I am a hadoop newbie.
I want to get a partition number on output file.
At first, I made a customized partitioner.
public static class MyPartitioner extends Partitioner<Text, LongWritable> {
public int getPartition(Text key, LongWritable value, int numReduceTasks) {
int numOfChars = key.toString().length();
return numOfChars % numReduceTasks;
}
}
It works. But, I want to output partition numbers 'visually' on Reducer.
How can I get a partiton number ??
Below is my reducer source.
public static class MyReducer extends Reducer<Text, LongWritable, Text, Text>{
private Text textList = new Text();
public void reduce(Text key, Iterable<LongWritable> values, Context context)
throws IOException, InterruptedException {
String list = new String();
for(LongWritable value: values) {
list = new String(list + "\t" + value.toString());
}
textList.set(list);
context.write(key, textList);
}
}
I want to put a partition number on 'list' respectively. There will be '0' or '1'.
list = new String(list + "\t" + value.toString() + "\t" + ??);
It would be great if someone helps me.
+
Thanks to the answer, I got a solution. But, It didn't work and I think I did something wrong.
Below is the modified MyPartitioner.
public static class MyPartitioner extends Partitioner {
public int getPartition(Text key, LongWritable value, int numReduceTasks) {
int numOfChars = key.toString().length();
return numOfChars % numReduceTasks;
private int bring_num = 0;
public void configure(JobConf job) {
bring_num = jobConf.getInt(numOfChars & numReduceTasks);
}
}
}
Add the below code to the Reducer class to get the partition number in a class variable which can be later used in the reducer method.
String partition;
protected void setup(Context context) throws IOException,
InterruptedException {
Configuration conf = context.getConfiguration();
partition = conf.get("mapred.task.partition");
}
Related
I'm completely new in Hadoop Framework and I want to write a "MapReduce" program (HadoopJoin.java) that joins on x attribute between two tables R and S. The structure of the two tables is :
R (tag : char, x : int, y : varchar(30))
and
S (tag : char, x : int, z : varchar(30))
For example we have for R table :
r 10 r-10-0
r 11 r-11-0
r 12 r-12-0
r 21 r-21-0
And for S table :
s 11 s-11-0
s 21 s-41-0
s 21 s-41-1
s 12 s-31-0
s 11 s-31-1
The result should look like :
r 11 r-11-0 s 11 s-11-0
etc.
Can anyone help me please ?
It will be very difficult to describe join in mapreduce for someone who is new to this Framework but here I provide a working implementation for your situation and I definitely recommend you to read section 9 of Hadoop The Definitive Guide 4th Eddition. It has described how to implement Join in mapreduce very well.
First of all you might consider using higher level frameworks such as Pig, Hive and Spark because they provide join operation in their core part of implementation.
Secondly There are many ways to implement mapreduce depending of the nature of your data. This ways include map-side join and reduce-side join. In this answer I have implemented the reduce-side join:
Implementation:
First of all we should have two different mapper for two different datset notice that in your case same mapper can be used for two dataset but in many situation you need different mappers for different dataset and because of that I have defined two mappers to make this solution more general:
I have used TextPair that have two attributes, one of them is the key that is used to join data and the other one is a tag that specify which dataset this record belongs to. If it belongs to the first dataset this tag will be 0. otherwise it will be 1.
I have implemented TextPair.FirstComparator to ensure that for each key(join by key) the record of the first dataset is the first key which is received by reducer. And all the other records in second dataset with that id are received after that. Actually this line of code will do the trick for us:
job.setGroupingComparatorClass(TextPair.FirstComparator.class);
So in reducer the first record that we will receive is the record from dataset1 and after that we receive record from dataset2. The only thing that should be done is that we have to write those records.
