Opentracing-and-Jaeger-in-a-real-Go-Microservices

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背景

微服务极大地改变了软件的开发和交付模式，单体应用被拆分为多个微服务，单个服务的复杂度大幅降低，库之间的依赖也转变为服务之间的依赖。由此带来的问题是部署的粒度变得越来越细，众多服务给运维带来巨大压力，不过好在我们有 Kubernetes，可以解决大部分运维方面的难题。

随着服务数量的增多和内部调用链的复杂化，仅凭借日志和性能监控很难做到“See the Whole Picture”，在进行问题排查或是性能分析的时候，无异于盲人摸象。分布式追踪能够帮助开发者直观分析请求链路，快速定位性能瓶颈，逐渐优化服务间依赖，也有助于开发者从更宏观的角度更好地理解整个分布式系统。

分布式追踪系统大体分为三个部分，数据采集、数据持久化、数据展示。数据采集是指在代码中埋点，设置请求中要上报的阶段，以及设置当前记录的阶段隶属于哪个上级阶段。数据持久化则是指将上报的数据落盘存储，例如 Jaeger 就支持多种存储后端，可选用 Cassandra 或者 Elasticsearch。数据展示则是前端根据 Trace ID 查询与之关联的请求阶段，并在界面上呈现。

微服务通讯架构图

Opentracing

发展历史

早在 2005 年，Google 就在内部部署了一套分布式追踪系统 Dapper，并发表了一篇论文《Dapper, a Large-Scale Distributed Systems Tracing Infrastructure》，阐述了该分布式追踪系统的设计和实现，可以视为分布式追踪领域的鼻祖。随后出现了受此启发的开源实现，如 Zipkin、SourceGraph 开源的 Appdash、Red Hat 的 Hawkular APM、Uber 开源的 Jaeger 等。但各家的分布式追踪方案是互不兼容的，这才诞生了 OpenTracing。OpenTracing 是一个 Library，定义了一套通用的数据上报接口，要求各个分布式追踪系统都来实现这套接口。这样一来，应用程序只需要对接 OpenTracing，而无需关心后端采用的到底什么分布式追踪系统，因此开发者可以无缝切换分布式追踪系统，也使得在通用代码库增加对分布式追踪的支持成为可能。

目前，主流的分布式追踪实现基本都已经支持 OpenTracing，包括 Jaeger、Zipkin、Appdash 等，具体可参考官方文档《Supported Tracer Implementations》。

数据模型

这部分在 OpenTracing 的规范中写的非常清楚，下面只大概翻译一下其中的关键部分，细节可参考原始文档《The OpenTracing Semantic Specification》。

Causal relationships between Spans in a single Trace

        [Span A]  ←←←(the root span)
            |
     +------+------+
     |             |
 [Span B]      [Span C] ←←←(Span C is a `ChildOf` Span A)
     |             |
 [Span D]      +---+-------+
               |           |
           [Span E]    [Span F] >>> [Span G] >>> [Span H]
                                       ↑
                                       ↑
                                       ↑
                         (Span G `FollowsFrom` Span F)

Trace 是调用链，每个调用链由多个 Span 组成。Span 的单词含义是范围，可以理解为某个处理阶段。Span 和 Span 的关系称为 Reference。上图中，总共有标号为 A-H 的 8 个阶段。

Temporal relationships between Spans in a single Trace

––|–––––––|–––––––|–––––––|–––––––|–––––––|–––––––|–––––––|–> time

 [Span A···················································]
   [Span B··············································]
      [Span D··········································]
    [Span C········································]
         [Span E·······]        [Span F··] [Span G··] [Span H··]

上图是按照时间顺序呈现的调用链。

每个阶段（Span）包含如下状态：

  • 操作名称
  • 起始时间
  • 结束时间
  • 一组 KV 值，作为阶段的标签（Span Tags）
  • 阶段日志（Span Logs）
  • 阶段上下文（SpanContext），其中包含 Trace ID 和 Span ID
  • 引用关系（References）

阶段（Span）可以有 ChildOf 和 FollowsFrom 两种引用关系。ChildOf 用于表示父子关系，即在某个阶段中发生了另一个阶段，是最常见的阶段关系，典型的场景如调用 RPC 接口、执行 SQL、写数据。FollowsFrom 表示跟随关系，意为在某个阶段之后发生了另一个阶段，用来描述顺序执行关系。

ChildOf relationship means that the rootSpan has a logical dependency on the child span before rootSpan can complete its operation. Another standard reference type in OpenTracing is FollowsFrom, which means the rootSpan is the ancestor in the DAG, but it does not depend on the completion of the child span, for example if the child represents a best-effort, fire-and-forget cache write.

