Instructions for Contribution

Oct 13, 2025 by ludoplayers7.com 49 views

Overview of the Project

In the realm of vector search, comparing the performance of various libraries and databases is crucial for making informed decisions. To facilitate this comparison, we aim to integrate Cassandra with the ANN-Benchmark, a popular industrial benchmark for approximate nearest neighbor libraries. This integration will enable us to compare the performance of Cassandra with ScyllaDB and other libraries using the ANN-Benchmark.

Background and Motivation

The Qdrant-Benchmark, a vector search benchmark, has an existing integration with Cassandra, prepared by ScyllaDB engineers. However, the ANN-Benchmark, a pure benchmark for approximate nearest neighbor libraries, covers a broader range of libraries, making it an attractive choice for comparison. By integrating Cassandra with the ANN-Benchmark, we can leverage its extensive coverage of libraries and gain a deeper understanding of Cassandra's performance in vector search applications.

Technical Requirements

To achieve this integration, we will create a fork of the ANN-Benchmark repository and implement the Cassandra integration. The existing integration with Qdrant-Benchmark can serve as a valuable source of snippets for efficient integration with the benchmark. Once the integration is complete, we will contribute it to the original ANN-Benchmark repository.

Instructions for Contribution

For those interested in contributing to the ANN-Benchmark, the instructions can be found in the repository's README file. Specifically, the section on including your algorithm provides a clear guide on how to contribute.

Existing Integration as a Reference

The existing integration with Qdrant-Benchmark can be a valuable reference for implementing the Cassandra integration. By leveraging the snippets and code from this integration, we can ensure a smooth and efficient implementation.

Scope of the Project

The scope of this project is to integrate Cassandra with the ANN-Benchmark, ensuring that the integration works as expected. Conducting extended benchmarks, apart from testing the integration, is not within the scope of this project. Additionally, extracting common code from the Qdrant and ANN-Benchmark integrations is not necessary at this stage, as the common code will be minimal.

Benefits of the Integration

The integration of Cassandra with the ANN-Benchmark will provide several benefits, including:

Enhanced comparison: By leveraging the ANN-Benchmark's extensive coverage of libraries, we can gain a deeper understanding of Cassandra's performance in vector search applications.
Improved performance evaluation: The integration will enable us to compare the performance of Cassandra with ScyllaDB and other libraries, providing valuable insights for developers and researchers.
Increased adoption: By contributing the integration to the original ANN-Benchmark repository, we can increase the adoption of Cassandra in vector search applications.

Conclusion

In conclusion, integrating Cassandra with the ANN-Benchmark is a crucial step in enhancing vector search comparison. By leveraging the existing integration with Qdrant-Benchmark and contributing to the original ANN-Benchmark repository, we can ensure a smooth and efficient implementation. The benefits of this integration will be multifaceted, providing valuable insights for developers and researchers, and increasing the adoption of Cassandra in vector search applications.

Technical Details

Cassandra Integration

To integrate Cassandra with the ANN-Benchmark, we will create a fork of the ANN-Benchmark repository and implement the Cassandra integration. The existing integration with Qdrant-Benchmark can serve as a valuable source of snippets for efficient integration with the benchmark.

Existing Integration as a Reference

Scope of the Project

Implementation Plan

Step 1: Create a Fork of the ANN-Benchmark Repository

We will create a fork of the ANN-Benchmark repository to implement the Cassandra integration.

Step 2: Implement the Cassandra Integration

We will implement the Cassandra integration, leveraging the existing integration with Qdrant-Benchmark as a reference.

Step 3: Test the Integration

We will test the integration to ensure that it works as expected.

Step 4: Contribute to the Original ANN-Benchmark Repository

Once the integration is complete, we will contribute it to the original ANN-Benchmark repository.

Timeline

The timeline for this project is as follows:

Week 1-2: Create a fork of the ANN-Benchmark repository and implement the Cassandra integration.
Week 3-4: Test the integration and address any issues that arise.
Week 5: Contribute the integration to the original ANN-Benchmark repository.

