Elasticsearch優化 & filebeat配置文件優化 & logstash格式配置 & grok實踐

時間 2021-01-19

標籤 java react linux ios golang web 正則表達式 redis spring json 欄目日誌分析简体版

原文原文鏈接

Elasticsearch優化 & filebeat配置文件優化 & logstash格式配置 & grok實踐

編碼轉換問題（主要就是中文亂碼）

（1）input 中的codec => plain 轉碼

codec => plain {
         charset => "GB2312"
}

將GB2312 的文本編碼，轉爲UTF-8 的編碼java

（2）也能夠在filebeat中實現編碼的轉換（推薦）

filebeat.prospectors:
- input_type: log
  paths:
    - c:\Users\Administrator\Desktop\performanceTrace.txt
  encoding: GB2312

刪除多餘日誌中的多餘行

（1）logstash filter 中drop 刪除

if ([message] =~ "^20.*-\ task\ request,.*,start\ time.*") {   #用正則需刪除的多餘行
            drop {}
    }

（2）日誌示例

2020-03-20 10:44:01,523 [33]DEBUG Debug - task request,task Id:1cbb72f1-a5ea-4e73-957c-6d20e9e12a7a,start time:2018-03-20 10:43:59   #需刪除的行
-- Request String : {"UserName":"15046699923","Pwd":"ZYjyh727","DeviceType":2,"DeviceId":"PC-20170525SADY","EquipmentNo":null,"SSID":"pc","RegisterPhones":null,"AppKey":"ab09d78e3b2c40b789ddfc81674bc24deac","Version":"2.0.5.3"} -- End
-- Response String : {"ErrorCode":0,"Success":true,"ErrorMsg":null,"Result":null,"WaitInterval":30} -- End

grok 處理多種日誌不一樣的行（重點）

（1）日誌示例：react

2020-03-20 10:44:01,523 [33]DEBUG Debug - task request,task Id:1cbb72f1-a5ea-4e73-957c-6d20e9e12a7a,start time:2018-03-20 10:43:59
-- Request String : {"UserName":"15046699923","Pwd":"ZYjyh727","DeviceType":2,"DeviceId":"PC-20170525SADY","EquipmentNo":null,"SSID":"pc","RegisterPhones":null,"AppKey":"ab09d78e3b2c40b789ddfc81674bc24deac","Version":"2.0.5.3"} -- End
-- Response String : {"ErrorCode":0,"Success":true,"ErrorMsg":null,"Result":null,"WaitInterval":30} -- End

在logstash filter中grok 分別處理3行

match => {
    "message" => "^20.*-\ task\ request,.*,start\ time\:%{TIMESTAMP_ISO8601:RequestTime}"
}

match => {
    "message" => "^--\ Request\ String\ :\ \{\"UserName\":\"%{NUMBER:UserName:int}\",\"Pwd\":\"(?<Pwd>.*)\",\"DeviceType\":%{NUMBER:DeviceType:int},\"DeviceId\":\"(?<DeviceId>.*)\",\"EquipmentNo\":(?<EquipmentNo>.*),\"SSID\":(?<SSID>.*),\"RegisterPhones\":(?<RegisterPhones>.*),\"AppKey\":\"(?<AppKey>.*)\",\"Version\":\"(?<Version>.*)\"\}\ --\ \End.*"    
}

match => {
    "message" => "^--\ Response\ String\ :\ \{\"ErrorCode\":%{NUMBER:ErrorCode:int},\"Success\":(?<Success>[a-z]*),\"ErrorMsg\":(?<ErrorMsg>.*),\"Result\":(?<Result>.*),\"WaitInterval\":%{NUMBER:WaitInterval:int}\}\ --\ \End.*"
}

... 等多行

（2）日誌示例：linux

# 這是一條INFO 日誌
2018-09-06 21:21:40.536 [490343b4207b39e5,490343b4207b39e5] [reactor-http-epoll-4] INFO  c.w.w.p.i.config.SecurityFilter - [filter,75] - skipFlag:false  uri:/report-server/daily/queryDailyReportChannel authorization:GbUzq6IElKkvRswreIHd8Xv/YMDd885jyINObc543vx2H+0lhdu0p5bOu0Vd9PT+jgxJpXHYyZiPgQmyio5Sfg==

