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.
101,701
101,702
101,703
102,701
103,705
103,707

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}
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.

Update Fixed number of MongoDB Documents

Recently I worked on a project which uses MongoDB as source data system and uses R for analysis and MongoDB again for output storage.

In this project we faced a different problem. We were using R to process source data present in MongoDB and if we gave large number of documents for analysis to R it was becoming slower and a bottleneck. To avoid this bottleneck we had to implement processing of a fixed number of documents in R for a batch.

To achieve this we needed some kind of record number in MongoDB, but being a distributed database getting some sequential number in MongoDB was not supported. Also our MongoDB source was getting populated by a distributed real-time stream so implementing some logic on application side was also deterrent.

To have some batchId field for a fixed number of documents in MongoDB we implemented below algorithm :
1. Find for documents which didn't had batchId field.
2. Sort by some timestamp field.
3. Limit the number of documents (say 10000).
4. Append batchId field to documents and save them (get value of batchId from audit table).

MongoDB shell command for this is :

db['collection1'].find({batchId:null}).sort({systemTime:1}).limit(10000).forEach(
    function (e) {
// get value of batchId from audit table
        e.batchId = 1;
        db['collection1'].save(e);
    }
);

Using the above code we appeneded batchId to MongoDB documents and picked only current batchId for analysis in R.

Java code for above MongoDB shell command is :

MapReduce on Avro data files

In this post we are going to write a MapReduce program to consume Avro input data and also produce data in Avro format.
We will write a program to calculate average of student marks.

 

Data Preparation

The schema for the records is:

student.avsc
{
  "type" : "record",
  "name" : "student_marks",
  "namespace" : "com.rishav.avro",
  "fields" : [ {
    "name" : "student_id",
    "type" : "int"
  }, {
    "name" : "subject_id",
    "type" : "int"
  }, {
    "name" : "marks",
    "type" : "int"
  } ]
}

And some sample records are:

student.json
{"student_id":1,"subject_id":63,"marks":19}
{"student_id":2,"subject_id":64,"marks":74}
{"student_id":3,"subject_id":10,"marks":94}
{"student_id":4,"subject_id":79,"marks":27}
{"student_id":1,"subject_id":52,"marks":95}
{"student_id":2,"subject_id":34,"marks":16}
{"student_id":3,"subject_id":81,"marks":17}
{"student_id":4,"subject_id":60,"marks":52}
{"student_id":1,"subject_id":11,"marks":66}
{"student_id":2,"subject_id":84,"marks":39}
{"student_id":3,"subject_id":24,"marks":39}
{"student_id":4,"subject_id":16,"marks":0}
{"student_id":1,"subject_id":65,"marks":75}
{"student_id":2,"subject_id":5,"marks":52}
{"student_id":3,"subject_id":86,"marks":50}
{"student_id":4,"subject_id":55,"marks":42}
{"student_id":1,"subject_id":30,"marks":21}

Now we will convert the above sample records to avro format and upload the avro data file to HDFS:

java -jar avro-tools-1.7.5.jar fromjson student.json --schema-file student.avsc > student.avro
hadoop fs -put student.avro student.avro

Avro MapReduce Program

In my program I have used Avro Java class for student_marks schema. To generate Java class from the schema file use below command:

java -jar avro-tools-1.7.5.jar compile schema student.avsc .

Then add the generated Java class to IDE.

I have written a MapReduce program which reads Avro data file student.avro (passed as argument) and calculates average marks for each student and store the output also in Avro format. The program is given below:

• In the program the input key to mapper is AvroKey<student_marks> and the input value is null. The output key of map method is student_id and output value is an IntPair having marks and 1.
• We have a combiner also which aggregates partial sums for each student_id.
• Finally reducer takes student_id and partial sums and counts and uses them to calculate average for each student_id. The reducer writes the output in Avro format.

For Avro job setup we have added these properties:

// set InputFormatClass to AvroKeyInputFormat and define input schema
    job.setInputFormatClass(AvroKeyInputFormat.class);
    AvroJob.setInputKeySchema(job, student_marks.getClassSchema());

// set OutputFormatClass to AvroKeyValueOutputFormat and key as INT type and value as FLOAT type
    job.setOutputFormatClass(AvroKeyValueOutputFormat.class);
    AvroJob.setOutputKeySchema(job, Schema.create(Schema.Type.INT));
    AvroJob.setOutputValueSchema(job, Schema.create(Schema.Type.FLOAT));

Job Execution

We package our Java program to avro_mr.jar and add Avro jars to libjars and hadoop classpath using below commands:

export LIBJARS=avro-1.7.5.jar,avro-mapred-1.7.5-hadoop1.jar,paranamer-2.6.jar
export HADOOP_CLASSPATH=avro-1.7.5.jar:avro-mapred-1.7.5-hadoop1.jar:paranamer-2.6.jar
hadoop jar avro_mr.jar com.rishav.avro.mapreduce.AvroAverageDriver -libjars ${LIBJARS} student.avro output

You can verify the output using avro-tool command.

