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}
k-itemset
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.

Writing Complex MongoDB Queries Using QueryBuilder

Writing Complex MongoDB Queries Using QueryBuilder

MongoDB provides a lot of query selectors for filtering documents from a collection.  Writing complex queries for MongoDB in Java can be tricky sometimes.
Consider below data present in student_marks collection

{"sid" : 1,"fname" : "Tom","lname" : "Ford","marks" : [ {"english" : 48}, {"maths" : 49}, {"science" : 50}]}
{"sid" : 2,"fname" : "Tim","lname" : "Walker","marks" : [ {"english" : 35}, {"maths" : 42}, {"science" : 37}]}
{"sid" : 3,"fname" : "John","lname" : "Ward","marks" : [ {"english" : 45}, {"maths" : 41}, {"science" : 37}]}

If we want to get students whose last name is Ford and have obtained more than 35 marks in english then the MongoDB shell command for this will be -

db.student_marks.find({$and:[{"lname":"Ford"},{"marks.english": {$gt:35}}]})

The same query written in Java will look something like this -

        DBObject query = new BasicDBObject();
        List<BasicDBObject> andQuery = new ArrayList<BasicDBObject>();
        andQuery.add(new BasicDBObject("lname", "Ford"));
        andQuery.add(new BasicDBObject("marks.english", new BasicDBObject("$gt", 35)));
        query.put("$and", andQuery);

Using MongoDB QueryBuilder we can rewrite above query as -

         DBObject query = new QueryBuilder()
                .start()
                .and(new QueryBuilder().start().put("lname").is("Ford").get(),
                        new QueryBuilder().start().put("marks.english")
                                .greaterThan(35).get()).get();

You can see that by using QueryBuilder we can write complex queries with ease. QueryBuilder class provides many methods like and, not, greaterThan, exists, etc. which helps in writing MongoDB queries more efficiently and less prone to error/mistakes.

Introduction To MongoDB Aggregation Pipeline

MongoDB Aggregation pipeline is a framework for data aggregation. It is modelled on the concept of data processing pipelines. Documents enter a multi-stage pipeline that transforms the documents into an aggregated results. It was introduced in MongoDB 2.2 to do aggregation operations without needing to use map-reduce.

Aggregation Pipeline

• The $match and $sort pipeline operators can take advantage of an index when they occur at the beginning of the pipeline [Reference].
• There are no restrictions on result size as a cursor is returned [Reference].
• The output can be returned inline or written to a collection [Reference].
• Pipeline stages have a limit of 100MB of RAM. To handle large datasets use allowDiskUse option [Reference].
• Aggregation Pipeline have an optimization phase which attempts to reshape the pipeline for improved performance [Reference].

For most aggregation operations, the Aggregation Pipeline provides better performance and more coherent interface. However, map-reduce operations provide some flexibility that is presently not available in the aggregation pipeline.

The syntax for aggregation pipeline is

db.collection.aggregate( [ { <stage> }, ... ] )

Stages

The MongoDB aggregation pipeline consists of stages. Each stage transforms the documents as they pass through the pipeline. Pipeline stages do not need to produce one output document for every input document; e.g., some stages may generate new documents or filter out documents. Pipeline stages can appear multiple times in the pipeline.

Various stage operators supported by MongoDB are listed below-

Name	Description
$geoNear	Returns an ordered stream of documents based on the proximity to a geospatial point. Incorporates the functionality of $match, $sort, and $limit for geospatial data. The output documents include an additional distance field and can include a location identifier field.
$group	Groups input documents by a specified identifier expression and applies the accumulator expression(s), if specified, to each group. Consumes all input documents and outputs one document per each distinct group. The output documents only contain the identifier field and, if specified, accumulated fields.
$limit	Passes the first n documents unmodified to the pipeline where n is the specified limit. For each input document, outputs either one document (for the first n documents) or zero documents (after the first n documents).
$match	Filters the document stream to allow only matching documents to pass unmodified into the next pipeline stage. $match uses standard MongoDB queries. For each input document, outputs either one document (a match) or zero documents (no match).
$out	Writes the resulting documents of the aggregation pipeline to a collection. To use the $out stage, it must be the last stage in the pipeline.
$project	Reshapes each document in the stream, such as by adding new fields or removing existing fields. For each input document, outputs one document.
$redact	Reshapes each document in the stream by restricting the content for each document based on information stored in the documents themselves. Incorporates the functionality of $project and $match. Can be used to implement field level redaction. For each input document, outputs either one or zero document.
$skip	Skips the first n documents where n is the specified skip number and passes the remaining documents unmodified to the pipeline. For each input document, outputs either zero documents (for the first n documents) or one document (if after the first n documents).
$sort	Reorders the document stream by a specified sort key. Only the order changes; the documents remain unmodified. For each input document, outputs one document.
$unwind	Deconstructs an array field from the input documents to output a document for each element. Each output document replaces the array with an element value. For each input document, outputs n documents where n is the number of array elements and can be zero for an empty array.

