This article examines whether adding an index to a status field in a multi‑million‑row MySQL table improves query performance, covering the index lookup process, extreme cases, selectivity, result‑set size impact, composite indexes, partitioning, and how to verify plans with EXPLAIN.
This article explores how high cardinality in Prometheus metrics impacts AI large‑model and autonomous‑driving observability, explains the underlying concepts, outlines the performance and cost challenges, and presents practical design, collection, and query‑side solutions—including metric modeling, pre‑aggregation, and remote‑read pushdown—to keep monitoring efficient and scalable.
The article examines performance bottlenecks in Prometheus‑Grafana monitoring dashboards caused by high metric cardinality, explains the internal query processing steps, demonstrates how to analyze and reduce cardinality with PromQL and recording rules, and shows concrete command‑line examples that dramatically improve query latency.
This article explains Redis HyperLogLog, its underlying principles, memory efficiency, typical use cases like UV/PV counting, and provides practical command examples (PFADD, PFCOUNT, PFMERGE) to perform high‑performance cardinality estimation on massive datasets.
This article explains how to select Redis data structures such as Bitmap, HyperLogLog, Set, List, Sorted Set, and Hash to efficiently handle massive statistical scenarios like user login status, UV counting, ranking, and set aggregation, while providing concrete command examples and best‑practice recommendations.
This article explains the concept of MySQL index cardinality, how it influences index selectivity, factors that affect cardinality values, methods to view them, and demonstrates with practical examples how different data distributions and query patterns impact the optimizer's execution plans.
This article explains the cardinality‑counting problem behind DAU/MAU and unique visitor metrics, compares naïve solutions like Set, Bitmap and Bloom filter, introduces big‑data algorithms such as Linear Counting, LogLog and HyperLogLog, and shows how Redis implements HyperLogLog with dense and sparse storage optimizations.
This article explains the concepts of cardinality and HINT in MySQL, demonstrates how inaccurate cardinality can lead to sub‑optimal execution plans, and shows how applying index_merge hints dramatically reduces query cost and row scans for multi‑column predicates.
