This article details Apache Fluss's dual-table model where LogTable handles high-throughput append-only streaming with columnar storage while PrimaryKeyTable supports real-time updates, partial merges, and CDC changelogs via integrated RocksDB state storage, sharing unified tiering and lakehouse sinking.
This article details Gaode Maps' end‑to‑end lakehouse solution for massive, high‑frequency trajectory data, covering the challenges of real‑time visibility, query performance, and storage cost, and explaining how a hot‑warm‑cold tiering architecture built on Apache Flink, Paimon, StarRocks, Redis and Lindorm delivers millisecond‑level queries while cutting storage expenses.
This article details Gaode Maps' end‑to‑end lakehouse solution for handling high‑frequency, high‑volume trajectory data, covering the challenges of real‑time visibility, multi‑scenario queries, storage cost, and data silos, and describing the layered storage architecture, performance validation, and future expansion plans.
This article explains the principles, configuration steps, monitoring metrics, leader selection, data migration granularity, compaction, invalid data cleanup, and cache mechanisms of cold‑hot data separation in Baidu Intelligent Cloud's Palo for Apache Doris, illustrating how tiered storage reduces costs while maintaining query performance.
Shopee ClickHouse implements a cold‑hot storage architecture by mounting JuiceFS‑backed S3 as a remote volume, using table‑level storage policies and TTL‑driven background moves to shift data from SSD to cold storage, achieving cost‑effective scalability, improved disk utilization, and validated performance despite operational challenges such as Redis memory pressure and small‑file handling.
Elasticsearch 7.10 adds searchable snapshots, letting users query indices stored directly in remote repositories such as S3 or COS, which halves storage costs, decouples storage from compute, supports manual mounting and ILM cold‑phase policies, and promises future full storage‑compute separation without local caching.
This article explains how to design a high‑throughput payment system using database sharding, Snowflake‑style globally unique order IDs, eventual consistency via message queues, high‑availability architectures, data tiering, and coarse‑fine traffic control to handle massive request spikes.
This article explains how a high‑throughput payment platform uses database sharding by user ID, Snowflake‑style globally unique order IDs, asynchronous replication for eventual consistency, multi‑level data caching, and coarse‑fine traffic pipelines to achieve millions of requests per second with robust high‑availability.
This article explains the challenges of growing data volumes, small files, and hot‑cold data in Hadoop HDFS, then details heterogeneous storage options, erasure‑coding techniques, and the open‑source SSM (Smart Storage Management) system that automates tiered storage based on data access patterns.
