This article explains how Redis implements dynamic string expansion using the Simple Dynamic String (SDS) structure, detailing the sdsMakeRoomFor function, its allocation strategy, type handling, and the architectural changes introduced in Redis 7.0.
Redis implements its own Simple Dynamic String (SDS) instead of plain C strings because SDS stores length for O(1) queries, pre‑allocates extra space and lazily reuses freed bytes, prevents buffer overflows, handles binary data, and uniformly backs all string keys, values and container elements.
Software-defined storage (SDS) combines distributed and virtualization technologies to decouple storage software from hardware, offering flexible scaling, higher utilization, and a market dominated by a few Chinese vendors, while outlining its architecture, benefits, and competitive landscape.
This article explains why Redis String types can consume excessive memory, examines the underlying Simple Dynamic String (SDS) structure and its three encoding modes, and demonstrates how using compressed lists (ziplist) and hash‑based storage can significantly reduce memory usage in backend applications.
This article reviews essential Redis string concepts, covering typical interview questions, encoding types, the EMBSTR threshold, SDS structure, and memory allocation details to help readers solidify their understanding of Redis string implementation.
This article explains how Redis achieves high performance by storing data in memory and using a variety of specialized data structures—such as SDS, quicklist, listpack, hash tables, and skip lists—to implement its key‑value store, covering their designs, trade‑offs, and evolution across Redis versions.
This article explains Redis String data type usage, the Simple Dynamic String (SDS) implementation details, memory‑saving techniques, encoding options, and demonstrates a practical distributed ID generator built with Redis commands and persistence strategies.
The article examines the development of hyper‑converged infrastructure (HCI), its relationship with software‑defined storage, the shift toward hybrid cloud solutions, hardware choices, and the suitability of distributed storage for critical business workloads, providing a comprehensive overview of modern cloud‑centric storage architectures.
This article explains the evolution of software‑defined and hyper‑converged storage, distinguishes it from converged infrastructure, discusses SDS appliances, and outlines how these technologies enable hybrid‑cloud deployments, data management, and reliable enterprise workloads.
This article explores Redis’s internal data structures—including simple dynamic strings, linked lists, and dictionaries—detailing their encoding constants, implementation differences across versions, performance advantages, and rehashing mechanisms that enable high‑throughput, memory‑efficient operations.
The article details Redis’s core low‑level data structures—Simple Dynamic Strings (SDS) with length‑aware headers, a custom doubly linked list for O(1) head/tail access, and a hash‑table dictionary employing MurmurHash, incremental rehashing, and packed headers to optimize memory and performance across versions.
This article explains why Redis is ultra‑fast by exploring its in‑memory design and the nine fundamental data structures—SDS, linked lists, ziplist, hash tables, skiplist, intset, quicklist, and listpack—that power keys, values, and internal objects, complete with code examples and version‑specific changes.
This article explains how Redis achieves high performance by using specialized in‑memory data structures such as SDS, quicklist, listpack, hash tables, skip‑lists and integer sets, detailing their design, evolution across versions, memory‑efficiency tricks, and the trade‑offs that make Redis a lightning‑fast key‑value store.
This article explains Redis's Simple Dynamic String (SDS) data structure, its internal layout, how it differs from traditional C strings, and the performance, memory‑reallocation, and data‑format benefits it brings to Redis's key‑value storage.
This article explains the Redis Simple Dynamic String (SDS) data structure, comparing it with traditional C strings, detailing its struct layout, O(1) length retrieval, pre‑allocation strategy, lazy space release, and provides code examples illustrating how SDS avoids buffer overflows and improves performance.
This article explains how computers store text using binary, introduces ASCII, Unicode and UTF‑8 encoding rules, discusses the limitations of C‑style null‑terminated strings, and describes Redis's Simple Dynamic String (SDS) data structure, its old and new versions, advantages, and related APIs.
