京东物流：康睿 姚再毅 李振 刘斌 王北永

说明：以下全部均基于 ElasticSearch 8.1 版本

一.跨集群检索 - ccr

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/modules-cross-cluster-search.html

跨集群检索的背景和意义

跨集群检索定义

跨集群检索环境搭建

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/modules-cross-cluster-search.html

步骤 1：搭建两个本地单节点集群，本地练习可取消安全配置

步骤 2：每个集群都执行以下命令

PUT _cluster/settings { "persistent": { "cluster": { "remote": { "cluster_one": { "seeds": [ "172.21.0.14:9301" ] },"cluster_two": { "seeds": [ "172.21.0.14:9302" ] } } } } }

步骤 3：验证集群之间是否互通

方案 1：Kibana 可视化查看：stack Management -> Remote Clusters -> status 应该是 connected！ 且必须打上绿色的对号。

​ 方案 2：GET _remote/info

跨集群查询演练

# 步骤1 在集群 1 中添加数据如下
PUT test01/_bulk
{"index":{"_id":1}}
{"title":"this is from cluster01..."}

# 步骤2 在集群 2 中添加数据如下：
PUT test01/_bulk
{"index":{"_id":1}}
{"title":"this is from cluster02..."}

# 步骤 3：执行跨集群检索如下: 语法：POST 集群名称1:索引名称,集群名称2:索引名称/_search
POST cluster_one:test01,cluster_two:test01/_search
{
  "took" : 7,
  "timed_out" : false,
  "num_reduce_phases" : 3,
  "_shards" : {
    "total" : 2,
    "successful" : 2,
    "skipped" : 0,
    "failed" : 0
  },
  "_clusters" : {
    "total" : 2,
    "successful" : 2,
    "skipped" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "cluster_two:test01",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "title" : "this is from cluster02..."
        }
      },
      {
        "_index" : "cluster_one:test01",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "title" : "this is from cluster01..."
        }
      }
    ]
  }
}

二.跨集群复制 - ccs - 该功能需付费

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/current/xpack-ccr.html

如何保障集群的高可用

1. 副本机制
2. 快照和恢复
3. 跨集群复制（类似 mysql 主从同步）

跨集群复制概述

跨集群复制配置

1. 准备两个集群，网络互通
2. 开启 license 使用，可试用 30 天

• 开启位置：Stack Management -> License mangement.

3.定义好谁是 Leads 集群，谁是 follower 集群

4.在 follower 集群配置 Leader 集群

5.在 follower 集群配置 Leader 集群的索引同步规则（kibana 页面配置）

a.stack Management -> Cross Cluster Replication -> create a follower index.

6.启用步骤 5 的配置

三索引模板

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/index-templates.html

8.X 之组件模板

1.创建组件模板 - 索引 setting 相关

# 组件模板 - 索引setting相关
PUT _component_template/template_sttting_part
{
  "template": {
    "settings": {
      "number_of_shards": 3,
      "number_of_replicas": 0
    }
  }
}

2.创建组件模板 - 索引 mapping 相关

# 组件模板 - 索引mapping相关
PUT _component_template/template_mapping_part
{
  "template": {
    "mappings": {
      "properties": {
        "hosr_name":{
          "type": "keyword"
        },
        "cratet_at":{
          "type": "date",
          "format": "EEE MMM dd HH:mm:ss Z yyyy"
        }
      }
    }
  }
}

3.创建组件模板 - 配置模板和索引之间的关联

// **注意：composed_of 如果多个组件模板中的配置项有重复，后面的会覆盖前面的，和配置的顺序有关**
# 基于组件模板，配置模板和索引之间的关联
# 也就是所有 tem_* 该表达式相关的索引创建时，都会使用到以下规则
PUT _index_template/template_1
{
  "index_patterns": [
    "tem_*"
  ],
  "composed_of": [
    "template_sttting_part",
    "template_mapping_part"
  ]
}

4.测试

# 创建测试
PUT tem_001

索引模板基本操作

实战演练

需求 1：默认如果不显式指定 Mapping,数值类型会被动态映射为 long 类型，但实际上业务数值都比较小，会存在存储浪费。需要将默认值指定为 Integer

索引模板，官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/index-templates.html

mapping-动态模板,官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/dynamic-templates.html

