Introduction to Graph Database on NoSQL
As we all know the graph is a pictorial representation of data in the form of nodes and relationships which are represented by edges. A graph database is a type of database used to represent the data in the form of a graph. It has three components: nodes, relationships, and properties. These components are used to model the data. The concept of a Graph Database is based on the theory of graphs. It was introduced in the year 2000. They are commonly referred to NoSql databases as data is stored using nodes, relationships and properties instead of traditional databases. A graph database is very useful for heavily interconnected data. Here relationships between data are given priority and therefore the relationships can be easily visualized. They are flexible as new data can be added without hampering the old ones. They are useful in the fields of social networking, fraud detection, AI Knowledge graphs etc.
The description of components are as follows:
- Nodes: represent the objects or instances. They are equivalent to a row in database. The node basically acts as a vertex in a graph. The nodes are grouped by applying a label to each member.
- Relationships: They are basically the edges in the graph. They have a specific direction, type and form patterns of the data. They basically establish relationship between nodes.
- Properties: They are the information associated with the nodes.
Some examples of Graph Databases software are Neo4j, Oracle NoSQL DB, Graph base etc. Out of which Neo4j is the most popular one.
In traditional databases, the relationships between data is not established. But in the case of Graph Database, the relationships between data are prioritized. Nowadays mostly interconnected data is used where one data is connected directly or indirectly. Since the concept of this database is based on graph theory, it is flexible and works very fast for associative data. Often data are interconnected to one another which also helps to establish further relationships. It works fast in the querying part as well because with the help of relationships we can quickly find the desired nodes. join operations are not required in this database which reduces the cost. The relationships and properties are stored as first-class entities in Graph Database.
Graph databases allow organizations to connect the data with external sources as well. Since organizations require a huge amount of data, often it becomes cumbersome to store data in the form of tables. For instance, if the organization wants to find a particular data that is connected with another data in another table, so first join operation is performed between the tables, and then search for the data is done row by row. But Graph database solves this big problem. They store the relationships and properties along with the data. So if the organization needs to search for a particular data, then with the help of relationships and properties the nodes can be found without joining or without traversing row by row. Thus the searching of nodes is not dependent on the amount of data.
Types of Graph Databases:
- Property Graphs: These graphs are used for querying and analyzing data by modelling the relationships among the data. It comprises of vertices that has information about the particular subject and edges that denote the relationship. The vertices and edges have additional attributes called properties.
- RDF Graphs: It stands for Resource Description Framework. It focuses more on data integration. They are used to represent complex data with well defined semantics. It is represented by three elements: two vertices, an edge that reflect the subject, predicate and object of a sentence. Every vertex and edge is represented by URI(Uniform Resource Identifier).
When to Use Graph Database?
- Graph databases should be used for heavily interconnected data.
- It should be used when amount of data is larger and relationships are present.
- It can be used to represent the cohesive picture of the data.
How Graph and Graph Databases Work?
Graph databases provide graph models They allow users to perform traversal queries since data is connected. Graph algorithms are also applied to find patterns, paths and other relationships this enabling more analysis of the data. The algorithms help to explore the neighboring nodes, clustering of vertices analyze relationships and patterns. Countless joins are not required in this kind of database.
Example of Graph Database:
- Recommendation engines in E commerce use graph databases to provide customers with accurate recommendations, updates about new products thus increasing sales and satisfying the customer’s desires.
- Social media companies use graph databases to find the “friends of friends” or products that the user’s friends like and send suggestions accordingly to user.
- To detect fraud Graph databases play a major role. Users can create graph from the transactions between entities and store other important information. Once created, running a simple query will help to identify the fraud.
Advantages of Graph Database:
- Potential advantage of Graph Database is establishing the relationships with external sources as well
- No joins are required since relationships is already specified.
- Query is dependent on concrete relationships and not on the amount of data.
- It is flexible and agile.
- it is easy to manage the data in terms of graph.
Disadvantages of Graph Database:
- Often for complex relationships speed becomes slower in searching.
- The query language is platform dependent.
- They are inappropriate for transactional data
- It has smaller user base.
Future of Graph Database:
Graph Database is an excellent tool for storing data but it cannot be used to completely replace the traditional database. This database deals with a typical set of interconnected data. Although Graph Database is in the developmental phase it is becoming an important part as business and organizations are using big data and Graph databases help in complex analysis. Thus these databases have become a must for today’s needs and tomorrow success.