关于elasticsearch入门教程: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] 能够，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，你优先要思考 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，可保留最近、最常搜寻的工夫序列数据。
• 要求读写快，如 ssd 硬盘。

• 1.4 data_warm

  node.roles: [data_warm]

• 和煦层通常保留最近几周的数据；
• 工夫序列上，过了 hot 的领域；
• 更新依然容许的，但可能不频繁；
• 节点性能能够比 hot 层的差些

1.5 data_cold

node.roles: [data_cold]

1. 扔可搜寻；

2. 不须要正本；

   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 (正本无)

1.6 data_frozen

1. 可搜寻

2. 须要一个快照库；

   The frozen tier requires a snapshot repository