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Map Reduce - How to group and aggregate multiple attributes in a single job

I am currently struggling a bit with MapReduce.
I have the following dataset:
1,John,Computer
2,Anne,Computer
3,John,Mobile
4,Julia,Mobile
5,Jack,Mobile
6,Jack,TV
7,John,Computer
8,Jack,TV
9,Jack,TV
10,Anne,Mobile
11,Anne,Computer
12,Julia,Mobile
Now I want to apply MapReduce with grouping and
aggregation on this data set, in order that the output
doesn't only show how many times which person bought something,
but also what the product is, which the person ordered most.
So the output should look like:
John 3 Computer
Anne 3 Mobile
Jack 4 TV
Julia 2 Mobile
My current implementation of the mapper as well as reducer
looks like that, which perfectly returns how many orders were
made by the individuals, however, I am really clueless how
to get the desired output.
static class CountMatchesMapper extends Mapper<Object,Text,Text,IntWritable> {
#Override
protected void map(Object key, Text value, Context ctx) throws IOException, InterruptedException {
String row = value.toString();
String[] row_part = row.split(",");
try{
ctx.write(new Text(row_part[1]), new IntWritable(1));
catch (IOException e) {
}
catch (InterruptedException e) {
}
}
}
}
static class CountMatchesReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
#Override
protected void reduce(Text key, Iterable<IntWritable> values, Context ctx) throws IOException, InterruptedException {
int i = 0;
for (IntWritable value : values) i += value.get();
try{
ctx.write(key, new IntWritable(i));
}
catch (IOException e) {
}
catch (InterruptedException e) {
}
}
}
I would really appreciate any efficient solution and help.
Thanks in advance!

If I understand correctly what you want, I think the 2nd output line should be:
Anne 3 Computer
based on the input. Anne has bought 3 products in total: 2 Computers and 1 Mobile.
I have here a very basic and simplistic approach, which doesn't take into account edge cases etc, but could give you some direction:
static class CountMatchesMapper extends Mapper<LongWritable, Text, Text, Text> {
private Text outputKey = new Text();
private Text outputValue = new Text();
#Override
protected void map(LongWritable key, Text value, Context ctx) throws IOException, InterruptedException {
String row = value.toString();
String[] row_part = row.split(",");
outputKey.set(row_part[1]);
outputValue.set(row_part[2]);
ctx.write(outputKey, outputValue);
}
}
static class CountMatchesReducer extends Reducer<Text, Text, Text, NullWritable> {
private Text output = new Text();
#Override
protected void reduce(Text key, Iterable<Text> values, Context ctx) throws IOException, InterruptedException {
HashMap<String, Integer> productCounts = new HashMap();
int totalProductsBought = 0;
for (Text value : values) {
String productBought = value.toString();
int count = 0;
if (productCounts.containsKey(productBought)) {
count = productCounts.get(productBought);
}
productCounts.put(productBought, count + 1);
totalProductsBought += 1;
}
String topProduct = getTopProductForPerson(productCounts);
output.set(key.toString() + " " + totalProductsBought + " " + topProduct);
ctx.write(output, NullWritable.get());
}
private String getTopProductForPerson(Map<String, Integer> productCounts) {
String topProduct = "";
int maxCount = 0;
for (Map.Entry<String, Integer> productCount : productCounts.entrySet()) {
if (productCount.getValue() > maxCount) {
maxCount = productCount.getValue();
topProduct = productCount.getKey();
}
}
return topProduct;
}
}
The above will give the output that you described.
If you want a proper solution that scales etc then probably you need a composite key and custom GroupComparator. This way you will be able to add Combiner as well and make it much more efficient. However, the approach above should work for an average case.

