Docker中一鍵安裝ELK
對於這種工具類的東西,第一步就直接到docker的hub中查找了,很幸運,不僅有Elasticsearch,kibana,logstash 單獨的鏡像,而且還直接 有ELK的鏡像。
sudo docker run -p 5601:5601 -p 9200:9200 -p 5044:5044 -d --name log-platform --restart always sebp/elk
這當然能少好多配置,毫不猶豫就選擇了elk的鏡像, 運行起來!如果沒有異常的話相信就很容易的跑起來了(最有可能出現的問題就是虛擬內存不足了,可以百度找解決方案這里就不在詳細說了)
項目中添加log4net到Elasticseach的Appender
因為在.net core 之前就有搭建過日志中心,所以對於appender還記得有一個Log4net.Elasticsearch的dll,但是在查看資料之后發現很久沒有更新 也不支持.net standard。在決定自己實現appender之前,抱着僥幸心理去查找了一翻,既然找到一個支持.net core的開源項目log4stash。很幸運,又可以不要造輪子了,哈哈。log4Stash使用很簡單,但是配置的東西還挺多的而且作者也沒有很好的文檔介紹,先不管其他的用起來在說。
- 項目中添加log4stash
Install-Package log4stash -Version 2.2.1
- 修改log4net.config
在log4net.config中添加appender
<appender name="ElasticSearchAppender" type="log4stash.ElasticSearchAppender, log4stash">
<Server>localhost</Server>
<Port>9200</Port>
<IndexName>log_test_%{+yyyy-MM-dd}</IndexName>
<IndexType>LogEvent</IndexType>
<Bulksize>2000</Bulksize>
<BulkIdleTimeout>10000</BulkIdleTimeout>
<IndexAsync>True</IndexAsync>
</appender>
另外附上全部的配置信息
<appender name="ElasticSearchAppender" type="log4stash.ElasticSearchAppender, log4stash">
<Server>localhost</Server>
<Port>9200</Port>
<!-- optional: in case elasticsearch is located behind a reverse proxy the URL is like http://Server:Port/Path, default = empty string -->
<Path>/es5</Path>
<IndexName>log_test_%{+yyyy-MM-dd}</IndexName>
<IndexType>LogEvent</IndexType>
<Bulksize>2000</Bulksize>
<BulkIdleTimeout>10000</BulkIdleTimeout>
<IndexAsync>False</IndexAsync>
<DocumentIdSource>IdSource</DocumentIdSource> <!-- obsolete! use IndexOperationParams -->
<!-- Serialize log object as json (default is true).
-- This in case you log the object this way: `logger.Debug(obj);` and not: `logger.Debug("string");` -->
<SerializeObjects>True</SerializeObjects>
<!-- optional: elasticsearch timeout for the request, default = 10000 -->
<ElasticSearchTimeout>10000</ElasticSearchTimeout>
<!--You can add parameters to the request to control the parameters sent to ElasticSearch.
for example, as you can see here, you can add a routing specification to the appender.
The Key is the key to be added to the request, and the value is the parameter's name in the log event properties.-->
<IndexOperationParams>
<Parameter>
<Key>_routing</Key>
<Value>%{RoutingSource}</Value>
</Parameter>
<Parameter>
<Key>_id</Key>
<Value>%{IdSource}</Value>
</Parameter>
<Parameter>
<Key>key</Key>
<Value>value</Value>
</Parameter>
</IndexOperationParams>
<!-- for more information read about log4net.Core.FixFlags -->
<FixedFields>Partial</FixedFields>
<Template>
<Name>templateName</Name>
<FileName>path2template.json</FileName>
</Template>
<!--Only one credential type can used at once-->
<!--Here we list all possible types-->
<AuthenticationMethod>
<!--For basic authentication purposes-->
<Basic>
<Username>Username</Username>
<Password>Password</Password>
</Basic>
<!--For AWS ElasticSearch service-->
<Aws>
<Aws4SignerSecretKey>Secret</Aws4SignerSecretKey>
<Aws4SignerAccessKey>AccessKey</Aws4SignerAccessKey>
<Aws4SignerRegion>Region</Aws4SignerRegion>
</Aws>
</AuthenticationMethod>
<!-- all filters goes in ElasticFilters tag -->
<ElasticFilters>
<Add>
<Key>@type</Key>
<Value>Special</Value>
</Add>
<!-- using the @type value from the previous filter -->
<Add>
<Key>SmartValue</Key>
<Value>the type is %{@type}</Value>
</Add>
<Remove>
<Key>@type</Key>
</Remove>
<!-- you can load custom filters like I do here -->
<Filter type="log4stash.Filters.RenameKeyFilter, log4stash">
<Key>SmartValue</Key>
<RenameTo>SmartValue2</RenameTo>
</Filter>
<!-- converts a json object to fields in the document -->
<Json>
<SourceKey>JsonRaw</SourceKey>
<FlattenJson>false</FlattenJson>
<!-- the separator property is only relevant when setting the FlattenJson property to 'true' -->
<Separator>_</Separator>
</Json>
<!-- converts an xml object to fields in the document -->
<Xml>
<SourceKey>XmlRaw</SourceKey>
<FlattenXml>false</FlattenXml>
</Xml>
<!-- kv and grok filters similar to logstash's filters -->
<Kv>
<SourceKey>Message</SourceKey>
<ValueSplit>:=</ValueSplit>
<FieldSplit> ,</FieldSplit>
</kv>
<Grok>
<SourceKey>Message</SourceKey>
<Pattern>the message is %{WORD:Message} and guid %{UUID:the_guid}</Pattern>
<Overwrite>true</Overwrite>
</Grok>
<!-- Convert string like: "1,2, 45 9" into array of numbers [1,2,45,9] -->
<ConvertToArray>
<SourceKey>someIds</SourceKey>
<!-- The separators (space and comma) -->
<Seperators>, </Seperators>
</ConvertToArray>
<Convert>
<!-- convert given key to string -->
<ToString>shouldBeString</ToString>
<!-- same as ConvertToArray. Just for convenience -->
<ToArray>
<SourceKey>anotherIds</SourceKey>
</ToArray>
</Convert>
</ElasticFilters>
</appender>
最后別忘了在root中添加上appender
<root>
<level value="WARN" />
<appender-ref ref="ElasticSearchAppender" />
</root>
OK,項目的配置就到這里結束了,可以運行項目寫入一些測試的日志了。
在kibana建立Index Pattern
Elasticsearch的Index跟關系數據庫中的Database挺類似的,雖然我們項目在寫了測試數據后,Elasticsearch中就已經有Index了,但是如果我們需要在可視化工具中查詢數據的話建立Index Pattern
進入Management - Create Index Pattern,輸入我們項目日志配置文件中的Index名稱log_test-*(如果有數據,這邊應該是會自動帶出來的),然后創建,之后就可以在Kibana中瀏覽,查詢我們的日志信息了。