LinkedIn is the world’s largest professional network with over 700 million members. As a social media platform built to connect professionals, LinkedIn stores and analyzes vast amounts of data on users, companies, jobs, skills, and professional relationships. This complexity and volume of highly interconnected data has led many to speculate that LinkedIn likely uses graph database technology to manage its data and drive core functionalities.
What is a Graph Database?
A graph database is a type of NoSQL database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. In a graph database, each node represents an entity (e.g. a person, company, etc.) and each edge represents a connection or relationship between nodes (e.g. friends, co-workers, etc.). Nodes and edges can have properties that provide attributes and context for the data.
Graph databases allow you to rapidly traverse millions or billions of connections between data points. They provide superior performance for connected data compared to traditional relational databases. Queries can be conducted quickly through graph traversals, pattern matching, and vectorized operations across massive datasets. This makes graph databases ideal for managing highly relational, interconnected data at scale.
Benefits of a Graph Database for LinkedIn
Here are some key advantages a graph database could provide LinkedIn:
- Manage complex, highly relational user data at massive scale – billions of user profiles and trillions of professional connections.
- Provide real-time insights and recommendations by rapidly traversing relationship graphs – people you may know, jobs you may be interested in, relevant content, etc.
- Enable powerful network analytics to identify connections, clusters, patterns, and anomalies.
- Simplify mapping of professional domains and hierarchies – skills, titles, industries, geographies.
- Accelerate pattern-based queries across the professional graph – find experts based on skill sets, experience, connections, etc.
- Continually evolve the underlying data model without migrations or impacting performance.
Evidence LinkedIn Uses a Graph Database
While LinkedIn has not publicly confirmed its database architecture, there are several indications that LinkedIn likely incorporates graph database technology:
- Job postings – LinkedIn has repeatedly recruited for roles requiring graph database experience including graph database engineers and data scientists.
- Patents – LinkedIn has published patents referencing graph database use cases including mapping professional identity graphs and optimizing how graphs are traversed.
- Publications – LinkedIn researchers have published papers on graph data mining, algorithms, and analytics which require graph database knowledge.
- Feature capabilities – Key LinkedIn features like People You May Know rely on traversing relationship graphs which graph databases are designed to optimize.
- Engineering blog posts – LinkedIn engineers have blogged about leveraging connected graph data and diagrams which map neatly to graph database constructs.
Based on these signals, it appears highly likely that LinkedIn utilizes graph database technology as an integral part of managing profile data, powering analytics, and driving core product capabilities.
Potential LinkedIn Graph Database Use Cases
Here are some potential specific use cases where LinkedIn may leverage a graph database architecture:
Professional Identity Graph
A graph database could store LinkedIn’s professional identity graph, mapping the connections between member profiles, companies, jobs, skills, content, and other entities. This would allow rapid relationship traversal queries to power features like People You May Know, skills gap analysis, and expertise searches.
News Feed Ranking
LinkedIn could analyze their content graph with a graph database to optimize news feed relevance through metrics like connectedness centrality and collaborative filtering approaches.
Career Trajectory Analysis
The career trajectories of LinkedIn members could be modeled as relationship pathways over time within a graph database. This allows holistic analysis of career progression patterns across industries, geographies, and skill sets.
Security & Fraud Detection
Analyzing the relationship graph could help identify fake profiles, spam accounts, and fraudulent activity through techniques like community detection and outlier analysis.
Advertising Targeting
LinkedIn’s interest, job function, company, location, and other member attributes could be modeled as a graph to precisely target highly relevant audiences for advertising.
Challenges of Graph Databases
While graph databases provide many benefits for interconnected data, LinkedIn would still face some technology challenges including:
- Scalability – Handling the volume and velocity of LinkedIn’s growing professional graph requires immense scalability.
- Data integrity – Ensuring consistency and data integrity as the graph data rapidly evolves.
- Query complexity – Tuning complex graph algorithms and queries to perform efficiently at scale.
- Data modeling – Capturing diverse data types and relationships in a flexible but well-structured graph model.
- Security – Authenticating users and securing data in a graph model that links sensitive profile information.
Conclusion
While LinkedIn has not publicly revealed the technical architecture powering their massive professional network, there is significant evidence indicating their use of graph database technology. Graph databases provide natural advantages for storing LinkedIn’s member data graph and enabling important product capabilities and analytics. However, operating a graph at LinkedIn’s scale also poses considerable data, performance, and security challenges. How LinkedIn tackles these technology and infrastructure demands provides key insights for any large-scale graph database implementation.