This step‑by‑step tutorial shows how to set up two Easysearch clusters, configure Cross‑Cluster Replication (CCR), enable the Auto‑Follow feature, apply minimal‑privilege roles, tune performance parameters, and avoid common pitfalls, enabling seamless multi‑region data sync and zero‑downtime migrations.
This article presents a comprehensive, source‑code‑derived list of every Elasticsearch configuration option—including hidden and undocumented settings—explains their scopes, default values, and types, and shows how the list can be used for quick lookups, performance tuning, debugging, and automation.
The data loss in a Flink‑Kafka job was caused by a mis‑configured bootstrap.servers list that mixed production and pre‑release Kafka clusters, leading different subtasks to connect to different clusters, resulting in inconsistent partition discovery and offset fetching, which omitted several partitions until the list was corrected.
This guide explains the different Elasticsearch node types, their default roles, how to configure master‑eligible, data, ingest, and coordinating‑only nodes, and provides best‑practice recommendations for planning and scaling large clusters to ensure stability and performance.
This guide walks through installing kubectl on an EC2 instance, configuring the kubeconfig to point to a specific Amazon EKS cluster, and verifying the setup by listing cluster nodes, providing all commands and expected outputs.
Apache Pulsar’s built‑in Geo‑Replication lets multiple clusters across different regions synchronize data, offering both synchronous and asynchronous modes; this guide explains the three asynchronous patterns—full‑mesh, unidirectional, and failover—detailing required configurations, operational principles, and current limitations.
This article explains Elasticsearch's data ingestion, retrieval, and search workflows, details the underlying indexing mechanisms, and provides comprehensive system‑level, shard‑level, and query‑level tuning recommendations—including configuration snippets and best‑practice strategies for high‑throughput and low‑latency deployments.
This article provides a comprehensive guide to improving Elasticsearch performance and stability by covering configuration file tweaks, system‑level settings, and usage‑level optimizations such as hot‑thread analysis, pending tasks, field storage, translog handling, refresh intervals, shard management, and best practices for routing and alias usage.
This article consolidates practical Elasticsearch tuning techniques—including configuration file adjustments, system‑level optimizations, and usage‑level settings—to improve cluster performance, stability, and resource efficiency for production environments.
This article provides a comprehensive overview of Elasticsearch architecture and presents practical index and search optimization techniques, configuration recommendations, stress‑testing methods, and monitoring tools to improve cluster performance and reliability.
This article explains how to optimize Hive on Spark running on YARN, covering YARN node resource configuration, Spark executor and driver memory settings, dynamic allocation, parallelism, and key Hive parameters to achieve superior performance compared to Hive on MapReduce.
This article, based on Xu Peng’s Gdevops 2017 talk, details the rationale for choosing Elasticsearch, outlines the overall architecture, provides step‑by‑step OS, JVM, and index parameter settings, and explains comprehensive monitoring strategies to ensure high‑availability and performance of large‑scale ES clusters.
