Figma · 2026
How Figma manages sandboxes at scale
Figma's AI products needed secure places to run untrusted code, so the team built Nimbus, a centralized sandbox platform that product teams call through a simple namespace config and GetOrCreate/Get/Delete APIs. Nimbus picks a cloud provider, applies networking and security policies (trusted-host allowlists plus read-only GET/HEAD/OPTIONS to the rest of the internet, credentials decrypted only on the way out to their intended destination), and runs sandboxd inside each box for process supervision and metrics. Warm pools of already-running sandboxes cut provisioning latency by about 90% versus cold starts, raised availability from 99% to 99.95%, and absorb minutes of provider outages while Postgres claim transactions and a Redis health-check queue keep the pool correctly sized and singly assigned.
tens of thousands of sandboxes across productswarm pools cut provisioning latency ~90%warm poolsmulti-provider failovernetwork policy isolationNimbusPostgresRedis
Cloudflare · 2026
Origin Range Requests: Serving large files without range-request tuning
Cloudflare's old range-request caching sliced files into a per-zone configured range size, so a small video seek could pull 128 MiB from origin for R2, while small ranges caused floods of tiny origin requests and memory pressure, and concurrent requests in the same range blocked each other. Moving from nginx to its Pingora-based cache, it reused Pingora's fixed-size on-disk chunks as the unit: a coordinator serves cached bytes, attaches to in-flight downloads, fetches only missing chunk-aligned ranges from origin, and validates size and ETag before composing one 206 response. For R2, p99 time to first byte dropped by about half, and letting one response combine disk and upstream bytes cut tiered cache traffic about 6% week over week.
R2 p99 time to first byte roughly halved (hits and misses)~6% less tiered cache traffic for R2-like workloads, petabytes/daycachingrequest coalescingtiered storagePingoraR2Tiered Cache
Cloudflare · 2026
Inside Hyperdrive: How we solved a rare Hyperdrive MySQL caching bug
Hyperdrive, Cloudflare's distributed connection pooler for Workers, keeps warm database connections on its network and can cache common query results in the CDN cache. Taking its MySQL support from beta to GA meant finding bugs like utf8 being a 3-byte alias (fixed with utf8mb4) and COM_RESET_CONNECTION silently resetting the charset to the server default. The hardest was an empty-result bug seen by the highest-volume MySQL customer in about 0.001% of queries, only with caching on. Each Hyperdrive instance has a Client node next to the Worker and an Endpoint node near the database, and they share a cache when they run in the same data center. Because drivers like Node's mysql2 don't support CLIENT_DEPRECATE_EOF, the Client rewrites results into the Worker's capabilities, so the two nodes were caching different bytes for the same query and could read each other's entries. Caching one canonical encoding no matter which node stores it fixed the bug, and Client connection success reached 99.99%.
99.99% production availability targetbug hit ~0.001% of queriesconnection poolingcachingprotocol translationMySQLWorkersHyperdrive
GitHub · 2026
Building Git infrastructure for agent-scale development
GitHub explains why agent workloads strain Spokes, which keeps five durable copies of each repo on fileserver disks, so every added read replica slows writes. The new design coordinates only the ref update, moves compaction and GC to separate workers, and separates storage from compute: Azure Blob Storage holds the authoritative data while lightweight caching workers serve reads.
473.3 billion Git events/month7.38 billion commits in Sept 2026control plane separationread replicascachingGitSpokesAzure Blob Storage