Reduce does not start, After map completes

Below is the code for my Implementation of a simple MapReduce Job using a custom writable comparable.
public class MapReduceKMeans {
public static class MapReduceKMeansMapper extends
Mapper<Object, Text, SongDataPoint, Text> {
public void map(Object key, Text value, Context context)
throws InterruptedException, IOException {
String str = value.toString();
// Reading Line one by one from the input CSV.
String split[] = str.split(",");
String trackId = split[0];
String title = split[1];
String artistName = split[2];
SongDataPoint songDataPoint =
new SongDataPoint(new Text(trackId), new Text(title),
new Text(artistName));
context.write(songDataPoint, new Text());
}
}
public static class MapReduceKMeansReducer extends
Reducer<SongDataPoint, Text, Text, NullWritable> {
public void reduce(SongDataPoint key, Iterable<Text> values,
Context context) throws IOException, InterruptedException {
StringBuilder sb = new StringBuilder();
sb.append(key.getTrackId()).append("\t").
append(key.getTitle()).append("\t")
.append(key.getArtistName()).append("\t");
String write = sb.toString();
context.write(new Text(write), NullWritable.get());
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args)
.getRemainingArgs();
if (otherArgs.length != 2) {
System.err
.println("Usage:<CsV Out Path> <Final Out Path>");
System.exit(2);
}
Job job = new Job(conf, "Song Data Trial");
job.setJarByClass(MapReduceKMeans.class);
job.setMapperClass(MapReduceKMeansMapper.class);
job.setReducerClass(MapReduceKMeansReducer.class);
job.setOutputKeyClass(SongDataPoint.class);
job.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
When I debug my code reads all the rows in the CSV file but it does not enter the reduce job at all.
I also have made use of the SongDataPoint as my custom writable.
Its code is as below.
public class SongDataPoint implements WritableComparable<SongDataPoint> {
Text trackId;
Text title;
Text artistName;
public SongDataPoint() {
this.trackId = new Text();
this.title = new Text();
this.artistName = new Text();
}
public SongDataPoint(Text trackId, Text title, Text artistName) {
this.trackId = trackId;
this.title = title;
this.artistName = artistName;
}
#Override
public void readFields(DataInput in) throws IOException {
this.trackId.readFields(in);
this.title.readFields(in);
this.artistName.readFields(in);
}
#Override
public void write(DataOutput out) throws IOException {
}
public Text getTrackId() {
return trackId;
}
public void setTrackId(Text trackId) {
this.trackId = trackId;
}
public Text getTitle() {
return title;
}
public void setTitle(Text title) {
this.title = title;
}
public Text getArtistName() {
return artistName;
}
public void setArtistName(Text artistName) {
this.artistName = artistName;
}
#Override
public int compareTo(SongDataPoint o) {
// TODO Auto-generated method stub
int compare = getTrackId().compareTo(o.getTrackId());
return compare;
}
}
Any help is appreciated. Thanks.

Your output key class class as per Driver is SongDataPoint.class and output value class as Text.class but actually you are writing Text as key in Reducer and Nullwritable as value in Reducer.

you should also specify the Mapper output values as following.
job.setMapOutputKeyClass(SongDataPoint.class);
job.setMapOutputValueClass(Text.class);

My write method in my CustomWritable Class was left blank by mistake. It solved the problem after writing the proper code in it.
public void write(DataOutput out) throws IOException {
}

generating a number between a range using json

How can we generate a number between a range using Json.
Like we have to generate a number between 0 to 50, how can we perform this in Java using a Json.
This is my Json Data
{
"rand": {
"type': "number",
"minimum": 0,
"exclusiveMinimum": false,
"maximum": 50,
"exclusiveMaximum": true
}
}
This is what I have tried in Java
public class JavaApplication1 {
public static void main(String[] args) {
try {
for (int i=0;i<5;i++)
{
FileInputStream fileInputStream = new FileInputStream("C://users/user/Desktop/V.xls");
HSSFWorkbook workbook = new HSSFWorkbook(fileInputStream);
HSSFSheet worksheet = workbook.getSheet("POI Worksheet");
HSSFRow row1 = worksheet.getRow(0);
String e1Val = cellE1.getStringCellValue();
HSSFCell cellF1 = row1.getCell((short) 5);
System.out.println("E1: " + e1Val);
JSONObject obj = new JSONObject();
obj.put("value", e1Val);
System.out.print(obj + "\n");
Map<String,Object> c_data = mapper.readValue(e1Val, Map.class);
System.out.println(a);
}
} catch (FileNotFoundException e) {
} catch (IOException e) {
}
}
}
Json Data is stored in excel sheet, from there I am reading it in Java program

Get a Json-reader like GSON.
Read in the JSON to an equivalent Object like
public class rand{
private String type;
private int minimum;
private boolean exclusiveMinimum;
private int maximum;
private boolean exclusiveMaximum;
//this standard-constructor is needed for the JsonReader
public rand(){
}
//Getter for all Values
}
and after reading in your JSON you can access your Data via your getter-methods