This article explains the design and implementation details of Redis's Simple Dynamic String (SDS) and the underlying structures of its core collections—List, Set, Sorted Set (Zset), and Hash—covering their memory layouts, advantages, and the conditions that trigger different internal representations.
This article explains why Redis uses a custom SDS string structure instead of native C strings, describes the SDS layout, expansion rules, redisObject metadata, and the three string encodings (int, embstr, raw) that optimize performance and memory usage.
This article explains the design and evolution of Redis's Simple Dynamic Strings (SDS), covering the original pre‑3.2 structure, the newer type‑specific headers, the core API functions for creating, clearing, and concatenating SDS objects, and the memory‑reallocation strategy that ensures binary safety and efficient storage.
This article explains Redis's custom Simple Dynamic String (SDS) implementation, its dynamic expansion mechanism, and the performance benefits such as O(1) length retrieval, buffer overflow avoidance, and efficient memory allocation strategies for high‑throughput applications.
This article explains Redis's memory model by describing how to query memory usage, the roles of used_memory, used_memory_rss, mem_fragmentation_ratio, and mem_allocator, and then dives into the internal structures such as redisObject, SDS, jemalloc, and the encoding strategies for strings, lists, hashes, sets, and sorted sets.
This article explains the internal implementations of Redis’s five major data structures—simple dynamic strings (SDS), linked lists, dictionaries, skiplists, integer sets, and ziplists—detailing their definitions, memory layouts, key characteristics, and how Redis leverages them for high‑performance storage and retrieval.
The article explains Redis's custom Simple Dynamic String (SDS) implementation, its memory layout, dynamic resizing rules, and performance benefits such as O(1) length retrieval, buffer‑overflow protection, and reduced allocation overhead through pre‑allocation and lazy reclamation.
This article explains how Redis implements its own string type using Simple Dynamic Strings (SDS), details the internal structure and dynamic expansion mechanism, and highlights the performance benefits such as O(1) length retrieval, buffer‑overflow protection, and reduced memory allocation overhead.
This article explains Redis string basics, its mutable SDS implementation, common commands, internal memory layout, and the three encoding formats (int, embstr, raw) that determine how strings are stored and optimized in the database.
This article explains Redis's memory model—including memory statistics, allocation strategies, object types, internal encodings, and practical optimization techniques—so developers can accurately estimate memory usage, reduce fragmentation, and improve overall system efficiency.
This article explains Redis's memory model—including memory statistics, allocation, object structures, internal encoding, and practical optimization techniques—so developers can accurately estimate memory usage, reduce fragmentation, and choose the most efficient data representations for high‑performance applications.
The article explains how data‑control separation architecture is used across traditional and distributed file systems, high‑performance SAN designs, software‑defined storage solutions, and cloud platforms like OpenStack, highlighting its impact on performance, scalability, and resource contention.
The article analyzes how cloud-native, distributed, and software‑defined storage solutions are reshaping the enterprise storage market, compares them with traditional high‑reliability systems, and offers guidance on selecting, integrating, and migrating storage technologies based on business scenarios, cost, and performance considerations.
This article introduces the concept, origins, technical background, classifications, and future directions of software-defined storage (SDS), while recommending the book “Software-Defined Storage: Principles, Practices, and Ecosystem” as a comprehensive guide for practitioners and enthusiasts.
This article compares the open‑source Ceph storage system and Huawei's OceanStor 9000 commercial SDS, detailing their service components, software stacks, OpenStack compatibility, and differing strengths in scalability, client architecture, and management for cloud and enterprise storage scenarios.
NetApp's $870 million cash purchase of flash startup SolidFire signals a shift in its flash storage roadmap, integrating SolidFire's scale‑out architecture into the Data Fabric and emphasizing ONTAP software value over traditional storage hardware.
This article explains the concept, classifications, and evolution of Software‑Defined Storage (SDS), covering its origins, control‑plane and data‑plane distinctions, various vendor implementations, and its impact on modern data‑center automation and cloud infrastructures.
