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This article details JD's large‑scale HDFS unified storage implementation, covering cross‑region storage challenges, topology design, asynchronous block replication, flow‑control mechanisms, tiered storage strategies, automatic hot‑cold data migration, and the resulting performance and cost improvements for big‑data workloads.
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Xiaohongshu's big-data storage team built cloud-native architecture with tiered storage, containerized Kafka, and custom load balancer, cutting storage costs up to 60%, enabling minute‑level elastic migration, improving scaling efficiency tenfold, and boosting performance via caching and batch reads.
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The article explains how RocketMQ 5.1.0 introduces a tiered storage module that offloads messages to cheaper media, describes its design, architecture layers, quick‑start configuration, upload and read mechanisms, prefetch cache, fault recovery, current development plans, and remaining challenges.
This article presents JD's large‑scale big‑data platform, detailing its overall architecture, the challenges of cross‑region storage, the design of a unified cross‑domain data synchronization mechanism, and the implementation of tiered storage to improve performance, cost efficiency, and data reliability across multi‑datacenter clusters.
The article explains how traditional SAN storage creates CPU‑I/O bottlenecks, how Google’s distributed file system inspired hyper‑converged designs that fuse compute and storage, and why tiered storage with SSD and HDD offers scalable, high‑performance infrastructure for modern data‑center workloads.