## 1. Project Overview & Quickstart (apache/apisix)
## File: README.md
# Apache APISIX API Gateway ｜ AI Gateway
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**Apache APISIX** is a dynamic, real-time, high-performance API Gateway.
APISIX API Gateway provides rich traffic management features such as load balancing, dynamic upstream, canary release, circuit breaking, authentication, observability, and more. You can use it to handle traditional north-south traffic, as well as east-west traffic between services. It can also be used as a [Kubernetes ingress controller](https://github.com/apache/apisix-ingress-controller).
## Table of Contents
- [Why APISIX](#why-apisix)
- [AI Gateway](#ai-gateway)
- [Get Started](#get-started)
- [Features](#features)
- [Benchmark](#benchmark)
- [Community](#community)
- [User Stories](#user-stories)
- [Who Uses APISIX API Gateway?](#who-uses-apisix-api-gateway)
- [Logos](#logos)
- [Acknowledgments](#acknowledgments)
- [License](#license)
## Why APISIX
APISIX is built on top of NGINX and etcd. Compared with traditional API gateways, APISIX has dynamic routing and hot-loading of plugins, which is especially suitable for API management under a microservice architecture.
The technical architecture of Apache APISIX:
## AI Gateway
APISIX can serve as an **[AI Gateway](https://apisix.apache.org/ai-gateway/)** through its flexible plugin system, providing:
- **AI proxying** to route traffic to different LLM providers through a unified interface.
- **Load balancing, retries, and fallbacks** across multiple LLMs to ensure the efficiency and reliability of AI agents.
- **Token-based rate limiting** to control cost and protect upstream models.
- **Robust security** for authentication, authorization, and traffic control on AI workloads.
APISIX also provides the [`mcp-bridge`](https://apisix.apache.org/blog/2025/04/21/host-mcp-server-with-api-gateway/) plugin to seamlessly convert stdio-based MCP servers to scalable HTTP SSE services.
## Get Started
Install and run APISIX with a single command using the quickstart script (requires [Docker](https://docs.docker.com/get-docker/)):
```shell
curl -sL https://run.api7.ai/apisix/quickstart | sh
```
This starts APISIX (listening on port `9080`) together with its etcd configuration store. Verify it is running:
```shell
curl "http://127.0.0.1:9080" --head | grep Server
```
Create your first route via the Admin API (port `9180`) to proxy requests to an upstream service:
```shell
curl -i "http://127.0.0.1:9180/apisix/admin/routes/1" -X PUT -d '
{
"uri": "/get",
"upstream": {
"type": "roundrobin",
"nodes": {
"httpbin.org:80": 1
}
}
}'
```
Send a request through APISIX to confirm the route works:
```shell
curl "http://127.0.0.1:9080/get"
```
To learn more, follow the [Getting Started](https://apisix.apache.org/docs/apisix/getting-started/) guide and the [installation documentation](https://apisix.apache.org/docs/apisix/installation-guide/) for other deployment methods. To extend APISIX, see the [plugin development guide](docs/en/latest/plugin-develop.md), the [plugin concept](docs/en/latest/terminology/plugin.md), and the [REST Admin API](docs/en/latest/admin-api.md) reference.
For more documents, please refer to the [Apache APISIX Documentation site](https://apisix.apache.org/docs/apisix/getting-started/).
## Features
You can use APISIX API Gateway as a traffic entrance to process all business data, including dynamic routing, dynamic upstream, dynamic certificates,
A/B testing, canary release, blue-green deployment, limit rate, defense against malicious attacks, metrics, monitoring alarms, service observability, service governance, etc.
- **All platforms**
- Cloud-Native: Platform agnostic, No vendor lock-in, APISIX API Gateway can run from bare-metal to Kubernetes.
- Supports ARM64: Don't worry about the lock-in of the infra technology.
- **Multi protocols**
- [TCP/UDP Proxy](docs/en/latest/stream-proxy.md): Dynamic TCP/UDP proxy.
- [Dubbo Proxy](docs/en/latest/plugins/dubbo-proxy.md): Dynamic HTTP to Dubbo proxy.
- [Dynamic MQTT Proxy](docs/en/latest/plugins/mqtt-proxy.md): Supports to load balance MQTT by `client_id`, both support MQTT [3.1.\*](http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/os/mqtt-v3.1.1-os.html), [5.0](https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html).
- [gRPC proxy](docs/en/latest/grpc-proxy.md): Proxying gRPC traffic.
- [gRPC Web Proxy](docs/en/latest/plugins/grpc-web.md): Proxying gRPC Web traffic to gRPC Service.
- [gRPC transcoding](docs/en/latest/plugins/grpc-transcode.md): Supports protocol transcoding so that clients can access your gRPC API by using HTTP/JSON.
- Proxy Websocket
- Proxy Protocol
- HTTP(S) Forward Proxy
- [SSL](docs/en/latest/certificate.md): Dynamically load an SSL certificate
- [HTTP/3 with QUIC](docs/en/latest/http3.md)
- **Full Dynamic**
- [Hot Updates And Hot Plugins](docs/en/latest/terminology/plugin.md): Continuously updates its configurations and plugins without restarts!
- [Proxy Rewrite](docs/en/latest/plugins/proxy-rewrite.md): Support rewrite the `host`, `uri`, `schema`, `method`, `headers` of the request before send to upstream.
- [Response Rewrite](docs/en/latest/plugins/response-rewrite.md): Set customized response status code, body and header to the client.
- Dynamic Load Balancing: Round-robin load balancing with weight.
- Hash-based Load Balancing: Load balance with consistent hashing sessions.
- [Health Checks](docs/en/latest/tutorials/health-check.md): Enable health check on the upstream node and will automatically filter unhealthy nodes during load balancing to ensure system stability.
- Circuit-Breaker: Intelligent tracking of unhealthy upstream services.
- [Proxy Mirror](docs/en/latest/plugins/proxy-mirror.md): Provides the ability to mirror client requests.
- [Traffic Split](docs/en/latest/plugins/traffic-split.md): Allows users to incrementally direct percentages of traffic between various upstreams.
- **Fine-grained routing**
- [Supports full path matching and prefix matching](docs/en/latest/router-radixtree.md#how-to-use-libradixtree-in-apisix)
- [Support all Nginx built-in variables as conditions for routing](docs/en/latest/router-radixtree.md#how-to-filter-route-by-nginx-builtin-variable), so you can use `cookie`, `args`, etc. as routing conditions to implement canary release, A/B testing, etc.
- Support [various operators as judgment conditions for routing](https://github.com/iresty/lua-resty-radixtree#operator-list), for example `{"arg_age", ">", 24}`
- Support [custom route matching function](https://github.com/iresty/lua-resty-radixtree/blob/master/t/filter-fun.t#L10)
- IPv6: Use IPv6 to match the route.
- Support [TTL](docs/en/latest/admin-api.md#route)
- [Support priority](docs/en/latest/router-radixtree.md#3-match-priority)
- [Support Batch Http Requests](docs/en/latest/plugins/batch-requests.md)
- [Support filtering route by GraphQL attributes](docs/en/latest/router-radixtree.md#how-to-filter-route-by-graphql-attributes)
- **Security**
- Rich authentication & authorization support:
* [key-auth](docs/en/latest/plugins/key-auth.md)
* [JWT](docs/en/latest/plugins/jwt-auth.md)
* [basic-auth](docs/en/latest/plugins/basic-auth.md)
* [wolf-rbac](docs/en/latest/plugins/wolf-rbac.md)
* [casbin](docs/en/latest/plugins/authz-casbin.md)
* [keycloak](docs/en/latest/plugins/authz-keycloak.md)
* [casdoor](docs/en/latest/plugins/authz-casdoor.md)
- [IP Whitelist/Blacklist](docs/en/latest/plugins/ip-restriction.md)
- [Referer Whitelist/Blacklist](docs/en/latest/plugins/referer-restriction.md)
- [IdP](docs/en/latest/plugins/openid-connect.md): Support external Identity platforms, such as Auth0, okta, etc..
- [Limit-req](docs/en/latest/plugins/limit-req.md)
- [Limit-count](docs/en/latest/plugins/limit-count.md)
- [Limit-concurrency](docs/en/latest/plugins/limit-conn.md)
- Anti-ReDoS(Regular expression Denial of Service): Built-in policies to Anti ReDoS without configuration.
- [CORS](docs/en/latest/plugins/cors.md) Enable CORS(Cross-origin resource sharing) for your API.
- [URI Blocker](docs/en/latest/plugins/uri-blocker.md): Block client request by URI.
- [Request Validator](docs/en/latest/plugins/request-validation.md)
- [CSRF](docs/en/latest/plugins/csrf.md) Based on the [`Double Submit Cookie`](https://en.wikipedia.org/wiki/Cross-site_request_forgery#Double_Submit_Cookie) way, protect your API from CSRF attacks.
- **OPS friendly**
- Zipkin tracing: [Zipkin](docs/en/latest/plugins/zipkin.md)
- Open source APM: support [Apache SkyWalking](docs/en/latest/plugins/skywalking.md)
- Works with external service discovery: In addition to the built-in etcd, it also supports [Consul](docs/en/latest/discovery/consul.md), [Consul_kv](docs/en/latest/discovery/consul_kv.md), [Nacos](docs/en/latest/discovery/nacos.md), [Eureka](docs/en/latest/discovery/eureka.md) and [Zookeeper (CP)](https://github.com/api7/apisix-seed/blob/main/docs/en/latest/zookeeper.md).
- Monitoring And Metrics: [Prometheus](docs/en/latest/plugins/prometheus.md)
- Clustering: APISIX nodes are stateless, creates clustering of the configuration center, please refer to [etcd Clustering Guide](https://etcd.io/docs/v3.5/op-guide/clustering/).
- High availability: Support to configure multiple etcd addresses in the same cluster.
- [Dashboard](https://github.com/apache/apisix-dashboard)
- Version Control: Supports rollbacks of operations.
- CLI: start\stop\reload APISIX through the command line.
- [Standalone](docs/en/latest/deployment-modes.md#standalone): Supports to load route rules from local YAML file, it is more friendly such as under the kubernetes(k8s).
- [Global Rule](docs/en/latest/terminology/global-rule.md): Allows to run any plugin for all request, eg: limit rate, IP filter etc.
- High performance: The single-core QPS reaches 18k with an average delay of fewer than 0.2 milliseconds.
- [Fault Injection](docs/en/latest/plugins/fault-injection.md)
- [REST Admin API](docs/en/latest/admin-api.md): Using the REST Admin API to control Apache APISIX, which only allows 127.0.0.1 access by default, you can modify the `allow_admin` field in `conf/config.yaml` to specify a list of IPs that are allowed to call the Admin API. Also, note that the Admin API uses key auth to verify the identity of the caller.
- External Loggers: Export access logs to external log management tools. ([HTTP Logger](docs/en/latest/plugins/http-logger.md), [TCP Logger](docs/en/latest/plugins/tcp-logger.md), [Kafka Logger](docs/en/latest/plugins/kafka-logger.md), [UDP Logger](docs/en/latest/plugins/udp-logger.md), [RocketMQ Logger](docs/en/latest/plugins/rocketmq-logger.md), [SkyWalking Logger](docs/en/latest/plugins/skywalking-logger.md), [Alibaba Cloud Logging(SLS)](docs/en/latest/plugins/sls-logger.md), [Google Cloud Logging](docs/en/latest/plugins/google-cloud-logging.md), [Splunk HEC Logging](docs/en/latest/plugins/splunk-hec-logging.md), [File Logger](docs/en/latest/plugins/file-logger.md), [SolarWinds Loggly Logging](docs/en/latest/plugins/loggly.md), [TencentCloud CLS](docs/en/latest/plugins/tencent-cloud-cls.md)).
- [ClickHouse](docs/en/latest/plugins/clickhouse-logger.md): push logs to ClickHouse.
- [Elasticsearch](docs/en/latest/plugins/elasticsearch-logger.md): push logs to Elasticsearch.
- [Datadog](docs/en/latest/plugins/datadog.md): push custom metrics to the DogStatsD server, comes bundled with [Datadog agent](https://docs.datadoghq.com/agent/), over the UDP protocol. DogStatsD basically is an implementation of StatsD protocol which collects the custom metrics for Apache APISIX agent, aggregates it into a single data point and sends it to the configured Datadog server.
- [Helm charts](https://github.com/apache/apisix-helm-chart)
- [HashiCorp Vault](https://www.vaultproject.io/): Support secret management solution for accessing secrets from Vault secure storage backed in a low trust environment. Currently, RS256 keys (public-private key pairs) or secret keys can be linked from vault in jwt-auth authentication plugin using [APISIX Secret](docs/en/latest/terminology/secret.md) resource.
- **Highly scalable**
- [Custom plugins](docs/en/latest/plugin-develop.md): Allows hooking of common phases, such as `rewrite`, `access`, `header filter`, `body filter` and `log`, also allows to hook the `balancer` stage.
- [Plugin can be written in Java/Go/Python](docs/en/latest/external-plugin.md)
- [Plugin can be written with Proxy Wasm SDK](docs/en/latest/wasm.md)
- Custom load balancing algorithms: You can use custom load balancing algorithms during the `balancer` phase.
- Custom routing: Support users to implement routing algorithms themselves.
- **Multi-Language support**
- Apache APISIX is a multi-language gateway for plugin development and provides support via `RPC` and `Wasm`.
- The RPC way, is the current way. Developers can choose the language according to their needs and after starting an independent process with the RPC, it exchanges data with APISIX through local RPC communication. Till this moment, APISIX has support for [Java](https://github.com/apache/apisix-java-plugin-runner), [Golang](https://github.com/apache/apisix-go-plugin-runner), [Python](https://github.com/apache/apisix-python-plugin-runner) and Node.js.
