Pydantic published a technical article on 2026-01-26 claiming that "shredding" JSON columns delivered up to 1000x faster queries in Logfire, its observability product. The post, titled "Shredding JSON columns gave Logfire up to 1000x faster queries," is live at pydantic.dev/articles/dynamic-shredding-2026-01-26. On Hacker News the item landed with 3 points and 1 comment — almost no community traction.
What dynamic shredding actually does
The 1000x figure comes from decomposing nested JSON into columnar storage. Instead of storing a blob of semi-structured data and digging through it row by row, the system breaks fields out into columns — the same principle behind analytical databases like ClickHouse and DuckDB. That architectural choice is what unlocks the speedup.
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For Logfire, the practical effect is that queries against messy, deeply nested JSON payloads stop being a bottleneck. That matters for any product ingesting logs or telemetry, where JSON is the default format and where developers have historically paid for that flexibility with slow scans.
The crypto angle isn't obvious, but it's there
Crypto data providers lean heavily on JSON. Indexers, analytics dashboards, and on-chain monitoring tools all parse nested blobs of transaction data, event logs, and contract state. Dune, Nansen, and TheGraph are among the projects that rely on that kind of semi-structured data at scale.
If dynamic shredding delivers what Pydantic claims, adopting the technique could meaningfully reduce the compute and storage burden on those teams. Cheaper queries mean cheaper analytics. Cheaper analytics mean more players can enter the space — and that's where things get interesting for incumbents whose pricing power depends on data access staying expensive.
None of this is a direct crypto story. Pydantic didn't mention blockchain, and there's no indication Logfire is being positioned for on-chain use. But the direction of travel is worth noting: columnar storage keeps proving itself against row-based systems for this class of workload, and crypto data infrastructure has been drifting the same way.
Three points on Hacker News
The Hacker News reception is the other half of the story. Three points and a single comment is close to silence. That's not necessarily a verdict on the work — timing, headline phrasing, and the general noise on HN all play a role — but it does suggest the broader developer community hasn't latched onto dynamic shredding as a must-have technique.
For crypto specifically, that gap is a pattern. The industry talks constantly about being on the cutting edge, yet adoption of generic developer tooling often lags. Teams build proprietary solutions when an off-the-shelf improvement exists. Sometimes that's justified by the unique constraints of blockchain data. Sometimes it's just not-invented-here.
What to watch
The article is dated 2026-01-26 and is available now. There's no follow-up announcement, no benchmark repository, and no indication of when or whether dynamic shredding might show up in downstream tooling. The next concrete signal would be someone outside Pydantic reproducing the 1000x figure — or a crypto data provider saying publicly that they're testing the approach. Until then, this is a technical write-up with a striking number and a quiet reception.
