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Apache Cassandra vs. Clickhouse: Choosing the Right Vector Database for Your AI Applications

Apache Cassandra vs. Clickhouse: Choosing the Right Vector Database for Your AI Applications

Sep 08, 202411 min read

As AI-driven applications become more prevalent, developers and engineers face the challenge of selecting the right database to handle vector data efficiently. Two popular options in this space are Apache Cassandra and Clickhouse. This article compares these technologies to help you make an informed decision for your vector database needs.

What is a Vector Database?

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

A vector database is specifically designed to store and query high-dimensional vector embeddings, which are numerical representations of unstructured data. These vectors encode complex information, such as text's semantic meaning, images' visual features, 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.

Vector databases are adopted in many use cases, including 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)
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.

Apache Cassandra is a NoSQL database with vector search as an add-on. Clickhouse is an open-source column-oriented database with vector search as an add-on.

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 vector similarity search through its Storage-Attached Indexes (SAI) feature. While this integration allows Cassandra to handle vector data, it's important to note that vector search is implemented as an extension of Cassandra's existing architecture rather than a native feature.

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.

Clickhouse: Overview and Core Technology

ClickHouse is an open-source real-time OLAP database known for its full SQL support and high-speed query processing. It excels at handling analytical queries due to its fully parallelized query pipeline, allowing it to perform vector search operations quickly. Its high levels of compression, customizable through codecs, enable ClickHouse to store and query large datasets effectively. One of its key strengths is that it can handle multi-TB datasets without being constrained by memory, making it a powerful tool for users dealing with large-scale vector data. It also supports filtering and aggregation on metadata, allowing developers to perform complex queries on both vectors and their associated metadata.

ClickHouse integrates vector search functionality through its SQL capabilities, where vector distance operations are treated like any other SQL function. This allows seamless combination with traditional filtering and aggregation, making it ideal for use cases where vector data needs to be queried alongside metadata or other information. Additionally, experimental features like Approximate Nearest Neighbour (ANN) indices offer faster, though approximate, matching capabilities. ClickHouse also supports exact matching through a linear scan over rows, with its parallelized processing ensuring high speed and efficiency.

ClickHouse is an excellent option for vector search when combining vector matching with metadata filtering or aggregation is important. It's especially useful for very large vector datasets that need to be processed in parallel across multiple CPU cores. ClickHouse is also advantageous when SQL support is necessary, and the vector dataset is too large to rely on memory-only indices. Additionally, if you already have related data in ClickHouse or wish to avoid learning another tool for managing millions of vectors, ClickHouse can save you both time and resources. Its strengths lie in fast, parallelized exact matching and handling large datasets, making it suitable for users with advanced search requirements.

ClickHouse stands out as a versatile platform for vector search, particularly when dealing with large datasets that require parallelized processing and when combining vector searches with SQL-based filtering and aggregation. While it may not be as specialized for small, memory-bound datasets or high-QPS scenarios as dedicated vector databases, its ability to handle complex queries, including metadata, makes it a powerful option for developers familiar with SQL who need high-speed vector search capabilities.

Key Differences: Apache Cassandra vs. Clickhouse

Search Methodology

Cassandra and ClickHouse both offer vector search capabilities, but their methodologies differ. Cassandra implements vector search through its Storage-Attached Indexes (SAI) feature, which allows for similarity search within its masterless, distributed architecture. It focuses on indexing vector data types and offers column-level indexing for efficient queries. ClickHouse, on the other hand, integrates vector search as an SQL function, allowing users to compute vector distances within SQL queries. It also supports exact and approximate matching using experimental Approximate Nearest Neighbor (ANN) indexes. While Cassandra focuses on extensibility within its existing architecture, ClickHouse's vector search is tightly integrated with its SQL query engine, offering more versatility in combining vector search with metadata filtering.

Data Handling

Cassandra excels at managing structured, semi-structured, and unstructured data across a distributed architecture, using its flexible, schema-optional data model. It is designed for handling large-scale distributed data across nodes. ClickHouse, as a columnar database, specializes in structured data, focusing on fast, analytical queries. It can handle semi-structured data but is more optimized for structured, high-compression workloads. While Cassandra is better suited for flexible data models across distributed systems, ClickHouse shines in environments where fast querying and analytics on structured data are key priorities.

Scalability and Performance

Cassandra is built for horizontal scalability across multiple nodes, making it highly suitable for large, distributed systems that prioritize availability and fault tolerance. It is designed to handle massive datasets with linear scalability as more nodes are added. ClickHouse, while also scalable, focuses more on vertical performance optimization, with its parallelized query execution allowing it to handle large datasets efficiently on fewer nodes. Its columnar architecture is designed for fast data retrieval, especially for analytics use cases. For large-scale distributed applications, Cassandra's scalability model is ideal, while ClickHouse's strength lies in its fast performance for real-time analytics.