Mapper for dataset1:
public class JoinDataSet1Mapper
extends Mapper<LongWritable, Text, TextPair, Text> {
#Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String[] data = value.toString().split(" ");
context.write(new TextPair(data[1], "0"), value);
}
}
Mapper for DataSet2:
public class JoinDataSet2Mapper
extends Mapper<LongWritable, Text, TextPair, Text> {
#Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String[] data = value.toString().split(" ");
context.write(new TextPair(data[1], "1"), value);
}
}
Reducer:
public class JoinReducer extends Reducer<TextPair, Text, NullWritable, Text> {
public static class KeyPartitioner extends Partitioner<TextPair, Text> {
#Override
public int getPartition(TextPair key, Text value, int numPartitions) {
return (key.getFirst().hashCode() & Integer.MAX_VALUE) % numPartitions;
}
}
#Override
protected void reduce(TextPair key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
Iterator<Text> iter = values.iterator();
Text stationName = new Text(iter.next());
while (iter.hasNext()) {
Text record = iter.next();
Text outValue = new Text(stationName.toString() + "\t" + record.toString());
context.write(NullWritable.get(), outValue);
}
}
}
Custom key:
public class TextPair implements WritableComparable<TextPair> {
private Text first;
private Text second;
public TextPair() {
set(new Text(), new Text());
}
public TextPair(String first, String second) {
set(new Text(first), new Text(second));
}
public TextPair(Text first, Text second) {
set(first, second);
}
public void set(Text first, Text second) {
this.first = first;
this.second = second;
}
public Text getFirst() {
return first;
}
public Text getSecond() {
return second;
}
#Override
public void write(DataOutput out) throws IOException {
first.write(out);
second.write(out);
}
#Override
public void readFields(DataInput in) throws IOException {
first.readFields(in);
second.readFields(in);
}
#Override
public int hashCode() {
return first.hashCode() * 163 + second.hashCode();
}
#Override
public boolean equals(Object o) {
if (o instanceof TextPair) {
TextPair tp = (TextPair) o;
return first.equals(tp.first) && second.equals(tp.second);
}
return false;
}
#Override
public String toString() {
return first + "\t" + second;
}
#Override
public int compareTo(TextPair tp) {
int cmp = first.compareTo(tp.first);
if (cmp != 0) {
return cmp;
}
return second.compareTo(tp.second);
}
public static class FirstComparator extends WritableComparator {
private static final Text.Comparator TEXT_COMPARATOR = new Text.Comparator();
public FirstComparator() {
super(TextPair.class);
}
#Override
public int compare(byte[] b1, int s1, int l1,
byte[] b2, int s2, int l2) {
try {
int firstL1 = WritableUtils.decodeVIntSize(b1[s1]) + readVInt(b1, s1);
int firstL2 = WritableUtils.decodeVIntSize(b2[s2]) + readVInt(b2, s2);
return TEXT_COMPARATOR.compare(b1, s1, firstL1, b2, s2, firstL2);
} catch (IOException e) {
throw new IllegalArgumentException(e);
}
}
#Override
public int compare(WritableComparable a, WritableComparable b) {
if (a instanceof TextPair && b instanceof TextPair) {
return ((TextPair) a).first.compareTo(((TextPair) b).first);
}
return super.compare(a, b);
}
}
}
JobDriver:
public class JoinJob extends Configured implements Tool {
#Override
public int run(String[] args) throws Exception {
Job job = Job.getInstance(getConf(), "Join two DataSet");
job.setJarByClass(getClass());
Path ncdcInputPath = new Path(getConf().get("job.input1.path"));
Path stationInputPath = new Path(getConf().get("job.input2.path"));
Path outputPath = new Path(getConf().get("job.output.path"));
MultipleInputs.addInputPath(job, ncdcInputPath,
TextInputFormat.class, JoinDataSet1Mapper.class);
MultipleInputs.addInputPath(job, stationInputPath,
TextInputFormat.class, JoinDataSet2Mapper.class);
FileOutputFormat.setOutputPath(job, outputPath);
job.setPartitionerClass(JoinReducer.KeyPartitioner.class);
job.setGroupingComparatorClass(TextPair.FirstComparator.class);
job.setMapOutputKeyClass(TextPair.class);
job.setReducerClass(JoinReducer.class);
job.setOutputKeyClass(Text.class);
return job.waitForCompletion(true) ? 0 : 1;
}
public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new JoinJob(), args);
System.exit(exitCode);
}
}
I have implemented Secondary sort on Hadoop and I don't really understand the behavior of the framework.
