Git packfiles use delta compression, storing only the diff when a 10MB file changes by one line, while the objects table stores each version in full. A file modified 100 times takes about 1GB in Postgres versus maybe 50MB in a packfile. Postgres does TOAST and compress large values, but that’s compressing individual objects in isolation, not delta-compressing across versions the way packfiles do, so the storage overhead is real. A delta-compression layer that periodically repacks objects within Postgres, or offloads large blobs to S3 the way LFS does, is a natural next step. For most repositories it still won’t matter since the median repo is small and disk is cheap, and GitHub’s Spokes system made a similar trade-off years ago, storing three full uncompressed copies of every repository across data centres because redundancy and operational simplicity beat storage efficiency even at hundreds of exabytes.
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What makes this particularly significant is Google's market position. Despite the rise of alternative AI search tools, Google still processes billions of searches daily and serves as the primary discovery mechanism for most internet users. When Google integrates AI-generated answers into its core search experience, it's not experimenting with a niche feature—it's fundamentally changing how the world's most popular search engine works.，详情可参考heLLoword翻译官方下载