Different expressions supported by MongoDB are listed here.

What is Write Concern in MongoDB?

In MongoDB there are multiple guarantee levels available for reporting the success of a write operation, called Write Concerns. The strength of the write concerns determine the level of guarantee. A weak Write Concern has better performance at the cost of lesser guarantee, while a strong Write Concern has higher guarantee as clients wait to confirm the write operations.

MongoDB provides different levels of write concern to better address the specific needs of applications. Clients may adjust write concern to ensure that the most important operations persist successfully to an entire MongoDB deployment. For other less critical operations, clients can adjust the write concern to ensure faster performance rather than ensure persistence to the entire deployment.

Write Concern Levels

MongoDB has the following levels of conceptual write concern, listed from weakest to strongest:

Unacknowledged
With an unacknowledged write concern, MongoDB does not acknowledge the receipt of write operations. Unacknowledged is similar to errors ignored; however, drivers will attempt to receive and handle network errors when possible. The driver’s ability to detect network errors depends on the system’s networking configuration.

 

Acknowledged
With a receipt acknowledged write concern, the mongod confirms the receipt of the write operation. Acknowledged write concern allows clients to catch network, duplicate key, and other errors. This is default write concern.

Journaled
With a journaled write concern, the MongoDB acknowledges the write operation only after committing the data to the journal. This write concern ensures that MongoDB can recover the data following a shutdown or power interruption.
You must have journaling enabled to use this write concern.

Replica Acknowledged
Replica sets present additional considerations with regards to write concern. The default write concern only requires acknowledgement from the primary. With replica acknowledged write concern, you can guarantee that the write operation propagates to additional members of the replica set.

Write operation to a replica set with write concern level of w:2 or write to the primary and at least one secondary.

Hive UDF to get Latitude and Longitude

In my previous post I explained about Hive GenericUDF.
In this post I will give an example of Hive GenericUDF to get Latitude and Longitude of a given location using Google Geocoding API. Lets call this Hive function as GeoEncodeUDF. GeoEncodeUDF function takes a String location and returns an array of Float containing latitude and longitude.


For obtaining latitude and longitude information I am using Google geocode API which is available here http://maps.googleapis.com/maps/api/geocode/json?address=<address>, this returns a JSON objects containg matching places and their latitude and longitude. This might return multiple address but for sake of simplicity I am taking the first address's latitude and longitude. I have created a helper method getLatLng in class GeoLatLng which takes location string and returns latitude and longitude in an array of float. This class is given below -
The GenericUDF is GeoEncodeUDF
I have overwritten initialize(), evaluate() and getDisplayString() methods which I have already described in my previous post.

Now to use this UDF in Hive we need to create a jar file of this UDF and add it to Hive. The commands to add this UDF to Hive are -

ADD JAR /path/to/HiveUDF.jar;
CREATE TEMPORARY FUNCTION geo_points AS 'com.rishav.hadoop.hive.ql.udf.generic.GeoEncodeUDF';

Now we can use geo_points function on any table having address string like this -

hive> select geo_points("india") from test.x limit 1;
[20.593683,78.96288]

This HQL will return an array containing lat-lng, to get them as separate columns use -

hive> select latlng[0], latlng[1] FROM (select geo_points("india") as latlng from test.x) tmp limit 1;
20.593683    78.96288

Introduction to Hive UDFs

Apache Hive comes with a set of pre-defined User Defined Functions (UDFs). A complete listing of Hive UDFs is available here. Some common UDFs are unix_timestamp(), to_date(string timestamp), concat(string|binary A, string|binary B...), etc. However sometimes custom UDF is needed to solve specific problems.

In this post I will go through the process of creating custom UDFs.

Difference between UDF and GenericUDF
Hive UDFs are written in Java. In order to create a Hive UDF you need to derive from one of two classes UDF or GenericUDF. GenericUDFis bit complex to develop compared to UDF but it offers better performance and it supports all non-primitive parameters as input parameters and return types.

