Master Thesis Intern - Vector support for Embeddings

MonetDB is a high-performance analytical database management system known for its efficiency in data warehousing and business intelligence applications. In the era of AI and machine learning, databases play a crucial role in storing and querying embeddings, which are vector representations of data points, such as word embeddings in natural language processing or feature embeddings in recommendation systems. To optimize the handling of embeddings, this master's thesis project aims to add vector support specifically tailored for AI use cases to the MonetDB database.


The primary objective of this master's thesis project is to enhance MonetDB's capabilities for storing and querying embeddings efficiently. This involves developing and integrating vectorized operations and data structures that are optimized for the types of queries and manipulations common in AI and machine learning workloads.


  • Literature Review: Start with a comprehensive review of the literature on embedding storage and querying techniques, as well as the existing features and architecture of MonetDB.
  • Database Analysis: Analyze the MonetDB codebase to identify areas where vector support can be introduced for efficient handling of embeddings. This may include the optimization of storage, indexing, and retrieval methods.
  • Design and Implementation: Develop a detailed design for adding vector support tailored for AI use cases. This may involve designing specialized data structures and query processing techniques to work with embeddings efficiently.
  • Testing and Benchmarking: Conduct thorough testing and benchmarking to ensure that the enhanced MonetDB system efficiently handles embeddings. Compare the performance against non-optimized methods to quantify the improvements.
  • Documentation and Reporting: Maintain detailed documentation of the changes made, including code comments, user manuals, and performance analysis reports. This documentation is crucial for the broader MonetDB community to understand and adopt your work.
  • Community Engagement: Share your findings and improvements with the MonetDB community through mailing lists, forums, or conferences. Collaborate with other developers and users to gather feedback and refine your implementation.

Expected Outcomes

A modified version of MonetDB with added vector support tailored for AI use cases, particularly embeddings. Performance benchmarks demonstrating the advantages of vectorized operations for embedding storage and retrieval. Documentation for users and developers on how to utilize the new vectorization features for AI workloads. Potential contributions and discussions within the MonetDB community.

Skills and Requirements:

  • Proficiency in C/C++ programming.
  • Understanding of database systems and SQL.
  • Knowledge of embedding storage and retrieval techniques.
  • Familiarity with MonetDB's architecture (prior experience is a plus.
  • Strong analytical and problem-solving skills.
  • Good documentation and communication skills.


The project is expected to be completed within the duration of a typical master's program, which is typically 1-2 academic semesters.


This project will advance MonetDB as a database system optimized for AI and machine learning workloads, making it a valuable asset for researchers and organizations working in these fields. It will enhance your knowledge and expertise in database systems, AI, and system-level programming, providing a strong foundation for further academic and professional pursuits.


This position is mainly targeted towards students of Dutch educational institutions. Unfortunately, we cannot support international students. Before starting the project, consult with your academic advisor or thesis committee to align the project with your program's requirements and expectations. Collaborate with MonetDB developers to receive guidance and support throughout the process, especially considering the specialized nature of this project. The position requires physical presence for at least three days per week for the duration of the project.

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