NATS文档
  • 欢迎
  • 发行备注
    • 最新情况
      • NATS 2.2
      • NATS 2.0
  • NATS 概念
    • 概览
      • 比较 NATS
    • 什么是NATS
      • 演练安装
    • 基于主题的消息
    • 核心NATS
      • 发布和订阅
        • 发布/订阅演 练
      • 请求和响应
        • 请求/响应 演练
      • 队列组
        • 队列 演练
    • JetStream
      • 流
      • 消费者
        • 示例
      • JetStream 演练
      • 键值对存储
        • 键值对存储演练
      • 对象存储
        • 对象存储演练
    • 主题映射与分区
    • NATS服务器基础架构
      • NATS部署架构适配
    • 安全
    • 连接性
  • 使用 NATS
    • NATS工具
      • nats
        • nats基准测试
      • nk
      • nsc
        • 基础
        • 流
        • 服务
        • 签名密钥
        • 撤销
        • 管理操作
      • nats-top
        • 教程
    • 用NATS开发
      • 一个NATS应用的解剖
      • 连接
        • 连接到默认服务器
        • 连接到特定服务器
        • 连接到群集
        • 连接名称
        • 用用户名和密码做认证
        • 用令牌做认证
        • 用NKey做认证
        • 用一个可信文件做认证
        • 用TLS加密连接
        • 设置连接超时
        • 乒乓协议
        • 关闭响应消息
        • 杂技功能
        • 自动恢复
          • 禁用自动重连
          • 设置自动重新连接的最大次数
          • 随机
          • 重连尝试之间暂停
          • 关注重连事件
          • 重连尝试期间缓存消息
        • 监视连接
          • 关注连接事件
          • 低速消费者
      • 接收消息
        • 同步订阅
        • 异步订阅
        • 取消订阅
        • N个消息后取消订阅
        • 回复一个消息
        • 通配符订阅
        • 队列订阅
        • 断开连接前清除消息
        • 接收结构化数据
      • 发送消息
        • 包含一个回复主题
        • 请求回复语义
        • 缓存刷入和乒
        • 发送结构化数据
      • JetStream
        • 深入JetStream模型
        • 管理流和消费者
        • 消费者详情
        • 发布到流
        • 使用键值对存储
        • 使用对象存储
      • 教程
        • 用go做个自定义拨号器
  • 运行一个NATS服务
    • 安装、运行和部署NATS服务
      • 安装一个NATS服务
      • 运行和部署一个NATS服务
      • Windows服务
      • 信号
    • 环境约束
    • NATS和Docker
      • 教程
      • Docker Swarm
      • Python 和 NGS 运行在Docker
      • JetStream
    • NATS和Kubernetes
      • 用Helm 部署NATS
      • 创建一个Kubernetes群集
      • NATS群集和认证管理
      • 用cfssl保护NATS群集
      • 用负载均衡来保护外部的NATS访问
      • 在Digital Ocean用Helm创建超级NATS群集
      • 使用Helm从0到K8S再到叶子节点
    • NATS服务的客户端
    • 配置 NATS服务
      • 配置 JetStream
        • 配置管理 Management
          • NATS管理命令行
          • 地形
          • GitHub Actions
          • Kubernetes控制器
      • 群集
        • 群集配置
        • JetStream 群集
          • 管理
      • 网关超级群集
        • 配置
      • 叶子节点
        • 配置
        • JetStream在叶子节点
      • 安全加固NATS
        • 使用 TLS
        • 认证
          • 令牌
          • 用户名/密码
          • TLS认证
            • 群集中的TLS认证
          • NKeys
          • 认证超时
          • 去中心化的 JWT 认证/授权
            • 使用解析器查找帐户
            • 内存解析器教程
            • 混合认证/授权安装
        • 授权
        • 基于账户的多租户
        • OCSP Stapling
      • 日志
      • 使用监控
      • MQTT
        • 配置
      • 配置主题映射
      • 系统事件
        • 系统时间和去中心化的JWT教程
      • WebSocket
        • 配置
    • 管理和监控你的NATS服务基础架构
      • 监控
        • 监控 JetStream
      • 管理 JetStream
        • 账号信息
        • 命名流,消费者和账号
        • 流
        • 消费者
        • 数据复制
        • 灾难回复
        • 加密Rest
      • 管理JWT安全
        • 深入JWT指南
      • 升级一个群集
      • 慢消费者
      • 信号
      • 跛脚鸭模式
  • 参考
    • 常见问题
    • NATS协议
      • 协议演示
      • 客户端协议
        • 开发一个客户端
      • NATS群集协议
      • JetStream API参考
  • 遗产
    • STAN='NATS流'
      • STAN概念
        • 和NATS的关系
        • 客户端连接
        • 频道
          • 消息日志
          • 订阅
            • 通常的
            • 持久化的
            • 队列组
            • 重新投递
        • 存储接口
        • 存储加密
        • 群集
          • Supported Stores
          • Configuration
          • Auto Configuration
          • Containers
        • Fault Tolerance
          • Active Server
          • Standby Servers
          • Shared State
          • Failover
        • Partitioning
        • Monitoring
          • Endpoints
      • Developing With STAN
        • Connecting to NATS Streaming Server
        • Publishing to a Channel
        • Receiving Messages from a Channel
        • Durable Subscriptions
        • Queue Subscriptions
        • Acknowledgements
        • The Streaming Protocol
      • STAN NATS Streaming Server
        • Installing
        • Running
        • Configuring
          • Command Line Arguments
          • Configuration File
          • Store Limits
          • Persistence
            • File Store
            • SQL Store
          • Securing
        • Process Signaling
        • Windows Service
        • Embedding NATS Streaming Server
        • Docker Swarm
        • Kubernetes
          • NATS Streaming with Fault Tolerance.
    • nats账号服务
      • Basics
      • Inspecting JWTs
      • Directory Store
      • Update Notifications
由 GitBook 提供支持
在本页
  • Simple Mapping
  • Subject Token Reordering
  • Deterministic Subject token Partitioning
  • When is deterministic partitioning needed
  • Weighted Mappings for A/B Testing or Canary Releases
  • Traffic Shaping in Testing
  • Artificial Loss
  1. NATS 概念