# 這個一條ERROR日誌
2018-09-06 21:21:15.863 [548809be071dd887,548809be071dd887] [reactor-http-epoll-4] ERROR c.w.w.c.e.WebExceptionHandler - [handle,34] - 系統異常:/report-server/game/queryPartnerGameReport\ncom.wbgg.wbcommon.core.base.exception.BusinessException: 您的帳號未登陸，請登陸後再操做！\n\tat com.wbgg.wbcommon.core.base.wrapper.Wrapper.check(Wrapper.java:155)\n\tat com.wbgg.wbgateway.pc.infrastructure.config.SecurityFilter.filter(SecurityFilter.java:86)\n\tat org.springframework.cloud.gateway.handler.FilteringWebHandler$GatewayFilterAdapter.filter(FilteringWebHandler.java:135)\n\tat org.springframework.cloud.gateway.filter.OrderedGatewayFilter.filter(OrderedGatewayFilter.java:44)\n\tat org.springframework.cloud.gateway.handler.FilteringWebHandler$DefaultGatewayFilterChain.lambda$filter$0(FilteringWebHandler.java:117)\n\tat reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:44)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.Mono.subscribe(Mono.java:3695)\n\tat reactor.core.publisher.MonoIgnoreThen$ThenIgnoreMain.drain(MonoIgnoreThen.java:172)\n\tat reactor.core.publisher.MonoIgnoreThen.subscribe(MonoIgnoreThen.java:56)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoFlatMap$FlatMapMain.onNext(MonoFlatMap.java:150)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxSwitchIfEmpty$SwitchIfEmptySubscriber.onNext(FluxSwitchIfEmpty.java:67)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.MonoNext$NextSubscriber.onNext(MonoNext.java:76)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.innerNext(FluxConcatMap.java:275)\n\tat reactor.core.publisher.FluxConcatMap$ConcatMapInner.onNext(FluxConcatMap.java:849)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxMap$MapSubscriber.onNext(FluxMap.java:114)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxSwitchIfEmpty$SwitchIfEmptySubscriber.onNext(FluxSwitchIfEmpty.java:67)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1505)\n\tat reactor.core.publisher.MonoFlatMap$FlatMapInner.onNext(MonoFlatMap.java:241)\n\tat reactor.core.publisher.Operators$ScalarSubscription.request(Operators.java:2070)\n\tat reactor.core.publisher.MonoFlatMap$FlatMapInner.onSubscribe(MonoFlatMap.java:230)\n\tat reactor.core.publisher.MonoJust.subscribe(MonoJust.java:54)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoFlatMap$FlatMapMain.onNext(MonoFlatMap.java:150)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxMap$MapSubscriber.onNext(FluxMap.java:114)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.MonoNext$NextSubscriber.onNext(MonoNext.java:76)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.innerNext(FluxConcatMap.java:275)\n\tat reactor.core.publisher.FluxConcatMap$ConcatMapInner.onNext(FluxConcatMap.java:849)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxOnErrorResume$ResumeSubscriber.onNext(FluxOnErrorResume.java:73)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxPeek$PeekSubscriber.onNext(FluxPeek.java:192)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1505)\n\tat reactor.core.publisher.MonoFilterWhen$MonoFilterWhenMain.innerResult(MonoFilterWhen.java:193)\n\tat reactor.core.publisher.MonoFilterWhen$FilterWhenInner.onNext(MonoFilterWhen.java:260)\n\tat reactor.core.publisher.MonoFilterWhen$FilterWhenInner.onNext(MonoFilterWhen.java:228)\n\tat reactor.core.publisher.Operators$ScalarSubscription.request(Operators.java:2070)\n\tat reactor.core.publisher.MonoFilterWhen$FilterWhenInner.onSubscribe(MonoFilterWhen.java:249)\n\tat reactor.core.publisher.MonoJust.subscribe(MonoJust.java:54)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.Mono.subscribe(Mono.java:3695)\n\tat reactor.core.publisher.MonoFilterWhen$MonoFilterWhenMain.onNext(MonoFilterWhen.java:150)\n\tat reactor.core.publisher.Operators$ScalarSubscription.request(Operators.java:2070)\n\tat reactor.core.publisher.MonoFilterWhen$MonoFilterWhenMain.onSubscribe(MonoFilterWhen.java:103)\n\tat reactor.core.publisher.MonoJust.subscribe(MonoJust.java:54)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoFilterWhen.subscribe(MonoFilterWhen.java:56)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoPeek.subscribe(MonoPeek.java:71)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoOnErrorResume.subscribe(MonoOnErrorResume.java:44)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.Mono.subscribe(Mono.java:3695)\n\tat reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.drain(FluxConcatMap.java:442)\n\tat reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.onNext(FluxConcatMap.java:244)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxDematerialize$DematerializeSubscriber.onNext(FluxDematerialize.java:114)\n\tat reactor.core.publisher.FluxDematerialize$DematerializeSubscriber.onNext(FluxDematerialize.java:42)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.drainAsync(FluxFlattenIterable.java:395)\n\tat reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.drain(FluxFlattenIterable.java:638)\n\tat reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.onNext(FluxFlattenIterable.java:242)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxPeekFuseable$PeekFuseableSubscriber.onNext(FluxPeekFuseable.java:204)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1505)\n\tat reactor.core.publisher.MonoCollectList$MonoBufferAllSubscriber.onComplete(MonoCollectList.java:118)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)\n\tat reactor.core.publisher.DrainUtils.postCompleteDrain(DrainUtils.java:131)\n\tat reactor.core.publisher.DrainUtils.postComplete(DrainUtils.java:186)\n\tat reactor.core.publisher.FluxMaterialize$MaterializeSubscriber.onComplete(FluxMaterialize.java:134)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)\n\tat reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.drainAsync(FluxFlattenIterable.java:325)\n\tat reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.drain(FluxFlattenIterable.java:638)\n\tat reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.onComplete(FluxFlattenIterable.java:259)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)\n\tat reactor.core.publisher.FluxMapFuseable$MapFuseableSubscriber.onComplete(FluxMapFuseable.java:144)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)\n\tat reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1508)\n\tat reactor.core.publisher.MonoCollectList$MonoBufferAllSubscriber.onComplete(MonoCollectList.java:118)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)\n\tat reactor.core.publisher.FluxFlatMap$FlatMapMain.checkTerminated(FluxFlatMap.java:794)\n\tat reactor.core.publisher.FluxFlatMap$FlatMapMain.drainLoop(FluxFlatMap.java:560)\n\tat reactor.core.publisher.FluxFlatMap$FlatMapMain.drain(FluxFlatMap.java:540)\n\tat reactor.core.publisher.FluxFlatMap$FlatMapMain.onComplete(FluxFlatMap.java:426)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)\n\tat reactor.core.publisher.FluxIterable$IterableSubscription.slowPath(FluxIterable.java:265)\n\tat reactor.core.publisher.FluxIterable$IterableSubscription.request(FluxIterable.java:201)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.request(ScopePassingSpanSubscriber.java:79)\n\tat reactor.core.publisher.FluxFlatMap$FlatMapMain.onSubscribe(FluxFlatMap.java:335)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onSubscribe(ScopePassingSpanSubscriber.java:71)\n\tat reactor.core.publisher.FluxIterable.subscribe(FluxIterable.java:139)\n\tat reactor.core.publisher.FluxIterable.subscribe(FluxIterable.java:63)\n\tat reactor.core.publisher.FluxLiftFuseable.subscribe(FluxLiftFuseable.java:70)\n\tat reactor.core.publisher.FluxFlatMap.subscribe(FluxFlatMap.java:97)\n\tat reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)\n\tat reactor.core.publisher.MonoCollectList.subscribe(MonoCollectList.java:59)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoMapFuseable.subscribe(MonoMapFuseable.java:59)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoFlattenIterable.subscribe(MonoFlattenIterable.java:101)\n\tat reactor.core.publisher.FluxLiftFuseable.subscribe(FluxLiftFuseable.java:70)\n\tat reactor.core.publisher.FluxMaterialize.subscribe(FluxMaterialize.java:40)\n\tat reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)\n\tat reactor.core.publisher.MonoCollectList.subscribe(MonoCollectList.java:59)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoPeekFuseable.subscribe(MonoPeekFuseable.java:74)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoFlattenIterable.subscribe(MonoFlattenIterable.java:101)\n\tat reactor.core.publisher.FluxLiftFuseable.subscribe(FluxLiftFuseable.java:70)\n\tat reactor.core.publisher.FluxDematerialize.subscribe(FluxDematerialize.java:39)\n\tat reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)\n\tat reactor.core.publisher.FluxDefer.subscribe(FluxDefer.java:54)\n\tat reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)\n\tat reactor.core.publisher.FluxConcatMap.subscribe(FluxConcatMap.java:121)\n\tat reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)\n\tat reactor.core.publisher.MonoNext.subscribe(MonoNext.java:40)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoMap.subscribe(MonoMap.java:55)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoFlatMap.subscribe(MonoFlatMap.java:60)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoSwitchIfEmpty.subscribe(MonoSwitchIfEmpty.java:44)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoMap.subscribe(MonoMap.java:55)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.Mono.subscribe(Mono.java:3695)\n\tat reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.drain(FluxConcatMap.java:442)\n\tat reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.onNext(FluxConcatMap.java:244)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)\n\tat reactor.core.publisher.FluxIterable$IterableSubscription.slowPath(FluxIterable.java:243)\n\tat reactor.core.publisher.FluxIterable$IterableSubscription.request(FluxIterable.java:201)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.request(ScopePassingSpanSubscriber.java:79)\n\tat reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.onSubscribe(FluxConcatMap.java:229)\n\tat org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onSubscribe(ScopePassingSpanSubscriber.java:71)\n\tat reactor.core.publisher.FluxIterable.subscribe(FluxIterable.java:139)\n\tat reactor.core.publisher.FluxIterable.subscribe(FluxIterable.java:63)\n\tat reactor.core.publisher.FluxLiftFuseable.subscribe(FluxLiftFuseable.java:70)\n\tat reactor.core.publisher.FluxConcatMap.subscribe(FluxConcatMap.java:121)\n\tat reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)\n\tat reactor.core.publisher.MonoNext.subscribe(MonoNext.java:40)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoSwitchIfEmpty.subscribe(MonoSwitchIfEmpty.java:44)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoFlatMap.subscribe(MonoFlatMap.java:60)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoFlatMap.subscribe(MonoFlatMap.java:60)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat org.springframework.cloud.sleuth.instrument.web.TraceWebFilter$MonoWebFilterTrace.subscribe(TraceWebFilter.java:180)\n\tat reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoOnErrorResume.subscribe(MonoOnErrorResume.java:44)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoOnErrorResume.subscribe(MonoOnErrorResume.java:44)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.MonoPeekTerminal.subscribe(MonoPeekTerminal.java:61)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoOnErrorResume.subscribe(MonoOnErrorResume.java:44)\n\tat reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)\n\tat reactor.core.publisher.Mono.subscribe(Mono.java:3695)\n\tat reactor.core.publisher.MonoIgnoreThen$ThenIgnoreMain.drain(MonoIgnoreThen.java:172)\n\tat reactor.core.publisher.MonoIgnoreThen.subscribe(MonoIgnoreThen.java:56)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoPeekFuseable.subscribe(MonoPeekFuseable.java:70)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.core.publisher.MonoPeekTerminal.subscribe(MonoPeekTerminal.java:61)\n\tat reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)\n\tat reactor.netty.http.server.HttpServerHandle.onStateChange(HttpServerHandle.java:64)\n\tat reactor.netty.tcp.TcpServerBind$ChildObserver.onStateChange(TcpServerBind.java:226)\n\tat reactor.netty.http.server.HttpServerOperations.onInboundNext(HttpServerOperations.java:434)\n\tat reactor.netty.channel.ChannelOperationsHandler.channelRead(ChannelOperationsHandler.java:141)\n\tat io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:374)\n\tat io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:360)\n\tat io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:352)\n\tat reactor.netty.http.server.HttpTrafficHandler.channelRead(HttpTrafficHandler.java:160)\n\tat io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:374)\n\tat io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:360)\n\tat io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:352)\n\tat io.netty.channel.CombinedChannelDuplexHandler$DelegatingChannelHandlerContext.fireChannelRead(CombinedChannelDuplexHandler.java:438)\n\tat io.netty.handler.codec.ByteToMessageDecoder.fireChannelRead(ByteToMessageDecoder.java:328)\n\tat io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:302)\n\tat io.netty.channel.CombinedChannelDuplexHandler.channelRead(CombinedChannelDuplexHandler.java:253)\n\tat io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:374)\n\tat io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:360)\n\tat io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:352)\n\tat io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1422)\n\tat io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:374)\n\tat io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:360)\n\tat io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:931)\n\tat io.netty.channel.epoll.AbstractEpollStreamChannel$EpollStreamUnsafe.epollInReady(AbstractEpollStreamChannel.java:799)\n\tat io.netty.channel.epoll.EpollEventLoop.processReady(EpollEventLoop.java:433)\n\tat io.netty.channel.epoll.EpollEventLoop.run(EpollEventLoop.java:330)\n\tat io.netty.util.concurrent.SingleThreadEventExecutor$6.run(SingleThreadEventExecutor.java:1044)\n\tat io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)\n\tat java.lang.Thread.run(Thread.java:748)

