09.需要加强练习

本文主要是介绍09.需要加强练习，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

1. 集群配置

1. 有三个节点，node1是专用的master
2. node2,node3是data+ingest节点
3. ip node1 192.168.1.123, 192.168.1.124,192.168.1.125

2. security一个单节点的集群

对单节点进行security操作，elastic的初始化密码为elastic-password
其余账户的也是如此
和下面的创建用户可以同时使用

3. 使用api来创建role,user,并使用kibana登录测试

创建一个role，对所有role*的index有所有权限，对cluster有monitor权限，能够登陆和使用kibana
使用这个账户重新登录kibana进行测试

4. script的使用题型

文档的数据是

PUT hamlet02/_bulk
{"index":{"_id":0}}
{"line_number":"1","speaker":"BERNARDO","text_entry":"Whos there?"}
{"index":{"_id":1}}
{"line_number":"2","speaker":"FRANCISCO","text_entry":"Nay, answer me:stand ","reindexBatch":1}

下面这样的
编写一个script,name是control_reindex_batch，用来对hamlet02进行update,如果原文档中有reindexBatch的话，reindexBatch的值会add update的时候传进来的increment参数。如果原文档没有reindexBatch字段的话，就直接初始化为传进来的init参数,同时将speak重命名为leader。

5. search template 使用方式

对于kibana_sample_data_logs中的数据
编写一个search_template name是with_response_and_tag,有三个参数
with_min_response,with_max_response,with_tag
对应的query满足 with_min_response<=response<=with_max_response
tags contains with_tag value

再来一个复杂点的，夹带条件判断，如果
(i) if the “with_max_response” parameter is not set, then don’t set an upper bound to the response value

(ii) if the “with_tag” parameter is not set, then do not apply that filter at all

答案

PUT _scripts/with_response_and_tag05
{"script": {"lang": "mustache","source":"""{"query": {"bool": {"must": [{"range": {"response": {"gte": {{with_min_response}}, "lte": {{with_max_response}}}}},{"match": {"tags": "{{with_tag}}"}}]}}}"""}
}

POST _scripts/with_response_and_tag03
{"script": {"lang": "mustache","source": """{"query": {"bool": {"must": [{"range": {"response": {"gte": {{with_min_response}}{{#with_max_response}},"lte": {{with_max_response}}{{/with_max_response}}}}}{{#with_tag}},{"match": {"tags": "{{with_tag}}"}}{{/with_tag}}]}}}
"""}
}

还有就是json化的方式

6. pipeline使用

文档是数据是这个样子的

POST hamlet-2/_doc/4
{"text_entry": "With turbulent and dangerous lunacy?","line_number": "3.1.4","number_act": "3","speaker": "KING CLAUDIUS","date":"09/12/2020"
}

写一个pipeline叫split_act_scene_line，将line_number使用点号"."切分成三个字段number_act, number_scene，number_line

这个挂一个答案吧，自己写写试试哦

POST _ingest/pipeline/_simulate
{"pipeline": {"description": "string split by dot","processors": [{"split": {"field": "line_number","separator": "\\.","target_field":"temp_arry"}},{"script": {"lang": "painless","source": """ctx.number_act=ctx.temp_arry[0];ctx.number_scene=ctx.temp_arry[1];ctx.number_line=ctx.temp_arry[2];
"""}},{"remove": {"field": "temp_arry"}}]},"docs": [{"_source": {"line_number": "1.1.3","text_entry": "Long live the king!","reindexBatch": 2,"speaker": "BERNARDO"}}]
}

7. index filter的使用场景

部署三个节点的ES节点,有一个属性叫warm_hot
node01为hot，node02和node03是warm节点。
创建两个索引task701,task702,两个索引都是2个shard。一个shard都存在hot一个都存在warm当中。

第二题
三个节点有一个属性area
node01,node02为rack01, node03为rack02

创建一个索引task703,2个shard,1个replica,
让task703的所有shard能够实现在rack01,rack02上的互备份。

8. search中的几个组合查询,附加alias

filter fuzzy compound term

9. cross_cluster search

在cluster1中写入如下数据

PUT hamlet/_bulk
{"index":{"_id":0}}
{"line_number":"1","speaker":"BERNARDO","text_entry":"Whos there?"}
{"index":{"_id":1}}
{"line_number":"2","speaker":"FRANCISCO","text_entry":"Nay answer me: stand, and unfold yourself."}
{"index":{"_id":2}}
{"line_number":"3","speaker":"BERNARDO","text_entry":"Long live theking!"}
{"index":{"_id":3}}
{"line_number":"4","speaker":"FRANCISCO","text_entry":"Bernardo?"}
{"index":{"_id":4}}
{"line_number":"5","speaker":"BERNARDO","text_entry":"He."}

在cluster2中写入如下数据

PUT hamlet02/_bulk
{"index":{"_id":0}}
{"line_number":"1","speaker":"BERNARDO","text_entry":"Whos there?"}
{"index":{"_id":1}}
{"line_number":"2","speaker":"FRANCISCO","text_entry":"Nay answer me: stand, and unfold yourself."}
{"index":{"_id":2}}
{"line_number":"3","speaker":"BERNARDO","text_entry":"Long live theking!"}
{"index":{"_id":3}}
{"line_number":"4","speaker":"FRANCISCO","text_entry":"Bernardo?"}
{"index":{"_id":4}}
{"line_number":"5","speaker":"BERNARDO","text_entry":"He."}

在cluster1中同时搜索两个集群中speaker为FRANCISCO 的doc

10. snapshot restore

单节点,写入数据

PUT hamlet02/_bulk
{"index":{"_id":0}}
{"line_number":"1","speaker":"BERNARDO","text_entry":"Whos there?"}
{"index":{"_id":1}}
{"line_number":"2","speaker":"FRANCISCO","text_entry":"Nay answer me: stand, and unfold yourself."}
{"index":{"_id":2}}
{"line_number":"3","speaker":"BERNARDO","text_entry":"Long live theking!"}
{"index":{"_id":3}}
{"line_number":"4","speaker":"FRANCISCO","text_entry":"Bernardo?"}
{"index":{"_id":4}}
{"line_number":"5","speaker":"BERNARDO","text_entry":"He."}

shared file system repository可以放在home/repo,home/my_repo 两个目录下
在home/my_repo下创建back_repo的repository,然后对hamlet02 进行备份，备份名称为hamlet_backup

删除hamlet02 验证一下是否能正常恢复(这个要注意可以先做一个reindex保命。。。)

11. dynaic_maping and template

创建一个template,要求

1. number_开头的string类型会设置为int
2. 其他类型的string设置为keyword
3. text_entry字段分别使用standard,english,dutch analyzer
4. 日期格式为"MM/dd/yyyy"
5. 所有hamlet开头的索引都会使用这个pattern

测试写入

POST hamlet-2/_doc/4
{"text_entry": "With turbulent and dangerous lunacy?","line_number": "3.1.4","number_act": "3","speaker": "KING CLAUDIUS","date":"09/12/2020"
}

12. join mapping

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