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Graph Database

1. Overview

  • Equally prioritizing relationships and entities
  • Utilizing graph structures for data representation
  • abstracting storage as nodes, edges, and properties

1.1. Key Concepts

  1. Nodes (Vertices): Individual data entities, similar to records in relational databases.
  2. Edges (Relationships): Connections between nodes, representing relationships.
  3. Properties: Attributes of nodes and edges, providing additional context.

1.2. Advantages

  • Performance: Efficient for querying hierarchical and network structures.
  • Flexibility: Easily accommodate changes in data structures.
  • Intuitive Visualization: Naturally mapped to real-world relationships, enhancing understanding.

1.3. Use Cases

  • Social Networks: Mapping relationships between users.
  • Recommendation Engines: Connecting users with products based on preferences.
  • Fraud Detection: Analyzing transactional patterns and relationships to identify anomalies.

2. Diff w/ Relational Databases

2.1. Efficient Query Performance

  • Complex Queries
    • Graph databases handle complex queries involving numerous joins or traversals more efficiently than relational databases.
    • Example: Quickly finding the shortest path between two nodes or determining the network of direct and indirect relationships.

2.2. Direct Representation of Relationships

  • First-Class Citizens
    • Relationships are explicitly modeled in graph databases, making it easier to manage and query them.
    • Relevant Domains
      • Social Networks
      • Recommendation Systems
      • Fraud Detection

2.3. Flexibility and Schema-less Nature

  • Schema-less Design
    • Graph databases do not require a predefined schema, allowing for agile development.
    • Adaptability
      • Easily accommodate changes in data structures.
      • Evolve relationships over time without significant restructuring.

2.4. Intuitive Data Modeling

  • Direct Representation
    • Nodes and edges in graph databases directly represent entities and their relationships.
  • User Understanding
    • More intuitive to understand and visualize compared to tables and foreign keys in relational databases.

2.5. Handling Semi-Structured Data

  • Versatility
    • Excel in managing semi-structured or heterogeneous data.
    • Integration
      • Seamlessly incorporate different data types (e.g., structured and unstructured).
Tags::cs:database: