elastic学习-data的多层角色

1. data角色

1.1 data多层角色

data_content,data_hot, data_warm, data_cold, data_frozen
一个节点如果设置了其中一个data的role, 就不能再作为通用的data角色了。
如 node.roles: [ data_content, data_hot, data_warm ]可以， 而node.roles: [ data, data_hot ] 就不对了~

In a multi-tier deployment architecture, you use specialized data roles to assign data nodes to specific tiers: data_content,data_hot, data_warm, data_cold, or data_frozen. A node can belong to multiple tiers, but a node that has one of the specialized data roles cannot have the generic data role.

1.2 data_content

node.roles: [ data_content ]
data_content角色的节点，你优先要考虑query性能，而不是IO吞吐量；

Content tier nodes are usually optimized for query performance—​they prioritize processing power over IO throughput so they can process complex searches and aggregations and return results quickly.

1.3 data_hot

node.roles: [ data_hot ]

• hot层，可保存最近、最常搜索的时间序列数据。
• 要求读写快，如ssd硬盘。

• 1.4 data_warm

  node.roles: [ data_warm ]

• 温暖层通常保存最近几周的数据；
• 时间序列上，过了hot的范畴；
• 更新仍然允许的，但可能不频繁；
• 节点性能可以比hot层的差些

1.5 data_cold

node.roles: [ data_cold ]

1. 扔可搜索；

2. 不需要副本；

   Unlike regular indices, these fully mounted indices don’t require replicas for reliability. In the event of a failure, they can recover data from the underlying snapshot instead.

3. 较便宜的硬件、较少的磁盘(副本无)

1.6 data_frozen

1. 可搜索

2. 需要一个快照库；

   The frozen tier requires a snapshot repository

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