主题映射与分区

Subject mapping and partitioning is a very powerful feature of the NATS server, useful for scaling some forms of distributed message processing through partitioning, for canary deployments, A/B testing, chaos testing, and migrating to a new subject namespace.

There are two places where you can apply subject mappings: each account has its own set of subject mappings, which will apply to any message published by client applications, and you can also use subject mappings as part of the imports and exports between accounts.

When not using operator JWT security, you can define the subject mappings in server configuration files, and you simply need to send a signal for the nats-server process to reload the configuration whenever you change a mapping for the change to take effect.

When using operator JWT security with the built-in resolver you define the mappings and the import/exports in the account JWT so after modifying them they will take effect as soon as you push the updated account JWT to the servers.

Simple Mapping

The example of foo:bar is straightforward. All messages the server receives on subject foo are remapped and can be received by clients subscribed to bar.

Subject Token Reordering

Wildcard tokens may be referenced by position number in the destination mapping using (only for versions 2.8.0 and above of nats-server) {{wildcard(position)}}. E.g. {{wildcard(1)}} references the first wildcard token, {{wildcard(2)}} references the second wildcard token, etc...

You can also (for all versions of nats-server) use the legacy notation of $position. E.g. $1 references the first wild card token, $2 the second wildcard token, etc...

Example: with this mapping "bar.*.*" : "baz.{{wildcard(2)}}.{{wildcard(1)}}", messages that were originally published to bar.a.b are remapped in the server to baz.b.a. Messages arriving at the server on bar.one.two would be mapped to baz.two.one, and so forth.

Deterministic Subject token Partitioning

Deterministic token partitioning allows you to use subject based addressing to deterministically divide (partition) a flow of messages where one or more of the subject tokens make up the key upon which the partitioning will be based, into a number of smaller message flows.

For example: new customer orders are published on neworders.<customer id>, you can partition those messages over 3 partition numbers (buckets), using the partition(number of partitions, wildcard token positions...) function which returns a partition number (between 0 and number of partitions-1) by using the following mapping "neworders.*" : "neworders.{{wildcard(1)}}.{{partition(3,1)}}".

This particular mapping means that any message published on neworders.<customer id> will be mapped to neworders.<customer id>.<a partition number 0, 1, or 2>. i.e.:

Published on
Mapped to

neworders.customerid1

neworders.customerid1.0

neworders.customerid2

neworders.customerid2.2

neworders.customerid3

neworders.customerid3.1

neworders.customerid4

neworders.customerid4.2

neworders.customerid5

neworders.customerid5.1

neworders.customerid6

neworders.customerid6.0

The mapping is deterministic because (as long as the number of partitions is 3) 'customerid1' will always map to the same partition number. The mapping is hash based, it's distribution is random but tending towards 'perfectly balanced' distribution (i.e. the more keys you map the more the number of keys for each partition will tend to converge to the same number).

