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Hot-Cold Tiering

5 articles · Page 1 of 1
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Aug 18, 2026 · Artificial Intelligence

Why DeepSeek’s Cache Costs Jumped 11‑Fold: Long‑Context Surge and the New “Storage Tax”

DeepSeek raised its cache‑hit price up to 11 times as exploding long‑context demand forces a shift to tiered KV storage, exposing hidden storage, I/O and scheduling costs that turn GPU compute into costly data‑movement, prompting developers to rethink cache strategies.

Cache CompressionDeepSeekHot-Cold Tiering
0 likes · 10 min read
Why DeepSeek’s Cache Costs Jumped 11‑Fold: Long‑Context Surge and the New “Storage Tax”
Lakehouse Research Base
Lakehouse Research Base
Nov 15, 2025 · Big Data

StarRocks Lakehouse: Internal Tables vs. Paimon/Iceberg – 5-10x Performance Gains

The article benchmarks StarRocks internal tables against Paimon and Iceberg lake tables using TPCH 100G, shows internal tables excel at real-time analytics while lake tables optimize cold storage cost and multi-engine sharing, and details optimization strategies (metadata, data cache, predicate pushdown) achieving 5-10x speedup, illustrated by a retail case study cutting query latency from seconds to milliseconds and storage cost by 65%.

Data CacheHot-Cold TieringLakehouse
0 likes · 9 min read
StarRocks Lakehouse: Internal Tables vs. Paimon/Iceberg – 5-10x Performance Gains
ITPUB
ITPUB
Jun 10, 2023 · Databases

How Apache Doris 2.0 Cuts Storage Costs with Hot‑Cold Data Tiering

The article explains how Apache Doris 2.0 introduces hot‑cold data tiering to move infrequently accessed data from expensive SSDs to cheaper object storage, dramatically reducing storage costs while maintaining query performance through automatic lifecycle management, storage policies, and cache mechanisms.

Apache DorisHot-Cold TieringObject Storage
0 likes · 19 min read
How Apache Doris 2.0 Cuts Storage Costs with Hot‑Cold Data Tiering
dbaplus Community
dbaplus Community
Aug 5, 2020 · Databases

How JIMKV Unifies Cache and Storage to Power High‑Performance Distributed Databases

The article details JD Retail's JIMKV distributed database, explaining its unified cache‑storage architecture, fault‑detection and elastic‑scaling mechanisms, hot‑cold data tiering, read/write amplification mitigation, real‑world product‑detail use case, and future plans for intelligent operations and OLAP support.

Elastic ScalingHot-Cold TieringKV store
0 likes · 18 min read
How JIMKV Unifies Cache and Storage to Power High‑Performance Distributed Databases