I have created a composite key which contains original key and part of value, that is used for sorting.
To achieve this I have implemented my own partitioner
public class CustomPartitioner extends Partitioner<CoupleAsKey, LongWritable>{
#Override
public int getPartition(CoupleAsKey couple, LongWritable value, int numPartitions) {
return Long.hashCode(couple.getKey1()) % numPartitions;
}
My own group comparator
public class GroupComparator extends WritableComparator {
protected GroupComparator()
{
super(CoupleAsKey.class, true);
}
#Override
public int compare(WritableComparable w1, WritableComparable w2) {
CoupleAsKey c1 = (CoupleAsKey)w1;
CoupleAsKey c2 = (CoupleAsKey)w2;
return Long.compare(c1.getKey1(), c2.getKey1());
}
}
And defined the couple in the following way
public class CoupleAsKey implements WritableComparable<CoupleAsKey>{
private long key1;
private long key2;
public CoupleAsKey() {
}
public CoupleAsKey(long key1, long key2) {
this.key1 = key1;
this.key2 = key2;
}
public long getKey1() {
return key1;
}
public void setKey1(long key1) {
this.key1 = key1;
}
public long getKey2() {
return key2;
}
public void setKey2(long key2) {
this.key2 = key2;
}
#Override
public void write(DataOutput output) throws IOException {
output.writeLong(key1);
output.writeLong(key2);
}
#Override
public void readFields(DataInput input) throws IOException {
key1 = input.readLong();
key2 = input.readLong();
}
#Override
public int compareTo(CoupleAsKey o2) {
int cmp = Long.compare(key1, o2.getKey1());
if(cmp != 0)
return cmp;
return Long.compare(key2, o2.getKey2());
}
#Override
public String toString() {
return key1 + "," + key2 + ",";
}
}
And here is the driver
Configuration conf = new Configuration();
Job job = new Job(conf);
job.setJarByClass(SSDriver.class);
job.setMapperClass(SSMapper.class);
job.setReducerClass(SSReducer.class);
job.setMapOutputKeyClass(CoupleAsKey.class);
job.setMapOutputValueClass(LongWritable.class);
job.setPartitionerClass(CustomPartitioner.class);
job.setGroupingComparatorClass(GroupComparator.class);
FileInputFormat.addInputPath(job, new Path("/home/marko/WORK/Whirlpool/input.csv"));
FileOutputFormat.setOutputPath(job, new Path("/home/marko/WORK/Whirlpool/output"));
job.waitForCompletion(true);
Now, this works, but what is really strange is that while iterating in reducer for a key, second part of the key (the value part) changes in each iteration. Why and how?
#Override
protected void reduce(CoupleAsKey key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
for (LongWritable value : values) {
//key.key2 changes during iterations, why?
context.write(key, value);
}
}
Definition says that "if you want all your relevant rows within a partition of data sent to a single reducer you must implement a grouping comparator". This only ensures that those set of keys will be sent to a single reduce call, and not that the key will change from composite (or whatever) to something that only contains that part of key on which grouping was done.
However, when you iterate over values, the corresponding keys will also change. We normally do not observe this happening, as by default the values are grouped on the same (non-composite) key, and thus, even when the value changes, the (value of-) key remains the same.
You can try printing the object reference of the key, and you shall notice that with every iteration, the object reference of the key is also changing (like this:)
IntWritable#1235ft
IntWritable#6635gh
IntWritable#9804as
Alternatively, you can also try applying a group-comparator on an IntWritable in a following way (you will have to write your own logic to do so):
Group1:
1 a
1 b
2 c
Group2:
3 c
3 d
4 a
and you shall see that with every iteration of value, your key is also changing.
