February 10, 2026
GIFs vs gigabytes
Lance table format explained simply, stupid (Animated)
Shiny animations wow, but one dev says LanceDB crawls in the real world
TLDR: Lance pitches itself as a faster, more flexible way to store and query data, with handy indexes for AI search. The comments erupt: one user admires the slick explainer animations, while another says LanceDB crawled in production and they reverted to Postgres + pgvector—classic demo‑day shine versus real‑world grind
The blog drops flashy, AI-made animations to explain Lance—a new way to store and organize big data that promises faster “find this one thing” lookups and easy add-on columns. It even boasts built-in indexes (think speed-dials for search), including vector search for AI. Cue the community split: the “ooh shiny” crowd versus the “try it in production” crowd.
On one side, pottereric wants the behind‑the‑scenes: were those animations hand‑coded or whipped up with web magic? On the other, fcanesin shows up with a bucket of cold water, saying their LanceDB tests were “abysmal” at real scale and they bailed to Postgres plus pgvector—with “absolutely no issues.” That turned the thread into a familiar tech soap opera: slick demos vs. real‑world grit.
Meanwhile, the article hints at a bigger wave: a year of big buys (Datadog snagging Quickwit, Databricks scooping Neon), Iceberg’s new spec, and a rival format from SpiralDB called vortex. The subtext? AI is pushing everyone to reinvent data plumbing, and Lance wants to be the new star. But the comments keep it honest: pretty animations don’t ship production. Expect more debates, more benchmarks, and more “we rolled back to Postgres” confessions before this one is settled.
Key Points
- •Lance is presented as a successor to Iceberg/Delta Lake, combining file, table, and catalog specs.
- •Lance’s file format targets faster random reads while maintaining strong sequential scan performance, compared to Parquet.
- •Lance’s table format allows adding ad-hoc columns without copying existing data and preserves MVCC.
- •Lance tables support indexes including B-tree, inverted (FTS), and vector indexes like HNSW.
- •The article notes 2025 ecosystem updates: Iceberg V3 adds VARIANT; turbopuffer vector search; Apache Fluss for Flink; Datadog–Quickwit and Databricks–Neon acquisitions.