You can partition on more than one subject wildcard token at a time, e.g.: {{partition(10,1,2)}} distributes the union of token wildcards 1 and 2 over 10 partitions.

Published on
Mapped to

foo.1.a

foo.1.a.1

foo.1.b

foo.1.b.0

foo.2.b

foo.2.b.9

foo.2.a

foo.2.a.2

What this deterministic partition mapping enables is the distribution of the messages that are subscribed to using a single subscriber (on neworders.*) into three separate subscribers (respectively on neworders.*.0, neworders.*.1 and neworders.*.2) that can operate in parallel.

When is deterministic partitioning needed

The core NATS queue-groups and JetStream durable consumer mechanisms to distribute messages amongst a number of subscribers are partition-less and non-deterministic, meaning that there is no guarantee that two sequential messages published on the same subject are going to be distributed to the same subscriber. While in most use cases a completely dynamic, demand-driven distribution is what you need, it does come at the cost of guaranteed ordering because if two subsequent messages can be sent to two different subscribers which would then both process those messages at the same time at different speeds (or the message has to be re-transmitted, or the network is slow, etc...) and that could result in potential 'out of order' message delivery.

This means that if the application requires strictly ordered message processing, you need to limit distribution of messages to 'one at a time' (per consumer/queue-group, i.e. using the 'max acks pending' setting), which in turns hurts scalability because it means no matter how many workers you have subscribed only one at a time is doing any processing work.

Being able to evenly split (i.e. partition) subjects in a deterministic manner (meaning that all the messages on a particular subject are always mapped to the same partition) allows you to distribute and scale the processing of messages in a subject stream while still maintaining strict ordering per subject.

Another reason to need deterministic mapping is in the extreme message rates scenarios where you are reaching the limits of the throughput of incoming messages into a stream capturing messages using a wildcard subject. This limit can be ultimately reached at very high message rates due to the fact that a single nats-server process is acting as the RAFT leader (coordinator) for any given stream and can therefore become a limiting factor. In that case, distributing (i.e. partitioning) that stream into a number of smaller streams (each one with their own RAFT leader and therefore all these RAFT leaders are spread over all of the JetStream-enabled nats-servers in the cluster rather than a single one) in order to scale.

Yet another use case where deterministic partitioning can help is if you want to leverage local data caching of data (context or potentially heavy historical data for example) that the subscribing process need to access as part of the processing of the messages.

Weighted Mappings for A/B Testing or Canary Releases

Traffic can be split by percentage from one subject to multiple subjects. Here's an example for canary deployments, starting with version 1 of your service.

Applications would make requests of a service at myservice.requests. The responders doing the work of the server would subscribe to myservice.requests.v1. Your configuration would look like this:

  myservice.requests: [
    { destination: myservice.requests.v1, weight: 100% }
  ]

All requests to myservice.requests will go to version 1 of your service.

When version 2 comes along, you'll want to test it with a canary deployment. Version 2 would subscribe to myservice.requests.v2. Launch instances of your service.

Update the configuration file to redirect some portion of the requests made to myservice.requests to version 2 of your service.

For example the configuration below means 98% of the requests will be sent to version 1 and 2% to version 2.

    myservice.requests: [
        { destination: myservice.requests.v1, weight: 98% },
        { destination: myservice.requests.v2, weight: 2% }
    ]

Once you've determined Version 2 is stable you can switch 100% of the traffic over to it and you can then shutdown the version 1 instance of your service.

Traffic Shaping in Testing

Traffic shaping is also useful in testing. You might have a service that runs in QA that simulates failure scenarios which could receive 20% of the traffic to test the service requestor.

myservice.requests.*: [{ destination: myservice.requests.$1, weight: 80% }, { destination: myservice.requests.fail.$1, weight: 20% }

Artificial Loss

Alternatively, introduce loss into your system for chaos testing by mapping a percentage of traffic to the same subject. In this drastic example, 50% of the traffic published to foo.loss.a would be artificially dropped by the server.

foo.loss.>: [ { destination: foo.loss.>, weight: 50% } ]

You can both split and introduce loss for testing. Here, 90% of requests would go to your service, 8% would go to a service simulating failure conditions, and the unaccounted for 2% would simulate message loss.

myservice.requests: [{ destination: myservice.requests.v3, weight: 90% }, { destination: myservice.requests.v3.fail, weight: 8% }] the remaining 2% is "lost"

上一页对象存储演练下一页NATS服务器基础架构

最后更新于2年前