Tuesday, 9 December 2014

Implementing Apriori Algorithm In Hadoop-HBase - Part 2 : Conversion to MapReduce Codes

Let us assume that the transaction data which we are getting is in csv format like this - tId,pId
where tId is transaction Id
and pId is product Id
a single transaction can have one or more product Ids spread across one or multiple csv records e.g.

I have implemented Apriori algorithm for 2-itemset using 3 MapReduce jobs. The jobs and their functions are described below -

1. PopulateTranBasketAndProdPairJob - The mapper class of this job reads transaction records from specified csv file and emits (tId, pId). This job's reducer class gets (tId, <pId1, pId2,..., pIdn>) as input, then it makes product pairs available for this tId and writes individual pId(s) and product-pair(s) to HBase table 'tranBasket' with tId as rowkey.

2. SupportCountJob - This job reads the 'tranBasket' table and calculates the support counts for all pId and product pair(s). The support counts of individual products are stored in 'pCount' table with pId as rowkey and the support counts for product pairs are stored in 'ppCount' table with product pair as rowkey. At the end of this job transaction count is also printed to screen which acts as input to next job.

3.CalcSupportConfidenceJob - This is the last job in this series and gives us support, confidence and lift values for different product pairs. This job takes transaction count from the previous job as input to calculate support values. In this job only mapper is there, which reads complete 'pCount' table in memory and then reads 'ppCount' table row by row and performs calculation of different Apriori measures like support, confidence and lift and writes the result to HBase table 'apprOut'.
For verifying the results we can check mapper sysout files which have the values in readable format.

The source code is available here.

Note - This is just a simple demo application and there is scope for improvements. Some modifications which I can think of now are -
  1. Generally transaction ids are sequential numbers and if they are stored in HBase as such we will experience region hot spotting. Hence rowkey design has to be reworked.
  2. HBase scanner caching value needs to be checked and optimised.
  3. Currently pId and tId are stored as Text which can be changed to Long.
References -
  • http://rakesh.agrawal-family.com/papers/vldb94apriori.pdf
  • http://www.slideshare.net/deepti92pawar/the-comparative-study-of-apriori-and-fpgrowth-algorithm
  • http://www3.cs.stonybrook.edu/~cse634/lecture_notes/07apriori.pdf

Implementing Apriori Algorithm In Hadoop-HBase - Part 1 : Introduction to Apriori Algorithm

Apriori algorithm is a frequent item set mining algorithm used over transactional databases, proposed by Rakesh Agrawal and Ramakrishnan Srikant in 1993. This algorithm proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database.

Before we go further and see how this algorithm works it is better to be familiar terminologies used in this algorithm-

Tid  | Items
1     | Bread, Milk
2     | Bread, Diaper, Beer, Milk
3     | Milk, Diaper, Beer, Coke
4     | Bread, Milk, Diaper, Beer
5     | Bread, Milk, Diaper,Coke
    • Itemset    
A collection of one or more items
Example: {Milk, Bread, Diaper}
An itemset that contains k items
  • Support count ()
Frequency of occurrence of an itemset
E.g.   ({Milk, Bread, Diaper}) = 2
  • Support
Fraction of transactions that contain an itemset
E.g.   s( {Milk, Bread, Diaper} ) = 2/5
  • Frequent Itemset
An itemset whose support is greater than or equal to a minsup threshold.

  • Association Rule
An implication expression of the form X  Y, where X and Y are itemsets.
Example: {Milk, Diaper}  {Beer}
  • Rule Evaluation Metrics
Support (s) - Fraction of transactions that contain both X and Y
Confidence (c) - Measures how often items in Y  appear in transactions that
contain X.

In next few post I will describe how to implement this algorithm in HBase and MapReduce.