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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
- 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
- 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
- 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 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*
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