MapReduce

问题 val jobConf = new JobConf(hbaseConf) jobConf.setOutputFormat(classOf[TableOutputFormat]) jobConf.set(TableOutputFormat.OUTPUT_TABLE, tablename) val indataRDD = sc.makeRDD(Array("1,jack,15","2,Lily,16","3,mike,16")) indataRDD.map(_.split(',')) val rdd = indataRDD.map(_.split(',')).map{arr=>{ val put = new Put(Bytes.toBytes(arr(0).toInt)) put.add(Bytes.toBytes("cf"),Bytes.toBytes("name"),Bytes.toBytes(arr(1))) put.add(Bytes.toBytes("cf"),Bytes.toBytes("age"),Bytes.toBytes(arr(2).toInt)) (new

问题 As i understood that File splitting at the time of copying the file into HDFS and input splits on file for mapper input are entirely two different approaches. Here it is my Question-- Suppose if my File1 size is 128MB which was split ted into two blocks and stored in two different data nodes (Node1,Node2) in hadoop cluster. I want to run MR job on this file and got two input splits of the sizes are 70MB and 58 MB respectively. First mapper will run on node1 by taking the input split data (Of

问题 Based on the following configuration i am expecting my log4j should write to HDFS folder (/myfolder/mysubfolder). But it's not even creating a file with the given name hadoop9.log. I tried by creating hadoop9.log manually on hdfs. Still it didn't work. Am i missing anything in log4j.properties.? # Define some default values that can be overridden by system properties hadoop.root.logger=INFO,console,RFA,DRFA hadoop.log.dir= /myfolder/mysubfolder hadoop.log.file=hadoop9.log # Define the root

问题 I'm trying to create a pagination index view in CouchDB that lists the doc._id for every Nth document found. I wrote the following map function, but the pageIndex variable doesn't reliably start at 1 - in fact it seems to change arbitrarily depending on the emitted value or the index length (e.g. 50, 55, 10, 25 - all start with a different file, though I seem to get the correct number of files emitted). function(doc) { if (doc.type == 'log') { if (!pageIndex || pageIndex > 50) { pageIndex = 1

问题 I have a multidimensional array that I want to index with CouchDB (really using Cloudant). I have users which have a list of the teams that they belong to. I want to search to find every member of that team. So, get me all the User objects that have a team object with id 79d25d41d991890350af672e0b76faed. I tried to make a json index on "Teams.id", but it didn't work because it isn't a straight array but a multidimensional array. User { "_id": "683be6c086381d3edc8905dc9e948da8", "_rev": "238

问题 I have a pretty basic question that I am trying to find an answer for. I was looking through the documentation to understand where is the data spilled to during the map phase, shuffle phase and reduce phase? As in if Mapper A has 16 GB of RAM, but if the allocated memory for a mapper has exceeded then the data is spilled. Is the data spilled to HDFS or will the data be spilled to a tmp folder on the disk? During the shuffle phase, is the data streamed from one node to another node and is

问题 I am new to Hive and MapReduce and would really appreciate your answer and also provide a right approach. I have defined an external table logs in hive partitioned on date and origin server with an external location on hdfs /data/logs/ . I have a MapReduce job which fetches these logs file and splits them and stores under the folder mentioned above. Like "/data/logs/dt=2012-10-01/server01/" "/data/logs/dt=2012-10-01/server02/" ... ... From MapReduce job I would like add partitions to the

问题 I have got questions about views in couchdb At the moment, I have a number of views (e.g. view_A, view_B.... view_Z), for each view they contains same range of keys but with different values. ie: view_A = {"key":"key_1", "value":10}, {"key":"key_2", "value":100} view_B = {"key":"key_1", "value":5}, {"key":"key_2", "value":2} view_C = {"key":"key_1", "value":1}, {"key":"key_2", "value":2} I am expecting to have a view to represent values in view_A divided by values in view_B => view_A_over_B =

问题 I'm trying to understand the example for mrjob better from mrjob.job import MRJob class MRWordFrequencyCount(MRJob): def mapper(self, _, line): yield "chars", len(line) yield "words", len(line.split()) yield "lines", 1 def reducer(self, key, values): yield key, sum(values) if __name__ == '__main__': MRWordFrequencyCount.run() I run it by $ python word_count.py my_file.txt and it works as expected but I don't get how it automatically knows that it's going to read a text file and split it by

问题 I need to store output of map-reduce program into database, so is there any way? If so, is it possible to store output into multiple columns & tables based on requirement?? please suggest me some solutions. Thank you.. 回答1: The great example is shown on this blog, I tried it and it goes really well. I quote the most important parts of the code. At first, you must create a class representing data you would like to store. The class must implement DBWritable interface: public class