Vector database vs. graph database

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Data storage and retrieval systems are essential in modern technology, powering things like personalized searches and social network analysis. In our data-driven world, choosing the right database is key to improve performance and managing data effectively.

In this blog, we'll explore Vector Database vs. Graph Database, two advanced and powerful database types. Each serves a unique purpose, and understanding when and how to use them can bring significant benefits in fields like AI, machine learning, and natural language processing.

Vectors are numerical representations of data, often generated by machine learning models like BERT or GPT. These vectors encapsulate the semantic meaning of the data (e.g., text, images) in multidimensional space where each vector’s numbers (dimensions) capture different important characteristics of the data (such as a word, an image, or a sentence), and all these characteristics together place the vector in a specific position in a high-dimensional space. The relationship between vectors in this space helps us understand how similar or different the data points are to each other.

Vector databases#

Today’s data-driven applications handle complex, multidimensional data, such as images, sound, videos, and text. These data types cannot be managed efficiently by traditional databases and search engines. In response to this challenge, vector databases have emerged as a solution, offering high-accuracy search capabilities and efficiently handling such diverse data.

What are vector databases?#

Vector databases are specialized databases designed to store, search, and manage high-dimensional data vectorsArrays or lists of numbers, where each number in the vector corresponds to a specific feature or attribute of the data.. Vector databases excel at tasks involving similarity search, where the objective is to identify items close to one another in the vector space based on their numerical representations. Similarity search is crucial when comparing data points and finding the most alike according to certain criteria.

How do vector databases work?#

Here’s a simplified breakdown of how vector databases handle data:

• Data transformation: Data such as text, images, or audio is first transformed into numerical vectors using machine learning models or embeddings like Word2Vec or BERT.

• Storage: These vectors, representing high-dimensional data, are stored in the vector database, allowing for efficient data management and retrieval.

• Indexing: The vector database indexes the stored vectors using specialized algorithms (e.g., HNSW or IVF) to optimize similarity search and ensure fast query results.

• Querying: When a query vector is provided, the database compares it against stored vectors to find the closest in vector space using distance metrics like cosine similarity or Euclidean distance.

• Resulting action: The closest vectors (representing the most similar data points) are returned, which can be used for tasks like recommendations, search results, or classification.

• Continuous update: The database is continuously updated with new vectors as more data is ingested, ensuring the system evolves with the application’s needs over time.

List of popular vector databases#

Here is the list of popular vector databases:

• Redis: An in-memory data structure store commonly used as a database, cache, and message broker, known for its speed and scalability.

• Faiss: A library developed by Facebook AI for efficient similarity search and clustering of dense vectors and optimized for large-scale data.

• Vespa: A search engine and data processing platform for real-time, large-scale machine learning models and vector-based retrieval.

• Weaviate: An open-source vector search engine that uses machine learning to store and search data objects by their semantic meaning.

• Pinecone: A managed vector database service designed for fast and scalable similarity search and machine learning applications.

• Chroma: An open-source vector database that builds and serves AI-driven applications with high-performance vector search.

• Milvus: An open-source vector database designed for similarity search and managing large-scale unstructured data.

• Qdrant: A vector similarity search engine designed to handle high-dimensional data optimized for performance and accuracy in real-time AI applications.

AI applications that use vector databases#

The following are the common use cases of vector databases in the real world:

1. Natural language processing (NLP)#

Vector databases function as specialized storage systems for Natural Language Processing. Instead of storing words directly, they store vector representations that capture the meaning and relationships between words. This is like having a map where related words are clustered, regardless of their spelling, allowing the system to understand meaning beyond literal matches. This clustering helps in various NLP tasks like semantic search, recommendation systems, text classification, machine translation, and sentiment analysis by enabling efficient retrieval of related content based on semantic similarity rather than exact wording.

2. Recommendation systems#

Recommendation systems are algorithms that suggest items or content to users based on their preferences and past interactions. These systems represent user preferences and items as vectors, capturing important features like behavior or item attributes. Vector databases compare the user’s preference vector with item vectors, finding the closest similarity. This enables personalized recommendations by suggesting semantically similar items, even if they don’t share exact keywords. Vector databases make the process fast and efficient, especially for large-scale data.

