Introduction to Graph Database in System Design

Introduction

In various real-world scenarios, we need to understand relationships between data elements rather than individual data elements. To understand such relationships, graph databases provide an intuitive and efficient way to organize and analyze such data to get valuable insights. In other words, graph databases help us to efficiently traverse complex hierarchies, identify hidden connections, and uncover inter-relationships between elements. That’s why they are used in various applications, where things are interconnected.

In system design, a graph database is classified as a NoSQL database because it does not use the tabular structure of a traditional SQL database. Instead, it stores data in a graph-like structure, with nodes representing entities and edges representing relationships.

Graph database use cases in real life applications

We enjoy using social media platforms for connecting with friends and followers.
With location-based services, we can examine the connections between places, routes, and distances for optimized navigation experiences.
To ensure the safety of our transactions, we rely on fraud detection systems that monitor relationships between individuals and transactions, ensuring that no fraudulent activity goes unnoticed.
When shopping online, a graph database is a useful tool for storing links between products, categories, customers, their purchase history, and preferences. On such platform, Recommendation systems analyze user behavior between products and provide personalized product suggestions.
In the field of life sciences, graph databases help us understand the complex relationships between genes, proteins, and diseases, leading to advancements in drug discovery and personalized medicine.
Knowledge graphs allow us to establish and manage complex relationships between entities such as people, places, and events, which improves semantic search and information retrieval.

Graph database real life examples

Why we prefer graph database over relational databases?

Let’s take an example of social media. If we use traditional relational databases, we need to create a “users” table that contains data about each user (name, email, password, etc.). We also need to create a “connections” table to store data about relationships between users (date they connected and type of relationship like friend, family, etc.).

In such a scenario, we might need to perform a JOIN operation between the “users” and “connections” tables to analyze relationships. This can be time-consuming and resource-intensive if there are large amounts of data and relationships. For example, if we want to find all friends of a user, we might need to perform a JOIN operation that matches the user’s ID with the IDs of all their friends in the “connections” table. This operation can be slow with the increase in the number of relationships and users, which could lead to long wait times and poor user experience.
On another side, the rigid schema of traditional relational databases can limit the flexibility and scalability of the database. For example, if we want to add a new type of relationship, we might need to modify the schema of the “connections” table. This can be a complex and time-consuming process. In other words, this can make it difficult for us to adapt to changing requirements and increase latency.

To solve the above issues, graph databases offer a better solution. Graph databases provide a flexible way of storing relationships between data elements.

This eliminates the need for time-consuming JOIN operations or cross-lookups.
We can store the relationships between users as edges connecting two nodes, where nodes can store data about users and edges can store data about the relationship. This can help us to quickly traverse the data and find the relationships.

For example, if we want to find all friends of a user on social media, we can simply start from the node and follow edges (friend relationship) to reach all the friends. This will be much more efficient process than performing a JOIN operation in a relational database. Additionally, graph databases have a flexible schema, allowing us to easily add new types of relationships without modifying the schema. This makes it easier to adapt to changing requirements.

Some popular graph databases

Neo4j is a highly efficient and scalable graph database with powerful performance.
Amazon Neptune is a fully managed graph database service offered by Amazon Web Services.
ArangoDB is an open-source multi-model database.
TigerGraph is a fast and scalable graph database that is specifically designed for use cases such as real-time fraud detection, recommendation systems, and network analysis.
RedisGraph is an open-source graph database built on top of popular in-memory data store, Redis.
GraphQL is a powerful query language for APIs, which provides a complete and understandable description of the data and gives clients the power to ask for exactly what they need.

Advantages of using a graph database

Provide flexible data model that simplify the representation of complex relationships.
Handle large amounts of data and relationships (well-suited for big data applications).
Process and analyze large amounts of data in real-time (useful for real-time applications).
Offer fast performance for complex queries (useful for fast and efficient data analysis).
Easily integrate with other data sources (useful for analyzing data from multiple sources).
Handle dynamic and changing data (useful for applications where data is constantly changing).

How graph database is implemented under the hood?

At a high level, a graph database is composed of a collection of nodes and edges, which are stored in a data structure such as an adjacency list or matrix. The nodes represent entities in the data, while the edges represent relationships between the entities. But in a typical implementation at low level:

Graph database will use an index to efficiently store and retrieve the nodes and edges in the graph. For example, a hash table or B-tree can be used to index nodes based on some unique identifier. When performing a query, database will use indexes to quickly locate relevant nodes, and then traverse the edges to find related nodes.
In addition to indexing, graph databases may also use various optimization techniques to improve performance, such as caching frequently-used data, using algorithms such as breadth-first or depth-first search to traverse the graph, or partitioning the graph into smaller sub-graphs to reduce the size of the data that needs to be processed.

Overall, implementation of a graph database will depend on the specific requirements of the application, such as size and complexity of data, type of queries that need to be performed, and performance and scalability requirements.

Some common queries on graph database

Neighborhood queries: Used to find all nodes and edges that are directly connected to a specific node. For example, finding all friends of a user in a social network.
Pathfinding queries: Used to find the shortest path between two nodes in the graph. For example, finding shortest path between two cities in a transportation network.
Pattern matching queries: Used to find all instances of a specific pattern in the graph. For example, finding all triangles in a social network (three users who are friends with each other).
Centrality queries: Used to find the most important nodes in the graph based on some measure of centrality. For example, finding the most influential users in a social network based on number of connections they have.
Clustering queries: Used to find groups of nodes that are densely connected to each other, but less connected to other nodes in the graph. For example, finding clusters of users who are friends with each other but not friends with users outside the cluster.

These are just a few examples of the types of queries that can be performed on a graph database. The specific queries will depend on the nature of data and requirements of the application.

Enjoy learning, Enjoy system design!