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Troubleshooting SeaTunnel Cluster Split-Brain: A Deep Dive into Hazelcast Configuration and GC-Induced Failures
- The article delves into troubleshooting SeaTunnel cluster split-brain phenomena related to Hazelcast configuration and GC-induced failures.
- The cluster consisted of 3 Alibaba Cloud ECS servers using a static slot mode with specific memory configurations.
- Issues like split-brain occurrences, master and worker node problems, and cluster unavailability were traced back to network setup failures and full GC-induced delays.
- Solutions included optimizing the ST cluster heartbeat timeout and GC configurations to prevent split-brain instances and improve cluster stability.
- GC optimizations involved adjusting parameters like MaxGCPauseMillis, GCTimeRatio, G1ReservePercent, and ConcurrentGCThreads.
- JVM tuning parameters were refined progressively to enhance memory reclamation efficiency and reduce occurrences of Full GCs.
- Despite a slight impact on application throughput, the optimized configurations led to the eradication of split-brain incidents and improved system health.
- Through the adoption of community recommendations and fine-tuning JVM parameters, the stability of the cluster was maintained during critical operational periods.
- The article emphasizes the need for meticulous configuration adjustments to address cluster split-brain scenarios and optimize system performance in distributed environments.
- By implementing detailed GC optimizations and failure detection configurations, the SeaTunnel cluster achieved increased resilience and stability.
- Continuous monitoring, log analysis, and parameter tuning played a vital role in resolving cluster issues and ensuring the seamless operation of the system.
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