EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Jul 27·5 min read
A Fast Path for Fixed-Length Lists in Parquet
TL;DR
Apache Parquet is enhancing its support for fixed-length lists, like vector embeddings, to improve storage efficiency and decoding performance. A proposed FIXED_SIZE_LIST type aims to reduce costs associated with current variable-length encoding methods.
✦ Why It Matters
Consider advocating for the adoption of FIXED_SIZE_LIST in your data storage solutions to optimize performance.
Key Takeaways
- Activate hardwood.fixed-list-fast-path for faster list retrieval.
- Expect up to 3.9× speed improvements for row readers.
- Utilize this optimization for fixed-length data like embeddings.
- Monitor performance on larger files for best results.
How It Works
The optimization detects fixed-length lists by examining encoded definition and repetition levels. If all lists in a data page are of the same length, the parser bypasses regular reconstruction, allowing for faster data access.
This is achieved by checking for consistent patterns in the encoded streams, which indicates that the lists do not contain null values or varying lengths.