Monday, 8 September 2014

How To Set Counters In Hadoop MapReduce

Counters are a useful channel for gathering statistics about the job: for quality control or for application level-statistics.Lets see an example where Counters count the no of keys processed in reducer.

3 key points to set

1. Define counter in Driver class

static enum UpdateCount{

2. Increment or set counter in Reducer


3. Get counter in Driver class 

c = job.getCounters().findCounter(UpdateCount.CNT).getValue();

Full code :  GitHub

You will be able to see the counters in console also.

Saturday, 23 August 2014

Example for Apriori Algorithm

Lets take a store data
To start with Apriori follow the below steps.
Step 1: Initially we need to find Item 1 Frequent Dataset
book 6
chalk 3
eraser 6
pen 8
pencil 7
Ink 1
We will say that an item set is frequent if it appears in at least 3 transactions of the itemset: the value 3 is the support threshold.
Support count = 3
So the items less that support count can be discarded form F1 frequent Dataset.
so our new set will be
book 6
chalk 3
eraser 6
pen 8
pencil 7
Step 2: We need to generate size 2 frequent item pair sets by joining L1 set
eg:{book} U {chalk} => {book,chalk} and so on..



Once the transactions are joined we need to identify the no occurence of the above data items in original transaction(That will be the support count of C2)
{book,chalk} 2
{book,eraser} 2
{book,pen} 4
{book,pencil} 5

{chalk,eraser} 2
{chalk,pen} 2
{chalk,pencil} 0

{eraser,pen} 5
{eraser,pencil} 3

{pen,pencil} 5
Transactions less that support count can be discarded form C2 frequent Dataset
{book,pen} 4
{book,pencil} 5
{eraser,pen} 5
{eraser,pencil} 3
{pen,pencil} 5
To find C3 loop through L2
eg: {book,pen} U {book,pencil} => {book,pen,pencil}
{book,pen,pencil} 3
{chalk,eraser,pen} 2
{eraser,pen,pencil} 2
Transactions less that support count can be discarded form C3 frequent Dataset
{book,pen,pencil} 3
There are no transaction to join further.
So our Frequent item sets are
book 6
chalk 3
eraser 6
pen 8
pencil 7

{book,pen} 4
{book,pencil} 5
{eraser,pen} 5
{eraser,pencil} 3
{pen,pencil} 5

{book,pen,pencil} 3
Step 3: We need to generate Strong Assosiaction  Rules for frequent Set using L1,L2and L3

Say confidence is 60% and Support count is 3.So we have to find the Transactions with no.of item 3 (ie L3 as support count = 3) and which has a confidence >=60.Now we can identify L3 set
{book,pen,pencil} 3

Finding Ruleset
{book,pen} => pencil
{book,pencil} => pen
{pen,pencil} => book

pencil => {book,pen}
pen => {book,pencil}
book => {pen,pencil}

Now we need to find the confidence of each transaction
eg: {book,pen} => pencil
           = support Cnt{book,pen}/ support count({pen})

Therefore rules having confidence greater than and equal to 60 are
book,pen=>pencil 75.0
book,pencil=>pen 60.0
pen,pencil=>book 60.0
These are the strongest rules.
If a customer buys book and pen he have a tendency to buy a pencil too. Like wise if he buys book and pencil he may buy pen too.

Calculating Mean in Hadoop MapReduce

Given a csv files we will find mean of each column (Optimized approach)


 Takes each input line and calculate the sum and stores the no of lines it sumed.Then sum get stored in a hash map with key as column Id.cleanup emits the sum and total line count inorder to take the overall mean.As we know each map only gets a block of input data.So while summing up we need to know how many elements summed up.

//Calculating sum
if (sumVal.isEmpty()) {
 //if sumval is empty add elements to sumval
 } else {
//calculating sum
 double sum = 0;
 for (Integer colId : mapLine.keySet()) {
  double val1 = mapLine.get(colId);
  double val2 = sumVal.get(colId);
  * calculating sum
 sum = val1 + val2;
 sumVal.put(colId, sum);


 Sums of the values for each key.

Reducer calculates 2 sums.
  1. Sums the values for each key and 
  2. Sums total no.of linecount

for (TwovalueWritable value : values) {
 //Taking sum of values and total number of lines 
 sum += value.getSum();
 total += value.getTotalCnt();
 //sum contains total sum of all elements in each column
 //total contains total no of elements in each column
 mean = sum / total;
 context.write(key, valEmit);

This approach helps in avoiding a large no of communication with reducer.Reducer needs only to sum up few values from mapper.
Say we have only 3 mappers and 4 columns in input set.Reducer only want to wait for 4 values from each mapper(no.of columns also considered)

Complete code : GitHub Link

Tuesday, 20 May 2014

How To WorkOut Navie Bayes Algorithm

A Naive Bayes Classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions.The main advantage of naive Bayes is that it only requires a smaller amount of data for training inorder to estimate the class labels necessary for classification. Because independent variables are assumed.

In general all of Machine Learning Algorithms need to be trained for supervised learning tasks like classification, prediction etc. 

By training it means to train them on particular inputs so that later on we may test them for unknown inputs (which they have never seen before) for which they may classify or predict etc (in case of supervised learning) based on their learning. This is what most of the Machine Learning techniques like Neural Networks, SVM, Bayesian etc. are based upon. 

How to Apply NaiveBayes to Predict an Outcome
Let's try it out using an example.

In the above training data we have 2 class labels buys_computer No and Yes. And we know 4 characteristics.

1. Whether the age is youth,middle_aged or senior.
2. Whether income is high,low or medium.
3. Whether they have student or not.
4. Whether credit is excellent,fair.

There are many things to pre-compute from the training dataset for future prediction.