- The Wasm or WebAssembly, is an experimental way. APISIX can load and run Wasm bytecode via APISIX [wasm plugin](https://github.com/apache/apisix/blob/master/docs/en/latest/wasm.md) written with the [Proxy Wasm SDK](https://github.com/proxy-wasm/spec#sdks). Developers only need to write the code according to the SDK and then compile it into a Wasm bytecode that runs on Wasm VM with APISIX.
- **Serverless**
- [Lua functions](docs/en/latest/plugins/serverless.md): Invoke functions in each phase in APISIX.
- [AWS Lambda](docs/en/latest/plugins/aws-lambda.md): Integration with AWS Lambda function as a dynamic upstream to proxy all requests for a particular URI to the AWS API gateway endpoint. Supports authorization via api key and AWS IAM access secret.
- [Azure Functions](docs/en/latest/plugins/azure-functions.md): Seamless integration with Azure Serverless Function as a dynamic upstream to proxy all requests for a particular URI to the Microsoft Azure cloud.
- [Apache OpenWhisk](docs/en/latest/plugins/openwhisk.md): Seamless integration with Apache OpenWhisk as a dynamic upstream to proxy all requests for a particular URI to your own OpenWhisk cluster.
## Benchmark
Using AWS's eight-core server, APISIX's QPS reaches 140,000 with a latency of only 0.2 ms.
[Benchmark script](benchmark/run.sh) has been open sourced, welcome to try and contribute.
[APISIX also works perfectly in AWS graviton3 C7g.](https://apisix.apache.org/blog/2022/06/07/installation-performance-test-of-apigateway-apisix-on-aws-graviton3)
## Community
- [Kindly Write a Review](https://www.g2.com/products/apache-apisix/reviews) for APISIX in G2.
- Mailing List: Mail to dev-subscribe@apisix.apache.org, follow the reply to subscribe to the mailing list.
- Slack Workspace - [invitation link](https://apisix.apache.org/slack) (Please open an [issue](https://apisix.apache.org/docs/general/submit-issue) if this link is expired), and then join the #apisix channel (Channels -> Browse channels -> search for "apisix").
- - follow and interact with us using hashtag `#ApacheAPISIX`
- [Documentation](https://apisix.apache.org/docs/)
- [Discussions](https://github.com/apache/apisix/discussions)
- [Blog](https://apisix.apache.org/blog)
## User Stories
- [European eFactory Platform: API Security Gateway – Using APISIX in the eFactory Platform](https://www.efactory-project.eu/post/api-security-gateway-using-apisix-in-the-efactory-platform)
- [Copernicus Reference System Software](https://github.com/COPRS/infrastructure/wiki/Networking-trade-off)
- [More Stories](https://apisix.apache.org/blog/tags/case-studies/)
## Who Uses APISIX API Gateway?
A wide variety of companies and organizations use APISIX API Gateway for research, production and commercial product, below are some of them:
- Airwallex
- Bilibili
- CVTE
- European eFactory Platform
- European Copernicus Reference System
- Geely
- HONOR
- Horizon Robotics
- iQIYI
- Lenovo
- NASA JPL
- Nayuki
- OPPO
- QingCloud
- Swisscom
- Tencent Game
- Travelsky
- vivo
- Sina Weibo
- WeCity
- WPS
- XPENG
- Zoom
## Logos
- [Apache APISIX logo(PNG)](https://github.com/apache/apisix/tree/master/logos/apache-apisix.png)
- [Apache APISIX logo source](https://apache.org/logos/#apisix)
## Acknowledgments
Inspired by Kong and Orange.
## License
[Apache 2.0 License](https://github.com/apache/apisix/tree/master/LICENSE)
---
## File: docs/zh/latest/architecture-design/apisix.md
---
title: 软件架构
keywords:
- 网关
- Apache APISIX
- APISIX 架构
---
Apache APISIX 是一个动态、实时、高性能的云原生 API 网关。它构建于 NGINX + ngx_lua 的技术基础之上，充分利用了 LuaJIT 所提供的强大性能。 [为什么 Apache APISIX 选择 NGINX+Lua 技术栈？](https://apisix.apache.org/zh/blog/2021/08/25/why-apache-apisix-chose-nginx-and-lua/)。
APISIX 主要分为两个部分：
1. APISIX 核心：包括 Lua 插件、多语言插件运行时（Plugin Runner）、Wasm 插件运行时等；
2. 功能丰富的各种内置插件：包括可观测性、安全、流量控制等。
APISIX 在其核心中，提供了路由匹配、负载均衡、服务发现、API 管理等重要功能，以及配置管理等基础性模块。除此之外，APISIX 插件运行时也包含其中，提供原生 Lua 插件的运行框架和多语言插件的运行框架，以及实验性的 Wasm 插件运行时等。APISIX 多语言插件运行时提供多种开发语言的支持，比如 Golang、Java、Python、JS 等。
APISIX 目前也内置了各类插件，覆盖了 API 网关的各种领域，如认证鉴权、安全、可观测性、流量管理、多协议接入等。当前 APISIX 内置的插件使用原生 Lua 实现，关于各个插件的介绍与使用方式，可以查看相关[插件文档](https://apisix.apache.org/docs/apisix/plugins/batch-requests)。
## 插件加载流程
## 插件内部结构
---
## File: docs/zh/latest/discovery/control-plane-service-discovery.md
---
title: 控制面服务发现
keywords:
- API 网关
- APISIX
- ZooKeeper
- Nacos
- APISIX-Seed
---
本文档介绍了如何在 APISIX 控制面通过 Nacos 和 Zookeeper 实现服务发现。
## APISIX-Seed 架构
Apache APISIX 在早期已经支持了数据面服务发现，现在 APISIX 也通过 [APISIX-Seed](https://github.com/api7/apisix-seed) 项目实现了控制面服务发现，下图为 APISIX-Seed 架构图。
图中的数字代表的具体信息如下：
1. 通过 Admin API 向 APISIX 注册上游并指定服务发现类型。APISIX-Seed 将监听 etcd 中的 APISIX 资源变化，过滤服务发现类型并获取服务名称（如 ZooKeeper）；
2. APISIX-Seed 将在服务注册中心（如 ZooKeeper）订阅指定的服务名称，以监控和更新对应的服务信息；
3. 客户端向服务注册中心注册服务后，APISIX-Seed 会获取新的服务信息，并将更新后的服务节点写入 etcd；
4. 当 APISIX-Seed 在 etcd 中更新相应的服务节点信息时，APISIX 会将最新的服务节点信息同步到内存中。
:::note
引入 APISIX-Seed 后，如果注册中心的服务变化频繁，etcd 中的数据也会频繁变化。因此，需要在启动 etcd 时设置 `--auto-compaction` 选项，用来定期压缩历史记录，避免耗尽 etcd 存储空间。详细信息请参考 [revisions](https://etcd.io/docs/v3.5/learning/api/#revisions)。
:::
## 为什么需要 APISIX-Seed？
- 网络拓扑变得更简单
APISIX 不需要与每个注册中心保持网络连接，只需要关注 etcd 中的配置信息即可。这将大大简化网络拓扑。
- 上游服务总数据量变小
由于 `registry` 的特性，APISIX 可能会在 Worker 中存储全量的 `registry` 服务数据，例如 Consul_KV。通过引入 APISIX-Seed，APISIX 的每个进程将不需要额外缓存上游服务相关信息。
- 更容易管理
服务发现配置需要为每个 APISIX 实例配置一次。通过引入 APISIX-Seed，APISIX 将对服务注册中心的配置变化无感知。
## 支持的服务发现类型
目前已经支持了 ZooKeeper 和 Nacos，后续还将支持更多的服务注册中心，更多信息请参考：[APISIX Seed](https://github.com/api7/apisix-seed#apisix-seed-for-apache-apisix)。
- 如果你想启用控制面 ZooKeeper 服务发现，请参考：[ZooKeeper 部署教程](https://github.com/api7/apisix-seed/blob/main/docs/zh/latest/zookeeper.md)。
- 如果你想启用控制面 Nacos 服务发现，请参考：[Nacos 部署教程](https://github.com/api7/apisix-seed/blob/main/docs/zh/latest/nacos.md)。
---
## File: docs/zh/latest/discovery/dns.md
---
title: DNS
---
## 基于 DNS 的服务发现
某些服务发现系统如 Consul，支持通过 DNS 提供系统信息。我们可以使用这种方法直接实现服务发现，七层与四层均支持。
首先我们需要配置 DNS 服务器的地址：
```yaml
# 添加到 config.yaml
discovery:
dns:
servers:
- "127.0.0.1:8600" # 使用 DNS 服务器的真实地址
```
与在 Upstream 的 `nodes` 对象中配置域名不同的是，DNS 服务发现将返回所有的记录。例如按照以下的 upstream 配置：
```json
{
"id": 1,
"discovery_type": "dns",
"service_name": "test.consul.service",
"type": "roundrobin"
}
```
之后 `test.consul.service` 将被解析为 `1.1.1.1` 和 `1.1.1.2`，这个结果等同于：
```json
{
"id": 1,
"type": "roundrobin",
"nodes": [
{"host": "1.1.1.1", "weight": 1},
{"host": "1.1.1.2", "weight": 1}
]
}
```
注意所有来自 `test.consul.service` 的 IP 都有相同的权重。
解析的记录将根据它们的 TTL 来进行缓存。对于记录不在缓存中的服务，我们将默认按照 `SRV -> A -> AAAA -> CNAME` 的顺序进行查询，刷新缓存记录时，我们将从上次成功的类型开始尝试。也可以通过修改配置文件来自定义 DNS 的解析顺序。
```yaml
# 添加到 config.yaml
discovery:
dns:
servers:
- "127.0.0.1:8600" # 使用 DNS 服务器的真实地址
order: # DNS 解析的顺序
- last # "last" 表示从上次成功的类型开始
- SRV
- A
- AAAA
- CNAME
```
如果你想指定 upstream 服务器的端口，可以把以下内容添加到 `service_name`：
```json
{
"id": 1,
"discovery_type": "dns",
"service_name": "test.consul.service:1980",
"type": "roundrobin"
}
```
另一种方法是通过 SRV 记录，见如下。
### SRV 记录
通过使用 SRV 记录你可以指定一个服务的端口和权重。
假设你有一条这样的 SRV 记录：
```
; under the section of blah.service
A 300 IN A 1.1.1.1
B 300 IN A 1.1.1.2
B 300 IN A 1.1.1.3
; name TTL type priority weight port
srv 86400 IN SRV 10 60 1980 A
srv 86400 IN SRV 20 20 1981 B
```
Upstream 配置是这样的：
```json
{
"id": 1,
"discovery_type": "dns",
"service_name": "srv.blah.service",
"type": "roundrobin"
}
```
效果等同于：
```json
{
"id": 1,
"type": "roundrobin",
"nodes": [
{"host": "1.1.1.1", "port": 1980, "weight": 60, "priority": -10},
{"host": "1.1.1.2", "port": 1981, "weight": 10, "priority": -20},
{"host": "1.1.1.3", "port": 1981, "weight": 10, "priority": -20}
]
}
```
注意 B 域名的两条记录均分权重。
对于 SRV 记录，低优先级的节点被先选中，所以最后一项的优先级是负数。
关于 0 权重的 SRV 记录，在 [RFC 2782](https://www.ietf.org/rfc/rfc2782.txt) 中是这么描述的：
> 当没有任何候选服务器时，域管理员应使用权重为 0 的，使 RR 更为易读（噪音更少）。当存在权重大于 0 的记录时，权重为 0 的记录被选中的可能性很小。
我们把权重为 0 的记录当作权重为 1，因此节点“被选中的可能性很小”，这也是处理此类记录的常用方法。
对于端口为 0 的 SRV 记录，我们会使用上游协议的默认端口。
你也可以在“service_name”字段中直接指定端口，比如“srv.blah.service:8848”。
---
## File: docs/zh/latest/discovery/eureka.md
---
title: eureka
---
Apache APISIX 支持使用 [Eureka](https://github.com/Netflix/eureka#eureka) 做服务发现。
详情请阅读 [支持的服务注册发现](../discovery.md#当前支持的注册中心) 。
---
## File: docs/zh/latest/discovery/kubernetes.md
---
title: Kubernetes
keywords:
- Kubernetes
- Apache APISIX
- 服务发现
- 集群
- API 网关
---
## 基于 Kubernetes 的服务发现
Kubernetes 服务发现以 [_List-Watch_](https://kubernetes.io/docs/reference/using-api/api-concepts) 方式监听 [_Kubernetes_](https://kubernetes.io) 集群 [_Endpoints_](https://kubernetes.io/docs/concepts/services-networking/service) 资源的实时变化，并将其值存储到 ngx.shared.DICT 中。
同时遵循 [_APISIX Discovery 规范_](../discovery.md) 提供了节点查询接口。
## Kubernetes 服务发现的使用
目前 Kubernetes 服务发现支持单集群和多集群模式，分别适用于待发现的服务分布在单个或多个 Kubernetes 的场景。
### 单集群模式 Kubernetes 服务发现的配置格式
单集群模式 Kubernetes 服务发现的完整配置如下：
```
/* Detailed source-code truncated for AI context efficiency. */
```
如果 Kubernetes 服务发现运行在 Pod 内，你可以使用如下最简配置：
```yaml
discovery:
kubernetes: { }
```
如果 Kubernetes 服务发现运行在 Pod 外，你需要新建或选取指定的 [_ServiceAccount_](https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/), 获取其 Token 值，然后使用如下配置：
```yaml
discovery:
kubernetes:
service:
schema: https
host: # enter apiserver host value here
port: # enter apiServer port value here
client:
token: # enter serviceaccount token value here
#token_file: # enter token file path here
```
### 单集群模式 Kubernetes 服务发现的查询接口
单集群模式 Kubernetes 服务发现遵循 [_APISIX Discovery 规范_](../discovery.md) 提供节点查询接口。
