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 elasticsearch8.1 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/_searchPOST 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..." } } ] }}
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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
副本机制
快照和恢复
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
准备两个集群，网络互通
开启 license 使用，可试用 30 天
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 } }}
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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" } } } }}
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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" ]}
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4.测试
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
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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
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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
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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_extPUT 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":{} }}
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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 } } } } }}
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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": {} }}
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3.测试需要修改 lim rollover 刷新频率
PUT _cluster/settings{ "persistent": { "indices.lifecycle.poll_interval": "1s" }}
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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rolloverPUT kr-index-alias/_bulk{"index":{"_id":6}}{"title":"testing 06"}# 查看索引情况GET kr_index-0001
get _cat/indices?v
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过程总结
第一步：配置 lim pollicy
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）
Backing indices
每个 data stream 由多个隐藏的后台索引构成
rollover 滚动索引机制用于自动生成后台索引
应用场景
日志、事件、指标等其他持续创建（少更新）的业务数据
两大核心特点
时序性数据
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:
Create an index lifecycle policy
Create component templates
Create an index template
Create the data stream
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 policyPUT _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 - mappingPUT _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 - settingPUT _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*
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