在logstash filter中grok 規則進行匹配處理

input {
  kafka {
    id => "test-kafka-input"
    bootstrap_servers => ["192.168.0.250:9092"] # kafka地址
    group_id => "logstash"						# kafka group
    topics => ["test", "filebeat"]				# kafka topics
    codec => json 								# 設定輸入類型爲json
  }
}

filter {

#    mutate {
#        gsub => [ "message", "\r", "" ]		# 替換掉換行符
#    }


    grok {
        match => ["message","%{TIMESTAMP_ISO8601:timestamp}\s+%{SYSLOG5424SD:uid}\s+%{SYSLOG5424SD:threadid}\s+%{LOGLEVEL:loglevel}\s+%{JAVACLASS:javaclass}\s+.?\s+%{SYSLOG5424SD}\s+.?\s+%{GREEDYDATA:message}"]	 # 配置正則表達式和標籤匹配日誌
        overwrite => ["message"]				# 將上面%{GREEDYDATA:message} 標籤覆蓋到message上
    }

    date {
        match => [ "timestamp", "yyyy-MM-dd HH:mm:ss,SSS" ] # 配置timestamp 時間格式
        target => "@timestamp"								# 將上面grok正則匹配的標籤timestamp 覆蓋到默認date "@timestamp" 上面，以便kibana中看到打印的最新時間
    }
    
    # 下面這段是爲了解決Elasticsearch 默認時間是0時區，不是東八區，因此默認顯示時間比東八區少8個小時，這時咱們經過ruby 進行時間格式的修改，增長8個小時，示例以下:
    ruby { 
        code => "event.set('timestamp', event.get('@timestamp').time.localtime + 8*60*60)" 
    }
 
    ruby {
        code => "event.set('@timestamp',event.get('timestamp'))"
    }

	# 配置要刪除的多餘的一些字符串，經過mutate模塊進行刪除
    mutate {
        remove_field => ["timestamp","hostname","tags","stream","agent","ecs","input","[kubernetes][container][name]","[kubernetes][labels][pod-template-hash]","[kubernetes][pod][uid]","[kubernetes][replicaset]","@version","[log][offset]"]
    }

    json {
        source => "@fields"
        # 刪除filebeat 自帶的不須要的元數據
        remove_field => [ "beat","@fields","fields","index_name","offset","source","message","time","tags"]
      }



#    json {
#        source => "message" 
#        remove_field => [ "message" ]
#  }

#  multiline {
#    pattern => "^\d{4}-\d{1,2}-\d{1,2}\s\d{1,2}:\d{1,2}:\d{1,2}" 
#    negate => true   
#    what => "previous" 
#    }

}


output {
  elasticsearch {
    hosts => ["http://192.168.0.250:9200"]
    user => logstash_admin
    password => "YHkdypsPKqw5gaWKE"
    index => "game-filebeat-%{+YYYY.MM.dd}"
  }
  
  #file {
  #  path => "/test/bak/test.txt"
  #}
  
}

日誌多行合併處理—multiline插件（重點）

（1）示例：ios

① 日誌golang

2018-03-20 10:44:01,523 [33]DEBUG Debug - task request,task Id:1cbb72f1-a5ea-4e73-957c-6d20e9e12a7a,start time:2018-03-20 10:43:59
-- Request String : {"UserName":"15046699923","Pwd":"ZYjyh727","DeviceType":2,"DeviceId":"PC-20170525SADY","EquipmentNo":null,"SSID":"pc","RegisterPhones":null,"AppKey":"ab09d78e3b2c40b789ddfc81674bc24deac","Version":"2.0.5.3"} -- End
-- Response String : {"ErrorCode":0,"Success":true,"ErrorMsg":null,"Result":null,"WaitInterval":30} -- End

