This RFC proposes the integration of a vector database solution to
optimize search capabilities in our application. The new system aims to
improve performance for high-dimensional data queries, particularly for
machine learning models and recommendation systems.
Motivation
The current database architecture struggles with efficiently handling
high-dimensional vector data. As a result, search performance is poor,
especially for tasks such as similarity searches, where traditional
relational databases are inefficient. By introducing a vector database,
we expect significant performance improvements in these areas.
Proposal
We recommend adopting a vector database such as
Pinecone or Milvus to handle
high-dimensional vector searches. These databases are designed for
similarity search and are optimized for performance, scalability, and
real-time querying.
Key Features
High performance for similarity and nearest
neighbor searches.
Scalability to handle large datasets.
Integration with existing machine learning
pipelines and frameworks.
Alternatives Considered
Traditional Relational
Databases
Pros: Already integrated into our stack; familiar
to the team.
Cons: Poor performance for vector data queries;
lacks optimized algorithms for similarity searches.
Reason to Discard: Inefficient for the
high-dimensional search problems we need to solve.
Custom-Built Solution
Pros: Full control over implementation and
optimization.
Cons: Time-consuming; requires significant
development effort.
Reason to Discard: Higher development and
maintenance cost compared to adopting a specialized database
solution.
Impact
Performance: Expected to drastically improve search
query response times.
Development: Requires minimal integration effort as
vector databases offer APIs that are compatible with our current
architecture.
Maintenance: Ongoing maintenance costs will be
similar to existing databases, though specialized knowledge of vector
databases may be required.
Unresolved Questions
Should we run the vector database on-premises or opt for a
cloud-managed service?
What would be the best way to handle database backups and
replication for high availability?