**函数：**
nodes(service_name)
**说明：**
service_name 必须满足格式：[namespace]/[name]:[portName]
+ namespace: Endpoints 所在的命名空间
+ name: Endpoints 的资源名
+ portName: Endpoints 定义包含的 `ports.name` 值，如果 Endpoints 没有定义 `ports.name`，请依次使用 `targetPort`, `port` 代替。设置了 `ports.name` 的情况下，不能使用后两者。
**返回值：**
以如下 Endpoints 为例：
```yaml
apiVersion: v1
kind: Endpoints
metadata:
name: plat-dev
namespace: default
subsets:
- addresses:
- ip: "10.5.10.109"
- ip: "10.5.10.110"
ports:
- port: 3306
name: port
```
nodes("default/plat-dev:port") 调用会得到如下的返回值：
```
{
{
host="10.5.10.109",
port= 3306,
weight= 50,
},
{
host="10.5.10.110",
port= 3306,
weight= 50,
},
}
```
### 多集群模式 Kubernetes 服务发现的配置格式
多集群模式 Kubernetes 服务发现的完整配置如下：
```
/* Detailed source-code truncated for AI context efficiency. */
```
多集群模式 Kubernetes 服务发现没有为 `service` 和 `client` 域填充默认值，你需要根据集群配置情况自行填充。
### 多集群模式 Kubernetes 服务发现的查询接口
多集群模式 Kubernetes 服务发现遵循 [_APISIX Discovery 规范_](../discovery.md) 提供节点查询接口。
**函数：**
nodes(service_name)
**说明：**
service_name 必须满足格式：[id]/[namespace]/[name]:[portName]
+ id: Kubernetes 服务发现配置中定义的集群 id 值
+ namespace: Endpoints 所在的命名空间
+ name: Endpoints 的资源名
+ portName: Endpoints 定义包含的 `ports.name` 值，如果 Endpoints 没有定义 `ports.name`，请依次使用 `targetPort`, `port` 代替。设置了 `ports.name` 的情况下，不能使用后两者。
**返回值：**
以如下 Endpoints 为例：
```yaml
apiVersion: v1
kind: Endpoints
metadata:
name: plat-dev
namespace: default
subsets:
- addresses:
- ip: "10.5.10.109"
- ip: "10.5.10.110"
ports:
- port: 3306
name: port
```
nodes("release/default/plat-dev:port") 调用会得到如下的返回值：
```
{
{
host="10.5.10.109",
port= 3306,
weight= 50,
},
{
host="10.5.10.110",
port= 3306,
weight= 50,
},
}
```
## Q&A
**Q: 为什么只支持配置 token 来访问 Kubernetes APIServer?**
A: 一般情况下，我们有三种方式可以完成与 Kubernetes APIServer 的认证：
- mTLS
- Token
- Basic authentication
因为 lua-resty-http 目前不支持 mTLS, Basic authentication 不被推荐使用，所以当前只实现了 Token 认证方式。
**Q: APISIX 继承了 NGINX 的多进程模型，是否意味着每个 APISIX 工作进程都会监听 Kubernetes Endpoints？**
A: Kubernetes 服务发现只使用特权进程监听 Kubernetes Endpoints，然后将其值存储到 `ngx.shared.DICT` 中，工作进程通过查询 `ngx.shared.DICT` 来获取结果。
**Q: [_ServiceAccount_](https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/) 需要的权限有哪些？**
A: [_ServiceAccount_](https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/) 需要集群级 [ get,list,watch ] endpoints 和 endpointslices 资源的的权限，其声明式定义如下：
```yaml
kind: ServiceAccount
apiVersion: v1
metadata:
name: apisix-test
namespace: default
---
kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
name: apisix-test
rules:
- apiGroups: [ "" ]
resources: [ endpoints]
verbs: [ get,list,watch ]
- apiGroups: [ "discovery.k8s.io" ]
resources: [ endpointslices ]
verbs: [ get,list,watch ]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: apisix-test
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: apisix-test
subjects:
- kind: ServiceAccount
name: apisix-test
namespace: default
```
**Q: 怎样获取指定 [_ServiceAccount_](https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/) 的 Token 值？**
A: 假定你指定的 [_ServiceAccount_](https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/) 资源名为“kubernetes-discovery“, 命名空间为“apisix”, 请按如下步骤获取其 Token 值。
1. 获取 _Secret_ 资源名。执行以下命令，输出的第一列内容就是目标 _Secret_ 资源名：
```shell
kubectl -n apisix get secrets | grep kubernetes-discovery
```
2. 获取 Token 值。假定你获取到的 _Secret_ 资源名为 "kubernetes-discovery-token-c64cv", 执行以下命令，输出内容就是目标 Token 值：
```shell
kubectl -n apisix get secret kubernetes-discovery-token-c64cv -o jsonpath={.data.token} | base64 -d
```
## 调试 API
它还提供了用于调试的控制 api。
### 内存 Dump API
```shell
GET /v1/discovery/kubernetes/dump
```
例子
```shell
# curl http://127.0.0.1:9090/v1/discovery/kubernetes/dump | jq
{
"endpoints": [
{
"endpoints": [
{
"value": "{\"https\":[{\"host\":\"172.18.164.170\",\"port\":6443,\"weight\":50},{\"host\":\"172.18.164.171\",\"port\":6443,\"weight\":50},{\"host\":\"172.18.164.172\",\"port\":6443,\"weight\":50}]}",
"name": "default/kubernetes"
},
{
"value": "{\"metrics\":[{\"host\":\"172.18.164.170\",\"port\":2379,\"weight\":50},{\"host\":\"172.18.164.171\",\"port\":2379,\"weight\":50},{\"host\":\"172.18.164.172\",\"port\":2379,\"weight\":50}]}",
"name": "kube-system/etcd"
},
{
"value": "{\"http-85\":[{\"host\":\"172.64.89.2\",\"port\":85,\"weight\":50}]}",
"name": "test-ws/testing"
}
],
"id": "first"
}
],
"config": [
{
"default_weight": 50,
"id": "first",
"client": {
"token": "xxx"
},
"service": {
"host": "172.18.164.170",
"port": "6443",
"schema": "https"
},
"shared_size": "64m"
}
]
}
```
---
## File: docs/zh/latest/discovery/nacos.md
---
title: nacos
---
## 基于 [Nacos](https://nacos.io/zh-cn/docs/what-is-nacos.html) 的服务发现
当前模块的性能有待改进：
1. 并行发送请求。
### Nacos 配置
在文件 `conf/config.yaml` 中添加以下配置到：
```yaml
discovery:
nacos:
host:
- "http://${username}:${password}@${host1}:${port1}"
prefix: "/nacos/v1/"
fetch_interval: 30 # default 30 sec
weight: 100 # default 100
timeout:
connect: 2000 # default 2000 ms
send: 2000 # default 2000 ms
read: 5000 # default 5000 ms
```
也可以这样简洁配置（未配置项使用默认值）：
```yaml
discovery:
nacos:
host:
- "http://192.168.33.1:8848"
```
### Upstream 设置
#### 七层
例如，转发 URI 匹配 "/nacos/*" 的请求到一个上游服务，
该服务在 Nacos 中的服务名是 APISIX-NACOS，查询地址是 http://192.168.33.1:8848/nacos/v1/ns/instance/list?serviceName=APISIX-NACOS，创建路由时指定服务发现类型为 nacos。
:::note
您可以这样从 `config.yaml` 中获取 `admin_key` 并存入环境变量：
```bash
admin_key=$(yq '.deployment.admin.admin_key[0].key' conf/config.yaml | sed 's/"//g')
```
:::
```shell
$ curl http://127.0.0.1:9180/apisix/admin/routes/1 -H "X-API-KEY: $admin_key" -X PUT -i -d '
{
"uri": "/nacos/*",
"upstream": {
"service_name": "APISIX-NACOS",
"type": "roundrobin",
"discovery_type": "nacos"
}
}'
```
响应如下：
```json
{
"node": {
"key": "\/apisix\/routes\/1",
"value": {
"id": "1",
"create_time": 1615796097,
"status": 1,
"update_time": 1615799165,
"upstream": {
"hash_on": "vars",
"pass_host": "pass",
"scheme": "http",
"service_name": "APISIX-NACOS",
"type": "roundrobin",
"discovery_type": "nacos"
},
"priority": 0,
"uri": "\/nacos\/*"
}
}
}
```
#### 四层
nacos 服务发现也支持在四层中使用，配置方式与七层的类似。
```shell
$ curl http://127.0.0.1:9180/apisix/admin/stream_routes/1 -H "X-API-KEY: $admin_key" -X PUT -i -d '
{
"remote_addr": "127.0.0.1",
"upstream": {
"scheme": "tcp",
"discovery_type": "nacos",
"service_name": "APISIX-NACOS",
"type": "roundrobin"
}
}'
```
### 参数
| 名字 | 类型 | 可选项 | 默认值 | 有效值 | 说明 |
| ------------ | ------ | ----------- | ------- | ----- | ------------------------------------------------------------ |
| namespace_id | string | 可选 | public | | 服务所在的命名空间 |
| group_name | string | 可选 | DEFAULT_GROUP | | 服务所在的组 |
#### 指定命名空间
例如，转发 URI 匹配 "/nacosWithNamespaceId/*" 的请求到一个上游服务，
该服务在 Nacos 中的服务名是 APISIX-NACOS，命名空间是 test_ns，查询地址是 http://192.168.33.1:8848/nacos/v1/ns/instance/list?serviceName=APISIX-NACOS&namespaceId=test_ns，创建路由时指定服务发现类型为 nacos。
```shell
$ curl http://127.0.0.1:9180/apisix/admin/routes/2 -H "X-API-KEY: $admin_key" -X PUT -i -d '
{
"uri": "/nacosWithNamespaceId/*",
"upstream": {
"service_name": "APISIX-NACOS",
"type": "roundrobin",
"discovery_type": "nacos",
"discovery_args": {
"namespace_id": "test_ns"
}
}
}'
```
响应如下：
```json
{
"node": {
"key": "\/apisix\/routes\/2",
"value": {
"id": "2",
"create_time": 1615796097,
"status": 1,
"update_time": 1615799165,
"upstream": {
"hash_on": "vars",
"pass_host": "pass",
"scheme": "http",
"service_name": "APISIX-NACOS",
"type": "roundrobin",
"discovery_type": "nacos",
"discovery_args": {
"namespace_id": "test_ns"
}
},
"priority": 0,
"uri": "\/nacosWithNamespaceId\/*"
}
}
}
```
#### 指定组
例如，转发 URI 匹配 "/nacosWithGroupName/*" 的请求到一个上游服务，
该服务在 Nacos 中的服务名是 APISIX-NACOS，组名是 test_group，查询地址是 http://192.168.33.1:8848/nacos/v1/ns/instance/list?serviceName=APISIX-NACOS&groupName=test_group，创建路由时指定服务发现类型为 nacos。
```shell
$ curl http://127.0.0.1:9180/apisix/admin/routes/3 -H "X-API-KEY: $admin_key" -X PUT -i -d '
{
"uri": "/nacosWithGroupName/*",
"upstream": {
"service_name": "APISIX-NACOS",
"type": "roundrobin",
"discovery_type": "nacos",
"discovery_args": {
"group_name": "test_group"
}
}
}'
```
响应如下：
```json
{
"node": {
"key": "\/apisix\/routes\/3",
"value": {
"id": "3",
"create_time": 1615796097,
"status": 1,
"update_time": 1615799165,
"upstream": {
"hash_on": "vars",
"pass_host": "pass",
"scheme": "http",
"service_name": "APISIX-NACOS",
"type": "roundrobin",
"discovery_type": "nacos",
"discovery_args": {
"group_name": "test_group"
}
},
"priority": 0,
"uri": "\/nacosWithGroupName\/*"
}
}
}
```
#### 同时指定命名空间和组
例如，转发 URI 匹配 "/nacosWithNamespaceIdAndGroupName/*" 的请求到一个上游服务，
该服务在 Nacos 中的服务名是 APISIX-NACOS，命名空间是 test_ns，组名是 test_group，查询地址是 http://192.168.33.1:8848/nacos/v1/ns/instance/list?serviceName=APISIX-NACOS&namespaceId=test_ns&groupName=test_group，创建路由时指定服务发现类型为 nacos。
```shell
$ curl http://127.0.0.1:9180/apisix/admin/routes/4 -H "X-API-KEY: $admin_key" -X PUT -i -d '
{
"uri": "/nacosWithNamespaceIdAndGroupName/*",
"upstream": {
"service_name": "APISIX-NACOS",
"type": "roundrobin",
"discovery_type": "nacos",
"discovery_args": {
"namespace_id": "test_ns",
"group_name": "test_group"
}
}
}'
```
响应如下：
```json
{
"node": {
"key": "\/apisix\/routes\/4",
"value": {
"id": "4",
"create_time": 1615796097,
"status": 1,
"update_time": 1615799165,
"upstream": {
"hash_on": "vars",
"pass_host": "pass",
"scheme": "http",
"service_name": "APISIX-NACOS",
"type": "roundrobin",
"discovery_type": "nacos",
