京东物流:康睿 姚再毅 李振 刘斌 王北永
阐明:以下全副均基于 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,本地练习可勾销平安配置
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
计划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。
计划2:GET _remote/info
跨集群查问演练
# 步骤1 在集群 1 中增加数据如下PUT test01/_bulk{"index":{"_id":1}}{"title":"this is from cluster01..."}# 步骤2 在集群 2 中增加数据如下: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..." } } ] }}
二.跨集群复制 - ccs - 该性能需付费
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
- 正本机制
- 快照和复原
- 跨集群复制(相似mysql 主从同步)
跨集群复制概述
跨集群复制配置
- 筹备两个集群,网络互通
- 开启 license 应用,可试用30天
- 开启地位: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集群的索引同步规定(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.创立组件模板-索引setting相干
# 组件模板 - 索引setting相干PUT _component_template/template_sttting_part{ "template": { "settings": { "number_of_shards": 3, "number_of_replicas": 0 } }}
2.创立组件模板-索引mapping相干
# 组件模板 - 索引mapping相干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.创立组件模板-配置模板和索引之间的关联
// **留神:composed_of 如果多个组件模板中的配置项有反复,前面的会笼罩后面的,和配置的程序无关**# 基于组件模板,配置模板和索引之间的关联# 也就是所有 tem_* 该表达式相干的索引创立时,都会应用到以下规定PUT _index_template/template_1{ "index_patterns": [ "tem_*" ], "composed_of": [ "template_sttting_part", "template_mapping_part" ]}
4.测试
# 创立测试PUT tem_001
索引模板基本操作
实战演练
需要1:默认如果不显式指定Mapping,数值类型会被动静映射为long类型,但实际上业务数值都比拟小,会存在存储节约。须要将默认值指定为Integer
索引模板,官网文档地址:
https://www.elastic.co/guide/en/elasticsearch/reference/8.1/index-templates.htmlmapping-动静模板,官网文档地址:
https://www.elastic.co/guide/en/elasticsearch/reference/8.1/dynamic-templates.html
# 联合mapping 动静模板 和 索引模板# 1.创立组件模板之 - mapping模板PUT _component_template/template_mapping_part_01{ "template": { "mappings": { "dynamic_templates": [ { "integers": { "match_mapping_type": "long", "mapping": { "type": "integer" } } } ] } }}# 2. 创立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.创立测试数据POST tem1_001/_doc/1{ "age":18}# 4.查看mapping构造验证get tem1_001/_mapping
需要2:date_*结尾的字段,对立匹配为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.htmlmapping-动静模板,官网文档地址:
https://www.elastic.co/guide/en/elasticsearch/reference/8.1/dynamic-templates.html
# 联合mapping 动静模板 和 索引模板# 1.创立组件模板之 - 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. 创立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.创立测试数据POST tem1_001/_doc/2{ "age":19, "date_aoe":"2022-01-01 18:18:00"}# 4.查看mapping构造验证get tem1_001/_mapping
四.LIM 索引生命周期治理
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
是否有过思考,如果一个索引,创立之后,就不再去治理了?会产生什么?
什么是索引生命周期治理
索引太大了会如何?
大索引的复原工夫,要远比小索引复原慢的多的多索引大了当前,检索会很慢,写入和更新也会受到不同水平的影响索引大到肯定水平,当索引呈现衰弱问题,会导致整个集群外围业务不可用
最佳实际
集群的单个分片最大文档数下限:2的32次幂减1,即20亿左右官网倡议:分片大小管制在30GB-50GB,若索引数据量有限增大,必定会超过这个值
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天大索引会将全副历史数据会集在一起,不利于这种场景的查问
索引生命周期治理的历史演变
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. 创立索引,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 滚动规定配置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立滚动索引PUT my-test-index-alias/_bulk{"index":{"_id":7}}{"title":"testing 07"}# 5.查问验证滚动是否胜利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外围步骤:
1. 将数据全副迁徙至一个独立的节点
2. 索引禁止写入
3. 方可进行压缩
# 1.筹备测试数据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 :压缩后正本为0# index.routing.allocation.include._tier_preference 数据分片全副路由到hot节点# "index.blocks.write 压缩后索引不再容许数据写入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.施行压缩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
全局认知建设 - 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
生命周期治理阶段(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.创立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,热节点迁徙到warm节点,正本数设置0,优先级:50
- Cold阶段设置: min_age 30s, warm迁徙到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.创立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.测试须要批改lim rollover刷新频率
PUT _cluster/settings{ "persistent": { "indices.lifecycle.poll_interval": "1s" }}
4.进行测试
# 创立索引,并制订可写别名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"}# 查看索引状况GET kr_index-0001get _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:创立模板 绑定policy,指定别名
第三步:创立起始索引
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
个性解析
Data Stream让咱们跨多个索引存储时序数据,同时给了惟一的对外接口(data stream名称)
- 写入和检索申请发给data stream
- data stream将这些申请路由至 backing index(后盾索引)
Backing indices
每个data stream由多个暗藏的后盾索引形成
- 主动创立
- 要求模板索引
rollover 滚动索引机制用于主动生成后盾索引
- 将成为data stream 新的写入索引
利用场景
- 日志、事件、指标等其余继续创立(少更新)的业务数据
- 两大外围特点
- 时序性数据
- 数据极少更新或没有更新
创立Data Stream 外围步骤
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. 创立一个data stream,名称为my-data-stream
2. index_template 名称为 my-index-template
3. 满足index格局【"my-data-stream*"】的索引都要被利用到
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 。创立 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 创立组件模板 - 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 创立组件模板 - 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 创立索引模板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 创立 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 后盾索引信息GET /_resolve/index/my-data-stream*