Flexibility and Customization

Cassandra offers significant flexibility in terms of data modeling and consistency, allowing for tunable consistency across different nodes, which can be adjusted based on the use case. ClickHouse, while flexible in query execution and vector search, is more rigid in its data modeling, focusing on structured data with limited support for dynamic schemas. However, ClickHouse excels in query customization, allowing developers to combine vector search with filtering, aggregation, and advanced SQL queries. Cassandra provides more flexibility in data storage, while ClickHouse offers more customization in search queries and analytical functions.

Integration and Ecosystem

Cassandra integrates well with distributed systems and cloud environments, offering strong support for integrations with other NoSQL databases, big data frameworks, and cloud-native tools. It is often used in environments involving Apache Spark, Kafka, and Kubernetes. ClickHouse also integrates with a variety of tools, especially within the data analytics and real-time reporting ecosystem. Its compatibility with popular analytics platforms and big data tools like Kafka and its SQL interface make it easier to plug into existing analytics stacks. Both systems have rich ecosystems, but Cassandra's is more focused on distributed data systems, while ClickHouse leans toward real-time analytics and OLAP systems.

Ease of Use

Cassandra has a steeper learning curve due to its distributed architecture and the need to manage replication, consistency, and availability. Setting it up and maintaining it requires an understanding of distributed systems concepts. ClickHouse, while powerful, is generally easier to use for developers familiar with SQL, as it offers a familiar query language and extensive documentation for its analytical capabilities. However, for complex use cases involving large-scale vector searches, ClickHouse may require additional tuning. Overall, ClickHouse is easier to get started with, especially for SQL-savvy developers, whereas Cassandra requires more expertise to manage and scale effectively.

Cost Considerations

Cassandra's operational costs can vary significantly depending on the size of the deployment, as its distributed architecture requires multiple nodes to achieve its scalability and availability benefits. This can lead to higher infrastructure costs, especially in cloud environments. ClickHouse can also incur significant costs for large datasets due to its parallel processing, but its focus on compression and efficient query execution can help optimize storage and compute resources. Both technologies can scale, but Cassandra might have higher operational costs due to its need for more nodes, while ClickHouse's columnar storage and compression can result in lower storage costs.

Security Features

Cassandra offers robust security features, including encryption at rest, authentication mechanisms like Kerberos and LDAP, and access control with role-based security. It also supports data encryption during transit. ClickHouse also provides encryption for data at rest and in transit, but its security model is more focused on SQL-level access control and user-defined functions. Both systems offer standard security features, but Cassandra is more oriented toward enterprise-level, distributed security needs, while ClickHouse provides sufficient security for analytics environments.

When to Choose Clickhouse or Apache Cassandra

Apache Cassandra Cassandra is best suited for large-scale, distributed systems that prioritize high availability, fault tolerance, and horizontal scalability. It's ideal when you need to handle massive datasets across multiple nodes with minimal downtime, making it a strong choice for applications that demand real-time data replication and consistency tuning. Cassandra's strength lies in managing structured, semi-structured, and unstructured data at scale, and with the addition of vector search through Storage-Attached Indexes (SAI), it becomes a solid option for AI-driven workloads where vector embeddings and large-scale data operations are needed.

ClickHouse ClickHouse is the better option when you need fast, real-time analytics on large datasets with advanced query capabilities. It shines in environments that require efficient vector search combined with metadata filtering and aggregation, making it suitable for OLAP use cases. With its parallelized query execution and ability to handle large datasets without being memory-bound, ClickHouse is ideal for scenarios involving complex analytics, high-performance vector matching, and integration with existing SQL-based workflows. If your use case involves both vector search and high-performance analytics, ClickHouse offers a powerful solution.

Conclusion

When deciding between Apache Cassandra and ClickHouse, it’s important to consider your specific needs. Cassandra excels with large-scale, distributed data and is great for applications needing high availability and fault tolerance. It’s well-suited for scenarios where vector search is an added requirement in a distributed system. On the other hand, ClickHouse is ideal for fast, real-time analytics and complex queries, especially when you need to combine vector search with detailed metadata filtering and aggregation. If you need robust analytics with vector search and efficient handling of large datasets, ClickHouse might be the better choice.

While this article provides an overview of Cassandra and Clickhouse, it's key to evaluate these databases based on your specific use case. One tool that can assist in this process is VectorDBBench, an open-source benchmarking tool designed for comparing vector database performance. Ultimately, thorough benchmarking with specific datasets and query patterns will be essential in making an informed decision between these two powerful, yet distinct, approaches to vector search in distributed database systems.

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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