February 24, 2026
One file, zero chill
A distributed queue in a single JSON file on object storage
One file, fewer headaches—fans say genius, skeptics smell hidden costs
TLDR: A team swapped a complex queue for one JSON file in cloud storage and claims far faster, simpler results. Commenters are split between praising bold simplicity and warning about hidden costs, bottlenecks, and vendor bias—making this a flashy “genius or chaos?” moment worth watching.
Engineers tossed their clunky, slow job queue and replaced it with… a single JSON file on cloud object storage. The result? A claimed 10x drop in nasty slowdowns, plus a simple “stateless broker” that keeps tasks in order. The internet promptly split into teams Genius vs Yikes.
Cheerleaders loved the “so simple it just works” vibe. One fan asked, “What actually needs to be in the database?” while others applauded using compare-and-set (think: only write if no one else changed it) and batching writes to dodge slow cloud saves. Memes flew: “One file to rule them all,” “JSON Jenga,” and “queue.txt when?”
But skeptics pounced. “Free lunch? Where’s the catch?” asked one, eyeing trade-offs like costs and throughput. Another threw shade that this praise-heavy post comes from an object storage vendor—like a pizza place reviewing its own slices. Pragmatists shrugged: the old system choked when one node got slow; any central queue might’ve fixed it, not just this one-file magic. Veterans warned that a single file can bottleneck fast, even if the team cleverly batches to overcome the 200ms write lag.
Bottom line: bold simplicity versus hidden gotchas. The crowd is fascinated—and a little terrified.
Key Points
- •The team replaced a sharded indexing job queue with a single JSON file on object storage, coordinated by a stateless broker.
- •The new design provides FIFO execution, at-least-once delivery, and achieves about 10x lower tail latency than the prior system.
- •Push and claim operations use compare-and-set (CAS) to ensure atomic writes and strong consistency without complex locking.
- •The simplest queue design functions up to ~1 request per second, limited by GCS; higher throughput requires batching.
- •Group commit buffers requests during object storage writes (up to ~200ms) and flushes them as batched CAS writes, similar to WAL batching and database fsync coalescing.