3. Anomaly detection#

Vector databases enhance anomaly detection in fields like fraud detection, network security, and healthcare by efficiently handling high-dimensional data. They store data as vectors that capture complex relationships, allowing quick comparison of new data points with historical patterns. Using advanced similarity search algorithms, vector databases can identify outliers and anomalies in real time, helping businesses quickly detect unusual activity—such as fraudulent transactions or security breaches—and respond proactively.

Graph databases#

Graph databases are a type of NoSQL database, distinct from traditional SQL-based relational databases that have dominated since the1980s. Rooted in graph theory, a field introduced by mathematician Leonhard Euler in the 18th century, this concept has evolved to become fundamental in various modern applications. Although the theory has long existed, graph databases have only begun to take shape in the last decade, well after the advent of the internet and modern computing.

What are graph databases?#

Graph databases organize data as a network of connected entities, where relationships (edges) link entities (nodes). This differs from the table-based format of relational databases or the high-dimensional space used by vector databases.

Graph databases visualize data as nodes and edges, where nodes represent entities and edges represent the relationships between those entities. This structure allows for intuitive data modeling and complex relationship-based queries. In a social media platform, users and posts can be represented as nodes, while relationships like friendships, likes, and comments are edges connecting them. Graph databases efficiently model these interactions, allowing for complex queries such as finding posts liked by friends or suggesting new connections. By visualizing data as nodes and edges, graph databases enable intuitive relationship-based data retrieval, making them ideal for handling complex queries in social media applications.

How do graph databases work?#

The graph databases work in the following steps:

• Nodes and edges: The nodes represent entities (e.g., users, products, posts), and the edges represent relationships between entities (e.g., friendships, likes, purchases).

• Building the graph: Based on the data, nodes, and edges are created. For example, a social media graph might include nodes for users and posts, with edges indicating friendships, likes, or comments.

• Efficient storage: The graph structure is stored in a database optimized for handling and querying interconnected data. This structure allows for efficient storage and retrieval of complex relationships.

• Graph traversal: Graph databases use traversal algorithms to navigate through nodes and edges. For example, the database traverses the graph to find relevant connections to find a user’s friends or recommend products based on user behavior.

• Insightful queries: Queries can explore various paths and connections in the graph, enabling insights based on relationships rather than just isolated data points.

List of popular graph databases#

Here is the list of popular graph databases:

• Neo4j: Neo4j is a leading graph database that efficiently handles complex queries and relationships. It uses a property-graph model to represent and query data, making it ideal for use cases like social networks and recommendation engines.

• OrientDB: OrientDB is a multi-model database supporting graph and document data models. It combines document databases’ flexibility with graph databases’ power, enabling versatile data management and querying.

• TigerGraph: TigerGraph specializes in real-time, large-scale graph processing and analytics. It is designed to handle complex queries on massive datasets, making it suitable for fraud detection and customer insights applications.

• ArangoDB: ArangoDB is a multi-model database that integrates graph, document, and key-value data models. It provides a unified query language optimized for complex data relationships and real-time analytics.

• JanusGraph: JanusGraph is an open-source database that handles large-scale graphs and complex queries. It integrates with big data technologies like Apache Hadoop and Apache Cassandra for scalable and distributed graph processing.

• Dgraph: Dgraph is a distributed graph database designed for high performance and scalability. It offers a highly efficient querying system and is built to handle massive amounts of data across multiple nodes.

• Azure Cosmos DB: Microsoft’s Azure Cosmos DB is a globally distributed, multi-model database service that includes graph database capabilities. It supports various data models and provides low-latency, scalable access to data across different geographic regions.

Applications of graph databases#

The following are the common use cases of graph databases in the real world:

1. Social network analysis#

Graph databases excel at mapping relationships between users, posts, and other elements in social networks by providing the infrastructure for storing and managing interconnected data. They efficiently uncover connections, analyze interactions, and identify influential nodes. Graph algorithms then analyze this data to derive meaningful insights, such as understanding user behavior and network dynamics. Together, graph databases and graph algorithms enable comprehensive social network analysis.

The diagram below visually represents the interconnectedness of individuals within a social network. It illustrates different roles, such as energizers, connectors, brokers, and challengers, and how they contribute to the overall structure and dynamics of the network.