Concepts and Terminology, 概念与术语

Traces

一个 trace 代表一个潜在的，分布式的，存在并行数据或并行执行轨迹（潜在的分布式、并行）的系统。一个 trace 可以认为是多个 span 的有向无环图（DAG）。

Spans

一个 span 代表系统中具有开始时间和执行时长的逻辑运行单元。span 之间通过嵌套或者顺序排列建立逻辑因果关系。

Operation Names

每一个 span 都有一个操作名称，这个名称简单，并具有可读性高。（例如：一个 RPC 方法的名称，一个函数名，或者一个大型计算过程中的子任务或阶段）。span 的操作名应该是一个抽象、通用的标识，能够明确的、具有统计意义的名称；更具体的子类型的描述，请使用 Tags
例如，假设一个获取账户信息的 span 会有如下可能的名称：

| Operation Name | Guidance |
|:—————|:——–|
| get | Too general |
| get_account/792 | Too specific |
| get_account | Good, and account_id=792 would make a nice Span tag |

References between Spans

A Span may reference zero or more other SpanContexts that are causally related. OpenTracing presently defines two types of references: ChildOf and FollowsFrom. Both reference types specifically model direct causal relationships between a child Span and a parent Span. In the future, OpenTracing may also support reference types for Spans with non-causal relationships (e.g., Spans that are batched together, Spans that are stuck in the same queue, etc).

ChildOf references: A Span may be the ChildOf a parent Span. In a ChildOf reference, the parent Span depends on the child Span in some capacity. All of the following would constitute ChildOf relationships:

  • A Span representing the server side of an RPC may be the ChildOf a Span representing the client side of that RPC
  • A Span representing a SQL insert may be the ChildOf a Span representing an ORM save method
  • Many Spans doing concurrent (perhaps distributed) work may all individually be the ChildOf a single parent Span that merges the results for all children that return within a deadline

These could all be valid timing diagrams for children that are the ChildOf a parent.

    [-Parent Span---------]
         [-Child Span----]

    [-Parent Span--------------]
         [-Child Span A----]
          [-Child Span B----]
        [-Child Span C----]
         [-Child Span D---------------]
         [-Child Span E----]

FollowsFrom references: Some parent Spans do not depend in any way on the result of their child Spans. In these cases, we say merely that the child Span FollowsFrom the parent Span in a causal sense. There are many distinct FollowsFrom reference sub-categories, and in future versions of OpenTracing they may be distinguished more formally.

These can all be valid timing diagrams for children that “FollowFrom” a parent.

    [-Parent Span-]  [-Child Span-]


    [-Parent Span--]
     [-Child Span-]


    [-Parent Span-]
                [-Child Span-]

Logs

每个 span 可以进行多次 Logs 操作，每一次 Logs 操作，都需要一个带时间戳的时间名称，以及可选的任意大小的存储结构。
标准中定义了一些日志（logging）操作的一些常见用例和相关的 log 事件的键值，可参考 Data Conventions Guidelines 数据约定指南。

Tags

每个 span 可以有多个键值对（key:value）形式的 Tags，Tags 是没有时间戳的，支持简单的对 span 进行注解和补充。
和使用 Logs 的场景一样，对于应用程序特定场景已知的键值对 Tags，tracer 可以对他们特别关注一下。更多信息，可参考 Data Conventions Guidelines 数据约定指南。

SpanContext

每个 span 必须提供方法访问 SpanContext。SpanContext 代表跨越进程边界，传递到下级 span 的状态。(例如，包含 <trace_id, span_id, sampled> 元组)，并用于封装 Baggage (关于 Baggage 的解释，请参考下文)。SpanContext 在跨越进程边界，和在追踪图中创建边界的时候会使用。(ChildOf 关系或者其他关系，参考 Span 间关系)。