I think that Jackson may be of help here.
I suggest that you create a data model in Java that reflects the JSON. This can along the lines of:
// This is the root object. It contains the input data (RandomizerInput) and a
// generate-function that is used for generating new random ints.
public class RandomData {
private RandomizerInput input;
#JsonCreator
public RandomData(#JsonProperty("rand") final RandomizerInput input) {
this.input = input;
}
#JsonProperty("rand")
public RandomizerInput getInput() {
return input;
}
#JsonProperty("generated")
public int generateRandomNumber() {
int max = input.isExclusiveMaximum()
? input.getMaximum() - 1 : input.getMaximum();
int min = input.isExclusiveMinimum()
? input.getMinimum() + 1 : input.getMinimum();
return new Random().nextInt((max - min) + 1) + min;
}
}
// This is the input data (pretty much what is described in the question).
public class RandomizerInput {
private final boolean exclusiveMaximum;
private final boolean exclusiveMinimum;
private final int maximum;
private final int minimum;
private final String type;
#JsonCreator
public RandomizerInput(
#JsonProperty("type") final String type,
#JsonProperty("minimum") final int minimum,
#JsonProperty("exclusiveMinimum") final boolean exclusiveMinimum,
#JsonProperty("maximum") final int maximum,
#JsonProperty("exclusiveMaximum") final boolean exclusiveMaximum) {
this.type = type; // Not really used...
this.minimum = minimum;
this.exclusiveMinimum = exclusiveMinimum;
this.maximum = maximum;
this.exclusiveMaximum = exclusiveMaximum;
}
public int getMaximum() {
return maximum;
}
public int getMinimum() {
return minimum;
}
public String getType() {
return type;
}
public boolean isExclusiveMaximum() {
return exclusiveMaximum;
}
public boolean isExclusiveMinimum() {
return exclusiveMinimum;
}
}
To use these classes the ObjectMapper from Jackson can be used like this:
public static void main(String... args) throws IOException {
String json =
"{ " +
"\"rand\": { " +
"\"type\": \"number\", " +
"\"minimum\": 0, " +
"\"exclusiveMinimum\": false, " +
"\"maximum\": 50, " +
"\"exclusiveMaximum\": true " +
"} " +
"}";
// Create the mapper
ObjectMapper mapper = new ObjectMapper();
// Convert JSON to POJO
final RandomData randomData = mapper.readValue(json, RandomData.class);
// Either you can get the random this way...
final int random = randomData.generateRandomNumber();
// Or, you can serialize the whole thing as JSON....
String str = mapper.writeValueAsString(randomData);
// Output is:
// {"rand":{"type":"number","minimum":0,"exclusiveMinimum":false,"maximum":50,"exclusiveMaximum":true},"generated":21}
System.out.println(str);
}
The actual generation of a random number is based on this SO question.

Map Reduce job generating empty output file

Program is generating empty output file. Can anyone please suggest me where am I going wrong.
Any help will be highly appreciated. I tried to put job.setNumReduceTask(0) as I am not using reducer but still output file is empty.
public static class PrizeDisMapper extends Mapper<LongWritable, Text, Text, Pair>{
int rating = 0;
Text CustID;
IntWritable r;
Text MovieID;
public void map(LongWritable key, Text line, Context context
) throws IOException, InterruptedException {
String line1 = line.toString();
String [] fields = line1.split(":");
if(fields.length > 1)
{
String Movieid = fields[0];
String line2 = fields[1];
String [] splitline = line2.split(",");
String Custid = splitline[0];
int rate = Integer.parseInt(splitline[1]);
r = new IntWritable(rate);
CustID = new Text(Custid);
MovieID = new Text(Movieid);
Pair P = new Pair();
context.write(MovieID,P);
}
else
{
return;
}
}
}
public static class IntSumReducer extends Reducer<Text,Pair,Text,Pair> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<Pair> values,
Context context
) throws IOException, InterruptedException {
for (Pair val : values) {
context.write(key, val);
}
}
public class Pair implements Writable
{
String key;
int value;
public void write(DataOutput out) throws IOException {
out.writeInt(value);
out.writeChars(key);
}
public void readFields(DataInput in) throws IOException {
key = in.readUTF();
value = in.readInt();
}
public void setVal(String aKey, int aValue)
{
key = aKey;
value = aValue;
}
Main class:
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setInputFormatClass (TextInputFormat.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Pair.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
Thanks #Pathmanaban Palsamy and #Chris Gerken for your suggestions. I have modified the code as per your suggestions but still getting empty output file. Can anyone please suggest me configurations in my main class for input and output. Do I need to specify Pair class in input to mapper & how?

I'm guessing the reduce method should be declared as
public void reduce(Text key, Iterable<Pair> values,
Context context
) throws IOException, InterruptedException
You get passed an Iterable (an object from which you can get an Iterator) which you use to iterate over all of the values that were mapped to the given key.

