Why AI Workloads Require Rebuilding Parquet: A Deep Dive into Lance

1. The core mismatch: scan‑oriented design vs AI random access

Traditional lakehouse stacks—Parquet as the file format, Iceberg or Delta Lake for table semantics, and engines such as Spark, Trino or Gluten/Velox—are built around high‑throughput sequential scans. This works for OLAP queries that aggregate over large partitions, but AI workloads such as RAG, embedding retrieval, or millisecond‑level feature services require extremely narrow random reads, high‑frequency vector distance calculations, and minimal I/O handshakes.

In a typical RAG query the data path is: Query Embedding → ANN → Top‑K Row IDs → Fetch Rows. The critical metric becomes latency to fetch a handful of rows, not GB‑per‑second throughput. Parquet’s layout (Footer → Row Group → Page) forces multiple parsing steps for each point lookup, inflating I/O.

Moreover, embeddings are stored as ordinary binary columns without dedicated index semantics, leading to a “stitch‑monster” architecture where Parquet/Iceberg hold raw data while an external vector DB (FAISS, Milvus) maintains a separate index. This separation creates consistency and maintenance overhead.

2. Lance’s design philosophy: rebuild from the ground up

Lance is not a faster Parquet; it aims to turn the data lake into an “AI‑native storage layer”. It treats file format, table format, and index as a unified three‑layer stack, breaking the long‑standing assumption that the file format should be oblivious to access patterns.

In the AI era the storage layer must be aware of which columns are vectors, which are indexed, and how to bind them physically.

3. Write‑path innovations that “solidify” the access path

Lance flips the traditional write process. Instead of passive encoding (chunk → compress → page → footer), it embeds three pieces of information at write time:

Physical location left‑shift : a precise mapping from logical Row ID to physical offset, enabling direct pointer‑like jumps during reads.

Object‑storage‑aware I/O reduction : by pre‑computing range information, the number of S3 range requests drops from 3‑4 to 1‑2, saving more time than reducing megabytes of data.

Native memory layout for vectors : embeddings are stored contiguously, allowing SIMD‑friendly consumption without decode or transpose steps.

This design trades higher write‑time computation for dramatically lower read latency.

4. Index becomes part of the dataset

Traditional systems keep index and data separate, leading to stale indexes or costly look‑ups. Lance embeds the index inside the dataset and synchronizes it with every atomic write under a single snapshot, eliminating version drift.

The query execution path therefore becomes:

Vector search using the built‑in index to obtain Row IDs.

Row ID → Offset lookup via the pre‑built mapping.

Direct read of the original rows.

This collapses the boundary between columnar storage and vector databases.

5. Tight coupling with native execution engines

Because Lance’s layout is Arrow‑compatible, data can be handed to native engines (e.g., Velox) without copying (zero‑copy). The format also enables deep compute push‑down: vector similarity and SQL predicates can be evaluated together inside the storage layer, and look‑ups become first‑class operations for join‑heavy workloads.

6. Broader trends in data systems

Three macro trends emerge: a shift from throughput‑centric to latency‑centric design, the elevation of embedding vectors to first‑class data types, and the blurring of storage‑retrieval boundaries. Continuing to patch Parquet + Iceberg will incur rising marginal costs as the physical layout cannot satisfy random‑access demands.

Lance’s bit‑level redesign, putting access patterns first, signals the next generation of data infrastructure where storage actively participates in computation and retrieval.