"discovery_args": {
"namespace_id": "test_ns",
"group_name": "test_group"
}
},
"priority": 0,
"uri": "\/nacosWithNamespaceIdAndGroupName\/*"
}
}
}
```
---
## File: docs/zh/latest/getting-started/configure-routes.md
---
title: 配置路由
slug: /getting-started/configure-routes
---
> 本教程由 [API7.ai](https://api7.ai/) 编写。
Apache APISIX 使用 _routes_ 来提供灵活的网关管理功能，在一个请求中，_routes_ 包含了访问路径和上游目标等信息。
本教程将引导你创建一个 route 并验证它，你可以参考以下步骤：
1. 创建一个指向 [httpbin.org](http://httpbin.org)的 _upstream_。
2. 使用 _cURL_ 发送一个请求，了解 APISIX 的代理和转发请求机制。
## Route 是什么
Route（也称之为路由）是访问上游目标的路径，在 [Apache APISIX](https://api7.ai/apisix) 中，Route 首先通过预定的规则来匹配客户端请求，然后加载和执行相应的插件，最后将请求转发至特定的 Upstream。
在 APISIX 中，一个最简单的 Route 仅由匹配路径和 Upstream 地址两个信息组成。
## Upstream 是什么
Upstream（也称之为上游）是一组具备相同功能的节点集合，它是对虚拟主机的抽象。Upstream 可以通过预先配置的规则对多个服务节点进行负载均衡。
## 前置条件
1. 参考[入门指南](./README.md)完成 APISIX 的安装。
## 创建路由
你可以创建一个路由，将客户端的请求转发至 [httpbin.org](http://httpbin.org)（这个网站能测试 HTTP 请求和响应的各种信息）。
通过下面的命令，你将创建一个路由，把请求`http://127.0.0.1:9080/ip` 转发至 [httpbin.org/ip](http://httpbin.org/ip)：
```shell
curl -i "http://127.0.0.1:9180/apisix/admin/routes" -X PUT -d '
{
"id": "getting-started-ip",
"uri": "/ip",
"upstream": {
"type": "roundrobin",
"nodes": {
"httpbin.org:80": 1
}
}
}'
```
如果配置成功，将会返回 `HTTP/1.1 201 Created`。
## 验证
```shell
curl "http://127.0.0.1:9080/ip"
```
你将会得到类似下面的返回：
```text
{
"origin": "183.94.122.205"
}
```
## 下一步
本教程创建的路由仅对应一个上游目标。在下个教程中，你将会学习如何配置多个上游目标的负载均衡。
---
## File: docs/zh/latest/getting-started/key-authentication.md
---
title: 密钥验证
slug: /getting-started/key-authentication
---
> 本教程由 [API7.ai](https://api7.ai/) 编写。
API 网关主要作用是连接 API 消费者和提供者。出于安全考虑，在访问内部资源之前，应先对消费者进行身份验证和授权。
APISIX 拥有灵活的插件扩展系统，目前有很多可用于用户身份验证和授权的插件。例如：
- [Key Authentication](https://apisix.apache.org/zh/docs/apisix/plugins/key-auth/)
- [Basic Authentication](https://apisix.apache.org/zh/docs/apisix/plugins/basic-auth/)
- [JSON Web Token (JWT) Authentication](https://apisix.apache.org/zh/docs/apisix/plugins/jwt-auth/)
- [Keycloak](https://apisix.apache.org/zh/docs/apisix/plugins/authz-keycloak/)
- [Casdoor](https://apisix.apache.org/zh/docs/apisix/plugins/authz-casdoor/)
- [Wolf RBAC](https://apisix.apache.org/zh/docs/apisix/plugins/wolf-rbac/)
- [OpenID Connect](https://apisix.apache.org/zh/docs/apisix/plugins/openid-connect/)
- [Central Authentication Service (CAS)](https://apisix.apache.org/zh/docs/apisix/plugins/cas-auth/)
- [HMAC](https://apisix.apache.org/zh/docs/apisix/plugins/hmac-auth/)
- [Casbin](https://apisix.apache.org/zh/docs/apisix/plugins/authz-casbin/)
- [LDAP](https://apisix.apache.org/zh/docs/apisix/plugins/ldap-auth/)
- [Open Policy Agent (OPA)](https://apisix.apache.org/zh/docs/apisix/plugins/opa/)
- [Forward Authentication](https://apisix.apache.org/zh/docs/apisix/plugins/forward-auth/)
- [Multiple Authentications](https://apisix.apache.org/docs/apisix/plugins/multi-auth/)
本教程中，你将创建一个带有 _密钥验证_ 插件的 _消费者_，并学习如何启用和停用身份验证插件。
## Consumer 是什么
Consumer（也称之为消费者）是指使用 API 的应用或开发人员。
在 APISIX 中，消费者需要一个全局唯一的 _名称_，并从上面的列表中选择一个身份验证 _插件_。
## Key Authentication 是什么
Key Authentication（也称之为密钥验证）是一个相对比较简单但是应用广泛的身份验证方法，它的设计思路如下：
1. 管理员为路由添加一个身份验证密钥（API 密钥）。
2. API 消费者在发送请求时，在查询字符串或者请求头中添加密钥。
## 启用 Key Authentication
### 前置条件
1. 参考[快入门指南](./README.md)完成 APISIX 的安装。
2. 完成[配置路由](./configure-routes.md#route-是什么)。
### 创建消费者
创建一个名为 `tom` 的消费者，并启用 `key-auth` 插件，密钥设置为 `secret-key`。所有携带密钥 `secret-key` 的请求都会被识别为消费者 `tom`。
:::caution
生产环境请使用复杂的密钥。
:::
```shell
curl -i "http://127.0.0.1:9180/apisix/admin/consumers" -X PUT -d '
{
"username": "tom",
"plugins": {
"key-auth": {
"key": "secret-key"
}
}
}'
```
如果消费者创建成功，你将得到返回 `HTTP/1.1 201 Created`。
### 启用 Authentication
在教程[配置路由](./configure-routes.md)中，我们已经创建了路由 `getting-started-ip`，我们通过 `PATCH` 方法为该路由增加 `key-auth` 插件：
```shell
curl -i "http://127.0.0.1:9180/apisix/admin/routes/getting-started-ip" -X PATCH -d '
{
"plugins": {
"key-auth": {}
}
}'
```
如果增加插件成功，你将得到返回 `HTTP/1.1 201 Created`。
### 验证
我们可以在以下场景中进行验证：
#### 1. 发送不带任何密钥的请求
发送一个不带请求头 `apikey` 的请求。
```shell
curl -i "http://127.0.0.1:9080/ip"
```
如果你已经启用了密钥身份验证，你将会得到返回 `HTTP/1.1 401 Unauthorized`，即未授权。
```text
HTTP/1.1 401 Unauthorized
Date: Wed, 08 Feb 2023 09:38:36 GMT
Content-Type: text/plain; charset=utf-8
Transfer-Encoding: chunked
Connection: keep-alive
Server: APISIX/3.1.0
```
#### 2. 发送携带错误密钥的请求
发送一个携带错误密钥（比如 `wrong-key`）的请求。
```shell
curl -i "http://127.0.0.1:9080/ip" -H 'apikey: wrong-key'
```
如果密钥错误，你也将得到返回 `HTTP/1.1 401 Unauthorized`，即未授权。
```text
HTTP/1.1 401 Unauthorized
Date: Wed, 08 Feb 2023 09:38:27 GMT
Content-Type: text/plain; charset=utf-8
Transfer-Encoding: chunked
Connection: keep-alive
Server: APISIX/3.1.0
```
#### 3. 发送携带正确密钥的请求
发送一个携带正确密钥（`secret-key`）的请求。
```shell
curl -i "http://127.0.0.1:9080/ip" -H 'apikey: secret-key'
```
你将会得到返回 `HTTP/1.1 200 OK`。
```text
HTTP/1.1 200 OK
Content-Type: application/json
Content-Length: 44
Connection: keep-alive
Date: Thu, 09 Feb 2023 03:27:57 GMT
Access-Control-Allow-Origin: *
Access-Control-Allow-Credentials: true
Server: APISIX/3.1.0
```
### 禁用 Authentication
将参数设置 `_meta.disable` 为 `true`，即可禁用密钥验证插件。
```shell
curl "http://127.0.0.1:9180/apisix/admin/routes/getting-started-ip" -X PATCH -d '
{
"plugins": {
"key-auth": {
"_meta": {
"disable": true
}
}
}
}'
```
你可以发送一个不带任何密钥的请求来验证：
```shell
curl -i "http://127.0.0.1:9080/ip"
```
因为你已经禁用了密钥验证插件，所以你将会得到返回 `HTTP/1.1 200 OK`。
## 下一步
你已经学习了如何为路由配置密钥验证。在下个教程中，你将学习如何配置限速。
---
## File: docs/zh/latest/getting-started/load-balancing.md
---
title: 负载均衡
slug: /getting-started/load-balancing
---
> 本教程由 [API7.ai](https://api7.ai/) 编写。
负载均衡管理客户端和服务端之间的流量。它决定由哪个服务来处理特定的请求，从而提高性能、可扩展性和可靠性。在设计需要处理大量流量的系统时，负载均衡是一个关键的考虑因素。
Apache APISIX 支持加权负载均衡算法，传入的流量按照预定顺序轮流分配给一组服务器的其中一个。
在本教程中，你将创建一个具有两个上游服务的路由，并且启用负载均衡来测试在两个服务之间的切换情况。
## 前置条件
1. 参考[入门指南](./README.md)完成 APISIX 的安装。
2. 了解 APISIX 中[路由及上游](./configure-routes.md#route-是什么)的概念。
## 启用负载均衡
创建一个具有两个上游服务的路由，访问 `/headers` 将被转发到 [httpbin.org](https://httpbin.org/headers) 和 [mock.api7.ai](https://mock.api7.ai/headers) 这两个上游服务，并且会返回请求头。
```shell
curl -i "http://127.0.0.1:9180/apisix/admin/routes" -X PUT -d '
{
"id": "getting-started-headers",
"uri": "/headers",
"upstream" : {
"type": "roundrobin",
"nodes": {
"httpbin.org:443": 1,
"mock.api7.ai:443": 1
},
"pass_host": "node",
"scheme": "https"
}
}'
```
如果路由创建成功，你将会收到返回 `HTTP/1.1 201 Created`。
:::info
1. 将 `pass_host` 字段设置为 `node`，将传递请求头给上游。
2. 将 `scheme` 字段设置为 `https`，向上游发送请求时将启用 TLS。
:::
## 验证
这两个服务返回不同的数据。
`httpbin.org` 返回：
```json
{
"headers": {
"Accept": "*/*",
"Host": "httpbin.org",
"User-Agent": "curl/7.58.0",
"X-Amzn-Trace-Id": "Root=1-63e34b15-19f666602f22591b525e1e80",
"X-Forwarded-Host": "localhost"
}
}
```
`mock.api7.ai` 返回：
```json
{
"headers": {
"accept": "*/*",
"host": "mock.api7.ai",
"user-agent": "curl/7.58.0",
"content-type": "application/json",
"x-application-owner": "API7.ai"
}
}
```
我们生成 100 个请求来测试负载均衡的效果：
```shell
hc=$(seq 100 | xargs -I {} curl "http://127.0.0.1:9080/headers" -sL | grep "httpbin" | wc -l); echo httpbin.org: $hc, mock.api7.ai: $((100 - $hc))
```
结果显示，请求几乎平均分配给这两个上游服务：
```text
httpbin.org: 51, mock.api7.ai: 49
```
## 下一步
你已经学习了如何配置负载均衡。在下个教程中，你将学习如何配置身份验证。
## 2. Official Technical Reference & Guides (apache/docs)
## File: README.md
## Apache HoraeDB (incubating) Website
> [!IMPORTANT]
> Apache HoraeDB (incubating) is an effort undergoing incubation at the Apache
> Software Foundation (ASF), sponsored by the Apache Incubator PMC.
>
> Please read the [DISCLAIMER](DISCLAIMER) and a full explanation of ["incubating"](https://incubator.apache.org/policy/incubation.html).
This repository stores all the source files of [HoraeDB website](https://horaedb.apache.org), it's built with [hugo](https://gohugo.io/) and [docsy](https://www.docsy.dev/).
## How to run
```bash
# Start a local server for preview
hugo serve
```
By default hugo will listen on http://localhost:1313
## Contributing
Welcome any contributions from the community. You can
- Open an [issue](https://github.com/apache/horaedb-docs/issues) with any suggestions, or
- Submit a [pull request](https://github.com/apache/horaedb-docs/pulls) to improve the website (trivial fixes are welcome).
---
## File: content/en/blog/2024/release-2.0.0.md
---
title: Release 2.0.0
date: 2024-04-23
---
## Upgrade from 1.x.x to 2.0.0
The transition from CeresDB to Apache HoraeDB introduces several breaking changes. To facilitate upgrading from older versions to v2.0.0, specific alterations are necessary.
#### Upgrade Steps
1. Setup required envs
```
export HORAEDB_DEFAULT_CATALOG=ceresdb
```
2. Update config
Etcd's root should be configured both in horaedb and horaemeta
For horaedb
```
[cluster_deployment.etcd_client]
server_addrs = ['127.0.0.1:2379']
root_path = "/rootPath"
```
For horaemeta
```
storage-root-path = "/rootPath"
```
3. Upgrade horaemeta
Horaedb will throw following errors, which is expected
```
2024-01-23 14:37:57.726 ERRO [src/cluster/src/cluster_impl.rs:136] Send heartbeat to meta failed, err:Failed to send heartbeat, cluster:defaultCluster, err:status: Unimplemented, message: "unknown service meta_service.MetaRpcService", details: [], metadata: MetadataMap { headers: {"content-type": "application/grpc"} }
```
4. Upgrade horaedb
After all server upgraded, the cluster should be ready for read/write, and old data could be queried like before.