# 结合mapping 动态模板 和 索引模板
# 1.创建组件模板之 - mapping模板
PUT _component_template/template_mapping_part_01
{
  "template": {
    "mappings": {
      "dynamic_templates": [
        {
          "integers": {
            "match_mapping_type": "long",
            "mapping": {
              "type": "integer"
            }
          }
        }
      ]
    }
  }
}

# 2. 创建组件模板与索引关联配置
PUT _index_template/template_2
{
  "index_patterns": ["tem1_*"],
  "composed_of": ["template_mapping_part_01"]
}

# 3.创建测试数据
POST tem1_001/_doc/1
{
  "age":18
}

# 4.查看mapping结构验证
get tem1_001/_mapping

需求 2：date_* 开头的字段，统一匹配为 date 日期类型。

索引模板，官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/index-templates.html

mapping-动态模板,官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/dynamic-templates.html

# 结合mapping 动态模板 和 索引模板
# 1.创建组件模板之 - mapping模板
PUT _component_template/template_mapping_part_01
{
  "template": {
    "mappings": {
      "dynamic_templates": [
        {
          "integers": {
            "match_mapping_type": "long",
            "mapping": {
              "type": "integer"
            }
          }
        },
        {
        "date_type_process": {
          "match": "date_*",
          "mapping": {
            "type": "date",
            "format":"yyyy-MM-dd HH:mm:ss"
          }
        }
      }
      ]
    }
  }
}

# 2. 创建组件模板与索引关联配置
PUT _index_template/template_2
{
  "index_patterns": ["tem1_*"],
  "composed_of": ["template_mapping_part_01"]
}


# 3.创建测试数据
POST tem1_001/_doc/2
{
  "age":19,
  "date_aoe":"2022-01-01 18:18:00"
}

# 4.查看mapping结构验证
get tem1_001/_mapping

四.LIM 索引生命周期管理

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/index-lifecycle-management.html

什么是索引生命周期

索引的 生-> 老 -> 病 -> 死

是否有过考虑，如果一个索引，创建之后，就不再去管理了？会发生什么？

什么是索引生命周期管理

索引太大了会如何?

大索引的恢复时间，要远比小索引恢复慢的多的多索引大了以后，检索会很慢，写入和更新也会受到不同程度的影响索引大到一定程度，当索引出现健康问题，会导致整个集群核心业务不可用

最佳实践

集群的单个分片最大文档数上限：2 的 32 次幂减 1，即 20 亿左右官方建议：分片大小控制在 30GB-50GB，若索引数据量无限增大，肯定会超过这个值

用户不关注全量

某些业务场景，业务更关注近期的数据，如近 3 天、近 7 天大索引会将全部历史数据汇集在一起，不利于这种场景的查询

索引生命周期管理的历史演变

LIM 前奏 - rollover 滚动索引

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/index-rollover.html

# 0.自测前提，lim生命周期rollover频率。默认10分钟
PUT _cluster/settings
{
  "persistent": {
    "indices.lifecycle.poll_interval": "1s"
  }
}

# 1. 创建索引，并指定别名
PUT test_index-0001
{
  "aliases": {
    "my-test-index-alias": {
      "is_write_index": true
    }
  }
}

# 2.批量导入数据
PUT my-test-index-alias/_bulk
{"index":{"_id":1}}
{"title":"testing 01"}
{"index":{"_id":2}}
{"title":"testing 02"}
{"index":{"_id":3}}
{"title":"testing 03"}
{"index":{"_id":4}}
{"title":"testing 04"}
{"index":{"_id":5}}
{"title":"testing 05"}

# 3.rollover 滚动规则配置
POST my-test-index-alias/_rollover
{
  "conditions": {
    "max_age": "7d",
    "max_docs": 5,
    "max_primary_shard_size": "50gb"
  }
}

# 4.在满足条件的前提下创建滚动索引
PUT my-test-index-alias/_bulk
{"index":{"_id":7}}
{"title":"testing 07"}

# 5.查询验证滚动是否成功
POST my-test-index-alias/_search

LIM 前奏 - shrink 索引压缩

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/ilm-shrink.html

核心步骤：

1. 将数据全部迁移至一个独立的节点

2. 索引禁止写入

3. 方可进行压缩

# 1.准备测试数据
DELETE kibana_sample_data_logs_ext
PUT kibana_sample_data_logs_ext
{
  "settings": {
    "number_of_shards": 5,
    "number_of_replicas": 0
  }
}
POST _reindex
{
  "source": {
    "index": "kibana_sample_data_logs"
  },
  "dest": {
    "index": "kibana_sample_data_logs_ext"
  }
}


