The leading WeChat public account for Elasticsearch fundamentals, advanced topics, and hands‑on practice. Join us to dive deep into the ELK Stack (Elasticsearch, Logstash, Kibana, Beats).
This article explains the principles of vector search, compares Easysearch's approximate (LSH) and exact kNN APIs, and walks through a complete hands‑on example using Stanford's 50‑dimensional GloVe embeddings to index, import, and query semantically similar words.
MCP (Model Context Protocol) is an emerging open‑standard that lets large language models interact with tools, data sources, and services through a unified JSON‑RPC or gRPC interface, addressing fragmentation, data silos, and low development efficiency while offering a flexible, community‑driven ecosystem with clear advantages and current limitations.
A careless Kibana query—such as selecting the past 30 days without filters—can overload an Elasticsearch node, causing CPU spikes, GC storms, and service outages; the article explains why unrestricted access is risky and outlines a three‑layer safety strategy with concrete configuration examples.
This post outlines a five‑part video series that walks through what Coco AI is, how to deploy Easysearch, set up the server and client components, and integrate the open‑source cross‑platform AI search tool into a web application, with links to detailed guides.
This article explains how vector embeddings enhance Elasticsearch 8.x search, walks through the concepts of dense vectors, shows step‑by‑step Python and Logstash pipelines for generating and storing embeddings, compares their pros and cons, and offers guidance on selecting the right approach for large‑scale log data.
This visual guide walks readers through Elasticsearch fundamentals—from architecture and indexing to clustering, query DSL, aggregations, and performance tuning—using comic-style illustrations that simplify each concept for easy understanding, and security considerations, multilingual support, and real‑time search capabilities.
The article explains two ways to recover access to INFINI Console when the password is lost: locating the original console_configuration.json file to retrieve the stored credentials, or using the built‑in Reset Password feature in the user management UI, with step‑by‑step instructions and screenshots.
The article examines why front‑end product search fails during large marketing events, evaluates Elasticsearch's join feature and its drawbacks, compares nested, reverse‑modeling and flattened approaches, recommends reverse modeling for massive activity‑product data, and provides concrete DSL code, pagination and caching tips.
The article analyzes the pain points of managing several Elasticsearch clusters separately, compares native Kibana, custom scripts, and commercial tools, and then walks through a practical implementation using the lightweight INFINI Console to achieve unified, version‑agnostic monitoring and alerting.
This article provides a detailed errata for the Elasticsearch book, correcting page numbers, fixing typographical errors, updating default cluster‑level parameters, and amending inaccurate terms and index names to ensure the technical content is accurate.