② logstash grok 對合並後多行的處理（合併多行後續都同樣，以下）web

filter {
　　grok {
　　　　match => {
　　　　　　"message" => "^%{TIMESTAMP_ISO8601:InsertTime}\ .*-\ task\ request,.*,start\ time:%{TIMESTAMP_ISO8601:RequestTime}\n--\ Request\ String\ :\ \{\"UserName\":\"%{NUMBER:UserName:int}\",\"Pwd\":\"(?<Pwd>.*)\",\"DeviceType\":%{NUMBER:DeviceType:int},\"DeviceId\":\"(?<DeviceId>.*)\",\"EquipmentNo\":(?<EquipmentNo>.*),\"SSID\":(?<SSID>.*),\"RegisterPhones\":(?<RegisterPhones>.*),\"AppKey\":\"(?<AppKey>.*)\",\"Version\":\"(?<Version>.*)\"\}\ --\ \End\n--\ Response\ String\ :\ \{\"ErrorCode\":%{NUMBER:ErrorCode:int},\"Success\":(?<Success>[a-z]*),\"ErrorMsg\":(?<ErrorMsg>.*),\"Result\":(?<Result>.*),\"WaitInterval\":%{NUMBER:WaitInterval:int}\}\ --\ \End"
　　　　}
　　}
}

（2）在filebeat中使用multiline 插件（推薦）正則表達式

① 介紹multilineredis

pattern：正則匹配從哪行合併spring

negate：true/false，匹配到pattern 部分開始合併，仍是不配到的合併json

match：after/before（需本身理解）

　　after：匹配到pattern 部分後合併，注意：這種狀況最後一行日誌不會被匹配處理

　　before：匹配到pattern 部分前合併（推薦）

② 5.5版本以後（before爲例）

filebeat.prospectors:
- input_type: log
  paths:
    - /root/performanceTrace*
  fields:
    type: zidonghualog
  multiline.pattern: '.*\"WaitInterval\":.*--\ End'
  multiline.negate: true
  multiline.match: before

③ 5.5版本以前（after爲例）

filebeat.prospectors:
- input_type: log 
     paths:
      - /root/performanceTrace*
      input_type: log 
      multiline:
           pattern: '^20.*'
           negate: true
           match: after

（3）在logstash input中使用multiline 插件（沒有filebeat 時推薦）

① 介紹multiline

pattern：正則匹配從哪行合併

negate：true/false，匹配到pattern 部分開始合併，仍是不配到的合併

what：previous/next（需本身理解）

　　previous：至關於filebeat 的after

　　next：至關於filebeat 的before

② 用法

input {
        file {
                path => ["/root/logs/log2"]
                start_position => "beginning"
                codec => multiline {
                        pattern => "^20.*"
                        negate => true
                        what => "previous"
                }
        }
}

（4）在logstash filter中使用multiline 插件（不推薦）

（a）不推薦的緣由：

　　① filter設置multiline後，pipline worker會自動將爲1

　　② 5.5 版本官方把multiline 去除了，要使用的話需下載，下載命令以下：

　　/usr/share/logstash/bin/logstash-plugin install logstash-filter-multiline

（b）示例：

filter {
  multiline {
    pattern => "^20.*"
    negate => true
    what => "previous"
  }
}

logstash filter 中的date使用

（1）日誌示例

2018-03-20 10:44:01 [33]DEBUG Debug - task request,task Id:1cbb72f1-a5ea-4e73-957c-6d20e9e12a7a,start time:2018-03-20 10:43:59

（2） date 使用

date {
                match => ["InsertTime","YYYY-MM-dd HH:mm:ss "]
                remove_field => "InsertTime"
        }

注：

match => ["timestamp" ,"dd/MMM/YYYY H:m:s Z"]