To enable snappy compression for output add below lines to run method and add snappy-java jar to libjars and hadoop classpath:

        FileOutputFormat.setCompressOutput(job, true);
        FileOutputFormat.setOutputCompressorClass(job, SnappyCodec.class);

Convert csv data to Avro data

In one of my previous post I explained how we can convert json data to avro data and vice versa using avro tools command line option. Today I was trying to see what options we have for converting csv data to avro format, as of now we don't have any avro tool option to accomplish this . Now, we can either write our own java program (MapReduce program or a simple java program) or we can use various SerDe's available with Hive to do this quickly and without writing any code :)

To convert csv data to Avro data using Hive we need to follow below steps:

1. Create a Hive table stored as textfile and specify your csv delimiter also.
2. Load csv file to above table using "load data" command.
3. Create another Hive table using AvroSerDe.
4. Insert data from former table to new Avro Hive table using "insert overwrite" command.

To demonstrate this I will use use below data (student.csv):

0,38,91
0,65,28
0,78,16
1,34,96
1,78,14
1,11,43

Now execute below queries in Hive:

Now you can get data in Avro format from Hive warehouse folder. To dump this file to local file system use below command:
hadoop fs -cat /path/to/warehouse/test.db/avro_table/* > student.avro

If you want to get json data from this avro file you can use avro tools command:
java -jar avro-tools-1.7.5.jar tojson student.avro > student.json

So we can easily convert csv to avro and csv to json also by just writing 4 HQLs.

Implementing Custom WritableComparable

In one of my previous post I wrote about Implementing Custom Writable which can be used as values in MapReduce program. For using customized key in MapReduce we need to implement WritableComparable interface.

WritableComparable interface is just a subinterface of the Writable and java.lang.Comparable interfaces. For implementing a WritableComparable we must have compareTo method apart from readFields and write methods, as shown below:

public interface WritableComparable extends Writable, Comparable
{
    void readFields(DataInput in);
    void write(DataOutput out);
    int compareTo(WritableComparable o)
}

Comparison of types is crucial for MapReduce, where there is a sorting phase during which keys are compared with one another.

The code for IntPair class which is used in In-mapper Combiner Program to Calculate Average post is given below:


As you can see in compareTo(IntPair tp) of above class that IntPair needs to be deserialized for comparison to take place, we can implement a RawComparator which can compare two keys by just checking their serialized representation. More on RawComparator is available in Hadoop: The Definitive Guide.

Avro Schema Evolution

Avro can use different schemas for serialization and deserialization, and it can handle removed, added and modified fields. Thus it helps in building decoupled and robust systems.

In this post we will serialize data using this schema:

and deserialize it using a different schema
which has following modifications:

1. university_id field is removed.
2. age field is added.
3. result_score field is renamed to score.

Before we actually see how Avro handles these modification I would like to mention below points:

• If a new field is added then it must have a default value. Also specify type as an array of types starting with null e.g. "type": ["null", "string"] otherwise you will get this error:

    Exception in thread "main" java.lang.NoSuchMethodError: org.codehaus.jackson.node.ValueNode.asText()Ljava/lang/String;

• If a field is renamed then the old name must be present as aliases.

In the this java program we serialize data using StudentActivity.avsc schema and deserialize data using StudentActivityNew.avsc schema

On executing this code we see that Avro handles the modifications without any issues and our data is deserialized properly.

Getting started with Avro Part2

In the previous post we used avro-tools commands to serialize and deserialize data. In this post we post we will use Avro Java API for achieving the same. We will use same sample data and schema from our previous post.

The java code for serializing and deserializing data without generating the code for schema is given below:

For generating the schema java code from Avro json schema we can use avro-tools jar. The command for same is given below:

java -jar avro-tools-1.7.5.jar compile schema StudentActivity.avsc <output_path>

Output path can be source folder for the project or we can add the generated java class files to Eclipse IDE manually.

The java code for serializing and deserializing data with generating the code for schema is similar to above code except that in previous code we were assiging values to a GenericRecord and in this one we are assigning values to the generated Avro object:

In next post we will see how Avro deals with schema evolution.

Getting started with Avro Part1

In our previous post we got some basic idea about Avro, in this post we will use Avro for serializing and deserializing data.

We will use these 3 methods in which we can use Avro for serialization/deserialization:

1. Using Avro command line tools.
2. Using Avro Java API without code generation.
3. Using Avro Java API with code generation.

Sample Data

We will use below sample data (StudentActivity.json):
Note that the JSON records are nested ones.