Resources

The resources required for this project include:

Cassandra: The Cassandra database will be used for the integration.
ANN-Benchmark: The ANN-Benchmark repository will be forked and modified to include the Cassandra integration.
Qdrant-Benchmark: The existing integration with Qdrant-Benchmark will be used as a reference for implementing the Cassandra integration.

Risks and Assumptions

The risks and assumptions associated with this project include:

Integration complexity: The integration of Cassandra with the ANN-Benchmark may be complex, requiring significant time and effort to implement.
Testing challenges: Testing the integration may be challenging, requiring careful consideration of edge cases and performance metrics.
Contribution to the original repository: Contributing the integration to the original ANN-Benchmark repository may be subject to review and approval, which may introduce delays.
Q&A: Integrating Cassandra with ANN-Benchmark =====================================================

Frequently Asked Questions

Q: What is the purpose of integrating Cassandra with ANN-Benchmark?

A: The purpose of integrating Cassandra with ANN-Benchmark is to enable comparison of the performance of Cassandra with ScyllaDB and other libraries using the ANN-Benchmark. This will provide valuable insights for developers and researchers, and increase the adoption of Cassandra in vector search applications.

Q: What is the scope of the project?

A: The scope of the project is to integrate Cassandra with the ANN-Benchmark, ensuring that the integration works as expected. Conducting extended benchmarks, apart from testing the integration, is not within the scope of this project. Additionally, extracting common code from the Qdrant and ANN-Benchmark integrations is not necessary at this stage, as the common code will be minimal.

Q: What is the timeline for the project?

A: The timeline for the project is as follows:

Week 1-2: Create a fork of the ANN-Benchmark repository and implement the Cassandra integration.
Week 3-4: Test the integration and address any issues that arise.
Week 5: Contribute the integration to the original ANN-Benchmark repository.

Q: What resources are required for the project?

A: The resources required for the project include:

Cassandra: The Cassandra database will be used for the integration.
ANN-Benchmark: The ANN-Benchmark repository will be forked and modified to include the Cassandra integration.
Qdrant-Benchmark: The existing integration with Qdrant-Benchmark will be used as a reference for implementing the Cassandra integration.

Q: What are the risks and assumptions associated with the project?

A: The risks and assumptions associated with the project include:

Integration complexity: The integration of Cassandra with the ANN-Benchmark may be complex, requiring significant time and effort to implement.
Testing challenges: Testing the integration may be challenging, requiring careful consideration of edge cases and performance metrics.
Contribution to the original repository: Contributing the integration to the original ANN-Benchmark repository may be subject to review and approval, which may introduce delays.

Q: How will the integration be tested?

A: The integration will be tested to ensure that it works as expected. This will involve testing the integration with various datasets and scenarios to ensure that it performs correctly and efficiently.

Q: What is the expected outcome of the project?

A: The expected outcome of the project is to have a working integration of Cassandra with the ANN-Benchmark, which will enable comparison of the performance of Cassandra with ScyllaDB and other libraries using the ANN-Benchmark.

Q: How will the project be contributed to the original ANN-Benchmark repository?

A: The project will be contributed to the original ANN-Benchmark repository once the integration is complete and has been tested. This will involve submitting a pull request to the repository and addressing any feedback or issues that arise during the review process.

Q: What are the benefits of the integration?

A: The benefits of the integration include:

Enhanced comparison: By leveraging the ANN-Benchmark's extensive coverage of libraries, we can gain a deeper understanding of Cassandra's performance in vector search applications.
Improved performance evaluation: The integration will enable us to compare the performance of Cassandra with ScyllaDB and other libraries, providing valuable insights for developers and researchers.
Increased adoption: By contributing the integration to the original ANN-Benchmark repository, we can increase the adoption of Cassandra in vector search applications.

Q: What are the next steps for the project?

A: The next steps for the project are to create a fork of the ANN-Benchmark repository, implement the Cassandra integration, test the integration, and contribute the integration to the original ANN-Benchmark repository.

Q: Who is responsible for the project?

A: The project is being led by [Your Name], with contributions from [Contributor 1], [Contributor 2], etc.

Q: What is the budget for the project?

A: The budget for the project is [Budget Amount], which will be used to cover the costs of resources, testing, and contribution to the original ANN-Benchmark repository.