## What's Changed
### Breaking Changes
- refactor!: refactor shard version logic by @ZuLiangWang in https://github.com/apache/horaedb/pull/1286
### Features
- feat: support re-acquire shard lock in a fast way by @ShiKaiWi in https://github.com/apache/horaedb/pull/1251
- feat: support alter partition table by @chunshao90 in https://github.com/apache/horaedb/pull/1244
- feat: support access etcd with tls by @ShiKaiWi in https://github.com/apache/horaedb/pull/1254
- feat: support schema validate in remote write by @ShiKaiWi in https://github.com/apache/horaedb/pull/1256
- feat: avoid flush when drop table by @jiacai2050 in https://github.com/apache/horaedb/pull/1257
- feat: opentsdb api support gzip body by @tanruixiang in https://github.com/apache/horaedb/pull/1261
- feat: infer timestamp constraint for single-timestamp column by @Dennis40816 in https://github.com/apache/horaedb/pull/1266
- feat: primary keys support sample by @jiacai2050 in https://github.com/apache/horaedb/pull/1243
- feat: cache space total memory by @jiacai2050 in https://github.com/apache/horaedb/pull/1278
- feat: skip record column values for level0 sst by @jiacai2050 in https://github.com/apache/horaedb/pull/1282
- feat: support write wal logs in columnar format by @ShiKaiWi in https://github.com/apache/horaedb/pull/1179
- feat: support stack size of read threads configurable by @ShiKaiWi in https://github.com/apache/horaedb/pull/1305
- feat: impl DoNothing wal by @jiacai2050 in https://github.com/apache/horaedb/pull/1311
- feat: slow log include remote query by @jiacai2050 in https://github.com/apache/horaedb/pull/1316
- feat: use string for request id by @jiacai2050 in https://github.com/apache/horaedb/pull/1349
- feat: support metrics for number of bytes fetched from object storage by @ShiKaiWi in https://github.com/apache/horaedb/pull/1363
- feat: avoid building dictionary for massive unique column values by @ShiKaiWi in https://github.com/apache/horaedb/pull/1365
- feat: utilize the column cardinality for deciding whether to do dict by @ShiKaiWi in https://github.com/apache/horaedb/pull/1372
- feat: avoid pulling unnecessary columns when querying append mode table by @Rachelint in https://github.com/apache/horaedb/pull/1307
- feat: dist sql analyze by @baojinri in https://github.com/apache/horaedb/pull/1260
- feat: impl priority runtime for read by @jiacai2050 in https://github.com/apache/horaedb/pull/1303
- feat: upgrade horaedbproto by @chunshao90 in https://github.com/apache/horaedb/pull/1408
- feat: block rules support query by @jiacai2050 in https://github.com/apache/horaedb/pull/1420
- feat: try load page indexes by @jiacai2050 in https://github.com/apache/horaedb/pull/1425
- feat: support setting meta_addr&etcd_addrs by env by @chunshao90 in https://github.com/apache/horaedb/pull/1427
- feat: add table status check by @ZuLiangWang in https://github.com/apache/horaedb/pull/1418
- feat: support docker-compose and update README by @chunshao90 in https://github.com/apache/horaedb/pull/1429
- feat: impl layered memtable to reduce duplicated encode during scan by @Rachelint in https://github.com/apache/horaedb/pull/1271
- feat: update disk cache in another thread to avoid blocking normal query process by @jiacai2050 in https://github.com/apache/horaedb/pull/1431
- feat: update pgwire to 0.19 by @sunng87 in https://github.com/apache/horaedb/pull/1436
- feat: filter out MySQL federated components' emitted statements by @chunshao90 in https://github.com/apache/horaedb/pull/1439
- feat: add system_stats lib to collect system stats by @ShiKaiWi in https://github.com/apache/horaedb/pull/1442
- feat(horaectl): initial commit by @chunshao90 in https://github.com/apache/horaedb/pull/1450
- feat: support collect statistics about the engine by @ShiKaiWi in https://github.com/apache/horaedb/pull/1451
- feat: persist sst meta size by @jiacai2050 in https://github.com/apache/horaedb/pull/1440
- feat: add sst level config for benchmark by @zealchen in https://github.com/apache/horaedb/pull/1482
- feat: add exponential backoff when retry by @zealchen in https://github.com/apache/horaedb/pull/1486
### Refactor
- refactor: move wal structs and traits to wal crate by @tisonkun in https://github.com/apache/horaedb/pull/1263
- refactor: improve error readability by @jiacai2050 in https://github.com/apache/horaedb/pull/1265
- refactor: move wal crate to under src folder by @tisonkun in https://github.com/apache/horaedb/pull/1270
- refactor: use `notifier::RequestNotifiers` instead of `dedup_requests::RequestNotifiers` by @baojinri in https://github.com/apache/horaedb/pull/1249
- refactor: conditionally compile wal impls by @tisonkun in https://github.com/apache/horaedb/pull/1272
- refactor: remove unused min/max timestamp in the RowGroup by @ShiKaiWi in https://github.com/apache/horaedb/pull/1297
- refactor: avoid duplicate codes by @ShiKaiWi in https://github.com/apache/horaedb/pull/1371
- refactor: avoid returning metrics in non-analyze sql by @baojinri in https://github.com/apache/horaedb/pull/1410
- refactor: move sub crates to the src directory by @chunshao90 in https://github.com/apache/horaedb/pull/1443
- refactor: adjust cpu's stats by @ShiKaiWi in https://github.com/apache/horaedb/pull/1457
- refactor: refactor compaction process for remote compaction by @Rachelint in https://github.com/apache/horaedb/pull/1476
### Fixed
- fix: dist query dedup by @Rachelint in https://github.com/apache/horaedb/pull/1269
- fix: log third party crates by @jiacai2050 in https://github.com/apache/horaedb/pull/1289
- fix: ensure primary key order by @jiacai2050 in https://github.com/apache/horaedb/pull/1292
- fix: use flag in preflush to indicate whether reorder is required by @jiacai2050 in https://github.com/apache/horaedb/pull/1298
- fix: alter partition table tag column by @chunshao90 in https://github.com/apache/horaedb/pull/1304
- fix: increase wait duration for flush by @jiacai2050 in https://github.com/apache/horaedb/pull/1315
- fix: add license to workspace members by @jiacai2050 in https://github.com/apache/horaedb/pull/1317
- fix: ensure channel size non zero by @jiacai2050 in https://github.com/apache/horaedb/pull/1345
- fix: fix create table result by @ZuLiangWang in https://github.com/apache/horaedb/pull/1354
- Revert "fix: fix create table result" by @ZuLiangWang in https://github.com/apache/horaedb/pull/1355
- fix: fix test create table result by @ZuLiangWang in https://github.com/apache/horaedb/pull/1357
- fix: no write stall by @ShiKaiWi in https://github.com/apache/horaedb/pull/1388
- fix: collect metrics for `get_ranges` by @ShiKaiWi in https://github.com/apache/horaedb/pull/1364
- fix: ignore collecting fetched bytes stats when sst file is read only once by @ShiKaiWi in https://github.com/apache/horaedb/pull/1369
- fix: publich nightly image by @chunshao90 in https://github.com/apache/horaedb/pull/1396
- fix: missing and verbose logs by @ShiKaiWi in https://github.com/apache/horaedb/pull/1398
- fix: fix broken link by @caicancai in https://github.com/apache/horaedb/pull/1399
- fix: the broken link about the issue status by @ShiKaiWi in https://github.com/apache/horaedb/pull/1402
- fix: skip wal encoding when data wal is disabled by @jiacai2050 in https://github.com/apache/horaedb/pull/1401
- fix: disable percentile for distributed tables by @jiacai2050 in https://github.com/apache/horaedb/pull/1406
- fix: compatible for old table options by @Rachelint in https://github.com/apache/horaedb/pull/1432
- fix: get_ranges is not spawned in io-runtime by @ShiKaiWi in https://github.com/apache/horaedb/pull/1426
- fix: table name is normalized when find timestamp column by @jiacai2050 in https://github.com/apache/horaedb/pull/1446
- fix: changes required for migrate dev to main by @jiacai2050 in https://github.com/apache/horaedb/pull/1455
- fix: missing filter index over the primary keys by @ShiKaiWi in https://github.com/apache/horaedb/pull/1456
- fix: random failure of test_collect_system_stats by @ShiKaiWi in https://github.com/apache/horaedb/pull/1459
- fix(ci): refactor ci trigger conditions by @jiacai2050 in https://github.com/apache/horaedb/pull/1474
### Docs
- chore(docs): rename CeresDB to HoraeDB by @caicancai in https://github.com/apache/horaedb/pull/1337
- chore/docs: remove broken link by @caicancai in https://github.com/apache/horaedb/pull/1341
- docs: update CONTRIBUTING.md by @suyanhanx in https://github.com/apache/horaedb/pull/1382
- doc: fix broken link by @caicancai in https://github.com/apache/horaedb/pull/1358
- chore/doc: rename ceresdb to horaedb by @caicancai in https://github.com/apache/horaedb/pull/1332
- doc: add MySQL-Client in README by @jackwener in https://github.com/apache/horaedb/pull/1331
- doc: fix link in illegal markdown format by @jackwener in https://github.com/apache/horaedb/pull/1334
- style: normalize comments/doc in rustfmt by @jackwener in https://github.com/apache/horaedb/pull/1335
- docs: add sudo for install commands by @caicancai in https://github.com/apache/horaedb/pull/1347
- docs: sync GH activities to commits only by @tisonkun in https://github.com/apache/horaedb/pull/1385
- chore(docs): fix invalid repo links by @SYaoJun in https://github.com/apache/horaedb/pull/1452
- chore(docs): fix invalid repo links by @Apricity001 in https://github.com/apache/horaedb/pull/1472
### Chore
- chore(deps): bump golang.org/x/net from 0.5.0 to 0.17.0 in /integration_tests/sdk/go by @dependabot in https://github.com/apache/horaedb/pull/1258
- chore: delete the configuration related to github cache by @tanruixiang in https://github.com/apache/horaedb/pull/1259
- chore: remove backtrace of blocked table by @chunshao90 in https://github.com/apache/horaedb/pull/1267
- ci: setup golang in CI by @tisonkun in https://github.com/apache/horaedb/pull/1275
- chore: remove default features in analytic_engine by @jiacai2050 in https://github.com/apache/horaedb/pull/1277
- chore(deps): bump google.golang.org/grpc from 1.53.0 to 1.56.3 in /integration_tests/sdk/go by @dependabot in https://github.com/apache/horaedb/pull/1280
- test: simplify ceresmeta-server installation by @tisonkun in https://github.com/apache/horaedb/pull/1287
- chore: enable blank issue by @ShiKaiWi in https://github.com/apache/horaedb/pull/1290
- chore: add metrics to inspect write path by @Rachelint in https://github.com/apache/horaedb/pull/1264
- chore: refactor build_meta.sh in integration-test by @chunshao90 in https://github.com/apache/horaedb/pull/1306
- chore: rename ceresdb to horaedb by @chunshao90 in https://github.com/apache/horaedb/pull/1310
- edit: add schema id, schema name, catalog name in TableData by @dust1 in https://github.com/apache/horaedb/pull/1294
- chore: ignore seq check for DoNothing wal by @jiacai2050 in https://github.com/apache/horaedb/pull/1314
- chore: remove community by @jiacai2050 in https://github.com/apache/horaedb/pull/1318
- chore: try to clear ceresdb stuff by @tisonkun in https://github.com/apache/horaedb/pull/1320
- chore: change copyright owner by @tisonkun in https://github.com/apache/horaedb/pull/1321
- ci: stop release docker image before we finish the rename and transfer by @tisonkun in https://github.com/apache/horaedb/pull/1323
- chore: bump deps by @jiacai2050 in https://github.com/apache/horaedb/pull/1325
- chore: rename ceresmeta to horaemeta by @chunshao90 in https://github.com/apache/horaedb/pull/1327
- chore: rename binary to horaedb-server and more by @tisonkun in https://github.com/apache/horaedb/pull/1330
- chore(license): rename `license-header.txt`'s CeresDB to HoraeDB by @caicancai in https://github.com/apache/horaedb/pull/1336
- chore: replace ceresdb with horaedb by @jackwener in https://github.com/apache/horaedb/pull/1338
- chore: more rename to horaedb by @tisonkun in https://github.com/apache/horaedb/pull/1340
- chore: update create table integration test result by @ZuLiangWang in https://github.com/apache/horaedb/pull/1344
- chore: disable frequently failed tests by @jiacai2050 in https://github.com/apache/horaedb/pull/1352
- test: add integration test for alter table options by @caicancai in https://github.com/apache/horaedb/pull/1346
- chore: ignore flush failure when flush by @ShiKaiWi in https://github.com/apache/horaedb/pull/1362
- chore: disable timeout for http api by @jiacai2050 in https://github.com/apache/horaedb/pull/1367
- chore: disable block for http api by @jiacai2050 in https://github.com/apache/horaedb/pull/1368
- config: add .asf.yaml by @chunshao90 in https://github.com/apache/horaedb/pull/1377
- ci: remove missing Required status by @tisonkun in https://github.com/apache/horaedb/pull/1383
- chore: git repo link type fix by @fengmk2 in https://github.com/apache/horaedb/pull/1378
- chore: apply ASF license header by @tanruixiang in https://github.com/apache/horaedb/pull/1384
- chore: add dev mail list and rename ceresdb to horaedb by @tanruixiang in https://github.com/apache/horaedb/pull/1375
- chore: more rename to horaedb by @chunshao90 in https://github.com/apache/horaedb/pull/1387
- chore: add push-nightly-image in workflow by @chunshao90 in https://github.com/apache/horaedb/pull/1389
- chore: update README by @chunshao90 in https://github.com/apache/horaedb/pull/1390
- chore: refactor for better readability by @jiacai2050 in https://github.com/apache/horaedb/pull/1400
- chore: add error log for remote server by @jiacai2050 in https://github.com/apache/horaedb/pull/1407
- chore: update website url by @chunshao90 in https://github.com/apache/horaedb/pull/1404
- chore: upload horaedb logo by @chunshao90 in https://github.com/apache/horaedb/pull/1409
- chore: add slack link by @tanruixiang in https://github.com/apache/horaedb/pull/1395
- chore: update logo by @chunshao90 in https://github.com/apache/horaedb/pull/1414
- chore: update horaedb logo by @chunshao90 in https://github.com/apache/horaedb/pull/1415
- chore: rename ceresformat to logformat by @ZuLiangWang in https://github.com/apache/horaedb/pull/1417
- chore: fix logo link in readme by @chunshao90 in https://github.com/apache/horaedb/pull/1416
- chore: update github pages by @chunshao90 in https://github.com/apache/horaedb/pull/1421
- chore: more rename to horaedb by @chunshao90 in https://github.com/apache/horaedb/pull/1419
- chore: fix error message by @jiacai2050 in https://github.com/apache/horaedb/pull/1412
- chore: remove github pages in asf.yaml by @chunshao90 in https://github.com/apache/horaedb/pull/1428
- chore: skip wal seq check when wal is disabled by @jiacai2050 in https://github.com/apache/horaedb/pull/1430
- chore: enable merge on github by @ShiKaiWi in https://github.com/apache/horaedb/pull/1435
- chore: merge change sets on the dev branch by @ShiKaiWi in https://github.com/apache/horaedb/pull/1423
- chore: fix issue status of README-CN.md by @ShiKaiWi in https://github.com/apache/horaedb/pull/1437
- chore(deps): bump h2 from 0.3.17 to 0.3.24 by @dependabot in https://github.com/apache/horaedb/pull/1448
- chore(deps): bump shlex from 1.1.0 to 1.3.0 by @dependabot in https://github.com/apache/horaedb/pull/1458
- chore: update create tables result by @ZuLiangWang in https://github.com/apache/horaedb/pull/1454
- chore: merge HoraeMeta code into HoreaDB repository by @ZuLiangWang in https://github.com/apache/horaedb/pull/1460
- chore(deps): bump google.golang.org/grpc from 1.47.0 to 1.56.3 in /horaemeta by @dependabot in https://github.com/apache/horaedb/pull/1464
- chore(deps): bump golang.org/x/net from 0.16.0 to 0.17.0 in /horaemeta by @dependabot in https://github.com/apache/horaedb/pull/1465
- chore(deps): bump golang.org/x/crypto from 0.14.0 to 0.17.0 in /horaemeta by @dependabot in https://github.com/apache/horaedb/pull/1462
- chore: rename ci's prefix name by @tanruixiang in https://github.com/apache/horaedb/pull/1467
- chore: fix github issue template by @ZuLiangWang in https://github.com/apache/horaedb/pull/1470
- chore(horaemeta&horaectl): refactor clusters/diagnose response body by @chunshao90 in https://github.com/apache/horaedb/pull/1475
- chore: free disk for ci by @jiacai2050 in https://github.com/apache/horaedb/pull/1484
- deps: bump datafusion by @tanruixiang in https://github.com/apache/horaedb/pull/1445
- horaectl: remove go implementation of horaectl by @chunshao90 in https://github.com/apache/horaedb/pull/1490
- chore: update version to 2.0.0, prepare for releasing v2.0.0 by @chunshao90 in https://github.com/apache/horaedb/pull/1487
## New Contributors
- @Dennis40816 made their first contribution in https://github.com/apache/horaedb/pull/1266
- @caicancai made their first contribution in https://github.com/apache/horaedb/pull/1332
- @jackwener made their first contribution in https://github.com/apache/horaedb/pull/1331
- @suyanhanx made their first contribution in https://github.com/apache/horaedb/pull/1382
- @fengmk2 made their first contribution in https://github.com/apache/horaedb/pull/1378
- @sunng87 made their first contribution in https://github.com/apache/horaedb/pull/1436
- @SYaoJun made their first contribution in https://github.com/apache/horaedb/pull/1452
- @Apricity001 made their first contribution in https://github.com/apache/horaedb/pull/1472
**Full Changelog**: https://github.com/apache/horaedb/compare/v1.2.7...v2.0.0
---
## File: content/en/blog/2024/release-2.1.0.md
---
title: Release 2.1.0
date: 2024-11-18
tags:
- release
---
The Apache HoraeDB(incubating) team are pleased to announce that v2.1.0 is released, which has closed over 60 issues, including two major features:
## 1. New WAL implementation based on local disk.
In previous version, there is a RocksDB-based WAL. Although it works well in most cases, it has following issues:
- Compiling from source can be a challenging task, especially since RocksDB is primarily written in C++.
- For WAL, RocksDB can be somewhat overkill. If you are not familiar with RocksDB, tuning it can be very challenging.
With this new WAL, the above two problems are solved very well, and in performance test, the new WAL slightly outperforms the previous implementation, giving a solid foundation for future optimizations.
Interested readers can refer to the design documentation [here]({{< ref "wal_on_disk" >}}) for more details on this feature.
### How to enable
```
[analytic.wal]
type = "Local"
data_dir = "/path/to/local/wal"
```
## 2. Access object store with [Apache OpenDAL](https://github.com/apache/opendal)
OpenDAL (Open Data Access Layer) is a project that provides a unified API for accessing various data storage backends.
It offers several advantages for developers and organizations. Here are some key benefits:
- Unified API. OpenDAL provides a consistent and unified API for accessing different storage backends, such as AWS S3, Azure Blob Storage, and local file systems.
- Optimized for Efficiency: OpenDAL is built with performance in mind. It includes optimizations to ensure efficient data access and manipulation, making it suitable for high-performance applications.
- Comprehensive Documentation: The project provides detailed documentation, making it easier for developers to get started and understand how to use the library effectively.
In newer versions of OpenDAL, [object_store integration](https://github.com/apache/opendal/tree/main/integrations/object_store) is provided, which is very beneficial for HoraeDB code migration, as the APIs used by the upper layers remain virtually unchanged, and only the object store part needs to be abstracted to a unified OpenDAL operator:
```rust
// Create a new operator
let operator = Operator::new(S3::default())?.finish();
// Create a new object store
let object_store = Arc::new(OpendalStore::new(operator));
```
Additionally, since the Apache OpenDAL implementation of `object_store` is based on the latest version of the object_store, which has breaking changes from the previous version that HoraeDB is using, we've chosen to make it compatible in order to keep the scope of this upgrade as manageable as possible.
In the process of adapting to the new API, the `put_multipart` interface has changed the most, so the main adaptation logic is also here, HoraeDB's approach is to encapsulate the underlying `put_multipart` interface to ensure that the upper layer code is not modified, the details can be found in the reference:
https://github.com/apache/horaedb/blob/v2.1.0/src/components/object_store/src/multi_part.rs
> Note: The adaptation logic is only practical when parquet version < 52.0.0.
## Download
Go to [download pages](/downloads).
## Conclusion
Other bug fixes and improvements can be seen here:
- https://github.com/apache/horaedb/releases/tag/v2.1.0
> As always, we warmly welcome you to join our [community](/community) and share your insights.
---
## File: content/en/blog/_index.md
---
title: "Blog"
weight: 1
simple_list: true
menu:
main:
weight: 30
pre:
---
---
## File: content/en/community/_index.md
---
title: "Community"
params:
contributingUrl: https://github.com/apache/horaedb/blob/main/CONTRIBUTING.md
menu:
main:
weight: 40
---
{{% blocks/section color="white" %}}
# How to Subscribe Mail List
Mailing lists are a form of communication used by the Apache community. Generally speaking, many things in the Apache community are hosted by mailing lists, For example: project q&A, technical discussion, transaction decision, release vote, etc，By subscribing, you can get the first update of the HoraeDB community and keep up with the community. The following list is the main list we are using:
- dev@horaedb.apache.org, Community activity information
- commits@horaedb.apache.org, Code repo update information
In order to subscribe to the dev@horaedb.apache.org mailing list, the steps are as follows：
1. Send an email without any content or subject: dev-subscribe@horaedb.apache.org
2. Wait until you receive an email with the subject line confirm subscribe to dev@horaedb.apache.org (if you have not received it for a long time, please confirm whether the email is blocked by your email, if you have not been blocked and will receive a reply for a long time, return to step 1)
3. Reply directly to the email without changing the subject line or adding the email content.
4. Wait until you receive an email with the subject line WELCOME to dev@horaedb.apache.org .
5. If you receive an email from dev, you have successfully subscribed to the email. To initiate a discussion, you can send an email directly to dev@horaedb.apache.org, which will be sent to everyone who subscribed to the mailing list.
## Unsubscribe from the mail list
The steps for unsubscribing to a mailing list are similar to those for subscribing to a mailing list:
1. Send an email without any content or subject to: dev-unsubscribe@horaedb.apache.org
2. Wait until you receive an email with the subject line confirm unsubscribe from dev@horaedb.apache.org
3. Reply directly to the email without changing the subject line or adding the email content
4. Wait until you receive an email with the subject line GOODBYE from dev@horaedb.apache.org
5. Unsubscribe success
{{% /blocks/section %}}
---
## File: content/en/docs/design/architecture.md
---
title: "Introduction to HoraeDB's Architecture"
---
## Target
- Provide the overview of HoraeDB to the developers who want to know more about HoraeDB but have no idea where to start.
- Make a brief introduction to the important modules of HoraeDB and the connections between these modules but details about their implementations are not be involved.
## Motivation
HoraeDB is a timeseries database (**TSDB**). However, HoraeDB's goal is to handle both timeseries and analytic workloads compared with the classic TSDB, which usually have a poor performance in handling analytic workloads.
In the classic timeseries database, the `Tag` columns (InfluxDB calls them `Tag` and Prometheus calls them `Label`) are normally indexed by generating an inverted index. However, it is found that the cardinality of `Tag` varies in different scenarios. And in some scenarios the cardinality of `Tag` is very high (we name this case after analytic workload), and it takes a very high cost to store and retrieve the inverted index. On the other hand, it is observed that scanning+pruning often used by the analytical databases can do a good job to handle such analytic workload.
The basic design idea of HoraeDB is to adopt a hybrid storage format and the corresponding query method for a better performance in processing both timeseries and analytic workloads.
## Architecture
```plaintext
┌──────────────────────────────────────────┐
│ RPC Layer (HTTP/gRPC/MySQL) │
└──────────────────────────────────────────┘
┌──────────────────────────────────────────┐
│ SQL Layer │
│ ┌─────────────────┐ ┌─────────────────┐ │
│ │ Parser │ │ Planner │ │
│ └─────────────────┘ └─────────────────┘ │
└──────────────────────────────────────────┘
┌───────────────────┐ ┌───────────────────┐
│ Interpreter │ │ Catalog │
└───────────────────┘ └───────────────────┘
┌──────────────────────────────────────────┐
│ Query Engine │
│ ┌─────────────────┐ ┌─────────────────┐ │
│ │ Optimizer │ │ Executor │ │
│ └─────────────────┘ └─────────────────┘ │
└──────────────────────────────────────────┘
┌──────────────────────────────────────────┐
│ Pluggable Table Engine │
│ ┌────────────────────────────────────┐ │
│ │ Analytic │ │
│ │┌────────────────┐┌────────────────┐│ │
│ ││ Wal ││ Memtable ││ │
│ │└────────────────┘└────────────────┘│ │
│ │┌────────────────┐┌────────────────┐│ │
│ ││ Flush ││ Compaction ││ │
│ │└────────────────┘└────────────────┘│ │
│ │┌────────────────┐┌────────────────┐│ │
│ ││ Manifest ││ Object Store ││ │
│ │└────────────────┘└────────────────┘│ │
│ └────────────────────────────────────┘ │
│ ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ Another Table Engine │ │
│ └ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
└──────────────────────────────────────────┘
```
The figure above shows the architecture of HoraeDB stand-alone service and the details of some important modules will be described in the following part.
### RPC Layer
module path: https://github.com/apache/incubator-horaedb/tree/main/server
The current RPC supports multiple protocols including HTTP, gRPC, MySQL.
Basically, HTTP and MySQL are used to debug HoraeDB, query manually and perform DDL operations (such as creating, deleting tables, etc.). And gRPC protocol can be regarded as a customized protocol for high-performance, which is suitable for massive reading and writing operations.
### SQL Layer
module path: https://github.com/apache/incubator-horaedb/tree/main/query_frontend
SQL layer takes responsibilities for parsing sql and generating the query plan.
Based on [sqlparser](https://github.com/sqlparser-rs/sqlparser-rs) a sql dialect, which introduces some key concepts including `Tag` and `Timestamp`, is provided for processing timeseries data. And by utilizing [DataFusion](https://github.com/apache/arrow-datafusion) the planner is able to generate both regular logical plans and tailored ones which is used to implement the special operators defined by timeseries queries, e.g `PromQL`.
### Interpreter
module path: https://github.com/apache/incubator-horaedb/tree/main/interpreters
The `Interpreter` module encapsulates the SQL `CRUD` operations. In the query procedure, a sql received by HoraeDB is parsed, converted into the query plan and then executed in some specific interpreter, such as `SelectInterpreter`, `InsertInterpreter` and etc.
### Catalog
module path: https://github.com/apache/incubator-horaedb/tree/main/catalog_impls
`Catalog` is actually the module managing metadata and the levels of metadata adopted by HoraeDB is similar to PostgreSQL: `Catalog > Schema > Table`, but they are only used as namespace.
At present, `Catalog` and `Schema` have two different kinds of implementation for standalone and distributed mode because some strategies to generate ids and ways to persist metadata differ in different mode.
### Query Engine
module path: https://github.com/apache/incubator-horaedb/tree/main/query_engine
`Query Engine` is responsible for optimizing and executing query plan given a basic SQL plan provided by SQL layer and now such work is mainly delegated to [DataFusion](https://github.com/apache/arrow-datafusion).
In addition to the basic functions of SQL, HoraeDB also defines some customized query protocols and optimization rules for some specific query plans by utilizing the extensibility provided by [DataFusion](https://github.com/apache/arrow-datafusion). For example, the implementation of `PromQL` is implemented in this way and read it if you are interested.
### Pluggable Table Engine
module path: https://github.com/apache/incubator-horaedb/tree/main/table_engine
`Table Engine` is actually a storage engine for managing tables in HoraeDB and the pluggability of `Table Engine` is a core design of HoraeDB which matters in achieving our long-term target, e.g supporting handle log or tracing workload by implementing new storage engines. HoraeDB will have multiple kinds of `Table Engine` for different workloads and the most appropriate one should be chosen as the storage engine according to the workload pattern.
