This guide walks through a real‑world Ceph cluster expansion from 500 TB to 1.2 PB, explaining the risks of automatic rebalancing, presenting a step‑by‑step, batch‑wise expansion plan with weight‑adjustment tricks, configuration tuning, monitoring, troubleshooting, and rollback procedures to keep business latency under control.
This article deep‑dives into Olric's concurrency architecture—sharding with local locks, goroutine‑driven pipelines, atomic operations, and its three‑step partition rebalancing process—showing how Go can power high‑throughput, consistent distributed caches.
The article demonstrates how kafka-go implements consumer groups by creating readers that spawn poll goroutines per partition, explains heartbeat‑driven rebalancing, and compares automatic versus manual offset‑commit strategies, highlighting their impact on throughput, reliability, and message‑processing semantics.
This article explains Kafka's fundamental architecture, including the roles of producers, brokers, and consumers, key concepts such as topics, partitions, replicas, ISR, and controller, as well as detailed mechanisms of producer client structure, interceptors, serializers, partitioners, and consumer group rebalancing strategies.
This article explains how Kafka's high‑level consumer rebalancing works, detailing the PartitionAssignor logic, assignment context creation, fetcher management, and the challenges such as herd effect and split‑brain, with Scala code examples illustrating each step.