Baggage

Baggage 是存储在 SpanContext 中的一个键值对 (SpanContext) 集合。它会在一条追踪链路上的所有 span 内全局传输，包含这些 span 对应的 SpanContexts。在这种情况下，”Baggage” 会随着 trace 一同传播，他因此得名（Baggage 可理解为随着 trace 运行过程传送的行李）。鉴于全栈 OpenTracing 集成的需要，Baggage 通过透明化的传输任意应用程序的数据，实现强大的功能。例如：可以在最终用户的手机端添加一个 Baggage 元素，并通过分布式追踪系统传递到存储层，然后再通过反向构建调用栈，定位过程中消耗很大的 SQL 查询语句。

Baggage 拥有强大功能，也会有很大的消耗。由于 Baggage 的全局传输，如果包含的数量量太大，或者元素太多，它将降低系统的吞吐量或增加 RPC 的延迟。

Baggage vs. Span Tags

  • Baggage 在全局范围内，（伴随业务系统的调用）跨进程传输数据。Span 的 tag 不会进行传输，因为他们不会被子级的 span 继承。
  • span 的 tag 可以用来记录业务相关的数据，并存储于追踪系统中。实现 OpenTracing 时，可以选择是否存储 Baggage 中的非业务数据，OpenTracing 标准不强制要求实现此特性。

Inject and Extract

SpanContexts 可以通过 Injected 操作向 Carrier 增加，或者通过 Extracted 从 Carrier 中获取，跨进程通讯数据（例如：HTTP 头）。通过这种方式，SpanContexts 可以跨越进程边界，并提供足够的信息来建立跨进程的 span 间关系（因此可以实现跨进程连续追踪）。

Global and No-op Tracers

每一个平台的 OpenTracing API 库(opentracing-go, opentracing-java 等)，都必须实现一个空的 Tracer，No-op Tracer 的实现必须不会出错，并且不会有任何副作用。这样在业务方没有指定 collector 服务、storage、和初始化全局 tracer 时，但是 rpc 组件，orm 组件或者其他组件加入了探针。这样全局默认是 No-op Tracer 实例，则对业务不会有任何影响。

jaeger

架构

Jaeger can be deployed either as all-in-one binary, where all Jaeger backend components run in a single process, or as a scalable distributed system, discussed below. There two main deployment options:

Collectors are writing directly to storage.
Collectors are writing to Kafka as a preliminary buffer.

Illustration of direct-to-storage architecture

Illustration of architecture with Kafka as intermediate buffer

An instrumented service creates spans when receiving new requests and attaches context information (trace id, span id, and baggage) to outgoing requests. Only ids and baggage are propagated with requests; all other information that compose a span like operation name, logs, etc. are not propagated. Instead sampled spans are transmitted out of process asynchronously, in the background, to Jaeger Agents.

The instrumentation has very little overhead, and is designed to be always enabled in production.

Note that while all traces are generated, only a few are sampled. Sampling a trace marks the trace for further processing and storage. By default, Jaeger client samples 0.1% of traces (1 in 1000), and has the ability to retrieve sampling strategies from the agent.

Agent

The Jaeger agent is a network daemon that listens for spans sent over UDP, which it batches and sends to the collector. It is designed to be deployed to all hosts as an infrastructure component. The agent abstracts the routing and discovery of the collectors away from the client.

Collector

The Jaeger collector receives traces from Jaeger agents and runs them through a processing pipeline. Currently our pipeline validates traces, indexes them, performs any transformations, and finally stores them.

Jaeger’s storage is a pluggable component which currently supports Cassandra, Elasticsearch and Kafka.

Query

Query is a service that retrieves traces from storage and hosts a UI to display them.

Ingester

Ingester is a service that reads from Kafka topic and writes to another storage backend (Cassandra, Elasticsearch).

部署

Agent

Jaeger client libraries expect jaeger-agent process to run locally on each host.

It can be executed directly on the host or via Docker, as follows:

## make sure to expose only the ports you use in your deployment scenario!
docker run \
  --rm \
  -p6831:6831/udp \
  -p6832:6832/udp \
  -p5778:5778/tcp \
  -p5775:5775/udp \
  jaegertracing/jaeger-agent:1.12

The agents can connect point to point to a single collector address, which could be load balanced by another infrastructure component (e.g. DNS) across multiple collectors. The agent can also be configured with a static list of collector addresses.

