0 views collected around this technical thread.
A full‑table scan of a 200 GB InnoDB table on a MySQL server with 100 GB RAM does not exhaust server memory because MySQL streams rows to the client, uses a fixed net_buffer, and InnoDB’s optimized LRU algorithm limits buffer‑pool pressure, ensuring stable performance.
This article explains how Redis uses memory as a cache, details the maxmemory setting and its configuration methods, describes various eviction policies—including LRU, LFU, and random strategies—and outlines expiration handling, replication considerations, and best‑practice recommendations for stable high‑load deployments.
Even when scanning tables with tens of millions of rows, MySQL avoids out‑of‑memory crashes by streaming data in small 16 KB net buffers, using socket buffers, and employing an improved LRU algorithm that isolates cold data in the buffer pool’s old generation.
The article explains how to alleviate MySQL bottlenecks in high‑traffic product services by introducing Redis as a local and remote cache, covering data structures, expiration policies, eviction strategies, persistence mechanisms, and a lightweight TCP protocol to achieve scalable, reliable performance.
This article reviews the long‑standing performance problems of TRUNCATE and DROP TABLE in MySQL, tracing their origins through official manuals and historical bugs, summarizing the optimizations introduced in MySQL 5.5.23, 5.7, 8.0, and 8.4, and offering practical guidance for mitigating remaining latency.
This article explains how Python dictionaries can serve as an efficient caching mechanism, covering basic dictionary concepts, common operations, simple cache examples, advanced techniques like LRU cache implementation, and practical use cases such as caching API responses, with complete code snippets.
This article explains MySQL InnoDB’s linear and random prefetch mechanisms, the role of the innodb_read_ahead_threshold and innodb_random_read_ahead variables, and how the LRU buffer‑pool list manages hot and cold pages to improve overall query efficiency.
This article explains Linux's page reclamation process, describing how the kernel recovers memory under pressure using PFRA, LRU algorithms, direct reclaim, compaction, and OOM handling, and provides detailed source code analysis of the involved kernel functions.
This article provides a comprehensive overview of Redis memory management, detailing how maxmemory limits trigger various eviction policies, explaining the internal freeMemoryIfNeeded algorithm, and describing expiration mechanisms—including active and lazy deletion—and offering guidance on selecting the appropriate eviction strategy for different workloads.
This article explains how Redis uses the maxmemory setting to trigger memory eviction, details the various eviction policies (including LRU and LFU), shows how to query and modify these settings with config commands, and briefly mentions promotional offers for a ChatGPT community.
This article explains the principles, implementation details, and trade‑offs of Redis’s LRU and LFU cache eviction algorithms, including their data structures, code snippets, configuration parameters, and practical guidance on choosing the appropriate strategy based on workload characteristics.
Redis implements approximate LRU and LFU eviction policies by sampling keys and using a compact 24‑bit field to store timestamps and counters, where LRU evicts the least recently accessed items and LFU evicts those with low, decay‑adjusted access frequency, each with trade‑offs for different workloads.
This article explains the MySQL Buffer Pool architecture, describing data pages, cache pages, the three page states, the various linked lists (Free, Flush, LRU), pre‑read mechanisms, hot‑cold separation, concurrency handling, and dynamic resizing using the chunk mechanism.
By eliminating unnecessary reverse‑geocode calls, aggregating nearby coordinates with GeoHash, and employing a multi‑layer LRU‑K cache with time‑ and access‑count eviction, the Hellobike map team cut daily requests from 200‑300 million to 20‑30 million while adding fallback and monitoring mechanisms.
This article explains the principles and practical implementation of in‑memory caching using Guava’s LoadingCache, covering cache initialization parameters, put and loading strategies, eviction policies, common algorithms such as LRU, LFU, FIFO, and tips for avoiding memory issues and monitoring cache performance.
This article explains the fundamentals of in‑memory caching, compares it with buffering, introduces Guava's LoadingCache configuration and operations, discusses eviction strategies, illustrates common cache algorithms (FIFO, LRU, LFU), provides a simple LRU implementation using LinkedHashMap, and offers practical guidelines for when and how to apply caching to improve backend performance.
This article explains how to size Redis memory, configure maxmemory and maxmemory‑policy settings, and choose among various eviction strategies—including no‑eviction, allkeys‑lru, allkeys‑lfu, and volatile options—while detailing the underlying LRU and LFU algorithms used by Redis.
This article explains the fundamentals of in‑memory caching, introduces Guava's LoadingCache API, discusses cache sizing, eviction policies, common algorithms like FIFO, LRU, LFU, shows a simple LRU implementation using LinkedHashMap, and provides practical guidance on when and how to apply caching for performance optimization.
Redis caches can fill up, requiring memory eviction; this guide explains how to set Redis maxmemory (e.g., 5GB), choose appropriate eviction policies such as allkeys-lru or allkeys-lfu, and details the underlying LRU and LFU algorithms and their configuration commands.
This article explains how to set Redis memory limits, choose appropriate eviction policies such as noeviction, allkeys‑lru, allkeys‑lfu, volatile‑ttl, and describes the underlying LRU and LFU algorithms, including their implementation details and practical configuration commands.