Elasticsearch优化 & filebeat配置文件优化 & logstash格式配置 & grok实践

Elasticsearch优化 & filebeat配置文件优化 & logstash格式配置 & grok实践

编码转换问题（主要就是中文乱码）

（1）input 中的codec => plain 转码

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

将GB2312 的文本编码，转为UTF-8 的编码

（2）也可以在filebeat中实现编码的转换（推荐）

filebeat.prospectors:
- input_type: log
  paths:
    - c:UsersAdministratorDesktopperformanceTrace.txt
  encoding: GB2312

删除多余日志中的多余行

（1）logstash filter 中drop 删除

    if ([message] =~ "^20.*- task request,.*,start time.*") {   #用正则需删除的多余行
            drop {}
    } 

（2）日志示例

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   #需删除的行
-- 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 处理多种日志不同的行（重点）

（1）日志示例：

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
-- 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 分别处理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）日志示例：

# 这是一条INFO 日志
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==

# 这个一条ERROR日志
2018-09-06 21:21:15.863 [548809be071dd887,548809be071dd887] [reactor-http-epoll-4] ERROR c.w.w.c.e.WebExceptionHandler - [handle,34] - 系统异常:/report-server/game/queryPartnerGameReport
com.wbgg.wbcommon.core.base.exception.BusinessException: 您的账号未登录，请登录后再操作！
	at com.wbgg.wbcommon.core.base.wrapper.Wrapper.check(Wrapper.java:155)
	at com.wbgg.wbgateway.pc.infrastructure.config.SecurityFilter.filter(SecurityFilter.java:86)
	at org.springframework.cloud.gateway.handler.FilteringWebHandler$GatewayFilterAdapter.filter(FilteringWebHandler.java:135)
	at org.springframework.cloud.gateway.filter.OrderedGatewayFilter.filter(OrderedGatewayFilter.java:44)
	at org.springframework.cloud.gateway.handler.FilteringWebHandler$DefaultGatewayFilterChain.lambda$filter$0(FilteringWebHandler.java:117)
	at reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:44)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.Mono.subscribe(Mono.java:3695)
	at reactor.core.publisher.MonoIgnoreThen$ThenIgnoreMain.drain(MonoIgnoreThen.java:172)
	at reactor.core.publisher.MonoIgnoreThen.subscribe(MonoIgnoreThen.java:56)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoFlatMap$FlatMapMain.onNext(MonoFlatMap.java:150)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxSwitchIfEmpty$SwitchIfEmptySubscriber.onNext(FluxSwitchIfEmpty.java:67)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.MonoNext$NextSubscriber.onNext(MonoNext.java:76)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.innerNext(FluxConcatMap.java:275)
	at reactor.core.publisher.FluxConcatMap$ConcatMapInner.onNext(FluxConcatMap.java:849)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxMap$MapSubscriber.onNext(FluxMap.java:114)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxSwitchIfEmpty$SwitchIfEmptySubscriber.onNext(FluxSwitchIfEmpty.java:67)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1505)
	at reactor.core.publisher.MonoFlatMap$FlatMapInner.onNext(MonoFlatMap.java:241)
	at reactor.core.publisher.Operators$ScalarSubscription.request(Operators.java:2070)
	at reactor.core.publisher.MonoFlatMap$FlatMapInner.onSubscribe(MonoFlatMap.java:230)
	at reactor.core.publisher.MonoJust.subscribe(MonoJust.java:54)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoFlatMap$FlatMapMain.onNext(MonoFlatMap.java:150)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxMap$MapSubscriber.onNext(FluxMap.java:114)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.MonoNext$NextSubscriber.onNext(MonoNext.java:76)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.innerNext(FluxConcatMap.java:275)
	at reactor.core.publisher.FluxConcatMap$ConcatMapInner.onNext(FluxConcatMap.java:849)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxOnErrorResume$ResumeSubscriber.onNext(FluxOnErrorResume.java:73)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxPeek$PeekSubscriber.onNext(FluxPeek.java:192)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1505)
	at reactor.core.publisher.MonoFilterWhen$MonoFilterWhenMain.innerResult(MonoFilterWhen.java:193)
	at reactor.core.publisher.MonoFilterWhen$FilterWhenInner.onNext(MonoFilterWhen.java:260)
	at reactor.core.publisher.MonoFilterWhen$FilterWhenInner.onNext(MonoFilterWhen.java:228)
	at reactor.core.publisher.Operators$ScalarSubscription.request(Operators.java:2070)
	at reactor.core.publisher.MonoFilterWhen$FilterWhenInner.onSubscribe(MonoFilterWhen.java:249)
	at reactor.core.publisher.MonoJust.subscribe(MonoJust.java:54)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.Mono.subscribe(Mono.java:3695)
	at reactor.core.publisher.MonoFilterWhen$MonoFilterWhenMain.onNext(MonoFilterWhen.java:150)
	at reactor.core.publisher.Operators$ScalarSubscription.request(Operators.java:2070)
	at reactor.core.publisher.MonoFilterWhen$MonoFilterWhenMain.onSubscribe(MonoFilterWhen.java:103)
	at reactor.core.publisher.MonoJust.subscribe(MonoJust.java:54)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoFilterWhen.subscribe(MonoFilterWhen.java:56)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoPeek.subscribe(MonoPeek.java:71)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoOnErrorResume.subscribe(MonoOnErrorResume.java:44)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.Mono.subscribe(Mono.java:3695)
	at reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.drain(FluxConcatMap.java:442)
	at reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.onNext(FluxConcatMap.java:244)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxDematerialize$DematerializeSubscriber.onNext(FluxDematerialize.java:114)
	at reactor.core.publisher.FluxDematerialize$DematerializeSubscriber.onNext(FluxDematerialize.java:42)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.drainAsync(FluxFlattenIterable.java:395)
	at reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.drain(FluxFlattenIterable.java:638)
	at reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.onNext(FluxFlattenIterable.java:242)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxPeekFuseable$PeekFuseableSubscriber.onNext(FluxPeekFuseable.java:204)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1505)
	at reactor.core.publisher.MonoCollectList$MonoBufferAllSubscriber.onComplete(MonoCollectList.java:118)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)
	at reactor.core.publisher.DrainUtils.postCompleteDrain(DrainUtils.java:131)
	at reactor.core.publisher.DrainUtils.postComplete(DrainUtils.java:186)
	at reactor.core.publisher.FluxMaterialize$MaterializeSubscriber.onComplete(FluxMaterialize.java:134)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)
	at reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.drainAsync(FluxFlattenIterable.java:325)
	at reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.drain(FluxFlattenIterable.java:638)
	at reactor.core.publisher.FluxFlattenIterable$FlattenIterableSubscriber.onComplete(FluxFlattenIterable.java:259)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)
	at reactor.core.publisher.FluxMapFuseable$MapFuseableSubscriber.onComplete(FluxMapFuseable.java:144)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)
	at reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1508)
	at reactor.core.publisher.MonoCollectList$MonoBufferAllSubscriber.onComplete(MonoCollectList.java:118)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)
	at reactor.core.publisher.FluxFlatMap$FlatMapMain.checkTerminated(FluxFlatMap.java:794)
	at reactor.core.publisher.FluxFlatMap$FlatMapMain.drainLoop(FluxFlatMap.java:560)
	at reactor.core.publisher.FluxFlatMap$FlatMapMain.drain(FluxFlatMap.java:540)
	at reactor.core.publisher.FluxFlatMap$FlatMapMain.onComplete(FluxFlatMap.java:426)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onComplete(ScopePassingSpanSubscriber.java:112)
	at reactor.core.publisher.FluxIterable$IterableSubscription.slowPath(FluxIterable.java:265)
	at reactor.core.publisher.FluxIterable$IterableSubscription.request(FluxIterable.java:201)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.request(ScopePassingSpanSubscriber.java:79)
	at reactor.core.publisher.FluxFlatMap$FlatMapMain.onSubscribe(FluxFlatMap.java:335)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onSubscribe(ScopePassingSpanSubscriber.java:71)
	at reactor.core.publisher.FluxIterable.subscribe(FluxIterable.java:139)
	at reactor.core.publisher.FluxIterable.subscribe(FluxIterable.java:63)
	at reactor.core.publisher.FluxLiftFuseable.subscribe(FluxLiftFuseable.java:70)
	at reactor.core.publisher.FluxFlatMap.subscribe(FluxFlatMap.java:97)
	at reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)
	at reactor.core.publisher.MonoCollectList.subscribe(MonoCollectList.java:59)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoMapFuseable.subscribe(MonoMapFuseable.java:59)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoFlattenIterable.subscribe(MonoFlattenIterable.java:101)
	at reactor.core.publisher.FluxLiftFuseable.subscribe(FluxLiftFuseable.java:70)
	at reactor.core.publisher.FluxMaterialize.subscribe(FluxMaterialize.java:40)
	at reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)
	at reactor.core.publisher.MonoCollectList.subscribe(MonoCollectList.java:59)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoPeekFuseable.subscribe(MonoPeekFuseable.java:74)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoFlattenIterable.subscribe(MonoFlattenIterable.java:101)
	at reactor.core.publisher.FluxLiftFuseable.subscribe(FluxLiftFuseable.java:70)
	at reactor.core.publisher.FluxDematerialize.subscribe(FluxDematerialize.java:39)
	at reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)
	at reactor.core.publisher.FluxDefer.subscribe(FluxDefer.java:54)
	at reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)
	at reactor.core.publisher.FluxConcatMap.subscribe(FluxConcatMap.java:121)
	at reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)
	at reactor.core.publisher.MonoNext.subscribe(MonoNext.java:40)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoMap.subscribe(MonoMap.java:55)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoFlatMap.subscribe(MonoFlatMap.java:60)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoSwitchIfEmpty.subscribe(MonoSwitchIfEmpty.java:44)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoMap.subscribe(MonoMap.java:55)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.Mono.subscribe(Mono.java:3695)
	at reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.drain(FluxConcatMap.java:442)
	at reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.onNext(FluxConcatMap.java:244)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onNext(ScopePassingSpanSubscriber.java:96)
	at reactor.core.publisher.FluxIterable$IterableSubscription.slowPath(FluxIterable.java:243)
	at reactor.core.publisher.FluxIterable$IterableSubscription.request(FluxIterable.java:201)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.request(ScopePassingSpanSubscriber.java:79)
	at reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.onSubscribe(FluxConcatMap.java:229)
	at org.springframework.cloud.sleuth.instrument.reactor.ScopePassingSpanSubscriber.onSubscribe(ScopePassingSpanSubscriber.java:71)
	at reactor.core.publisher.FluxIterable.subscribe(FluxIterable.java:139)
	at reactor.core.publisher.FluxIterable.subscribe(FluxIterable.java:63)
	at reactor.core.publisher.FluxLiftFuseable.subscribe(FluxLiftFuseable.java:70)
	at reactor.core.publisher.FluxConcatMap.subscribe(FluxConcatMap.java:121)
	at reactor.core.publisher.FluxLift.subscribe(FluxLift.java:46)
	at reactor.core.publisher.MonoNext.subscribe(MonoNext.java:40)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoSwitchIfEmpty.subscribe(MonoSwitchIfEmpty.java:44)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoFlatMap.subscribe(MonoFlatMap.java:60)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoFlatMap.subscribe(MonoFlatMap.java:60)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at org.springframework.cloud.sleuth.instrument.web.TraceWebFilter$MonoWebFilterTrace.subscribe(TraceWebFilter.java:180)
	at reactor.core.publisher.MonoDefer.subscribe(MonoDefer.java:52)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoOnErrorResume.subscribe(MonoOnErrorResume.java:44)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoOnErrorResume.subscribe(MonoOnErrorResume.java:44)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.MonoPeekTerminal.subscribe(MonoPeekTerminal.java:61)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoOnErrorResume.subscribe(MonoOnErrorResume.java:44)
	at reactor.core.publisher.MonoLift.subscribe(MonoLift.java:45)
	at reactor.core.publisher.Mono.subscribe(Mono.java:3695)
	at reactor.core.publisher.MonoIgnoreThen$ThenIgnoreMain.drain(MonoIgnoreThen.java:172)
	at reactor.core.publisher.MonoIgnoreThen.subscribe(MonoIgnoreThen.java:56)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoPeekFuseable.subscribe(MonoPeekFuseable.java:70)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.core.publisher.MonoPeekTerminal.subscribe(MonoPeekTerminal.java:61)
	at reactor.core.publisher.MonoLiftFuseable.subscribe(MonoLiftFuseable.java:55)
	at reactor.netty.http.server.HttpServerHandle.onStateChange(HttpServerHandle.java:64)
	at reactor.netty.tcp.TcpServerBind$ChildObserver.onStateChange(TcpServerBind.java:226)
	at reactor.netty.http.server.HttpServerOperations.onInboundNext(HttpServerOperations.java:434)
	at reactor.netty.channel.ChannelOperationsHandler.channelRead(ChannelOperationsHandler.java:141)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:374)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:360)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:352)
	at reactor.netty.http.server.HttpTrafficHandler.channelRead(HttpTrafficHandler.java:160)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:374)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:360)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:352)
	at io.netty.channel.CombinedChannelDuplexHandler$DelegatingChannelHandlerContext.fireChannelRead(CombinedChannelDuplexHandler.java:438)
	at io.netty.handler.codec.ByteToMessageDecoder.fireChannelRead(ByteToMessageDecoder.java:328)
	at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:302)
	at io.netty.channel.CombinedChannelDuplexHandler.channelRead(CombinedChannelDuplexHandler.java:253)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:374)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:360)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:352)
	at io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1422)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:374)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:360)
	at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:931)
	at io.netty.channel.epoll.AbstractEpollStreamChannel$EpollStreamUnsafe.epollInReady(AbstractEpollStreamChannel.java:799)
	at io.netty.channel.epoll.EpollEventLoop.processReady(EpollEventLoop.java:433)
	at io.netty.channel.epoll.EpollEventLoop.run(EpollEventLoop.java:330)
	at io.netty.util.concurrent.SingleThreadEventExecutor$6.run(SingleThreadEventExecutor.java:1044)
	at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
	at java.lang.Thread.run(Thread.java:748)

