MapReduce

问题 I am trying to execute a simple mapreduce program from eclipse .Following is my program package wordcount; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class WordCount { public static void main(String[] args) throws

问题 I'm running map-reduce and my inputs are gzipped, but do not have a .gz (file name) extension. Normally, when they do have the .gz extension, Hadoop takes care of unzipping them on the fly before passing them to the mapper. However, without the extension it doesn't do so. I can't rename my files, so I need some way of "forcing" Hadoop to unzip them, even though they do not have the .gz extension. I tried passing the following flags to Hadoop: step_args=[ "-jobconf", "stream.recordreader

问题 I'm currently trying to find the best way of processing two very large datasets. I have two BigQuery Tables : One table containing streamed events (Billion rows) One table containing a tags and the associated event properties (100 000 rows) I want to tag each event with the appropriate tags based on the event properties (an event can have multiple tags). However a SQL cross-join seems to be too slow for the dataset size. What is the best way to proceed using a pipeline of mapreduces and

问题 I have a MapReduce job written in Java. It depends on multiple classes. I want to run the MapReduce job on Spark. What steps should I follow to do the same? I need to make changes only to the MapReduce class? Thanks! 回答1: This is a very broad question, but the short of it is: Create an RDD of the input data. Call map with your mapper code. Output key-value pairs. Call reduceByKey with your reducer code. Write the resulting RDD to disk. Spark is more flexible than MapReduce: there is a great

问题 I've got a question regarding Map/Reduce Sort an inner Document in mongodb. The scheme is like following: { "_id" : 16, "days" : { "1" : 123, "2" : 129, "3" : 140, "4" : 56, "5" : 57, "6" : 69, "7" : 80 } So my question now is: How can i achieve to sum some specific days from the above data. For an example: I want to sum the values of day 1,3 and 7 an get the result out of this. I tried the solution from MapReduce aggregation based on attributes contained outside of document but didn't had

问题 I am currently working in pseudo-distributed mode in Hadoop. The way my reduce function works is: For each key it will create a arraylist of its value and then will make an instance of a singleton class [ This class is present in a library so I cannot change it ]. It then call a method of this instance. Now my problem is: Suppose the map function emits 2 keys, then the reducer will only process one key for the another one it will say that "Java.lang.exception" the class[ the singleton one]

问题 I am trying to submit an example map reduce oozie job and all the properties are configured properly with regards to the path and name node and job-tracker port etc. I validated the workflow.xml too . when I deploy the job I get a job id and when I check the status I see a status KILLED and the details basically say that /var/tmp/oozie/oozie-oozi7188507762062318929.dir/map-reduce-launcher.jar does not exist. 回答1: In order to resolve this error, just crate hdfs folders and give appropriate

问题 In my MapReduce jobs, I pass a product name to the Mapper as a string argument. The Mapper.py script imports a secondary script called Process.py that does something with the product name and returns some emit strings to the Mapper. The mapper then emits those strings to the Hadoop framework so they can be picked up by the Reducer. Everything works fine except for the following: The Process.py script contains a dictionary of lookup values that I want to move from inside the script to an xml

问题 I am running my hadoop job and it is failing on class not found. 4 java files in total. logProcessor.java logMapper.java logReducer.java logParser.java Everything is in a com folder on unix and I have "package com;" in the first line in all classes that means if you do below command head -5 *java You will see package com; in all 4 files. logProcessor is the Driver class. All files are in "com" folder on unix. ls -ltr com/ logProcessor.java logMapper.java logReducer.java logParser.java I

问题 I have simple dataset on HDFS that I'm loading into Spark. It looks like this: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ... basically, a matrix. I'm trying to implement something that requires grouping matrix rows, and so I'm trying to add a unique key for every row like so: (1, [1 1 1 1 1 ... ]) (2, [1 1 1 1 1 ... ]) (3, [1 1 1 1 1 ... ]) ... I tried something somewhat naive: set a global variable and write a lambda function to iterate over the global variable: # initialize global index