This guide explains how Elasticsearch index aliases and index templates work, illustrating their benefits for seamless index switching, grouping, view creation, and automated settings, and provides step‑by‑step code examples for creating, viewing, renaming, and deleting aliases and templates.
This guide explains Elasticsearch Index Lifecycle Management (ILM), detailing its five phases, the actions available in each stage, the criteria for phase transitions, and step‑by‑step configuration through Kibana and API commands, helping engineers automate index handling and reduce manual maintenance.
This guide dissects the Elastic Certified Engineer 8.15 exam syllabus, explains each core topic—from searchable snapshots and async search to ILM policies and cross‑cluster replication—while offering a step‑by‑step study roadmap, hands‑on lab ideas, and resource recommendations to help candidates pass efficiently.
To prevent IO saturation during Elasticsearch ILM hot‑to‑cold migrations, the article recommends batching migrations, fine‑tuning max_bytes_per_sec, upgrading cold‑node storage and adding hot nodes, and automating monitoring with scripts that pause or resume ILM based on real‑time utilization thresholds.
This article introduces Elasticsearch, explains its hot‑warm‑cold lifecycle management, demonstrates vector search and built‑in NLP capabilities, and describes how the 360 Zhihui Cloud Platform integrates these features with practical test cases and new visualization tools.
This guide explains how to deploy an Elasticsearch cluster with hot, warm, and cold nodes, configure Index Lifecycle Management policies, adjust polling intervals, create indices with rollover aliases, add documents, and observe automatic phase transitions and deletions, all using Docker Compose.
This article explains the principles behind Elasticsearch searchable snapshots, details the DataTier model and node role optimizations, and provides a full practical walkthrough—including cluster setup, COS repository creation, ILM policy configuration, index templates, mounting strategies, and performance considerations—using ES 7.14.2.
The article explains Elasticsearch 7.14’s production‑ready searchable snapshot feature, detailing tiered node roles, allocation preferences, full and partial mount types, and provides a step‑by‑step Tencent Cloud COS setup—including license activation, repository creation, ILM policy, index template and automatic mounting—to achieve cost‑effective, large‑scale data storage.
Elasticsearch 7.10 adds searchable snapshots, letting users query indices stored directly in remote repositories such as S3 or COS, which halves storage costs, decouples storage from compute, supports manual mounting and ILM cold‑phase policies, and promises future full storage‑compute separation without local caching.
This article walks through a mid‑size gaming company's journey of deploying, tuning, and scaling an Elasticsearch cluster for massive log volumes, covering hot‑cold node architecture, ILM policies, shard management, Logstash‑Kafka optimization, emergency expansions, and the promise of searchable snapshots to achieve petabyte‑scale storage with cost efficiency.
To handle a gaming company's million‑QPS log stream, the team built a hot‑cold Tencent Cloud Elasticsearch cluster with ILM‑driven tiering, scaled CPU/heap, reduced shard count via shrink and replica tweaks, tuned Logstash‑Kafka pipelines, and employed COS snapshots and searchable snapshots, achieving stable performance and lower cost.