在logstash filter中grok 规则进行匹配处理

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 								# 设定输入类型为json
  }
}

filter {

#    mutate {
#        gsub => [ "message", "
", "" ]		# 替换掉换行符
#    }


    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}"]	 # 配置正则表达式和标签匹配日志
        overwrite => ["message"]				# 将上面%{GREEDYDATA:message} 标签覆盖到message上
    }

    date {
        match => [ "timestamp", "yyyy-MM-dd HH:mm:ss,SSS" ] # 配置timestamp 时间格式
        target => "@timestamp"								# 将上面grok正则匹配的标签timestamp 覆盖到默认date "@timestamp" 上面，以便kibana中看到打印的最新时间
    }
    
    # 下面这段是为了解决Elasticsearch 默认时间是0时区，不是东八区，所以默认显示时间比东八区少8个小时，这时我们通过ruby 进行时间格式的修改，增加8个小时，示例如下:
    ruby { 
        code => "event.set('timestamp', event.get('@timestamp').time.localtime + 8*60*60)" 
    }
 
    ruby {
        code => "event.set('@timestamp',event.get('timestamp'))"
    }

	# 配置要删除的多余的一些字符串，通过mutate模块进行删除
    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"
        # 删除filebeat 自带的不需要的元数据
        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}sd{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"
  #}
  
}

日志多行合并处理—multiline插件（重点）

（1）示例：

① 日志

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 对合并后多行的处理（合并多行后续都一样，如下）

filter {
　　grok {
　　　　match => {
　　　　　　"message" => "^%{TIMESTAMP_ISO8601:InsertTime} .*- task request,.*,start time:%{TIMESTAMP_ISO8601:RequestTime}
-- 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
-- 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（推荐）

① 介绍multiline

pattern：正则匹配从哪行合并

negate：true/false，匹配到pattern 部分开始合并，还是不配到的合并

match：after/before（需自己理解）

　　after：匹配到pattern 部分后合并，注意：这种情况最后一行日志不会被匹配处理

　　before：匹配到pattern 部分前合并（推荐）

② 5.5版本之后（before为例）

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

③ 5.5版本之前（after为例）

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（没有filebeat 时推荐）

① 介绍multiline

pattern：正则匹配从哪行合并

negate：true/false，匹配到pattern 部分开始合并，还是不配到的合并

what：previous/next（需自己理解）

　　previous：相当于filebeat 的after

　　next：相当于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（不推荐）

（a）不推荐的原因：

　　① filter设置multiline后，pipline worker会自动将为1

　　② 5.5 版本官方把multiline 去除了，要使用的话需下载，下载命令如下：

　　/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） 日志示例

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"]

