The article explains Elasticsearch's cluster architecture, covering nodes, shards, replicas, two deployment models, data‑layer design, and the trade‑offs of each approach, while also comparing alternative distributed storage architectures and their impact on reliability and performance.
An OceanBase 4.2.1.4 deployment experienced random selection of data sources when adding tenant replicas, causing cross‑city bandwidth congestion; the article reproduces the issue, analyzes its cause, and presents three remediation methods—including setting the choose_migration_source_policy parameter, manual replica addition, and temporary network isolation.
This article explains how to enable JMX for Kafka, retrieve and interpret key metrics such as UnderReplicatedPartitions, and troubleshoot common issues like broker failures, disk outages, and replica lag by examining metric values and related logs.
This article explains Elasticsearch’s distributed cluster design, covering core concepts such as nodes, indices, shards, and replicas, compares mixed and tiered deployment models, examines data‑layer storage options, and discusses two typical distributed system architectures with their trade‑offs.
This guide shows how to build Undermoon, run a master and replica Memory Broker, verify data replication, and manually switch to the replica via the coordinator API when the master fails, ensuring continued service operation.
This article explains Elasticsearch’s cluster and data layer architecture, covering nodes, indices, shards, replicas, mixed and tiered deployment models, storage options, and the trade‑offs of different distributed system designs for scalable systems.
This article explains the core concepts of Elasticsearch’s distributed architecture—including nodes, indices, shards, replicas—and compares mixed and tiered deployment models, while also discussing data storage strategies, replica benefits, and the trade‑offs of local‑file versus shared‑storage distributed systems.
This article explains the core concepts of Elasticsearch—including nodes, indices, shards, and replicas—covers its cluster and data‑layer architectures, compares mixed and tiered deployment models, and discusses the advantages and drawbacks of replica‑based distributed storage systems.
This article explains Elasticsearch's core concepts—nodes, indices, shards, and replicas—describes the indexing flow, compares mixed and layered deployment models, and discusses data‑layer architectures, helping readers choose the appropriate cluster design for reliability and scalability.
This article analyzes Elasticsearch’s distributed architecture, explaining core concepts such as nodes, indices, shards and replicas, comparing mixed and layered deployment models, and discussing data‑layer design choices and their trade‑offs in modern distributed storage and search systems.
The article thoroughly explains Kafka’s message‑queue reliability design and implementation, covering use‑case scenarios, core concepts, storage format, producer acknowledgment settings, broker replication mechanisms (ISR, HW, LEO), consumer delivery semantics, the epoch solution for synchronization, and practical configuration guidelines for various consistency and availability requirements.
This article provides a comprehensive introduction to Elasticsearch, covering data types, the role of Lucene, cluster architecture, node roles, discovery mechanisms, shard and replica management, mapping, installation, health monitoring, indexing workflow, storage internals, refresh and translog processes, segment merging, and practical performance and JVM tuning tips.
This article explains Elasticsearch’s cluster and data layer architecture, detailing nodes, indices, shards, replicas, deployment models, and the trade‑offs of different distributed system designs, helping readers understand how Elasticsearch achieves scalability, reliability, and query performance.
This article explains Elasticsearch’s cluster architecture, detailing node roles, index, shard, and replica concepts, compares mixed and tiered deployment models, examines data storage mechanisms, and discusses the advantages and trade‑offs of different distributed system designs.