For writing custom UDFs by extending GenericUDF we need to overwrite 3 methods: initialize(), evaluate() and getDisplayString().

initialize()
This method only gets called once per JVM at the beginning to initilize the UDF. initilialize() is used to assert and validate the number and type of parameters that a UDF takes and the type of argument it returns. It also returns an ObjectInspector corresponding to the return type of the UDF.

evaluate()
This method is called once for every row of data being processed. Here the actual logic for transformation/processing of each row is written. It will return an object containing the result of processing logic. 

getDisplayString()
A simple method for returning the display string for the UDF when explain is used.

Apart from these we can have these Annotations also -

• @UDFType(deterministic = true)

A deterministic UDF is one which always gives the same result when passed the same parameters. An example of such UDF are length(string input), regexp_replace(string initial_string, string pattern, string replacement), etc. A non-deterministic UDF, on the other hand can return different result for the same set of parameters. For example, unix_timestamp() returns the current timestamp using the default time zone. Therefore, when unix_timestamp() is invoked with the same parameters (no parameters) at different times, different results are obtained, making it non-deterministic. This annotation allows Hive to perform some optimization if the UDF is deterministic.

• @Description(name="my_udf", value="This will be the result returned by explain statement.", extended="This will be result returned by the explain extended statement.")

This annotation tells Hive the name of your UDF. It will also be used to populate the result of queries like `DESCRIBE FUNCTION MY_UDF` or `DESCRIBE FUNCTION EXTENDED MY_UDF`.

In my next post I will give an example of GenericUDF to latitude and longitude of a location.

HBase: MapReduce On Multiple Input Table

Starting with version 0.94.5 HBase supports reading multiple tables as input to MapReduce jobs using MultiTableInputFormat class.
In this post I am giving an example of MapReduce job which reads from two HBase tables performs some aggregation on one table and merges (SQL UNION ALL operation) it with the content of second table and stores the result in an output table.

The first table is 'storeSales' table and it has store-wise sales for each date. The create statements are -

create 'storeSales', 'cf1'
put 'storeSales', '20130101#1', 'cf1:sSales', '100'
put 'storeSales', '20130101#2', 'cf1:sSales', '110'
put 'storeSales', '20130102#1', 'cf1:sSales', '200'
put 'storeSales', '20130102#2', 'cf1:sSales', '210'


The second table is 'onlineSales' table and it has online sale for each date. The create statements are -
create 'onlineSales', 'cf2'
put 'onlineSales', '20130101', 'cf2:oSales', '400'
put 'onlineSales', '20130102', 'cf2:oSales', '130'

Using a MapReduce job I am going to merge aggregated (at date level) store sales with online sales.
Lets create a output table for the same -
create 'totalSales', 'cf1'

The mapper class for this job is -

Note that in mapper I am getting table name of current split and using different context.write based on table name. If your source tables have rowkeys with different prefixes you can use that also for different context.write logic.

The reducer class for this job is -

Based on intermediate key value I am using aggregation in reducer.

Finally the driver class for this job is

In the driver there are 2 HBase Scan for 2 input tables and I am passing these scans in a list to TableMapReduceUtil.initTableMapperJob method.

Package jar file (to hbase-union.jar) and execute below commands to invoke MapReduce job -
export HADOOP_CLASSPATH=`hbase classpath`
hadoop jar hbase-union.jar com.rishav.hbase.union.UnionJob

Once the job is complete use HBase shell to verify output results -
hbase(main):034:0> scan 'totalSales'
ROW                                        COLUMN+CELL                                                                                                               
 o#20130101                                column=cf1:tSales, timestamp=1410848221034, value=\x00\x00\x01\x90                                                        
 o#20130102                                column=cf1:tSales, timestamp=1410848221034, value=\x00\x00\x00\x82                                                        
 s#20130101                                column=cf1:tSales, timestamp=1410848221034, value=\x00\x00\x00\xD2                                                        
 s#20130102                                column=cf1:tSales, timestamp=1410848221034, value=\x00\x00\x01\x9A                                                        
4 row(s) in 0.0410 seconds
hbase(main):035:0> org.apache.hadoop.hbase.util.Bytes.toInt("\x00\x00\x01\x90".to_java_bytes)
=> 400
hbase(main):036:0> org.apache.hadoop.hbase.util.Bytes.toInt("\x00\x00\x00\x82".to_java_bytes)
=> 130
hbase(main):037:0> org.apache.hadoop.hbase.util.Bytes.toInt("\x00\x00\x00\xD2".to_java_bytes)
=> 210
hbase(main):038:0> org.apache.hadoop.hbase.util.Bytes.toInt("\x00\x00\x01\x9A".to_java_bytes)
=> 410

MultiTableInputFormat can be used for doing HBase table joins too, I shall try that some time.