匹配这个字段，字段的格式为：日日/月月月/年年年年 时/分/秒 时区

也可以写为：match => ["timestamp","ISO8601"]（推荐）

（3）date 介绍

　　就是将匹配日志中时间的key 替换为@timestamp 的时间，因为@timestamp 的时间是日志送到logstash 的时间，并不是日志中真正的时间。

6、对多类日志分类处理（重点）

① 在filebeat 的配置中添加type 分类

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，可进行对不同类进行不同处理

filter {
   if [fields][type] == "WebApiDebugLog_request" {   #对request 类日志
        if ([message] =~ "^20.*- task report,.*,start time.*") {   #删除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"
} 

对elk 整体性能的优化

性能分析

（1）服务器硬件Linux：1cpu 4GRAM

假设每条日志250 Byte

（2）分析

① logstash硬件Linux：1cpu 4GRAM

每秒500条日志

去掉ruby每秒660条日志

去掉grok后每秒1000条数据

② filebeat硬件Linux：1cpu 4GRAM

每秒2500-3500条数据

每天每台机器可处理：24h*60min*60sec*3000*250Byte=64,800,000,000Bytes，约64G

③ 瓶颈在logstash 从redis中取数据存入ES，开启一个logstash，每秒约处理6000条数据；开启两个logstash，每秒约处理10000条数据（cpu已基本跑满）；

④ logstash的启动过程占用大量系统资源，因为脚本中要检查java、ruby以及其他环境变量，启动后资源占用会恢复到正常状态。

关于收集日志的选择：logstash/filter

（1）没有原则要求使用filebeat或logstash，两者作为shipper的功能是一样的，区别在于：

① logstash由于集成了众多插件，如grok，ruby，所以相比beat是重量级的；

② logstash启动后占用资源更多，如果硬件资源足够则无需考虑二者差异；

③ logstash基于JVM，支持跨平台；而beat使用golang编写，AIX不支持；

④ AIX 64bit平台上需要安装jdk（jre） 1.7 32bit，64bit的不支持；

⑤ filebeat可以直接输入到ES，但是系统中存在logstash直接输入到ES的情况，这将造成不同的索引类型造成检索复杂，最好统一输入到els 的源。

（2）总结

　　logstash/filter 总之各有千秋，但是，我推荐选择：在每个需要收集的日志服务器上配置filebeat，因为轻量级，用于收集日志；再统一输出给logstash，做对日志的处理；最后统一由logstash 输出给es。中间也开增加kafka消息队列进行缓存。

logstash的优化相关配置

（1）可以优化的参数，可根据自己的硬件进行优化配置

① pipeline 线程数，官方建议是等于CPU内核数

默认配置 ---> pipeline.workers: 2

可优化为 ---> pipeline.workers: CPU内核数（或几倍cpu内核数）

② 实际output 时的线程数

默认配置 ---> pipeline.output.workers: 1

可优化为 ---> pipeline.output.workers: 不超过pipeline 线程数

③ 每次发送的事件数

默认配置 ---> pipeline.batch.size: 125

可优化为 ---> pipeline.batch.size: 1000

④ 发送延时

默认配置 ---> pipeline.batch.delay: 5

可优化为 ---> pipeline.batch.size: 10

（2）总结

　　通过设置-w参数指定pipeline worker数量，也可直接修改配置文件logstash.yml。这会提高filter和output的线程数，如果需要的话，将其设置为cpu核心数的几倍是安全的，线程在I/O上是空闲的。

　　默认每个输出在一个pipeline worker线程上活动，可以在输出output 中设置workers设置，不要将该值设置大于pipeline worker数。

　　还可以设置输出的batch_size数，例如ES输出与batch size一致。

　　filter设置multiline后，pipline worker会自动将为1，如果使用filebeat，建议在beat中就使用multiline，如果使用logstash作为shipper，建议在input 中设置multiline，不要在filter中设置multiline。

（3）Logstash中的JVM配置文件

　　Logstash是一个基于Java开发的程序，需要运行在JVM中，可以通过配置jvm.options来针对JVM进行设定。比如内存的最大最小、垃圾清理机制等等。JVM的内存分配不能太大不能太小，太大会拖慢操作系统。太小导致无法启动。默认如下：

-Xms256m  # 最小使用内存
-Xmx1g 	  # 最大使用内存

引入Redis 的相关问题

（1）filebeat可以直接输入到logstash（indexer），但logstash没有存储功能，如果需要重启需要先停所有连入的beat，再停logstash，造成运维麻烦；另外如果logstash发生异常则会丢失数据；引入Redis作为数据缓冲池，当logstash异常停止后可以从Redis的客户端看到数据缓存在Redis中；

