The incident showed that Redis hashes encoded as ziplist incur O(N) HEXISTS latency, causing high CPU on hot keys, while hashtable‑encoded hashes remain O(1); adjusting hash‑max‑ziplist settings or redesigning hot‑key patterns can convert to faster, though more memory‑hungry, hashtable encoding.
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 explains how Redis stores set and sorted‑set objects using dual encodings—intset vs hashtable for sets and skiplist vs ziplist for sorted sets—detailing their internal structures, upgrade mechanisms, configuration thresholds, and the essential commands for manipulating these data types.
This article examines Redis List interview topics, covering FIFO misconceptions, underlying encodings across versions, ZIPLIST compression mechanics, backward traversal, and the time complexities of node count queries for both ZIPLIST and LINKEDLIST implementations.
This article introduces Redis List as a linked collection of strings, explains its size limits, common CRUD commands, read operations, and dives into the underlying encodings—ZIPLIST, LINKEDLIST, and the hybrid QUICKLIST—highlighting when each is used.
This article explains how Redis stores set and sorted‑set objects using two underlying data structures, details the intset and hashtable encodings, describes the upgrade process with concrete examples, and lists the common commands for manipulating these collections.
This article explains why Redis stores set and sorted‑set objects using two different internal encodings, describes the intset and hashtable representations for sets, the skiplist and ziplist representations for sorted sets, shows the upgrade process with code examples, and lists the common commands for manipulating these data structures.
This article explains how Redis internally stores set and sorted‑set objects using intset, hashtable, ziplist, and skiplist encodings, the conditions that trigger each representation, the upgrade process, and the essential Redis commands for manipulating these data structures.
The article explains Redis’s internal low‑level structures—ziplist, skiplist, intset, quicklist, zipmap, and stream—detailing their memory‑efficient layouts, operations, use cases, and how they combine (e.g., quicklist merging linked lists with ziplists) to deliver high performance in the in‑memory database.
This article explains how Redis stores Set and Sorted Set objects using dual internal representations—intset and hashtable for sets, skiplist and ziplist for sorted sets—detailing their structures, encoding selection criteria, upgrade processes, command usage, and the reasons behind combining dictionaries with skiplists for optimal performance.
This article explains Redis's ziplist—a compact list implementation used for small integers and short strings—by detailing its memory layout, field meanings, entry encoding schemes, the role of previous_entry, and the chain‑update behavior that occurs when nodes are added or removed.
This article explains Redis's ziplist—a compact list implementation used for small integers and short strings—detailing its memory layout, header fields, entry encoding, previous_entry handling, and the cascade update mechanism that occurs when node sizes change.
This article explains Redis's ziplist compressed list structure, its internal fields, lookup algorithm, performance characteristics, configuration thresholds for Hash and List types, and demonstrates a real‑world use case with memory‑saving calculations and experimental results.
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.
This article explains how to dramatically reduce Redis memory consumption—by up to 90%—through converting long string keys to integers, leveraging zipList‑encoded hashes, and distributing millions of key‑value pairs across many hash buckets while maintaining query performance.
This article explains the internal structure and encoding of Redis's ziplist (compressed list), describes its memory layout and node format, and analyzes how Redis leverages this data structure across different types such as lists, hashes, and sorted sets to reduce memory usage and fragmentation.
This article explains Redis’s ziplist (compressed list) memory layout, detailing its overall encoding, node structure—including previous length, encoding, and content fields—illustrates how various Redis data types employ ziplist encoding, and analyzes the benefits and trade‑offs of using this structure.
Redis’s hash‑max‑ziplist‑entries and hash‑max‑ziplist‑value settings control when hash tables switch to compressed ziplist storage, and by segmenting data into 1,000‑item hash tables, Instagram reduced memory usage from ~20 GB to about 5 GB, dramatically improving efficiency.