# 2.压缩前必要的条件设置
# number_of_replicas :压缩后副本为0
# index.routing.allocation.include._tier_preference 数据分片全部路由到hot节点
# "index.blocks.write 压缩后索引不再允许数据写入
PUT kibana_sample_data_logs_ext/_settings
{
  "settings": {
    "index.number_of_replicas": 0,
    "index.routing.allocation.include._tier_preference": "data_hot",
    "index.blocks.write": true
  }
}

# 3.实施压缩
POST kibana_sample_data_logs_ext/_shrink/kibana_sample_data_logs_ext_shrink
{
  "settings":{
    "index.number_of_replicas": 0,
    "index.number_of_shards": 1,
    "index.codec":"best_compression"
  },
  "aliases":{
    "kibana_sample_data_logs_alias":{}
  }
}

LIM 实战

全局认知建立 - 四大阶段

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/overview-index-lifecycle-management.html

生命周期管理阶段（Policy）：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/ilm-index-lifecycle.html

Hot 阶段(生)

Set priority

Unfollow

Rollover

Read-only

Shrink

Force Merge

Search snapshot

Warm 阶段（老）

Set priority

Unfollow

Read-only

Allocate

migrate

Shirink

Force Merge

Cold 阶段（病）

Search snapshot

Delete 阶段（死）

delete

演练

1.创建 policy

• Hot阶段设置，rollover: max_age:3d，max_docs:5, max_size:50gb, 优先级：100

• Warm 阶段设置：min_age:15s , forcemerage 段合并，热节点迁移到 warm 节点，副本数设置 0，优先级：50

• Cold 阶段设置: min_age 30s, warm 迁移到 cold 阶段

• Delete 阶段设置：min_age 45s，执行删除操作

PUT _ilm/policy/kr_20221114_policy
{
  "policy": {
    "phases": {
      "hot": {
        "min_age": "0ms",
        "actions": {
          "set_priority": {
            "priority": 100
          },
          "rollover": {
            "max_size": "50gb",
            "max_primary_shard_size": "50gb",
            "max_age": "3d",
            "max_docs": 5
          }
        }
      },
      "warm": {
        "min_age": "15s",
        "actions": {
          "forcemerge": {
            "max_num_segments": 1
          },
          "set_priority": {
            "priority": 50
          },
          "allocate": {
            "number_of_replicas": 0
          }
        }
      },
      "cold": {
        "min_age": "30s",
        "actions": {
          "set_priority": {
            "priority": 0
          }
        }
      },
      "delete": {
        "min_age": "45s",
        "actions": {
          "delete": {
            "delete_searchable_snapshot": true
          }
        }
      }
    }
  }
}

2.创建 index template

PUT _index_template/kr_20221114_template
{
  "index_patterns": ["kr_index-**"],
  "template": {
    "settings": {
      "index": {
        "lifecycle": {
          "name": "kr_20221114_policy",
          "rollover_alias": "kr-index-alias"
        },
        "routing": {
          "allocation": {
            "include": {
              "_tier_preference": "data-hot"
            }
          }
        },
        "number_of_shards": "3",
        "number_of_replicas": "1"
      }
    },
    "aliases": {},
    "mappings": {}
  }
}

3.测试需要修改 lim rollover 刷新频率

PUT _cluster/settings
{
  "persistent": {
    "indices.lifecycle.poll_interval": "1s"
  }
}

4.进行测试

# 创建索引，并制定可写别名
PUT kr_index-0001
{
  "aliases": {
    "kr-index-alias": {
      "is_write_index": true
    }
  }
}
# 通过别名新增数据
PUT kr-index-alias/_bulk
{"index":{"_id":1}}
{"title":"testing 01"}
{"index":{"_id":2}}
{"title":"testing 02"}
{"index":{"_id":3}}
{"title":"testing 03"}
{"index":{"_id":4}}
{"title":"testing 04"}
{"index":{"_id":5}}
{"title":"testing 05"}
# 通过别名新增数据，触发rollover
PUT kr-index-alias/_bulk
{"index":{"_id":6}}
{"title":"testing 06"}
# 查看索引情况
GET kr_index-0001