Defining a schema

Avro schemas are defined using JSON. The avro schema for our sample data is defined as below (StudentActivity.avsc):

1. Serialization/Deserialization using Avro command line tools

Avro provides a jar file by name avro-tools-<version>.jar which provides many command line tools as listed below:


For converting json sample data to Avro binary format use "fromjson" option and for getting json data back from Avro files use "tojson" option.

Command for serializing json
Without any compression

java -jar avro-tools-1.7.5.jar fromjson --schema-file StudentActivity.avsc StudentActivity.json > StudentActivity.avro

With snappy compression

java -jar avro-tools-1.7.5.jar fromjson --schema-file StudentActivity.avsc StudentActivity.json > StudentActivity.snappy.avro

Command for deserializing json
The same command is used for deserializing both compressed and uncompressed data

java -jar avro-tools-1.7.5.jar tojson StudentActivity.avro
java -jar avro-tools-1.7.5.jar tojson StudentActivity.snappy.avro

As Avro data file contains the schema also, we can retrieve it using this commmand:

java -jar avro-tools-1.7.5.jar getschema StudentActivity.avro
java -jar avro-tools-1.7.5.jar getschema StudentActivity.snappy.avro

In our next post we will use Avro Java API for serialization/deserialization.

Introduction to Avro

Apache Avro is a popular data serialization format and is gaining more users as many hadoop based tools natively support Avro for serialization and deserialization.
In this post we will understand some basics about Avro.

What is Avro?
Data serialization system
Uses JSON based schemas
Uses RPC calls to send data
Schema's sent during data exchange

Avro provides:
Rich data structures.
A compact, fast, binary data format.
A container file, to store persistent data.
Simple integration with many languages. Code generation is not required to read or write data files nor to use or implement RPC protocols. Code generation as an optional optimization, only worth implementing for statically typed languages.
Avro API's exist for these languages Java, C, C++, C#, Python and Ruby.

Avro Schema:
Avro relies on schemas for serialization/deserialization.
When Avro data is stored in a file, its schema is stored with it, so that files may be processed later by any program.
If the program reading the data expects a different schema this can be easily resolved, since both schemas are present.
Avro supports a wide range of datatypes which are listed below:
Primitive Types

• null: no value
• boolean: a binary value
• int: 32-bit signed integer
• long: 64-bit signed integer
• float: single precision (32-bit) IEEE 754 floating-point number
• double: double precision (64-bit) IEEE 754 floating-point number
• bytes: sequence of 8-bit unsigned bytes
• string: unicode character sequence

Complex Types
Avro supports six kinds of complex types: records, enums, arrays, maps, unions and fixed. Detailed information on these complex types is available here.

Schema Resolution:
A reader of Avro data, whether from an RPC or a file, can always parse that data because its schema is provided. But that schema may not be exactly the schema that was expected. For example, if the data was written with a different version of the software than it is read, then records may have had fields added or removed.

We call the schema used to write the data as the writer's schema, and the schema that the application expects the reader's schema. Differences between these should be resolved as follows:

• It is an error if the two schemas do not match.
  To match, one of the following must hold:
  
  • both schemas are arrays whose item types match
  • both schemas are maps whose value types match
  • both schemas are enums whose names match
  • both schemas are fixed whose sizes and names match
  • both schemas are records with the same name
  • either schema is a union
  • both schemas have same primitive type
  • the writer's schema may be promoted to the reader's as follows:
    • int is promotable to long, float, or double
    • long is promotable to float or double
    • float is promotable to double
• if both are records:
  • the ordering of fields may be different: fields are matched by name.
  • schemas for fields with the same name in both records are resolved recursively.
  • if the writer's record contains a field with a name not present in the reader's record, the writer's value for that field is ignored.
  • if the reader's record schema has a field that contains a default value, and writer's schema does not have a field with the same name, then the reader should use the default value from its field.
  • if the reader's record schema has a field with no default value, and writer's schema does not have a field with the same name, an error is signalled.
• if both are enums: if the writer's symbol is not present in the reader's enum, then an error is signalled.
  
• if both are arrays: This resolution algorithm is applied recursively to the reader's and writer's array item schemas.
  
• if both are maps: This resolution algorithm is applied recursively to the reader's and writer's value schemas.
  
• if both are unions: The first schema in the reader's union that matches the selected writer's union schema is recursively resolved against it. if none match, an error is signalled.
  
• if reader's is a union, but writer's is not The first schema in the reader's union that matches the writer's schema is recursively resolved against it. If none match, an error is signalled.
  
• if writer's is a union, but reader's is not If the reader's schema matches the selected writer's schema, it is recursively resolved against it. If they do not match, an error is signalled.

In next post we will see a program to serialization/deserialization some data using avro and also see how Avro handles schema evolution.