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A simple recommender system for your e-commerce store using a graph database

#graph #recommendation #orientdb #e-commerce #etl


In the last post, I have introduced you to a simple ETL use case for graph database like Orient DB. If you haven’t read it, I suggest you read this - OrientDB A simple use case note.

After loading data, you might want to play around with the graph structure and its possible traversal logic. As it is easy to represent the semantic relationships between them, the queries we will write also be designed based on the logic we come up with. In the last post, I have provided the query to find out the books bought by the buyers he know or befriended. In this post, I will provide some more simple examples to query such a graph in Orient DB. Here I a using the native query supported by the database.

How do we find out the books bought by a buyer named ‘Hary’?


select @rid, title from (select expand(out('Bought')) from Buyer where name='Hary')



Here this query will return the RecordId of the format <<cluster: position>> form. In OrientDB each record has its own self-assigned unique ID within the database called Record ID or RID. cluster-id is the id of the cluster and cluster-position is the position of the record inside the cluster. You can consider a cluster as a Table where each class (say, Buyer) of records are stored. Here the subquery uses expand function to expand the collection in the field and use it as result. It will fetch the records linked to the edge ‘Bought’.

How do we find out the people ‘Hary’ knows?


select expand(out('Knows')) from  Buyer where name='Hary'



Find out the books bought by friends of Hary?


select title from (
select expand(out('Bought')) from (select expand(out('Knows')
) from  Buyer where name='Hary'))



Here we combined both of the queries above it to make a logical decision as the interlinking of vertices is clearly identified.

Find out books bought by Hary but not by his friends, so that we can recommend some?

select title from (select expand(out('Bought')) from Buyer where name='Hary') 
let $temp = (
  select title from (
    select expand(out('Bought')) from (
      select expand(out('Knows')) from  Buyer where name='Hary'
    )
  )
)where title not in $temp




Here we used LET to assign the results of a subquery. In the subquery, we find the books bought by Hary’s friends. Then we find the books bought by Hary but not by friends.

Find out the books who also bought a book like The Shining? This is a common use case for recommend links where we may want to list the similar products bought by people who is about to buy the displayed product.

select expand(inE('Bought').outV().OutE('Bought').inV().title) 
from Book where title not in ['The Shining']

Orient DB - A simple ETL use case note

#orientdb #graph #etl #java #database

As someone who is familiar with graph data structures would like to know how we can map real-world models to a graph and process them. If you are trying to build them programmatically and approach them using traversal algorithms, you are going to have a hard time. If your application use a relational database to store data mapped to these models, then it will become complex while trying to link them with more relationships. How will you design the relationships between domains in a better  semantic way? How would you query them like a sql-like or DSL language? Graph databases should be a right candidate. Here I am trying to test out Orient DB.


In relational databases, we have primary and foreign-key columns references that helps joins that are computed at query time which is memory and compute intensive. Also we use junctions tables for many-to-many relationships with highly normalized tables which will  increase the query execution time and complexity. Graph databases are like relational databases, but with first class support for “relationships” defined by edges (stored as list) connected nodes (vertex/entity). Whenever you run a join operation, the database just uses this materialized list and has direct access to the connected nodes, eliminating the need for a expensive search / match computation.

Consider following tables,

Author Table
id name
1 Stephen King
2 George R. R. Martin
3 John Grisham
Book Table
id author_id title
1 1 Carrie
2 1 The Shining
Buyer Table
id name knows_id book_id
1 Hary 2 2
2 Mary 1 2

In graph database like orient db, we can define the relationships in amore semantic way. Graph databases operate on 3 structures: Vertex(sometimes called Node), Edge(or Arc) and Property(sometimes called Attribute).
  • Vertex. It’s data: Author, Book etc
  • Edge is physical relation between Vertices. Each Edge connects two different vertices, no more, no less. Additionally Edge has label and Direction, so If you label your edge as likes you know that Hary bought the book The Shining. The direction of relationship cane be either Out or In.
  • Property - it’s a value related to Vertex or Edge.
OrientDB comes with an ETL tool to import data. Also, you can use the libraries and write your own code to create nodes in the database. A generic framework for graph databases is available. More on Apache TinkerPop later.
You have to define configuration files for loading certain data into the graph store.
In the above sample configuration, you are defining,
  • “source”: { “file”: { “path”: “csv file location” } } // the source of file input for a model/entity
  • in transformer
    • vertex as the model or table
    • edge will define the edges in and out of the table
  • In the loader definition we define all the entities and constraints
More about the transformation definition can be read here
Import the csv files and configuration from the github repo. Please change the location of files and conf with respective to your environment.

Simply execute the oetl.sh tool from $ORIENTDB_HOME as sh oetl.sh ‘location of conf file’

loading author data
You have to execute all the configurations to load all the data.
After loading all the data you can query out and visualize them in the Orient DB’s web based console.

Here you can see the links between the entities.

how do you find the books bought by your friends?

select expand( both('Knows').out('Bought')) from Buyer where name = 'Hary'