Running High Performance And Fault Tolerant ElasticSearch ...
summary
Shows how to run Elasticsearch inside Docker with concrete commands, constraints and failure handling: use Docker run with environment vars and -m 2G memory limits, CPU pinning and port mapping 9200/9300, build custom images with a Dockerfile, persist data with host mounts or data-only volumes and Docker create/volumes-from, split roles into master/data/client and set minimum_master_nodes = (n/2)+1, tag hot/cold tiers for shard allocation, watch cluster health, shards, CPU, IO, JVM heap/GC, refresh and merge times; replication lets nodes be replaced safely and orchestration (Mesos) automates recovery.
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chapters (10)
- 0:00Introduction to the topic
- 0:55Traditional vs container env
- 3:26Why Docker and ElasticSearch
- 5:17Running ElasticSearch on Docker
- 6:55Resource and configuration
- 10:07Networking and clustering
- 12:57Handling storage and persistence
- 15:04Scaling and architectural tiers
- 17:24Key metrics for monitoring
- 20:56Q&A and conclusion
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