th HDFS, you're ready to perform the map-reduce job. I put the command into a separate bash script () because it was easier to maintain. Here is my file that will run the word count example (bash): After the command completes, you can view your output which is also stored on HDFS by executing the following commands > hadoop dfs -ls /user/hduser/ > hadoop dfs -cat /user/hduser/books-output/part-00000 That's it -- you just ran your first Hadoop streaming job. I plan on putting much more sophisticated examples (Mahout, Hive, etc) up in the future. Stay tuned. The code for all of this is on github: And you can follow me on twitter @josephmisiti [1] [2] [3]