I am new to Hadoop and Java. My mapper outputs text and Arraywritable. I having trouble to read ArrayWritable values. Unbale to cast .get() values to integer. Mapper and reducer code are attached. Can someone please help me to correct my reducer code in order to read ArrayWritable values?
public static class Temp2Mapper extends Mapper<LongWritable, Text, Text, ArrayWritable>{
private static final int MISSING=9999;
#Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException{
String line = value.toString();
String date = line.substring(07,14);
int maxTemp,minTemp,avgTemp;
IntArrayWritable carrier = new IntArrayWritable();
IntWritable innercarrier[] = new IntWritable[3];
maxTemp=Integer.parseInt(line.substring(39,45));
minTemp=Integer.parseInt(line.substring(47,53));
avgTemp=Integer.parseInt(line.substring(63,69));
if (maxTemp!= MISSING)
innercarrier[0]=new IntWritable(maxTemp); // maximum Temperature
if (minTemp!= MISSING)
innercarrier[1]=new IntWritable(minTemp); //minimum temperature
if (avgTemp!= MISSING)
innercarrier[2]=new IntWritable(avgTemp); // average temperature of 24 hours
carrier.set(innercarrier);
context.write(new Text(date), carrier); // Output Text and ArrayWritable
}
}
public static class Temp2Reducer
extends Reducer<Text, ArrayWritable, Text, IntWritable>{
#Override public void reduce(Text key, Iterable<ArrayWritable> values, Context context )
throws IOException, InterruptedException {
int max = Integer.MIN_VALUE;
int[] arr= new int[3];
for (ArrayWritable val : values) {
arr = (Int) val.get(); // Error: cannot cast Writable to int
max = Math.max(max, arr[0]);
}
context.write( key, new IntWritable(max) );
}
}
ArrayWritable#get method returns an array of Writable.
You can't cast an array of Writable to int. What you can do is:
iterate over this array
cast each item (which will be of type Writable) of the array to IntWritable
use IntWritable#get method to get the int value.
for (ArrayWritable val: values) {
for (Writable writable: val.get()) { // iterate
IntWritable intWritable = (IntWritable)writable; // cast
int value = intWritable.get(); // get
// do your thing with int value
}
}
I decided to create my own WritableComparable class to learn how Hadoop works with it. So I create an Order class with two instance variables (orderNumber cliente) and implemented the required methods. I also used Eclipse generators for getters/setters/hashCode/equals/toString.
In compareTo, I've decided to use only the orderNumber variable.
I created a simple MapReduce job only to count the occurrences of an order in a dataset. By mistake one of my test records is Ita instead of Itá, as you can see here:
123 Ita
123 Itá
123 Itá
345 Carol
345 Carol
345 Carol
345 Carol
456 Iza Smith
As I understand the first record should be treated as a different order, because record 1 hashCode is de different from record 2 and 3 hashCodes.
But in reduce phase the 3 records are grouped together. As you can see here:
Order [cliente=Ita, orderNumber=123] 3
Order [cliente=Carol, orderNumber=345] 4
Order [cliente=Iza Smith, orderNumber=456] 1
I thought it should have a line for Itá records with count 2 and Ita should have count 1.
Well as I used only orderNumber in compareTo, I tried to use the String cliente in this method (commented on code below). And then, it worked as I was expecting.
So, is that an expected result? Shouldn't hadoop use only hashCode to group key and its values?
