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Apache Cassandra vs pgvector: Choosing the Right Vector Database for Your Needs

Apache Cassandra vs pgvector: Choosing the Right Vector Database for Your Needs

Sep 04, 20248 min read

As AI and data-driven technologies advance, selecting an appropriate vector database for your application is becoming increasingly important. Apache Cassandra and pgvector are two options in this space. This article compares these technologies to help you make an informed decision for your project.

What is a Vector Database?

Before we compare Apache Cassandra and pgvector, let's first explore the concept of vector databases.

A vector database is specifically designed to store and query high-dimensional vectors, which are numerical representations of unstructured data. These vectors encode complex information, such as the semantic meaning of text, the visual features of images, or product attributes. By enabling efficient similarity searches, vector databases play a pivotal role in AI applications, allowing for more advanced data analysis and retrieval.

Common use cases for vector databases include e-commerce product recommendations, content discovery platforms, anomaly detection in cybersecurity, medical image analysis, and natural language processing (NLP) tasks. They also play a crucial role in Retrieval Augmented Generation (RAG), a technique that enhances the performance of large language models (LLMs) by providing external knowledge to reduce issues like AI hallucinations.

There are many types of vector databases available in the market, including:

Purpose-built vector databases such as Milvus, Zilliz Cloud (fully managed Milvus), and Weaviate
Vector search libraries such as Faiss and Annoy.
Lightweight vector databases such as Chroma and Milvus Lite.
Traditional databases with vector search add-ons capable of performing small-scale vector searches.

Cassandra and pgvector represent similar approaches to vector databases. Both are traditional databases that have evolved to include vector search capabilities.

Apache Cassandra: Overview and Core Technology

Apache Cassandra is an open-source, distributed NoSQL database known for its scalability and availability. Cassandra's features include a masterless architecture for availability, scalability, tunable consistency, and a flexible data model. With the release of Cassandra 5.0, it now supports vector embeddings and similarity search.

Cassandra's vector search functionality is built on its existing architecture. It allows users to store vector embeddings alongside other data and perform similarity searches. This integration enables Cassandra to support AI-driven applications while maintaining its strengths in handling large-scale, distributed data.

A key component of Cassandra's vector search is the use of Storage-Attached Indexes (SAI). SAI is a highly-scalable and globally-distributed index that adds column-level indexes to any vector data type column. It provides high I/O throughput for databases to use Vector Search as well as other search indexing. SAI offers extensive indexing functionality, capable of indexing both queries and content (including large inputs like documents, words, and images) to capture semantics.

Vector Search is the first instance of validating the extensibility of SAI, leveraging its new modularity. This combination of Vector Search and SAI enhances Cassandra's capabilities in handling AI and machine learning workloads, making it a strong contender in the vector database space.

pgvector: Overview and Core Technology

pgvector is an extension for PostgreSQL that adds support for vector operations. It allows users to store and query vector embeddings directly within their PostgreSQL database, providing vector similarity search capabilities without the need for a separate vector database.

Key features of pgvector include:

Support for exact and approximate nearest neighbor search
Integration with PostgreSQL's indexing mechanisms
Ability to perform vector operations like addition and subtraction
Support for various distance metrics (Euclidean, cosine, inner product)

pgvector, by default, employs exact nearest neighbor search, which guarantees perfect recall but can be slower for large datasets. To optimize performance, pgvector offers the option to create indexes for approximate nearest neighbor search. This approach trades some accuracy for significantly improved speed, which is often a worthwhile tradeoff in many real-world applications.

It's important to note that adding an approximate index can change the results of your queries. This is different from typical database indexes, which don't affect the actual results returned. The two types of approximate indexes supported by pgvector are:

HNSW (Hierarchical Navigable Small World): Introduced in pgvector version 0.5.0, HNSW is known for its high performance and quality of results. It builds a multi-layer graph structure that allows for fast traversal during searches.
IVFFlat (Inverted File Flat): This method divides the vector space into clusters. During a search, it first identifies the most relevant clusters and then performs an exact search within those clusters. This can significantly speed up searches in large datasets.

The choice between these index types depends on your specific use case, considering factors like dataset size, required query speed, and acceptable trade-off in accuracy. HNSW generally offers better performance but may use more memory, while IVFFlat can be more memory-efficient but might be slightly slower or less accurate in some cases.

When implementing pgvector in your project, try to experiment with both index types and their parameters to find the optimal configuration for your specific needs. This process of fine-tuning can impact the performance and accuracy of your vector search operations.

Wanna learn how to get started using pgvector? Check out this tutorial!

Key Differences Between Apache Cassandra and pgvector

Search Methodology

Cassandra's vector search is designed for similarity searches on high-dimensional data across a distributed system. It's suited for applications that require semantic understanding and contextual relevance at scale.

pgvector, being an extension of PostgreSQL, combines traditional relational database capabilities with vector operations. This allows for complex queries that can involve both structured data and vector similarity searches.