Prior Probabilities

Prior Probabilities

P(yes) = 9/14 = 0.643
  Given that the class label is "yes" the universe is 14 = yes(9) + no(5). 9 of them is yes
P(no) = 5/14 = 0.357
  Given that the class label is "no" the universe is 14 = yes(9) + no(5). 5 of them is no

Probability of Likelihood

Probability of Likelihood

P(youth/yes) = 2/9 = 0.222
  Given that the class label is "yes" the universe is 9. 2 of them are youth.
P(youth/no) = 3/5 = 0.600
P(fair/yes) = 6/9 = 0.667
P(fair/no) = 2/5 = 0.400

How to classify an outcome

Let's say we are given the properties of an unknown buys_computer (class). We are told that the properties are

X => age = youth, income = medium, student = yes, credit rating = fair

We need to 

 Maximize P(X|Ci )P(Ci ), for i = 1, 2

P(Ci ) - the prior probability of each class, can be computed based on the training tuples:

P(yes/youth,medium,yes and fair) 
      = P(youth/yes)* P(medium/yes)* P(yes/yes)* P(fair/yes) * P(yes)
      = (0.222* 0.444* 0.667* 0.667) * 0.643
      = 0.028

P(no/youth,income,medium,yes and fair) 
      = P(youth/no)* P(medium/no)* P(yes/no)* P(fair/no) * P(no)
      = (0.600* 0.400* 0.200* 0.400) * 0.357
      = 0.007

(0.028 >> 0.007), we classify this youth/medium/yes/fair  as likely to be yes.

Therefore, the naive Bayesian classifier predicts buys_computer = yes for tuple X.

Saturday, 17 May 2014

Count Frequency Of Values In A Column Using Apache Pig

There may be situations to count the occurence of a value in a field.
Let this be the sample input bag.

user_id   course_name user_name
1           Social      Anju
2           Maths       Malu
1           English     Anju
1           Maths       Anju

Say we need to calculate no of occurence of each user_name.
Anju 3
Malu 1

Inorder to achieve this - COUNT Built In Function can be used.

COUNT Function in Apache Pig

COUNT function  compute the number of elements in a bag.
To group count a preceding GROUP BY statement and for global counts GROUP ALL statement is required.

The basic idea to do the above example is to group by user_name and count the tuples in the bag.


 userAlias = LOAD '/home/sreeveni/myfiles/pig/count.txt' as 
 groupedByUser = group userAlias by user_name;
 counted = FOREACH groupedByUser GENERATE group as user_name,COUNT(userAlias) as cnt;
 result = FOREACH counted GENERATE user_name, cnt;
 store result into '/home/sreeveni/myfiles/pig/OUT/count';

The COUNT function ignores NULLs, that is tuple in the bag will not be counted if the first field in this tuple is NULL.
COUNT_STAR can be used to count fields including NULL values.

Monday, 12 May 2014

Configuring PasswordLess SSH for Apache Hadoop

In pseudo-distributed mode, we have to start daemons, and to do that, we need to have SSH installed. Hadoop doesn’t actually distinguish between pseudo-distributed and fully distributed modes: it merely starts daemons on the set of hosts in the cluster (defined by the slaves file) by SSH-ing to each host and starting a daemon process. Pseudo-distributed mode is just a special case of fully distributed mode in which the (single) host is localhost, so we need to make sure that we can SSH to localhost and log in without having to enter a password.

If you cannot ssh to localhost without a passphrase, execute the following commands:

unmesha@unmesha-hadoop-virtual-machine:~$ ssh-keygen
Generating public/private rsa key pair.
Enter file in which to save the key (/home/unmesha/.ssh/id_rsa): [press enter]
Enter passphrase (empty for no passphrase): [press enter]
Enter same passphrase again: [press enter]
Your identification has been saved in /home/unmesha/.ssh/id_rsa.
Your public key has been saved in /home/unmesha/.ssh/
The key fingerprint is:
61:c5:33:9f:53:1e:4a:5f:e9:4d:19:87:55:46:d3:6b unmesha@unmesha-virtual-machine
The key's randomart image is:
+--[ RSA 2048]----+
|         ..    *%|
|         .+ . ++*|
|        o  = *.+o|
|       . .  = oE.|
|        S    ..  |
|                 |
|                 |
|                 |
|                 |

unmesha@unmesha-hadoop-virtual-machine:~$ ssh-copy-id localhost
unmesha@localhost's password: 
Now try logging into the machine, with "ssh 'localhost'", and check in:


to make sure we haven't added extra keys that you weren't expecting.

Now you will be able to ssh without password.

unmesha@unmesha-hadoop-virtual-machine:~$ ssh localhost
Welcome to Ubuntu 12.04 LTS (GNU/Linux 3.2.0-23-generic x86_64)

 * Documentation:

Last login: Tue Apr 29 17:48:55 2014 from amma-hp-probook-4520s.local

Happy Hadooping ...

Sunday, 4 May 2014

Map-Only Jobs In Hadoop

There may be reasons where Map-Only job is needed,Where there is no Reducer to execute.Here Map does all its task with its InputSplit and no job for Reducer.This can be achieved by setting  job.setNumReduceTasks()  to Zero in Configuration.

Job job = new Job(getConf(), "Map-Only Job");


 * Set no of reducers to 0



FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));

boolean success = job.waitForCompletion(true);
return(success ? 0 : 1);

This sets Reducer task to 0 and turns off the Reducer.


So the no. of output files will be equal to no. of mappers and output files will be named as part-m-00000.

And once Reducer task is set to Zero the result will be unsorted.

If we are not specifying this property in Configuration, an Identity Reducer will get executed in which the same value is simply emitted along with the incoming key and the output file will be part-r-00000.

Happy Hadooping ...