Here is the Order class (I ommited the getters/setters):
public class Order implements WritableComparable<Order>
{
private String cliente;
private long orderNumber;
#Override
public void readFields(DataInput in) throws IOException
{
cliente = in.readUTF();
orderNumber = in.readLong();
}
#Override
public void write(DataOutput out) throws IOException
{
out.writeUTF(cliente);
out.writeLong(orderNumber);
}
#Override
public int compareTo(Order o) {
long thisValue = this.orderNumber;
long thatValue = o.orderNumber;
return (thisValue < thatValue ? -1 :(thisValue == thatValue ? 0 :1));
//return this.cliente.compareTo(o.cliente);
}
#Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + ((cliente == null) ? 0 : cliente.hashCode());
result = prime * result + (int) (orderNumber ^ (orderNumber >>> 32));
return result;
}
#Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
Order other = (Order) obj;
if (cliente == null) {
if (other.cliente != null)
return false;
} else if (!cliente.equals(other.cliente))
return false;
if (orderNumber != other.orderNumber)
return false;
return true;
}
#Override
public String toString() {
return "Order [cliente=" + cliente + ", orderNumber=" + orderNumber + "]";
}
Here is the MapReduce code:
public class TesteCustomClass extends Configured implements Tool
{
public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Order, LongWritable>
{
LongWritable outputValue = new LongWritable();
String[] campos;
Order order = new Order();
#Override
public void configure(JobConf job)
{
}
#Override
public void map(LongWritable key, Text value, OutputCollector<Order, LongWritable> output, Reporter reporter) throws IOException
{
campos = value.toString().split("\t");
order.setOrderNumber(Long.parseLong(campos[0]));
order.setCliente(campos[1]);
outputValue.set(1L);
output.collect(order, outputValue);
}
}
public static class Reduce extends MapReduceBase implements Reducer<Order, LongWritable, Order,LongWritable>
{
#Override
public void reduce(Order key, Iterator<LongWritable> values,OutputCollector<Order,LongWritable> output, Reporter reporter) throws IOException
{
LongWritable value = new LongWritable(0);
while (values.hasNext())
{
value.set(value.get() + values.next().get());
}
output.collect(key, value);
}
}
#Override
public int run(String[] args) throws Exception {
JobConf conf = new JobConf(getConf(),TesteCustomClass.class);
conf.setMapperClass(Map.class);
// conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);
conf.setJobName("Teste - Custom Classes");
conf.setOutputKeyClass(Order.class);
conf.setOutputValueClass(LongWritable.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
return 0;
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(),new TesteCustomClass(),args);
System.exit(res);
}
}
The default partitioner is the HashPartitioner, which uses the hashCode method to determine which reducer to send the K,V pair to.
Once in the reducer (or if you're using a Combiner which is run map side), the compareTo method is used to sort the keys and then also used (by default) to compare whether sequential keys should be grouped together and their associated values reduced in the same iteration.
If you don't use the cliente Key variable and only your orderNumber variable in your compareTo method, then any key with the same orderNumber will have its values reduced together - regardless of the cliente value (which is what you're currently observing)
Can somebody give one good example link for mapreduce with Hbase? My requirement is run mapreduce on hdfs file and store reducer output to hbase table. Mapper input will be hdfs file and output will be Text,IntWritable key value pairs. Reducers output will be Put object ie add reducer Iterable IntWritable values and store in hbase table.
Here is the code which will solve your problem
Driver
HBaseConfiguration conf = HBaseConfiguration.create();
Job job = new Job(conf,"JOB_NAME");
job.setJarByClass(yourclass.class);
job.setMapperClass(yourMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Intwritable.class);
FileInputFormat.setInputPaths(job, new Path(inputPath));
TableMapReduceUtil.initTableReducerJob(TABLE,
yourReducer.class, job);
job.setReducerClass(yourReducer.class);
job.waitForCompletion(true);
Mapper&Reducer
class yourMapper extends Mapper<LongWritable, Text, Text,IntWritable> {
//#overide map()
}
class yourReducer
extends
TableReducer<Text, IntWritable,
ImmutableBytesWritable>
{
//#override rdeuce()
}
**Ckeck the bellow code that works fine for me with Phoenix Hbase and map reduce **
This program will read data from Hbase table and inset result in to another table after map-reduce job .
