0 views collected around this technical thread.
The article explores the lifelong challenge of matching versus transcending standards, using vector distance, cosine similarity, and transformation matrices as metaphors, and illustrates the concept through personal examples and the film Nezha, urging readers to question and reshape the norms that guide them.
This article details the redesign of a company’s logging infrastructure—from an ELK‑based solution to a ClickHouse‑powered architecture—highlighting the motivations, key requirements, component choices, configuration examples, performance optimizations, and the resulting cost and storage benefits.
This article compares Elasticsearch and ClickHouse as log‑search solutions, detailing their architectures, Docker‑compose deployments, data‑ingestion pipelines with Vector, query syntax differences, and benchmark results that show ClickHouse generally outperforms Elasticsearch in speed and aggregation efficiency.
This article compares Elasticsearch and ClickHouse by describing their architectures, presenting Docker‑based test stacks, showing code snippets for deployment, data ingestion, and queries, and reporting performance results that demonstrate ClickHouse generally outperforms Elasticsearch in log‑analytics scenarios.
This article explains the concepts and importance of observability, introduces the Vector data‑pipeline tool and its architecture, demonstrates how to configure sources, transforms and sinks, and shows how to integrate Vector with the Honghu platform to build a complete, real‑time monitoring solution for modern distributed systems.
This article compares Elasticsearch and ClickHouse as log‑analytics solutions, detailing their architectures, node roles, data ingestion pipelines, query capabilities, and benchmark results, ultimately showing ClickHouse’s superior performance in most tested scenarios.
This article presents a practical comparison between Elasticsearch and ClickHouse, detailing their architectures, Docker‑Compose deployment, data ingestion pipelines, a series of representative queries, and benchmark results that show ClickHouse generally outperforms Elasticsearch in basic search and aggregation scenarios.
Rotating a 2‑D vector is performed by multiplying it with the matrix [[cos θ, ‑sin θ],[sin θ, cos θ]], yielding the new coordinates (x cos θ ‑ y sin θ, x sin θ + y cos θ); a short JavaScript function implements this, turning [3,2] into [‑2,3] for a 90° counter‑clockwise rotation and also rotates the basis vectors.
This article introduces matrices and vectors, explains special matrix types, covers matrix addition, scalar multiplication, and their properties, details matrix multiplication and its rules, and describes the transpose operation, providing clear definitions and illustrative examples for each concept.
This article compares Elasticsearch and ClickHouse in architecture, query capabilities, and performance for log search workloads, presenting Docker‑compose setups, data ingestion pipelines, sample queries, and benchmark results that show ClickHouse generally outperforms Elasticsearch, especially in aggregation scenarios.
This article provides a detailed comparison of Java's List interface implementations—ArrayList, LinkedList, Vector, and Stack—explaining their underlying data structures, key methods such as get, set, add, and remove, performance characteristics, thread safety, and appropriate usage scenarios.
This article introduces vector norms—including L0, L1, L2, L∞, and general Lp—explaining their mathematical definitions, geometric interpretations, and roles in machine learning such as compressive sensing and distance‑cosine equivalence after L2 normalization, supplemented with illustrative formulas and reference links.
The article explains the mathematical foundations and front‑end implementation of five common mobile gestures—drag, pinch, rotate, single‑finger pinch, and single‑finger rotate—using native touch events, vector calculations, and matrix transformations, and provides a lightweight library that emits incremental gesture events for easy integration.
This article explains the principles behind Java's mutable array implementations, details the characteristics and use‑cases of List, ArrayList, LinkedList, and Vector, and provides code examples for common operations such as adding, removing, setting, retrieving elements, and using iterators.