匹配這個字段，字段的格式爲：日日/月月月/年年年年時/分/秒時區

也能夠寫爲：match => ["timestamp","ISO8601"]（推薦）

（3）date 介紹

　　就是將匹配日誌中時間的key 替換爲@timestamp 的時間，由於@timestamp 的時間是日誌送到logstash 的時間，並非日誌中真正的時間。

六、對多類日誌分類處理（重點）

① 在filebeat 的配置中添加type 分類

filebeat:
  prospectors:
    - paths:
        - /mnt/data_total/WebApiDebugLog.txt*
      fields:
        type: WebApiDebugLog_total
    - paths:
        - /mnt/data_request/WebApiDebugLog.txt*
      fields:
        type: WebApiDebugLog_request
    - paths:
        - /mnt/data_report/WebApiDebugLog.txt*
      fields:
        type: WebApiDebugLog_report

② 在logstash filter中使用if，可進行對不一樣類進行不一樣處理

filter {
   if [fields][type] == "WebApiDebugLog_request" {   #對request 類日誌
        if ([message] =~ "^20.*-\ task\ report,.*,start\ time.*") {   #刪除report 行
                drop {}
        }
    grok {
        match => {"... ..."}
        }
}

③ 在logstash output中使用if

if [fields][type] == "WebApiDebugLog_total" {
    elasticsearch {
        hosts => ["6.6.6.6:9200"]
        index => "logstashl-WebApiDebugLog_total-%{+YYYY.MM.dd}"
        document_type => "WebApiDebugLog_total_logs"
}

對elk 總體性能的優化

性能分析

（1）服務器硬件Linux：1cpu 4GRAM

假設每條日誌250 Byte

（2）分析

① logstash硬件Linux：1cpu 4GRAM

每秒500條日誌

去掉ruby每秒660條日誌

去掉grok後每秒1000條數據

② filebeat硬件Linux：1cpu 4GRAM

每秒2500-3500條數據

天天每臺機器可處理：24h*60min*60sec*3000*250Byte=64,800,000,000Bytes，約64G

③ 瓶頸在logstash 從redis中取數據存入ES，開啓一個logstash，每秒約處理6000條數據；開啓兩個logstash，每秒約處理10000條數據（cpu已基本跑滿）；

④ logstash的啓動過程佔用大量系統資源，由於腳本中要檢查java、ruby以及其餘環境變量，啓動後資源佔用會恢復到正常狀態。

關於收集日誌的選擇：logstash/filter

（1）沒有原則要求使用filebeat或logstash，二者做爲shipper的功能是同樣的，區別在於：

① logstash因爲集成了衆多插件，如grok，ruby，因此相比beat是重量級的；

② logstash啓動後佔用資源更多，若是硬件資源足夠則無需考慮兩者差別；

③ logstash基於JVM，支持跨平臺；而beat使用golang編寫，AIX不支持；

④ AIX 64bit平臺上須要安裝jdk（jre） 1.7 32bit，64bit的不支持；

⑤ filebeat能夠直接輸入到ES，可是系統中存在logstash直接輸入到ES的狀況，這將形成不一樣的索引類型形成檢索複雜，最好統一輸入到els 的源。

（2）總結

　　logstash/filter 總之各有千秋，可是，我推薦選擇：在每一個須要收集的日誌服務器上配置filebeat，由於輕量級，用於收集日誌；再統一輸出給logstash，作對日誌的處理；最後統一由logstash 輸出給es。中間也開增長kafka消息隊列進行緩存。

logstash的優化相關配置

（1）能夠優化的參數，可根據本身的硬件進行優化配置

① pipeline 線程數，官方建議是等於CPU內核數

默認配置 ---> pipeline.workers: 2

可優化爲 ---> pipeline.workers: CPU內核數（或幾倍cpu內核數）

② 實際output 時的線程數

默認配置 ---> pipeline.output.workers: 1

可優化爲 ---> pipeline.output.workers: 不超過pipeline 線程數

③ 每次發送的事件數

默認配置 ---> pipeline.batch.size: 125

可優化爲 ---> pipeline.batch.size: 1000

④ 發送延時

默認配置 ---> pipeline.batch.delay: 5

可優化爲 ---> pipeline.batch.size: 10

（2）總結

　　經過設置-w參數指定pipeline worker數量，也可直接修改配置文件logstash.yml。這會提升filter和output的線程數，若是須要的話，將其設置爲cpu核心數的幾倍是安全的，線程在I/O上是空閒的。

　　默認每一個輸出在一個pipeline worker線程上活動，能夠在輸出output 中設置workers設置，不要將該值設置大於pipeline worker數。