2. Supply chain and logistics#

Graph databases manage and analyze complex supply chain relationships, including connections between suppliers, manufacturers, distributors, and retailers. They can model and query the intricate network of dependencies and interactions within the supply chain, helping to optimize logistics, detect bottlenecks, and improve overall efficiency.

3. Knowledge graphs#

A knowledge graph is a specialized graph database that focuses on representing and connecting diverse pieces of information to provide a rich, contextual understanding of data. The knowledge graph integrates data from multiple sources to create a unified, semantically meaningful representation of knowledge. It is used in applications like enhancing search engine results with contextual information, personalizing recommendations by linking user preferences with related content and integrating diverse datasets for comprehensive analysis in healthcare and enterprise data management.

4. Master data management (MDM)#

Master data management (MDM) is a practice focused on ensuring the accuracy, consistency, and integrity of an organization’s critical data assets across various systems. It involves consolidating data from different sources into a unified repository, implementing data quality management to correct errors and standardize formats, and establishing data governance policies for managing and securing the data. MDM also includes data modeling to define the structure and relationships of master data and data synchronization to keep information consistent across systems. By integrating and managing master data effectively, MDM supports better decision-making, enhances operational efficiency, and provides a single, authoritative source of truth for key business entities.

Key differences between vector database vs. graph database#

In the following table, we’ll explain how a vector database is different from a graph database:

Key similarities#

Despite their differences, vector and graph databases share several key similarities. For example, vector databases are used in image and text search engines to find similar items based on content features, while graph databases are used in fraud detection systems to uncover suspicious connections between entities. In each case, the databases help identify and analyze intricate patterns within the data. Here are some key similarities of these databases:

• Handling complex data and structures: Both are designed to handle complex data that traditional databases struggle with.

• Mathematical foundations: Both database types rely on advanced mathematical principles.

• Applications in data management: They are both used to extract insights and manage large, complex datasets.

• Complex query processing: They excel at processing complex queries that involve relationships or similarities.

• Versatility: Both vector and graph databases can be applied across various industries and use cases, from recommendation systems to fraud detection.

Choosing between vector vs. graph databases#

Despite the insights shared in this blog, choosing the right database can still feel overwhelming. To simplify this process, here’s a framework you can use to guide you toward making the best decision for your needs.

1. Assess your data type: Determine whether vectors or relationships best represent your data. Vectors can effectively represent text data, while graphs (relationships) can represent social network data.

2. Define key use cases: Consider whether you need similarity searches or relationship-based queries. For product recommendations, use similarity searches to find similar items, while for analyzing corporate networks, use relationship-based queries to explore connections between entities.

3. Consider performance and scalability: Evaluate the scalability requirements of your application. For large-scale image searches, ensure the vector database can handle high-dimensional data efficiently, while for expanding social networks, a graph database should scale to manage increasing nodes and relationships effectively.

4. Compare technology advantages: Vector databases handle high-dimensional data and perform similarity searches, making them ideal for applications like recommendation systems and image recognition. Graph databases are best for managing complex relationships and querying intricate connections, crucial for social network analysis and fraud detection applications. Understanding these unique strengths helps you choose the right database for your application’s needs.

Combining vector and graph databases#

In recommendation systems, vector databases can store and retrieve high-dimensional feature vectors representing user preferences and item characteristics. Meanwhile, graph databases can manage and analyze the complex relationships between users, items, and interactions. This combination allows the system to recommend products based on similarity (from the vector database) and user interactions or social connections (from the graph database). Combining vector and graph databases can be highly effective for complex tasks but comes with challenges. By integrating vector and graph databases, applications can benefit from enhanced data analysis capabilities that address similarity-based queries and relationship-based insights, resulting in more robust and intelligent systems.

Conclusion#

When building your next project, consider whether your data relies more on high-dimensional similarity (suited for vector databases) or complex relationships (ideal for graph databases). Choosing the right type can significantly enhance performance and scalability.

Both vector and graph databases offer unique strengths for different data challenges. By understanding their capabilities, you can select the best fit for your application's goals, whether dealing with high-dimensional vectors, intricate relationships, or a combination of both.

Next steps#

If you'd like to dive deeper into the world of vector databases, explore our hands-on course Vector Databases for LLMs, which focuses on implementing vector databases in language model projects and optimizing data handling for NLP and AI applications.