Elasticsearch ILM 策略与冷热分层存储

# Elasticsearch ILM 策略与冷热分层存储 ## 策略示例 ```json { "policy": { "phases": { "hot": { "actions": { "rollover": { "max_age": "7d", "max_size": "50gb" } } }, "warm": { "min_age": "14d", "actions": { "forcemerge": { "max_num_segments": 1 } } }, "cold": { "min_age": "30d", "actions": { "set_priority": { "priority": 0 } } } } } } ``` ## 索引模板绑定 ```json { "index_patterns": ["logs-*"], "template": { "settings": { "index.lifecycle.name": "logs-ilm" } } } ``` ## 验证 - 通过 `_ilm/explain` 查看索引生命周期执行状态 ## 总结 ILM 结合 rollover 与分层策略，可在保证查询性能的同时优化存储成本。