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
Netflix · 2026
TimeSeries Tiered Storage Journey: Kafka/Flink Streams to Native Cassandra Cold Reads
Netflix moved older data in its multi-petabyte TimeSeries Abstraction out of hot Cassandra storage in two steps. First, Kafka and Flink streamed events to S3 as Parquet files, which a daily job compacted into small, time-sorted row groups with Bloom filters cached in EVCache (p50 ~150 ms, p99 ~500 ms). The second design skips that streaming pipeline: a ReadOnlyCompactionStrategy aligns immutable SSTables to token ranges, and a custom pluggable Memtable (Cassandra 4.1) reads directly from existing Cassandra backups in S3. It now holds over 15 PB of compressed cold data, cut disk usage on affected clusters from 80% to 7%, and improved p90 latency by ~30%.
15+ PB compressed data in cold storagedisk usage cut from 80% to 7%tiered storagecompactioncost optimizationCassandraKafkaFlink
Tiger Data (Timescale) · 2023
How We Scaled PostgreSQL to 350 TB+
Timescale dogfooded its own Postgres extension to build a database-observability product, using hypertables, columnar compression and tiered storage to keep a 350 TB+ dataset queryable.
350 TB+10 billion new records/daypartitioningcompressiontiered storagePostgreSQLTimescaleDB