Now the requirements for a `Table Engine` are:
- Manage all the shared resources under the engine:
- Memory
- Storage
- CPU
- Manage metadata of tables such as table schema and table options;
- Provide `Table` instances which provides `read` and `write` methods;
- Take responsibilities for creating, opening, dropping and closing `Table` instance;
- ....
Actually the things that a `Table Engine` needs to process are a little complicated. And now in HoraeDB only one `Table Engine` called `Analytic` is provided and does a good job in processing analytical workload, but it is not ready yet to handle the timeseries workload (we plan to enhance it for a better performance by adding some indexes which help handle timeseries workload).
The following part gives a description about details of `Analytic Table Engine`.
#### WAL
module path: https://github.com/apache/incubator-horaedb/tree/main/wal
The model of HoraeDB processing data is `WAL` + `MemTable` that the recent written data is written to `WAL` first and then to `MemTable` and after a certain amount of data in `MemTable` is accumulated, the data will be organized in a query-friendly form to persistent devices.
Now three implementations of `WAL` are provided for standalone and distributed mode:
- For standalone mode, `WAL` is based on `RocksDB` and data is persisted on the local disk.
- For distributed mode, `WAL` is required as a distributed component and to be responsible for durability of the newly written data, so now we provide an implementation based on [OceanBase](https://github.com/oceanbase/oceanbase).
- For distributed mode, in addition to [OceanBase](https://github.com/oceanbase/oceanbase), we also provide a more lightweight implementation based on [`Apache Kafka`](https://github.com/apache/kafka).
#### MemTable
module path: https://github.com/apache/incubator-horaedb/tree/main/analytic_engine/src/memtable
For `WAL` can't provide efficient data retrieval, the newly written data is also stored in `Memtable` for efficient data retrieval, after a certain amount of data is reached, HoraeDB organizes the data in `MemTable` into a query-friendly storage format (`SST`) and stores it to the persistent device.
The current implementation of `MemTable` is based on [agatedb's skiplist](https://github.com/tikv/agatedb/blob/8510bff2bfde5b766c3f83cf81c00141967d48a4/skiplist). It allows concurrent reads and writes and can control memory usage based on [Arena](https://github.com/apache/incubator-horaedb/tree/main/components/skiplist).
#### Flush
module path: https://github.com/apache/incubator-horaedb/blob/main/analytic_engine/src/instance/flush_compaction.rs
What `Flush` does is that when the memory usage of `MemTable` reaches the threshold, some `MemTables` are selected for flushing into query-friendly `SST`s saved on persistent device.
During the flushing procedure, the data will be divided by a certain time range (which is configured by table option `Segment Duration`), and any `SST` is ensured that the timestamps of the data in it are in the same `Segment`. Actually this is also a common operation in most timeseries databases which organizes data in the time dimension to speed up subsequent time-related operations, such as querying data over a time range and assisting purge data outside the `TTL`.
#### Compaction
module path: https://github.com/apache/incubator-horaedb/tree/main/analytic_engine/src/compaction
The data of `MemTable` is flushed as `SST`s, but the file size of recently flushed `SST` may be very small. And too small or too many `SST`s lead to the poor query performance. Therefore, `Compaction` is then introduced to rearrange the `SST`s so that the multiple smaller `SST` files can be compacted into a larger `SST` file.
#### Manifest
module path: https://github.com/apache/incubator-horaedb/tree/main/analytic_engine/src/meta
`Manifest` takes responsibilities for managing tables' metadata of `Analytic Engine` including:
- Table schema and table options;
- The sequence number where the newest flush finishes;
- The information of all the `SST`s belonging to the table.
Now the `Manifest` is based on `WAL` and `Object Storage`. The newly written updates on the `Manifest` are persisted as logs in `WAL`, and in order to avoid infinite expansion of `Manifest` (actually every `Flush` leads to an update), `Snapshot` is also introduced to clean up the history of metadata updates, and the generated `Snapshot` will be saved to `Object Storage`.
#### Object Storage
module path: https://github.com/apache/incubator-horaedb/tree/main/components/object_store
The `SST` generated by `Flush` needs to be persisted and the abstraction of the persistent storage device is `ObjectStore` including multiple implementations:
- Based on local file system;
- Based on [Alibaba Cloud OSS](https://www.alibabacloud.com/product/object-storage-service).
The distributed architecture of HoraeDB separates storage and computing, which requires `Object Store` needs to be a highly available and reliable service independent of HoraeDB. Therefore, storage systems like [Amazon S3](https://aws.amazon.com/s3/), [Alibaba Cloud OSS](https://www.alibabacloud.com/product/object-storage-service) is a good choice and in the future implementations on storage systems of some other cloud service providers is planned to provide.
#### SST
module path: https://github.com/apache/incubator-horaedb/tree/main/analytic_engine/src/sst
`SST` is actually an abstraction that can have multiple specific implementations. The current implementation is based on [Parquet](https://parquet.apache.org/), which is a column-oriented data file format designed for efficient data storage and retrieval.
The format of `SST` is very critical for retrieving data and is also the most important part to perform well in handling both timeseries and analytic workloads. At present, our [Parquet](https://parquet.apache.org/)-based implementation is good at processing analytic workload but is poor at processing timeseries workload. In our roadmap, we will explore more storage formats in order to achieve a good performance in both workloads.
#### Space
module path: https://github.com/apache/incubator-horaedb/blob/main/analytic_engine/src/space.rs
In `Analytic Engine`, there is a concept called `space` and here is an explanation for it to resolve some ambiguities when read source code. Actually `Analytic Engine` does not have the concept of `catalog` and `schema` and only provides two levels of relationship: `space` and `table`. And in the implementation, the `schema id` (which should be unique across all `catalog`s) on the upper layer is actually mapped to `space id`.
The `space` in `Analytic Engine` serves mainly for isolation of resources for different tenants, such as the usage of memory.
## Critical Path
After a brief introduction to some important modules of HoraeDB, we will give a description for some critical paths in code, hoping to provide interested developers with a guide for reading the code.
### Query
```plaintext
┌───────┐ ┌───────┐ ┌───────┐
│ │──1──▶│ │──2──▶│ │
│Server │ │ SQL │ │Catalog│
│ │◀─10──│ │◀─3───│ │
└───────┘ └───────┘ └───────┘
│ ▲
4│ 9│
│ │
▼ │
┌─────────────────────────────────────┐
│ │
│ Interpreter │
│ │
└─────────────────────────────────────┘
│ ▲
5│ 8│
│ │
▼ │
┌──────────────────┐
│ │
│ Query Engine │
│ │
└──────────────────┘
│ ▲
6│ 7│
│ │
▼ │
┌─────────────────────────────────────┐
│ │
│ Table Engine │
│ │
└─────────────────────────────────────┘
```
Take `SELECT` SQL as an example. The figure above shows the query procedure and the numbers in it indicates the order of calling between the modules.
Here are the details:
- Server module chooses a proper rpc module (it may be HTTP, gRPC or mysql) to process the requests according the protocol used by the requests;
- Parse SQL in the request by the parser;
- With the parsed sql and the information provided by catalog/schema module, [DataFusion](https://github.com/apache/arrow-datafusion) can generate the logical plan;
- With the logical plan, the corresponding `Interpreter` is created and logical plan will be executed by it;
- For the logical plan of normal `Select` SQL, it will be executed through `SelectInterpreter`;
- In the `SelectInterpreter` the specific query logic is executed by the `Query Engine`:
- Optimize the logical plan;
- Generate the physical plan;
- Optimize the physical plan;
- Execute the physical plan;
- The execution of physical plan involves `Analytic Engine`:
- Data is obtained by `read` method of `Table` instance provided by `Analytic Engine`;
- The source of the table data is `SST` and `Memtable`, and the data can be filtered by the pushed down predicates;
- After retrieving the table data, `Query Engine` will complete the specific computation and generate the final results;
- `SelectInterpreter` gets the results and feeds them to the protocol module;
- After the protocol layer converts the results, the server module responds to the client with them.
The following is the flow of function calls in version [v1.2.2](https://github.com/apache/incubator-horaedb/releases/tag/v1.2.2):
```
/* Detailed source-code truncated for AI context efficiency. */
```
1. The received request will be forwarded to `handle_sql` after various protocol conversions, and since the request may not be processed by this node, it may need to be forwarded to `maybe_forward_sql_query` to handle the forwarding logic.
2. After constructing the `ForwardRequest` in `maybe_forward_sql_query`, call `forward`
3. After constructing the `RouteRequest` in `forward`, call `route`
4. Use `route` to get the destination node `endpoint` and return to `forward`.
5. Call `forward_with_endpoint` to forward the request
6. return `forward`
7. return `maybe_forward_sql_query`
8. return `handle_sql`
9. If this is a `Local` request, call `fetch_sql_query_output` to process it
10. Call `parse_sql` to parse `sql` into `Statment`
11. return `fetch_sql_query_output`
12. Call `statement_to_plan` with `Statment`
13. Construct `Planner` with `ctx` and `Statment`, and call the `statement_to_plan` method of `Planner`
14. The `planner` will call the corresponding `planner` method for the requested category, at this point our `sql` is a query and will call `sql_statement_to_plan`
15. Call `sql_statement_to_datafusion_plan` , which will generate the `datafusion` object, and then call `SqlToRel::sql_statement_to_plan`
16. The generated logical plan is returned from `SqlToRel::sql_statement_to_plan`
17. return
18. return
19. return
20. Call `execute_plan_involving_partition_table` (in the default configuration) for subsequent optimization and execution of this logical plan
21. Call `build_interpreter` to generate `Interpreter`
22. return
23. Call `Interpreter's` `interpreter_execute_plan` method for logical plan execution.
24. The corresponding `execute` function is called, at this time the `sql` is a query, so the execute of the `SelectInterpreter` will be called
25. call `execute_logical_plan` , which will call `build_df_session_ctx` to generate the optimizer
26. `build_df_session_ctx` will use the `config` information to generate the corresponding context, first using datafusion and some custom optimization rules (in logical_optimize_rules()) to generate the logical plan optimizer, using `apply_adapters_for_physical_optimize_rules` to generate the physical plan optimizer
27. return optimizer
28. Call `optimize_plan`, using the optimizer just generated to first optimize the logical plan and then the physical plan
29. Return to optimized physical plan
30. execute physical plan
31. returned after execution
32. After collecting the results of all slices, return
33. return
34. return
35. return
36. Return to the upper layer for network protocol conversion and finally return to the request sender
### Write
```plaintext
┌───────┐ ┌───────┐ ┌───────┐
│ │──1──▶│ │──2──▶│ │
│Server │ │ SQL │ │Catalog│
│ │◀─8───│ │◀─3───│ │
└───────┘ └───────┘ └───────┘
│ ▲
4│ 7│
│ │
▼ │
┌─────────────────────────────────────┐
│ │
│ Interpreter │
│ │
└─────────────────────────────────────┘
│ ▲
│ │
│ │
│ │
│ │ ┌──────────────────┐
│ │ │ │
5│ 6│ │ Query Engine │
│ │ │ │
│ │ └──────────────────┘
│ │
│ │
│ │
▼ │
┌─────────────────────────────────────┐
│ │
│ Table Engine │
│ │
└─────────────────────────────────────┘
```
Take `INSERT` SQL as an example. The figure above shows the query procedure and the numbers in it indicates the order of calling between the modules.
Here are the details:
- Server module chooses a proper rpc module (it may be HTTP, gRPC or mysql) to process the requests according the protocol used by the requests;
- Parse SQL in the request by the parser;
- With the parsed sql and the catalog/schema module, [DataFusion](https://github.com/apache/arrow-datafusion) can generate the logical plan;
- With the logical plan, the corresponding `Interpreter` is created and logical plan will be executed by it;
- For the logical plan of normal `INSERT` SQL, it will be executed through `InsertInterpreter`;
- In the `InsertInterpreter`, `write` method of `Table` provided `Analytic Engine` is called:
- Write the data into `WAL` first;
- Write the data into `MemTable` then;
- Before writing to `MemTable`, the memory usage will be checked. If the memory usage is too high, the flush process will be triggered:
- Persist some old MemTables as `SST`s;
- Store updates about the new `SST`s and the flushed sequence number of `WAL` to `Manifest`;
- Delete the corresponding `WAL` entries;
- Server module responds to the client with the execution result.
---
## File: content/en/docs/design/clustering.md
---
title: "Introduction to Architecture of HoraeDB Cluster"
---Note: Some of the features mentioned in the article have not yet been implemented.
## Overview
```
/* Detailed source-code truncated for AI context efficiency. */
```
The diagram above describes the architecture of a HoraeDB cluster, where some key concepts need to be explained:
- `HoraeMeta Cluster`: Takes responsibilities for managing the metadata and resource scheduling of the cluster;
- `Shard(L)/Shard(F)`: Leader shard and follower shard consisting of multiple tables;
- `HoraeDB`: One HoraeDB instance consisting of multiple shards;
- `WAL Service`: Write-ahead log service for storing new-written real-time data;
- `Object Storage`: Object storage service for storing SST converted from memtable;
From the architecture diagram above, it can be concluded that the compute and storage are separated in the HoraeDB cluster, which makes it easy to implement useful distributed features, such as elastic autoscaling of compute/storage resources, high availability, load balancing, and so on.
Let's dive into some of the key components mentioned above before explaining how these features are implemented.
### Shard
`Shard` is the basic scheduling unit in the cluster, which consists of a group of tables. And the tables in a shard share the same region for better storage locality in the `WAL Service`, and because of this, it is efficient to recover the data of all tables in the shard by scanning the entire WAL region. For most of implementations of `WAL Service`, without the shard concept, it costs a lot to recover the table data one by one due to massive random IO, and this case will deteriorate sharply when the number of tables grows to a certain level.
