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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。
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

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