云原生时代的数据监控与管理要求更为灵活、高效且能够轻松应对PB级别的数据规模。为此，一款名为OpenObserve（简称O2）的云原生可观测性平台应运而生，它为日志、指标、跟踪、分析以及RUM（实时用户监控 - 性能、错误、会话回放）提供了专业解决方案。
不同于Elasticsearch，后者需要用户对众多设置进行了解和调优，OpenObserve更加简洁易操作，可以在不到2分钟的时间内部署启用。
OpenObserve可以无缝替代Elasticsearch，适用于那些通过API摄取数据并执行搜索的用户群体。而且，OpenObserve自带用户界面，免去了额外安装的需要。
根据我们将生产Kubernetes集群的日志同时推送到Elasticsearch和OpenObserve的结果，使用OpenObserve可以将日志存储成本相比于Elasticsearch减少约140倍。
一、安装OpenObserve和Otel-collector使用docker-compose 安装记得替换下USER_MAIL xxx@xx.com和PASSWORD xxx这里解释下为啥要用named volume来映射openobserve的data目录，因为目前版本(0.7.2)在windows的docker desktop里使用会有bug，导致stream无法正确写入文件持久化，一直在生成新的stream文件，而使用named volume则正常，详情请见issue：write stream to disk failed · Issue #2166 · openobserve/openobserve (github.com)
openobserve占用了5080（http）、5081（grpc）两个端口otel-collector占用了5078和5079两个端口来作为对外提供服务的入口，后边的配置会有解释，其他端口有注释这里就不赘述了
新建一个openobserve目录，新建docker-compose.yaml文件，内容如下
version: '3'services:main:image: openobserve/openobservevolumes:- ob-v:/dataports:- 5080:5080- 5081:5081environment:- ZO_DATA_DIR=/data- ZO_ROOT_USER_EMAIL=xxx@xx.com- ZO_ROOT_USER_PASSWORD=xxxx- TZ=Asia/Shanghairestart: alwaysotel-collector:image: otel/opentelemetry-collector-contribrestart: alwaysvolumes:- ./otel-collector-config.yaml:/etc/otelcol-contrib/config.yamlports:- 1888:1888 # pprof extension- 8888:8888 # Prometheus metrics exposed by the Collector- 8889:8889 # Prometheus exporter metrics- 13133:13133 # health_check extension- 5078:5078 # OTLP gRPC receiver- 5079:5079 # OTLP/2 gRPC receiver#- 55679:55679 # zpages extensionvolumes:ob-v: {}otel-collector-config配置然后在当前目录新建otel-collector-config.yaml内容如下指定了两个端点，5079和5078，让测试环境和生产环境走不同的流水线处理。otel-collector-config配置参考文档记得替换下边的myapp为自己的项目名称
receivers:otlp:protocols:grpc:endpoint: 0.0.0.0:5079otlp/2:protocols:grpc:endpoint: 0.0.0.0:5078processors:batch:exporters:otlp:endpoint: main:5081headers:Authorization: "Basic xxxxxx=="organization: myapp_teststream-name: defaulttls:insecure: trueotlp/2:endpoint: main:5081headers:Authorization: "Basic xxxxxx=="organization: myapp_productionstream-name: defaulttls:insecure: trueextensions:health_check:service:extensions: [health_check]pipelines:traces:receivers: [otlp]processors: [batch]exporters: [otlp]metrics:receivers: [otlp]processors: [batch]exporters: [otlp]logs:receivers: [otlp]processors: [batch]exporters: [otlp]traces/2:receivers: [otlp/2]processors: [batch]exporters: [otlp/2]metrics/2:receivers: [otlp/2]processors: [batch]exporters: [otlp/2]logs/2:receivers: [otlp/2]processors: [batch]exporters: [otlp/2]然后使用命令行运行docker-compose up -d
浏览器访问 服务器ip:5080, 用户名和密码是上边设置的邮箱和密码点击左侧采集--Trace（OpenTelemetry）复制其中的Authorization: 后边的部分，替换otel-collector-config.yaml中的Basic xxxxxx==
最后执行docker-compose restart重启容器完成配置
二、asp.net core项目增加OpenTelemetry导出引用OpenTelemetry Nuget包在web api项目的csproj文件中增加以下nuget包引用
<PackageReference Include="OpenTelemetry.Exporter.Console" Version="1.7.0" /><PackageReference Include="OpenTelemetry.Exporter.OpenTelemetryProtocol" Version="1.7.0" /><PackageReference Include="OpenTelemetry.Extensions.Hosting" Version="1.7.0" /><PackageReference Include="OpenTelemetry.Instrumentation.AspNetCore" Version="1.7.0" /><PackageReference Include="OpenTelemetry.Instrumentation.EntityFrameworkCore" Version="1.0.0-beta.8" /><PackageReference Include="OpenTelemetry.Instrumentation.Http" Version="1.7.0" /><PackageReference Include="OpenTelemetry.Instrumentation.Runtime" Version="1.5.1" />在appsettings.json中配置启用开关 "OpenTelemetry": {"ServiceName": "UserService","EndPoint": "http://192.168.1.101:5079","Enable": true }其中ServiceName是该web api项目名称，EndPoint是otel-collector的一个grpc端点比如测试环境的项目使用了5079端口，那生成环境就改为5078端口，如果还有演示环境、预发布环境之类的就继续在配置里增加端口
代码中配置启用Logging Metrics Traces配置Logging导出由于ConfigureLogging方法是IHostBuilder的扩展方法，所以只能在CreateDefaultBuilder这里调用
Host.CreateDefaultBuilder(args) .ConfigureLogging((c,l) => {if (bool.TryParse(c.Configuration["OpenTelemetry:Enable"], out var e) && e) { l.AddOpenTelemetry(logging => logging.SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(c.Configuration["OpenTelemetry:ServiceName"])) .AddOtlpExporter(o => { o.Endpoint = new Uri(c.Configuration["OpenTelemetry:Endpoint"]); })); } })配置 Tracing和Metrics导出在有IServiceCollection的地方执行AddOpenTelemetry扩展方法
if (bool.TryParse(_appConfiguration["OpenTelemetry:Enable"], out var otelEnable) && otelEnable) { services .AddOpenTelemetry() .ConfigureResource(builder => builder .AddService(serviceName: _appConfiguration["OpenTelemetry:ServiceName"], serviceInstanceId: Environment.MachineName)) .WithTracing(builder => builder .SetSampler(new AlwaysOnSampler()) .AddEntityFrameworkCoreInstrumentation(options => options.SetDbStatementForText = true) .AddHttpClientInstrumentation() .AddAspNetCoreInstrumentation(options => { options.RecordException = true; }) .AddOtlpExporter(o => { o.Endpoint = new Uri(_appConfiguration["OpenTelemetry:Endpoint"]); }) ) .WithMetrics(builder => builder .AddRuntimeInstrumentation() .AddAspNetCoreInstrumentation() .AddOtlpExporter(o => { o.Endpoint = new Uri(_appConfiguration["OpenTelemetry:Endpoint"]); }) ); }C#复制全屏最后项目运行起来在OpenObserve的数据流中有对应数据就算成功了