On Docker, a command like the following can be used:

docker run \
  --rm \
  -p5775:5775/udp \
  -p6831:6831/udp \
  -p6832:6832/udp \
  -p5778:5778/tcp \
  jaegertracing/jaeger-agent:1.12 \
  --reporter.grpc.host-port=jaeger-collector.jaeger-infra.svc:14250

When using gRPC, you have several options for load balancing and name resolution:

  • Single connection and no load balancing. This is the default if you specify a single host:port. (example: –reporter.grpc.host-port=jaeger-collector.jaeger-infra.svc:14250)
  • Static list of hostnames and round-robin load balancing. This is what you get with a comma-separated list of addresses. (example: reporter.grpc.host-port=jaeger-collector1:14250,jaeger-collector2:14250,jaeger-collector3:14250)
  • Dynamic DNS resolution and round-robin load balancing. To get this behaviour, prefix the address with dns:/// and gRPC will attempt to resolve the hostname using SRV records (for external load balancing), TXT records (for service configs), and A records. Refer to the gRPC Name Resolution docs and the dns_resolver.go implementation for more info. (example: –reporter.grpc.host-port=dns:///jaeger-collector.jaeger-infra.svc:14250)

Collectors

The collectors are stateless and thus many instances of jaeger-collector can be run in parallel. Collectors require almost no configuration, except for the location of Cassandra cluster, via –cassandra.keyspace and –cassandra.servers options, or the location of Elasticsearch cluster, via –es.server-urls, depending on which storage is specified. To see all command line options run

go run ./cmd/collector/main.go -h

or, if you don’t have the source code

docker run -it --rm jaegertracing/jaeger-collector:1.12 -h

Storage Backends

Collectors require a persistent storage backend. Cassandra and Elasticsearch are the primary supported storage backends.

The storage type can be passed via SPAN_STORAGE_TYPE environment variable. Valid values are cassandra, elasticsearch, kafka (only as a buffer), grpc-plugin, badger (only with all-in-one) and memory (only with all-in-one).

Elasticsearch

Supported in Jaeger since 0.6.0 Supported versions: 5.x, 6.x

Elasticsearch does not require initialization other than installing and running Elasticsearch. Once it is running, pass the correct configuration values to the Jaeger collector and query service.

Configuration

Minimal

docker run \
  -e SPAN_STORAGE_TYPE=elasticsearch \
  -e ES_SERVER_URLS=<...> \
  jaegertracing/jaeger-collector:1.12

To view the full list of configuration options, you can run the following command:

docker run \
  -e SPAN_STORAGE_TYPE=elasticsearch \
  jaegertracing/jaeger-collector:1.12 \
  --help

more info

微服务框架接入 opentracing 流程

一个微服务框架包括两个部分，http(gin) & grpc 两部分，对外提供 rest，对内提供 grpc 服务。

微服务软件框架图:

以下是微服务框架接入 opentracing 的大概流程。

为每个 http 请求创建一个 tracer

tracer, closer := tracing.Init("hello-world")
defer closer.Close()
opentracing.SetGlobalTracer(tracer)

创建 Span，如果 http header 中有 trace 和 span 信息，则从头部获取，否则创建新的。

spanCtx, _ := tracer.Extract(opentracing.HTTPHeaders, opentracing.HTTPHeadersCarrier(r.Header))
span := tracer.StartSpan("format", ext.RPCServerOption(spanCtx))

defer span.Finish()

// 把 span 写入 context，函数见的内部调用需要传递 ctx，或者说 span 之间需要传递 ctx。ctx := opentracing.ContextWithSpan(context.Background(), span)

Http/GRPC 服务函数的进程内部

span, _ := opentracing.StartSpanFromContext(ctx, "formatString")
defer span.Finish()


// 跨进程调用，如调用一个 rest api，则需要把 span 信息注入 http header 中。// tracing.InjectToHeaders(ctx, "GET", url, req.Header)
func InjectToHeaders(ctx context.Context, method string, url string, header http.Header) {span := opentracing.SpanFromContext(ctx)
    if span != nil {ext.SpanKindRPCClient.Set(span)
        ext.HTTPUrl.Set(span, url)
        ext.HTTPMethod.Set(span, "GET")
        span.Tracer().Inject(span.Context(),
            opentracing.HTTPHeaders,
            opentracing.HTTPHeadersCarrier(header),
        )
    }
}

span.LogFields(log.String("event", "string-format"),
        log.String("value", helloStr),
)