Table :-> STOCK ,STOCK_STATS
StockComputationJob.java
public static class StockMapper extends Mapper<NullWritable, StockWritable, Text , DoubleWritable> {
private Text stock = new Text();
private DoubleWritable price = new DoubleWritable ();
#Override
protected void map(NullWritable key, StockWritable stockWritable, Context context) throws IOException, InterruptedException {
double[] recordings = stockWritable.getRecordings();
final String stockName = stockWritable.getStockName();
System.out.println("Map-"+recordings);
double maxPrice = Double.MIN_VALUE;
for(double recording : recordings) {
System.out.println("M-"+key+"-"+recording);
if(maxPrice < recording) {
maxPrice = recording;
}
}
System.out.println(stockName+"--"+maxPrice);
stock.set(stockName);
price.set(maxPrice);
context.write(stock,price);
}
}
public static void main(String[] args) throws Exception {
final Configuration conf = new Configuration();
HBaseConfiguration.addHbaseResources(conf);
conf.set(HConstants.ZOOKEEPER_QUORUM, zkUrl);
final Job job = Job.getInstance(conf, "stock-stats-job");
// We can either specify a selectQuery or ignore it when we would like to retrieve all the columns
final String selectQuery = "SELECT STOCK_NAME,RECORDING_YEAR,RECORDINGS_QUARTER FROM STOCK ";
// StockWritable is the DBWritable class that enables us to process the Result of the above query
PhoenixMapReduceUtil.setInput(job,StockWritable.class,"STOCK",selectQuery);
// Set the target Phoenix table and the columns
PhoenixMapReduceUtil.setOutput(job, "STOCK_STATS", "STOCK_NAME,MAX_RECORDING");
job.setMapperClass(StockMapper.class);
job.setReducerClass(StockReducer.class);
job.setOutputFormatClass(PhoenixOutputFormat.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(DoubleWritable.class);
job.setOutputKeyClass(NullWritable.class);
job.setOutputValueClass(StockWritable.class);
TableMapReduceUtil.addDependencyJars(job);
job.waitForCompletion(true);
}
}
StockReducer.java
public class StockReducer extends Reducer<Text, DoubleWritable, NullWritable , StockWritable> {
protected void reduce(Text key, Iterable<DoubleWritable> recordings, Context context) throws IOException, InterruptedException {
double maxPrice = Double.MIN_VALUE;
System.out.println(recordings);
for(DoubleWritable recording : recordings) {
System.out.println("R-"+key+"-"+recording);
if(maxPrice < recording.get()) {
maxPrice = recording.get();
}
}
final StockWritable stock = new StockWritable();
stock.setStockName(key.toString());
stock.setMaxPrice(maxPrice);
System.out.println(key+"--"+maxPrice);
context.write(NullWritable.get(),stock);
}
}
StockWritable.java
public class StockWritable implements DBWritable,Writable {
private String stockName;
private int year;
private double[] recordings;
private double maxPrice;
public void readFields(DataInput input) throws IOException {
}
public void write(DataOutput output) throws IOException {
}
public void readFields(ResultSet rs) throws SQLException {
stockName = rs.getString("STOCK_NAME");
setYear(rs.getInt("RECORDING_YEAR"));
final Array recordingsArray = rs.getArray("RECORDINGS_QUARTER");
setRecordings((double[])recordingsArray.getArray());
}
public void write(PreparedStatement pstmt) throws SQLException {
pstmt.setString(1, stockName);
pstmt.setDouble(2, maxPrice);
}
public int getYear() {
return year;
}
public void setYear(int year) {
this.year = year;
}
public double[] getRecordings() {
return recordings;
}
public void setRecordings(double[] recordings) {
this.recordings = recordings;
}
public double getMaxPrice() {
return maxPrice;
}
public void setMaxPrice(double maxPrice) {
this.maxPrice = maxPrice;
}
public String getStockName() {
return stockName;
}
public void setStockName(String stockName) {
this.stockName = stockName;
}
}