A specific role, `Leader` or `Follower`, should be assigned to a shard. A pair of leader-follower shards share the same set of tables, and the leader shard can serve the write and query requests from the client while the follower shard can only serve the read-only requests, and must synchronize the newly written data from the WAL service in order to provide the latest snapshot for data retrieval. Actually, the follower is not needed if the high availability is not required, while with at least one follower, it takes only a short time to resume service by simply switching the `Follower` to `Leader` when the HoraeDB instance on which the leader shard exists crashes.
The diagram below concludes the relationship between HoraeDB instance, `Shard`, `Table`. As shown in the diagram, the leader and follower shards are interleaved on the HoraeDB instance.
```plaintext
┌─HoraeDB Instance0──────┐ ┌─HoraeDB Instance1──────┐
│ ┌─Shard0(L)────────┐ │ │ ┌─Shard0(F)────────┐ │
│ │ ┌────┬────┬────┐ │ │ │ │ ┌────┬────┬────┐ │ │
│ │ │ T0 │ T1 │ T2 │ │ │ │ │ │ T0 │ T1 │ T2 │ │ │
│ │ └────┴────┴────┘ │ │ │ │ └────┴────┴────┘ │ │
│ └──────────────────┘ │ │ └──────────────────┘ │
│ │ │ │
│ ┌─Shard1(F)────────┐ │ │ ┌─Shard1(L)────────┐ │
│ │ ┌────┬────┬────┐ │ │ │ │ ┌────┬────┬────┐ │ │
│ │ │ T0 │ T1 │ T2 │ │ │ │ │ │ T0 │ T1 │ T2 │ │ │
│ │ └────┴────┴────┘ │ │ │ │ └────┴────┴────┘ │ │
│ └──────────────────┘ │ │ └──────────────────┘ │
└────────────────────────┘ └────────────────────────┘
```
Since `Shard` is the basic scheduling unit, it is natural to introduce some basic shard operations:
- Create/Drop table to/from a shard;
- Open/Close a shard;
- Split one shard into two shards;
- Merge two shards into one shard;
- Switch the role of a shard;
With these basic shard operations, some complex scheduling logic can be implemented, e.g. perform an expansion by splitting one shard into two shards and migrating one of them to the new HoraeDB instance.
### HoraeMeta
`HoraeMeta` is implemented by embedding an ETCD inside to ensure consistency and takes responsibilities for cluster metadata management and scheduling.
The cluster metadata includes:
- Table information, such as table name, table ID, and which cluster the table belongs to;
- The mapping between table and shard and between shard and HoraeDB instance;
- ...
As for the cluster scheduling work, it mainly includes:
- Receiving the heartbeats from the HoraeDB instances and determining the online status of these registered instances;
- Assigning specific role shards to the registered HoraeDB instances;
- Participating in table creation by assigning a unique table ID and the most appropriate shard to the table;
- Performing load balancing through shard operations according to the load information sent with the heartbeats;
- Performing expansion through shard operations when new instances are registered;
- Initiating failover through shard operations when old instances go offline;
### Route
In order to avoid the overhead of forwarding requests, the communication between clients and the HoraeDB instances is peer-to-peer, that is to say, the client should retrieve routing information from the server before sending any specific write/query requests.
Actually, the routing information is decided by the `HoraeMeta`, but clients are only allowed the access to it through the HoraeDB instances rather than `HoraeMeta`, to avoid potential performance issues on the `HoraeMeta`.
### WAL Service & Object Storage
In the HoraeDB cluster, `WAL Service` and `Object Storage` exist as separate distributed systems featured with HA, data replication and scalability. Current distributed implementations for `WAL Service` includes `Kafka` and `OBKV` (access `OceanBase` by its table api), and the implementations for `Object Storage` include popular object storage services, such as AWS S3, Azure object storage and Aliyun OSS.
The two components are similar in that they are introduced to serve as the underlying storage layer for separating compute and storage, while the difference between two components is obvious that `WAL Service` is used to store the newly written data from the real-time write requests whose individual size is small but quantity is large, and `Object Storage` is used to store the read-friendly data files (SST) organized in the background, whose individual size is large and aggregate size is much larger.
The two components make it much easier to implement the horaedb cluster, which features horizontal scalability, high availability and load balancing.
## Scalability
Scalability is an important feature for a distributed system. Let's take a look at to how the horizontal scalability of the HoraeDB cluster is achieved.
First, the two storage components (`WAL Service` and `Object Storage`) should be horizontally scalable when deciding on the actual implementations for them, so the two storage services can be expanded separately if the storage capacity is not sufficient.
It will be a little bit complex when discussing the scalability of the compute service. Basically, these cases will bring the capacity problem:
- Massive queries on massive tables;
- Massive queries on a single large table;
- Massive queries on a normal table;
For the first case, it is easy to achieve horizontal scalability just by assigning shards that are created or split from old shards to expanded HoraeDB instances.
For the second case, the table partitioning is proposed and after partitioning, massive queries are distributed across multiple HoraeDB instances.
And the last case is the most important and the most difficult. Actually, the follower shard can handle part of the queries, but the number of follower shards is limited by the throughput threshold of the synchronization from the WAL regions. As shown in the diagram below, a pure compute shard can be introduced if the followers are not enough to handle the massive queries. Such a shard is not required to synchronize data with the leader shard, and retrieves the newly written data from the leader/follower shard only when the query comes. As for the SSTs required by the query, they will be downloaded from `Object Storage` and cached afterwards. With the two parts of the data, the compute resources are fully utilized to execute the CPU-intensive query plan. As we can see, such a shard can be added with only a little overhead (retrieving some data from the leader/follower shard when it needs), so to some extent, the horizontal scalability is achieved.
```plaintext
┌HoraeDB─────┬┬─┐
┌──newly written─│ │ │TableN││ │
▼ └──Shard(L/F)┴┴─┘
┌───────┐ Query ┌HoraeDB─────┬┬─┐
│client │────────▶│ │ │TableN││ │
└───────┘ └──Shard─────┴┴─┘ ┌───────────────┐
▲ │ Object │
└───old SST──────│ Storage │
└───────────────┘
```
## High Availability
Assuming that `WAL service` and `Object Storage` are highly available, the high availability of the HoraeDB cluster can be achieved by such a procedure:
- When detecting that the heartbeat is broken, `HoraeMeta` determines that the HoraeDB instance is offline;
- The follower shards whose paired leader shards exist on the offline instance are switched to leader shards for fast failover;
- A slow failover can be achieved by opening the crashed shards on another instance if such follower shards don't exist.
```
/* Detailed source-code truncated for AI context efficiency. */
```
## Load Balancing
HoraeMeta collects the instance load information contained in the received heartbeats to create a load overview of the whole cluster, according to which the load balancing can be implemented as an automatic mechanism:
- Pick a shard on a low-load instance for the newly created table;
- Migrate a shard from a high-load instance load to another low-load instance;
- Split the large shard on the high-load instance and migrate the split shards to other low-load instances;
---
## File: content/en/docs/design/compaction_offload.md
---
title: "Compaction Offload"
---
**Note: This feature is still in development.**
This chapter discusses compaction offload, which is designed to separate the compaction workload from the local horaedb nodes and delegate it to external compaction nodes.
## Overview
```plaintext
┌─────────────────────────────────────────────────────────────────────────┐
│ │
│ HoraeMeta Cluster │
│ │
└─────────────────────────────────────────────────────────────────────────┘
▲ ▲ | |
│ │1.Fetch compaction │(Monitor compaction│
│ │ node info │node) │
| │ ▼ ▼
┌────────────┐ ┌────────────┐ ┌────────────┐ ┌────────────┐
│ │ │ │2.Offload Task│ │ │ │
│ HoraeDB │ │ HoraeDB │ ─────────▶ │ Compaction │ │ Compaction │
│ │ │ │ ◀───────── │ Node │ │ Node │
└────────────┘ └────────────┘ 4.Ret Result └────────────┘ └────────────┘
| | | |
│ 5.Update the │ │ 3.Compact │
│ SSTable │ │ │
▼ ▼ ▼ ▼
┌─────────────────────────────────────────────────────────────────────────┐
│ ┌─────────────────────┐ │
│ Object Storage │ Temporary Workspace │ │
│ └─────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
```
The diagram above describes the architecture of cluster for compaction offload, where some key concepts need to be explained:
- `Compaction Node`: Takes responsibility to handle offloaded compaction tasks. The compaction node receives the compaction task and performs the actual merging of SSTables, then sends back the task result to HoraeDB.
- `HoraeMeta Cluster`: HoraeMeta acts as a compaction nodes manager in the compaction offload scenario. It monitors the compaction nodes cluster and schedule the compaction nodes.
The procedure of remote compaction based above architecture diagram is:
1. HoraeDB servers fetch the information of suitable compaction nodes from the HoraeMeta.
2. HoraeDB submit the compaction task to the remote compaction node, according to the information fetched from HoraeMeta.
3. Compaction node executes the task and write results to the temporary workspace.
4. Compaction node sends compaction results back to HoraeDB.
5. HoraeDB receives the result, installs the data in temporary workspace and purges compaction input files.
The architecture above makes it easy to implement some wonderful features like load balancing and high availability. Let's dive into the key components in the architecture and talking about how these features are implemented.
### Compaction Node
`Compaction Node` runs the main logic of compaction. It is implemented based on HoraeDB and distinguished by:
- `NodeType`: A config parameter used to distinguished the `HoraeDB` and `CompactionNode`. This info would be sent to HoraeMeta through heartbeat.
The compaction service is implemented as grpc service.
### HoraeMeta
`HoraeMeta` manages the compaction nodes cluster with `CompactionNodeManager`, which takes responsibilities for compaction nodes metadata management and scheduling.
The compaction nodes metadata includes:
- Compaction node information, such as node name, node state;
- A compaction node name list, used as the key to access compaction node info, for better scheduling with round-robin strategy;
- ...
As for the compaction nodes scheduling work, it mainly includes:
- Receiving the heartbeats from the compaction node and determining the online status of these registered nodes.
- Performing load balancing according to the compaction nodes cluster info.
- Providing the info of suitable compaction node for HoraeDB when remote compaction execution is needed.
## Load Balancing
Load Balancing is critical for compaction nodes cluster to make their overall processing more efficient. The effect of load balancing mainly based on the schedule algorithm for compaction nodes impl in `CompactionNodeManager`.
_(ps: The current implementation of schedule algorithm is round-robin strategy for easiness.)_
The main process for the schedule algorithm based on real load is:
- HoraeMeta collects the compaction nodes load information through the heartbeats to create a load overview of the compaction nodes cluster.
- Pick a compaction node with low load according to the load overview.
## High Availability
The fault tolerance of above architecture can be achieved by such a procedure:
- When detecting that the heartbeat is broken, `HoraeMeta` determines that the compaction node is offline.
- When `HoraeMeta` can not provide suitable compaction node for HoraeDB or compaction node doesn't return the task result successfully, HoraeDB would switches to run compaction task locally.
---
## File: content/en/docs/design/storage.md
---
title: "Storage"
---
The storage engine mainly provides the following two functions：
1. Persistence of data
2. Under the premise of ensuring the correctness of the data, organize the data in the most reasonable way to meet the query needs of different scenarios.
This document will introduce the internal implementation of the storage engine in HoraeDB. Readers can refer to the content here to explore how to use HoraeDB efficiently.
# Overall Structure
HoraeDB is a distributed storage system based on the share-nothing architecture.
Data between different servers is isolated from each other and does not affect each other. The storage engine in each stand-alone machine is a variant of [log-structured merge-tree](https://en.wikipedia.org/wiki/Log-structured_merge-tree), which is optimized for time-series scenarios. The following figure shows its core components:
## Write Ahead Log (WAL)
A write request will be written to
1. memtable in memory
2. WAL in durable storage
Since memtable is not persisted to the underlying storage system in real time, so WAL is required to ensure the reliability of the data in memtable.
On the other hand, due to the design of the [distributed architecture](cluster.md), WAL itself is required to be highly available. Now there are following implementations in HoraeDB:
- [Local disk](wal_on_rocksdb.md) (based on [RocksDB](http://rocksdb.org/), no distributed high availability)
- [OceanBase](https://www.oceanbase.com)
- [Kafka](wal_on_kafka.md)
## Memtable
Memtable is a memory data structure used to hold recently written table data. Different tables have its corresponding memtable.
Memtable is read-write by default (aka active), and when the write reaches some threshold, it will become read-only and be replaced by a new memtable.
The read-only memtable will be flushed to the underlying storage system in SST format by background thread. After flush is completed, the read-only memtable can be destroyed, and the corresponding data in WAL can also be deleted.
## Sorted String Table（SST）
SST is a persistent format for data, which is stored in the order of primary keys of table. Currently, HoraeDB uses parquet format for this.
For HoraeDB, SST has an important option: segment_duration, only SST within the same segment can be merged, which is benefical for time-series data. And it is also convenient to eliminate expired data.
In addition to storing the original data, the statistical information of the data will also be stored in the SST to speed up the query, such as the maximum value, the minimum value, etc.
## Compactor
Compactor can merge multiple small SST files into one, which is used to solve the problem of too many small files. In addition, Compactor will also delete expired data and duplicate data during the compaction. In future, compaction maybe add more task, such as downsample.
The current compaction strategy in HoraeDB reference Cassandra:
- [SizeTieredCompactionStrategy](https://cassandra.apache.org/doc/latest/cassandra/operating/compaction/stcs.html)
- [TimeWindowCompactionStrategy](https://cassandra.apache.org/doc/latest/cassandra/operating/compaction/twcs.html)
## Manifest
Manifest records metadata of table, SST file, such as: the minimum and maximum timestamps of the data in an SST.
Due to the design of the distributed architecture, the manifest itself is required to be highly available. Now in HoraeDB, there are mainly the following implementations：
- WAL
- ObjectStore
## ObjectStore
ObjectStore is place where data (i.e. SST) is persisted.
Generally speaking major cloud vendors should provide corresponding services, such as Alibaba Cloud's OSS and AWS's S3.