Since no reducer required, I suspect below line
Pair P = new Pair();
context.write(MovieID,P);
empty Pair would be the issue.
also pls check your Driver class you have given correct keyclass and valueclass like
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Pair.class);

Performing multiple computations with Hadoop Map Reduce

I have a map reduce program for finding the min/max for 2 separate properties for each year. This works, for the most part, using a single node cluster in hadoop. Here is my currently setup:
public class MaxTemperatureReducer extends
Reducer<Text, Stats, Text, Stats> {
private Stats result = new Stats();
#Override
public void reduce(Text key, Iterable<Stats> values, Context context)
throws IOException, InterruptedException {
int maxValue = Integer.MIN_VALUE;
int minValue = Integer.MAX_VALUE;
int sum = 0;
for (Stats value : values) {
result.setMaxTemp(Math.max(maxValue, value.getMaxTemp()));
result.setMinTemp(Math.min(minValue, value.getMinTemp()));
result.setMaxWind(Math.max(maxValue, value.getMaxWind()));
result.setMinWind(Math.min(minValue, value.getMinWind()));
sum += value.getCount();
}
result.setCount(sum);
context.write(key, result);
}
}
public class MaxTemperatureMapper extends
Mapper<Object, Text, Text, Stats> {
private static final int MISSING = 9999;
private Stats outStat = new Stats();
#Override
public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
String[] split = value.toString().split("\\s+");
String year = split[2].substring(0, 4);
int airTemperature;
airTemperature = (int) Float.parseFloat(split[3]);
outStat.setMinTemp((float)airTemperature);
outStat.setMaxTemp((float)airTemperature);
outStat.setMinWind(Float.parseFloat(split[12]));
outStat.setMaxWind(Float.parseFloat(split[14]));
outStat.setCount(1);
context.write(new Text(year), outStat);
}
}
public class MaxTemperatureDriver extends Configured implements Tool {
public int run(String[] args) throws Exception {
if (args.length != 2) {
System.err
.println("Usage: MaxTemperatureDriver <input path> <outputpath>");
System.exit(-1);
}
Job job = new Job();
job.setJarByClass(MaxTemperatureDriver.class);
job.setJobName("Max Temperature");
job.setMapperClass(MaxTemperatureMapper.class);
job.setCombinerClass(MaxTemperatureReducer.class);
job.setReducerClass(MaxTemperatureReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Stats.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
boolean success = job.waitForCompletion(true);
return success ? 0 : 1;
}
public static void main(String[] args) throws Exception {
MaxTemperatureDriver driver = new MaxTemperatureDriver();
int exitCode = ToolRunner.run(driver, args);
System.exit(exitCode);
}
}
Currently it only prints the Min/Max for the temp and windspeed for each year. I am sure it is a simple implementation but cannot find a answer anywhere. I want to try and find the top 5 min/max for each year. Any suggestions?

Let me assume the following signature for your Stats class.
/* the stats class need to be a writable, the below is just a demo*/
public class Stats {
public float getTemp() {
return temp;
}
public void setTemp(float temp) {
this.temp = temp;
}
public float getWind() {
return wind;
}
public void setWind(float wind) {
this.wind = wind;
}
private float temp;
private float wind;
}
With this, let us change the reducer as below.
SortedSet<Float> tempSetMax = new TreeSet<Float>();
SortedSet<Float> tempSetMin = new TreeSet<Float>();
SortedSet<Float> windSetMin = new TreeSet<Float>();
SortedSet<Float> windSetMax = new TreeSet<Float>();
List<Stats> values = new ArrayList<Float>();
for (Stats value : values) {
float temp = value.getTemp();
float wind = value.getWind();
if (tempSetMax.size() < 5) {
tempSetMax.add(temp);
} else {
float currentMinValue = tempSetMax.first();
if (temp > currentMinValue) {
tempSetMax.remove(currentMinValue);
tempSetMax.add(temp);
}
}
if (tempSetMin.size() < 5) {
tempSetMin.add(temp);
} else {
float currentMaxValue = tempSetMin.last();
if (temp < currentMaxValue) {
tempSetMax.remove(currentMaxValue);
tempSetMax.add(temp);
}
}
if (windSetMin.size() < 5) {
windSetMin.add(wind);
} else {
float currentMaxValue = windSetMin.last();
if (wind < currentMaxValue) {
windSetMin.remove(currentMaxValue);
windSetMin.add(temp);
}
}
if (windSetMax.size() < 5) {
windSetMax.add(wind);
} else {
float currentMinValue = windSetMax.first();
if (wind > currentMinValue) {
windSetMax.remove(currentMinValue);
windSetMax.add(temp);
}
}
}
Now you can write to context the toString() of each list, or you can create a custom writable. In my code, please change the Stats according to your requirement. It needs to be a Writable. The above is just for demonstrating the example flow.

Here is the code from the MR Design Patterns Book to get the top 10. There is also code for other MR design patterns in the same GitHub location.