MapReduce

问题 I use this code below to get output result like ( Key , Value ) Apple 12 Bee 345 Cat 123 What I want is descending sorted by value ( 345 ) and place them before the key ( Value , Key ) 345 Bee 123 Cat 12 Apple I found there are something called "secondary sorted" not going to lie but I'm so lost - I tried to change .. context.write(key, result); but failed miserably. I'm new to Hadoop and not sure how can I start to tackle this problem. Any recommendation would be appreciated. Which function

问题 I am running a simple mapreduce job written in python and I noticed that when I test the script locally, i obtain a different out then when I run the job on hadoop. My input is something of a kind: key1 val1 key1 val2 key1 val3 key1 val4 key2 val1 key2 val3 key2 val5 key3 val5 key4 val4 My mapper creates a dictionary of values with their corresponding list (string) of keys (e.g. val1 key1,key2 ; val2 key1 ; val3 key1,key2 ....). Then for each value in the dictionary I print all the possible

问题 I have the below Logincount.js . Please tell me how can i Include the Date Field also while creating the collection LoginCount ?? Right now the js file creates a collection with two fields namely _id and value I want it to also create a field called date with yesterdays Date in it . This is my js file m = function() { emit(this.cust_id, 1); } r = function (k, vals) { var sum = 0; for (var i in vals) { sum += vals[i]; } return sum; } q = function() { var currentDate = new Date(); currentDate

问题 I want to emit a 2D double array using TwoDArrayWritable as value . how to write the context.write(key , ) EDIT And in Reducer how to get them in a Two Dimensional double array and print the values. I Wrote in Mapper row = E.length; col = E[0].length; TwoDArrayWritable array = new TwoDArrayWritable (DoubleWritable.class); DoubleWritable[][] myInnerArray = new DoubleWritable[row][col]; // set values in myInnerArray for (int k1 = 0; k1 < row; k1++) { for(int j1=0;j1< col;j1++){ myInnerArray[k1]

问题 I want to sort the output based on lastname of the user, the key being used is firstName. Following are the classes which i am using but i am not getting sorted output based on lastName. I am new to hadoop,this i wrote using help from various internet sources. Main Class :- public class WordCount { public static class Map extends Mapper<LongWritable, Text, CustomKey, Text> { public static final Log log = LogFactory.getLog(Map.class); private final static IntWritable one = new IntWritable(1);

问题 I am trying to aggregate session data without a true session "key" in PySpark. I have data where an individual is detected in an area at a specific time, and I want to aggregate that into a duration spent in each area during a specific visit (see below). The tricky part here is that I want to infer the time someone exits each area as the time they are detected in the next area. This means that I will need to use the start time of the next area ID as the end time for any given area ID. Area

问题 I have an extra question based on the one I asked before: calculate frequency using mongodb aggregate framework so my data in MongoDB looks like this now: { "data": { "interaction": { "created_at": "Wed, 09 Apr 2014 14:38:16 +0000" } }, "_id": { "$oid": "53455b59edcd5e4e3fdd4ebb" } } before I used to have it like: [ { created_at: "2014-03-31T22:30:48.000Z", id: 450762158586880000, _id: "5339ec9808eb125965f2eae1" } ] so to access created_at I was using mapper like: var mapper = function () {

问题 I configured hadoop 2.4.1 in a single machine (4-core) to use the Psedue Distributed mode, and I am able to run my map/reduce program via the hadoop shell command on the HDFS input files. But I notice that the map and reduce look like still running in single thread. So I tried to hard-code the properties mapreduce.tasktracker.map.tasks.maximum and mapreduce.tasktracker.reduce.tasks.maximum, both to 4. (Just for trying I know it is not ideal setting). But I still see the map and reduce tasks

问题 Say I have a collection of 'activities', each of which has a name, cost and location: {_id : 1 , name: 'swimming', cost: '3.40', location: 'kirkstall'} {_id : 2 , name: 'cinema', cost: '6.50', location: 'hyde park'} {_id : 3 , name: 'gig', cost: '10.00', location: 'hyde park'} I also have a people collection which records, for each activity, how many times they plan to do each in a year: {_id : 1 , name: 'russell', activities : { {1 : 9} , {2 : 4} , {3 : 21} }} I don't want to denormalise the

问题 abstract document in collection md given: { vals : [{ uid : string, val : string|array }] } the following, partially correct aggregation is given: db.md.aggregate( { $unwind : "$vals" }, { $match : { "vals.uid" : { $in : ["x", "y"] } } }, { $group : { _id : { uid : "$vals.uid" }, vals : { $addToSet : "$vals.val" } } } ); that may lead to the following result: "result" : [ { "_id" : { "uid" : "x" }, "vals" : [ [ "24ad52bc-c414-4349-8f3a-24fd5520428e", "e29dec2f-57d2-43dc-818a-1a6a9ec1cc64" ],