（2）Redis可以使用list(最长支持4,294,967,295条)或发布订阅存储模式；

（3）redis 做elk 缓冲队列的优化：

​ ① bind 0.0.0.0 #不要监听本地端口

​ ② requirepass ilinux.io #加密码，为了安全运行

​ ③ 只做队列，没必要持久存储，把所有持久化功能关掉：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 关闭RDB

​ ④ 把内存的淘汰策略关掉，把内存空间最大

　　maxmemory 0 #maxmemory为0的时候表示我们对Redis的内存使用没有限制

elasticsearch 节点优化配置

（1）服务器硬件配置，OS 参数

（a） /etc/sysctl.conf 配置

vim /etc/sysctl.conf

vm.swappiness = 1                     # ES 推荐将此参数设置为 1，大幅降低 swap 分区的大小，强制最大程度的使用内存，注意，这里不要设置为 0, 这会很可能会造成 OOM
net.core.somaxconn = 65535     		# 定义了每个端口最大的监听队列的长度
vm.max_map_count= 262144    			# 限制一个进程可以拥有的VMA(虚拟内存区域)的数量。虚拟内存区域是一个连续的虚拟地址空间区域。当VMA 的数量超过这个值，OOM
fs.file-max = 518144                  # 设置 Linux 内核分配的文件句柄的最大数量

[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）为了使以上参数永久生效，还要设置两个地方

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

添加如下属性：
session required pam_limits.so
可能需重启后生效

（2）elasticsearch 中的JVM配置文件

-Xms2g
-Xmx2g

① 将最小堆大小（Xms）和最大堆大小（Xmx）设置为彼此相等。

② Elasticsearch可用的堆越多，可用于缓存的内存就越多。但请注意，太多的堆可能会使您长时间垃圾收集暂停。

③ 设置Xmx为不超过物理RAM的50％，以确保有足够的物理内存留给内核文件系统缓存。

④ 不要设置Xmx为JVM用于压缩对象指针的临界值以上；确切的截止值有所不同，但接近32 GB。不要超过32G，如果空间大，多跑几个实例，不要让一个实例太大内存

（3）elasticsearch 配置文件优化参数

① vim elasticsearch.yml

bootstrap.memory_lock: true  #锁住内存，不使用swap
#缓存、线程等优化如下
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 设置环境变量

vim /etc/profile.d/elasticsearch.sh export ES_HEAP_SIZE=2g #Heap Size不超过物理内存的一半，且小于32G

（4）集群的优化（我未使用集群）

① ES是分布式存储，当设置同样的cluster.name后会自动发现并加入集群；

② 集群会自动选举一个master，当master宕机后重新选举；

③ 为防止"脑裂"，集群中个数最好为奇数个

④ 为有效管理节点，可关闭广播 discovery.zen.ping.multicast.enabled: false，并设置单播节点组discovery.zen.ping.unicast.hosts: ["ip1", "ip2", "ip3"]

性能的检查

（1）检查输入和输出的性能

Logstash和其连接的服务运行速度一致，它可以和输入、输出的速度一样快。

（2）检查系统参数

① CPU

注意CPU是否过载。在Linux/Unix系统中可以使用top -H查看进程参数以及总计。

如果CPU使用过高，直接跳到检查JVM堆的章节并检查Logstash worker设置。

② Memory

注意Logstash是运行在Java虚拟机中的，所以它只会用到你分配给它的最大内存。

检查其他应用使用大量内存的情况，这将造成Logstash使用硬盘swap，这种情况会在应用占用内存超出物理内存范围时。

③ I/O 监控磁盘I/O检查磁盘饱和度

使用Logstash plugin（例如使用文件输出）磁盘会发生饱和。

当发生大量错误，Logstash生成大量错误日志时磁盘也会发生饱和。

在Linux中，可使用iostat，dstat或者其他命令监控磁盘I/O

④ 监控网络I/O

当使用大量网络操作的input、output时，会导致网络饱和。

在Linux中可使用dstat或iftop监控网络情况。

（3）检查JVM heap

heap设置太小会导致CPU使用率过高，这是因为JVM的垃圾回收机制导致的。

一个快速检查该设置的方法是将heap设置为两倍大小然后检测性能改进。不要将heap设置超过物理内存大小，保留至少1G内存给操作系统和其他进程。

你可以使用类似jmap命令行或VisualVM更加精确的计算JVM heap