get _cat/indices?v

过程总结

第一步：配置 lim pollicy

• 横向：Phrase 阶段 (Hot、Warm、Cold、Delete) 生老病死

• 纵向：Action 操作（rollover、forcemerge、readlyonly、delete）

第二步：创建模板 绑定 policy,指定别名

第三步：创建起始索引

第四步：索引基于第一步指定的 policy 进行滚动

五.Data Stream

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/ilm-actions.html

特性解析

Data Stream 让我们跨多个索引存储时序数据，同时给了唯一的对外接口（data stream 名称）

• 写入和检索请求发给 data stream

• data stream 将这些请求路由至 backing index（后台索引）

Backing indices

每个 data stream 由多个隐藏的后台索引构成

• 自动创建

• 要求模板索引

rollover 滚动索引机制用于自动生成后台索引

• 将成为 data stream 新的写入索引

应用场景

1. 日志、事件、指标等其他持续创建（少更新）的业务数据
2. 两大核心特点
3. 时序性数据
4. 数据极少更新或没有更新

创建 Data Stream 核心步骤

官网文档地址：

https://www.elastic.co/guide/en/elasticsearch/reference/8.1/set-up-a-data-stream.html

Set up a data stream

To set up a data stream, follow these steps:

1. Create an index lifecycle policy
2. Create component templates
3. Create an index template
4. Create the data stream
5. Secure the data stream

演练

1. 创建一个 data stream，名称为 my-data-stream

2. index_template 名称为 my-index-template

3. 满足 index 格式【"my-data-stream*"】的索引都要被应用到

4. 数据插入的时候，在 data_hot 节点

5. 过 3 分钟之后要 rollover 到 data_warm 节点

6. 再过 5 分钟要到 data_cold 节点

# 步骤1 。创建 lim policy
PUT _ilm/policy/my-lifecycle-policy
{
  "policy": {
    "phases": {
      "hot": {
        "actions": {
          "rollover": {
            "max_size": "50gb",
            "max_age": "3m",
            "max_docs": 5
          },
          "set_priority": {
            "priority": 100
          }
        }
      },
      "warm": {
        "min_age": "5m",
        "actions": {
          "allocate": {
            "number_of_replicas": 0
          }, 
          "forcemerge": {
            "max_num_segments": 1
          },
          "set_priority": {
            "priority": 50
          }
        }
      },
      "cold": {
        "min_age": "6m",
        "actions": {
          "freeze":{}
        }
      },
      "delete": {
        "min_age": "45s",
        "actions": {
          "delete": {}
        }
      }
    }
  }
}

# 步骤2 创建组件模板 - mapping
PUT _component_template/my-mappings
{
  "template": {
    "mappings": {
      "properties": {
        "@timestamp": {
          "type": "date",
          "format": "date_optional_time||epoch_millis"
        },
        "message": {
          "type": "wildcard"
        }
      }
    }
  },
  "_meta": {
    "description": "Mappings for @timestamp and message fields",
    "my-custom-meta-field": "More arbitrary metadata"
  }
}

# 步骤3 创建组件模板 - setting
PUT _component_template/my-settings
{
  "template": {
    "settings": {
      "index.lifecycle.name": "my-lifecycle-policy",
      "index.routing.allocation.include._tier_preference":"data_hot"
    }
  },
  "_meta": {
    "description": "Settings for ILM",
    "my-custom-meta-field": "More arbitrary metadata"
  }
}

# 步骤4 创建索引模板
PUT _index_template/my-index-template
{
  "index_patterns": ["my-data-stream*"],
  "data_stream": { },
  "composed_of": [ "my-mappings", "my-settings" ],
  "priority": 500,
  "_meta": {
    "description": "Template for my time series data",
    "my-custom-meta-field": "More arbitrary metadata"
  }
}

# 步骤5 创建 data stream  并 写入数据测试
PUT my-data-stream/_bulk
{ "create":{ } }
{ "@timestamp": "2099-05-06T16:21:15.000Z", "message": "192.0.2.42 - - [06/May/2099:16:21:15 +0000] \"GET /images/bg.jpg HTTP/1.0\" 200 24736" }
{ "create":{ } }
{ "@timestamp": "2099-05-06T16:25:42.000Z", "message": "192.0.2.255 - - [06/May/2099:16:25:42 +0000] \"GET /favicon.ico HTTP/1.0\" 200 3638" }

POST my-data-stream/_doc
{
  "@timestamp": "2099-05-06T16:21:15.000Z",
  "message": "192.0.2.42 - - [06/May/2099:16:21:15 +0000] \"GET /images/bg.jpg HTTP/1.0\" 200 24736"
}


# 步骤6 查看data stream 后台索引信息
GET /_resolve/index/my-data-stream*