Data Handling

Cassandra handles structured and semi-structured data in a distributed environment. Its data model allows for storage and retrieval of vector embeddings alongside other data types across multiple nodes.

pgvector works within PostgreSQL's relational model. It can store vector data as a column type, allowing for seamless integration of vector data with traditional structured data in tables.

Scalability and Performance

Cassandra uses a masterless architecture that allows for linear scalability. This design enables it to handle large amounts of data across many nodes with consistent performance. Its SAI feature further enhances its ability to perform efficient vector searches at scale.

pgvector leverages PostgreSQL's scaling capabilities. While PostgreSQL can be scaled horizontally, it typically doesn't scale as easily as Cassandra for very large distributed systems. However, for many applications, pgvector's performance within a well-tuned PostgreSQL setup can be more than sufficient.

Flexibility and Customization

Cassandra offers flexibility in data modeling and consistency levels. Users can adjust these aspects to their specific use cases. The addition of vector search capabilities expands its use cases into AI and machine learning domains.

pgvector benefits from PostgreSQL's rich ecosystem of extensions and tools. It allows for complex queries that can combine traditional SQL operations with vector similarity searches, offering unique flexibility for applications that need both relational data and vector operations.

Integration and Ecosystem

Cassandra integrates well with other big data tools in the Apache ecosystem, such as Spark and Hadoop. Its vector search capabilities also allow it to work with machine learning frameworks for AI-driven applications.

pgvector, being a PostgreSQL extension, integrates seamlessly with the vast and super popular PostgreSQL ecosystem. This includes various ORMs, connection poolers, and other database tools that support PostgreSQL.

Ease of Use

Cassandra has a learning curve, especially for those new to distributed systems. Setting up and maintaining a Cassandra cluster requires understanding its architecture and data model. However, for teams already familiar with Cassandra, adding vector search capabilities is relatively straightforward.

pgvector, leveraging the familiar PostgreSQL environment, may have a gentler learning curve for teams already experienced with relational databases. Setting up pgvector is typically as simple as installing the extension on an existing PostgreSQL database

Cost Considerations

Both Cassandra and PostgreSQL (and by extension, pgvector) are open-source and free to use. However, operational costs can vary.

Cassandra may require more resources to run efficiently, especially for large clusters. However, its ability to run on commodity hardware can help manage costs for large-scale deployments.

PostgreSQL with pgvector can often run on smaller hardware for moderate-sized datasets, potentially leading to lower infrastructure costs for smaller to medium-sized applications.

Security Features

Cassandra offers features like authentication, authorization, and encryption. Its distributed nature requires careful configuration to ensure data security across all nodes.

PostgreSQL, and by extension pgvector, provides a robust set of security features including role-based access control, encryption, and audit logging. Being a mature relational database, PostgreSQL has a long history of security-focused development.

When to Choose Apache Cassandra or pgvector

Consider Cassandra when:

You need to handle very large amounts of data across a distributed system
High availability and fault tolerance are crucial
Your use case involves both traditional data storage and vector similarity searches at scale
You're already using or planning to use other tools in the Apache ecosystem

Consider pgvector when:

You're already using PostgreSQL and want to add vector search capabilities
You need to perform complex queries involving both relational data and vector similarity
Your data size is moderate and can be handled by a well-tuned PostgreSQL setup
You value the ease of use and familiar environment of a relational database

Conclusion

Both Apache Cassandra and pgvector offer powerful capabilities for vector search, but they cater to different use cases and scale requirements.

Cassandra, with its distributed architecture and newly added vector search capabilities, is well-suited for large-scale, highly available systems that need to perform vector similarity searches across massive datasets. Its integration with the Apache ecosystem makes it a strong choice for organizations already invested in these technologies.

pgvector, as an extension to PostgreSQL, offers a more accessible entry point into vector search for teams already familiar with relational databases. It shines in scenarios where vector search needs to be tightly integrated with traditional relational data and where the flexibility of SQL is valued.

Your choice between Cassandra and pgvector should depend on your specific use case, data volume, existing technology stack, and team expertise. Both technologies continue to evolve, so it's worth monitoring their progress as you make your decision.

Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own

VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems such as Milvus and Zilliz Cloud (the managed Milvus) using their own datasets, and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.

VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.

Download VectorDBBench from its GitHub repository to reproduce our benchmark results or obtain performance results on your own datasets.
Take a quick look at the performance of mainstream vector databases on the VectorDBBench Leaderboard.
Read the following blogs to learn more about vector database evaluation.

Further Resources about VectorDB, GenAI, and ML

Chloe Williams
Chloe Williams is a technical writer at Zilliz.

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