　　還能夠設置輸出的batch_size數，例如ES輸出與batch size一致。

　　filter設置multiline後，pipline worker會自動將爲1，若是使用filebeat，建議在beat中就使用multiline，若是使用logstash做爲shipper，建議在input 中設置multiline，不要在filter中設置multiline。

（3）Logstash中的JVM配置文件

　　Logstash是一個基於Java開發的程序，須要運行在JVM中，能夠經過配置jvm.options來針對JVM進行設定。好比內存的最大最小、垃圾清理機制等等。JVM的內存分配不能太大不能過小，太大會拖慢操做系統。過小致使沒法啓動。默認以下：

-Xms256m  # 最小使用內存
-Xmx1g 	  # 最大使用內存

引入Redis 的相關問題

（1）filebeat能夠直接輸入到logstash（indexer），但logstash沒有存儲功能，若是須要重啓須要先停全部連入的beat，再停logstash，形成運維麻煩；另外若是logstash發生異常則會丟失數據；引入Redis做爲數據緩衝池，當logstash異常中止後能夠從Redis的客戶端看到數據緩存在Redis中；

（2）Redis可使用list(最長支持4,294,967,295條)或發佈訂閱存儲模式；

（3）redis 作elk 緩衝隊列的優化：

① bind 0.0.0.0 #不要監聽本地端口

② requirepass ilinux.io #加密碼，爲了安全運行

③ 只作隊列，不必持久存儲，把全部持久化功能關掉：快照（RDB文件）和追加式文件（AOF文件），性能更好

　　save "" 禁用快照
　　appendonly no 關閉RDB

④ 把內存的淘汰策略關掉，把內存空間最大

　　maxmemory 0 #maxmemory爲0的時候表示咱們對Redis的內存使用沒有限制

elasticsearch 節點優化配置

（1）服務器硬件配置，OS 參數

（a） /etc/sysctl.conf 配置

vim /etc/sysctl.conf

vm.swappiness = 1                     # ES 推薦將此參數設置爲 1，大幅下降 swap 分區的大小，強制最大程度的使用內存，注意，這裏不要設置爲 0, 這會極可能會形成 OOM
net.core.somaxconn = 65535     		# 定義了每一個端口最大的監聽隊列的長度
vm.max_map_count= 262144    			# 限制一個進程能夠擁有的VMA(虛擬內存區域)的數量。虛擬內存區域是一個連續的虛擬地址空間區域。當VMA 的數量超過這個值，OOM
fs.file-max = 518144                  # 設置 Linux 內核分配的文件句柄的最大數量

[root@elasticsearch]# sysctl -p 生效一下

（b）limits.conf 配置

vim /etc/security/limits.conf
elasticsearch    soft    nofile          65535
elasticsearch    hard    nofile          65535
elasticsearch    soft    memlock         unlimited
elasticsearch    hard    memlock         unlimited

（c）爲了使以上參數永久生效，還要設置兩個地方

vim /etc/pam.d/common-session-noninteractive
vim /etc/pam.d/common-session

添加以下屬性：
session required pam_limits.so
可能需重啓後生效

（2）elasticsearch 中的JVM配置文件

-Xms2g
-Xmx2g

① 將最小堆大小（Xms）和最大堆大小（Xmx）設置爲彼此相等。

② Elasticsearch可用的堆越多，可用於緩存的內存就越多。但請注意，太多的堆可能會使您長時間垃圾收集暫停。

③ 設置Xmx爲不超過物理RAM的50％，以確保有足夠的物理內存留給內核文件系統緩存。

④ 不要設置Xmx爲JVM用於壓縮對象指針的臨界值以上；確切的截止值有所不一樣，但接近32 GB。不要超過32G，若是空間大，多跑幾個實例，不要讓一個實例太大內存

（3）elasticsearch 配置文件優化參數

① vim elasticsearch.yml

bootstrap.memory_lock: true  #鎖住內存，不使用swap
#緩存、線程等優化以下
bootstrap.mlockall: true
transport.tcp.compress: true
indices.fielddata.cache.size: 40%
indices.cache.filter.size: 30%
indices.cache.filter.terms.size: 1024mb
threadpool:
    search:
        type: cached
        size: 100
        queue_size: 2000

② 設置環境變量

vim /etc/profile.d/elasticsearch.sh export ES_HEAP_SIZE=2g #Heap Size不超過物理內存的一半，且小於32G