dgraph 单机的已经运行过了，以下是一个简单的集群环境运行，以及试用
环境准备
- docker-compose 文件
version: "3.2"services: zero1: image: dgraph/dgraph:latest volumes: - data-volume1:/dgraph ports: - 5080:5080 - 6080:6080 command: dgraph zero --my=zero1:5080 --replicas 3 --idx 1 zero2: image: dgraph/dgraph:latest volumes: - data-volume2:/dgraph ports: - 5081:5081 - 6081:6081 command: dgraph zero -o 1 --my=zero2:5081 --replicas 3 --peer zero1:5080 --idx 2 zero3: image: dgraph/dgraph:latest volumes: - data-volume3:/dgraph ports: - 5082:5082 - 6082:6082 command: dgraph zero -o 2 --my=zero3:5082 --replicas 3 --peer zero1:5080 --idx 3 alpha1: image: dgraph/dgraph:latest hostname: "alpha1" volumes: - data-volume-app1:/dgraph ports: - 8080:8080 - 9080:9080 command: dgraph alpha --my=alpha1:7080 --lru_mb=2048 --zero=zero1:5080,zero2:5080,zero3:5080 alpha2: image: dgraph/dgraph:latest hostname: "alpha2" volumes: - data-volume-app2:/dgraph ports: - 8081:8081 - 9081:9081 command: dgraph alpha --my=alpha2:7081 --lru_mb=2048 --zero=zero1:5080,zero2:5080,zero3:5080 -o 1 alpha3: image: dgraph/dgraph:latest hostname: "alpha3" volumes: - data-volume-app3:/dgraph ports: - 8082:8082 - 9082:9082 command: dgraph alpha --my=alpha3:7082 --lru_mb=2048 --zero=zero1:5080,zero2:5080,zero3:5080 -o 2 alpha4: image: dgraph/dgraph:latest hostname: "alpha4" volumes: - data-volume-app4:/dgraph ports: - 8083:8083 - 9083:9083 command: dgraph alpha --my=alpha4:7083 --lru_mb=2048 --zero=zero1:5080,zero2:5080,zero3:5080 -o 3 alpha5: image: dgraph/dgraph:latest hostname: "alpha5" volumes: - data-volume-app5:/dgraph ports: - 8084:8084 - 9084:9084 command: dgraph alpha --my=alpha5:7084 --lru_mb=2048 --zero=zero1:5080,zero2:5080,zero3:5080 -o 4 alpha6: image: dgraph/dgraph:latest hostname: "alpha6" volumes: - data-volume-app6:/dgraph ports: - 8085:8085 - 9085:9085 command: dgraph alpha --my=alpha6:7085 --lru_mb=2048 --zero=zero1:5080,zero2:5080,zero3:5080 -o 5 ratel: image: dgraph/dgraph:latest hostname: "ratel" ports: - 8000:8000 command: dgraph-ratelvolumes: data-volume1: data-volume2: data-volume3: data-volume-app1: data-volume-app2: data-volume-app3: data-volume-app4: data-volume-app5: data-volume-app6:- 启动
docker-compose up -d- 集群效果
- 说明
dgraph分不同的角色zero，以及alpha，以上是一个ha 的环境搭建，3个zero，6个alpha，以及配置，主要是方便学习，为了方便使用
同时添加了ratel
使用
方法与单机的类似，集群的数据是自动处理的
- schema 定义
schema.graphql
type Product { productID: ID! name: String @search(by: [term]) reviews: [Review] @hasInverse(field: about)}type Customer { username: String! @id @search(by: [hash, regexp]) reviews: [Review] @hasInverse(field: by)}type Review { id: ID! about: Product! by: Customer! comment: String @search(by: [fulltext]) rating: Int @search}- deploy schema
curl -X POST localhost:8080/admin/schema -d '@schema.graphql'- mutation
mutation { addProduct(input: [ { name: "GraphQL on Dgraph"}, { name: "Dgraph: The GraphQL Database"} ]) { product { productID name } } addCustomer(input: [{ username: "Michael"}]) { customer { username } }}// 后边执行mutation { addReview(input: [{ by: {username: "Michael"}, about: { productID: "0x3"}, comment: "Fantastic, easy to install, worked great. Best GraphQL server available", rating: 10}]) { review { comment rating by { username } about { name } } }}- 数据查询
query { queryProduct { productID name reviews { id rating comment } }}效果 
说明
对于graphql 的地址，实际上我们可以基于lb 的模式做负载处理，这样可以更好的利用资源，以及集群的能力
一个参考模式 
说明
https://github.com/dgraph-io/dgraph
https://graphql.dgraph.io/docs/quick-start/
https://dgraph.io/docs/design-concepts/
https://github.com/rongfengliang/dgraph-cluster-graphql-learning
来源：oschina
链接：https://my.oschina.net/u/4343139/blog/4286225