如何埋点

Gin 框架

router 埋点

在每个需要追踪请求的 http 路由方法上，添加「tracing.NewSpan」函数。

import ".../go_common/tracing"

...

authorized := r.Group("/v1")
authorized.Use(handlers.TokenCheck, handlers.MustLogin())
{authorized.GET("/user/:id", handlers.GetUserInfo)
    authorized.GET("/user", handlers.GetUserInfoByToken)
    authorized.PUT("/user/:id", tracing.NewSpan("put /user/:id", "handlers.Setting", false), handlers.Setting)
}

参数说明

NewSpan(service string, operationName string, abortOnErrors bool, opts …opentracing.StartSpanOption)

service generally fill with the endpoint of api.
operationName can be filled with HandleFunc’s name.

Handler 函数埋点

func Setting(c *gin.Context) {
    ...
    // 从 gin context 中获取 span；必须埋点！span, found := tracing.GetSpan(c)

    // 添加 tag 和 log
    if found == true && span != nil {span.SetTag("req", req)
        span.LogFields(log.Object("uid", uid),
        )
    }

    // opentracing.ContextWithSpan，将 span 和 context 绑定；在 handler 函数中，这个地方也是必须埋点的。ctx, cancel := context.WithTimeout(opentracing.ContextWithSpan(context.Background(), span), time.Second*3)
    defer cancel()

    // call by grpc；这块不需要特殊处理
    auth := passportpb.Authentication{LoginToken: c.GetHeader("Qsc-Peduli-Token"),
    }
    cli, _ := passportpb.Dial(ctx, grpc.WithPerRPCCredentials(&auth))
    reply, err := cli.Setting(ctx, req)

    // directly call by local rpc method；这块不需要特殊处理
    ctx = metadata.AppendToOutgoingContext(ctx, "logintoken", c.GetHeader("Qsc-Peduli-Token"))
    reply, err := rpc.Srv.Setting(ctx, req)

    ...
}

GRPC

grpc 客户端 SDK

import "github.com/grpc-ecosystem/go-grpc-middleware/tracing/opentracing"

...

// Dial grpc server
func (c *Client) Dial(serviceName string, opts ...grpc.DialOption) (*grpc.ClientConn, error) {
    
    ...
    
    unaryInterceptor := grpc_middleware.ChainUnaryClient(grpc_opentracing.UnaryClientInterceptor(),
    )

    c.Dialopts = append(c.Dialopts, grpc.WithUnaryInterceptor(unaryInterceptor))

    conn, err := grpc.Dial(serviceName, c.Dialopts...)
    if err != nil {return nil, fmt.Errorf("Failed to dial %s: %v", serviceName, err)
    }
    return conn, nil
}

grpc 服务端 SDK

import "github.com/grpc-ecosystem/go-grpc-middleware/tracing/opentracing"

...

func NewServer(serviceName, addr string) *Server {var opts []grpc.ServerOption
    opts = append(opts, grpc_middleware.WithUnaryServerChain(grpc_opentracing.UnaryServerInterceptor(),
    ))

    srv := grpc.NewServer(opts...)
    return &Server{
        serviceName: serviceName,
        addr:        addr,
        grpcServer:  srv,
    }
}

RPC 函数埋点

func (s *Service) Setting(ctx context.Context, req *passportpb.UserSettingRequest) (*passportpb.UserSettingReply, error) {
    // 如果不是 grpc 调用，即本地 rpc 函数调用方式，则从上下文中提取 span。if !s.meta.IsGrpcRequest(ctx) {span, _ := opentracing.StartSpanFromContext(ctx, "rpc.srv.Setting")
    defer span.Finish()}

    // 如果在 rpc 函数中，存在请求其它 grpc 函数，则正常调用即可，因为在 grpc 的请求上下文中已经有了 trace 和 span 信息，直接繁殖就行，无需额外操作。reqVerify := new(passportpb.VerifyRequest)
    reqVerify.UID = req.UserID
    cli, _ := passportpb.Dial(ctx)
    replyV, _ := cli.Verify(ctx, reqVerify)
}

Jaeger UI 最终效果

参考

jaegertracing

opentracing-tutorial

grpc-opentracing

gin-opentracing

go-opentracing-guides

opentracing 文档中文版

正文完
golang microservice 微服务
发表至：无分类
2019-09-04
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