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).
The author reflects on DeepSeek’s explosive popularity, compares it to early ChatGPT hype, and outlines ten observations—from early‑stage entry and tool comparison to long‑term AI marathon thinking—offering a forward‑looking perspective on how technologists should navigate the AI transformation.
The article reviews Elasticsearch performance testing options—including the official Rally benchmark suite, third‑party solutions such as Logz.io and JMeter, and the open‑source INFINI Loadgen—detailing their automation, version handling, metric reporting, sample benchmark results, and guidance on selecting the right tool for specific workloads.
This article explains how INFINI Loadgen’s response assertion feature works for Elasticsearch performance testing, showing configuration examples, the fields that can be asserted, handling of assertion failures, and the benefits for accurate load testing and debugging.
This article walks through creating a custom Elasticsearch 8.x query that first groups documents into time buckets (e.g., within 3 days, 4‑7 days, older) using a pre‑computed time_bucket field, then sorts each bucket by the _score relevance of the content field, covering pipeline definition, mapping, sample data, DSL query, and practical considerations.
This article provides concise answers and example configurations for eleven common Logstash interview questions, covering HTTP input/poller plugins, the Split filter, pipeline debugging, performance monitoring with Metricbeat, Grok failure handling, secure communication, multi‑source collection, multiple outputs, differences from Elasticsearch ingest pipelines, and Kibana pipeline management.
This article walks through the most common Logstash interview topics, covering its role in the ELK stack, key Input and Filter plugins, the difference between DATA and GREEDYDATA, Mutate gsub usage, parsing JSON/XML, CSV/KV handling, Grok patterns, the Date filter, and strategies to prevent duplicate documents.
This article introduces Easysearch, a lightweight, secure, Elasticsearch‑compatible search database, outlines its core features and applicable scenarios, and provides a step‑by‑step CentOS 7 deployment guide together with practical verification commands and future development considerations.
This article analyzes why organizations are seeking domestic replacements for Elasticsearch due to policy, licensing, and cost constraints, and evaluates several options—including OpenSearch, XunSearch, Lucene/Solr, native database extensions, Milvus, and EasySearch—highlighting their strengths, weaknesses, and suitable scenarios.
This article examines two practical Elasticsearch filter‑context scenarios, showing how converting a must clause to a filter can activate the query cache and reduce latency, while also revealing cases where filter context offers no speed benefit and explaining the underlying ConstantScore behavior.
In a micro‑service that uses Elasticsearch to fetch product listings, a series of targeted optimizations—including shard reduction, segment merging, keyword mapping, request‑cache activation, and PIT‑based sorting—slashed query latency from 50‑60 ms to under 1 ms and boosted throughput to about 50 k queries per second.
