HugeGraph-Server通过HugeGraph-API基于HTTP协议为Client提供操作图的接口，主要包括元数据和 图数据的增删改查，遍历算法，变量，图操作及其他操作。
除了下方的文档，你还可以通过 localhost:8080/swagger-ui/index.html 访问 swagger-ui 以查看 RESTful API。示例可以参考此处
This is the multi-page printable view of this section. Click here to print.
HugeGraph RESTful API
- 1: Schema API
- 2: PropertyKey API
- 3: VertexLabel API
- 4: EdgeLabel API
- 5: IndexLabel API
- 6: Rebuild API
- 7: Vertex API
- 8: Edge API
- 9: Traverser API
- 10: Rank API
- 11: Variable API
- 12: Graphs API
- 13: Task API
- 14: Gremlin API
- 15: Cypher API
- 16: Authentication API
- 17: Metrics API
- 18: Other API
1 - Schema API
1.1 Schema
HugeGraph 提供单一接口获取某个图的全部 Schema 信息，包括：PropertyKey、VertexLabel、EdgeLabel 和 IndexLabel。
Method & Url
GET http://localhost:8080/graphs/{graph_name}/schema
e.g: GET http://localhost:8080/graphs/hugegraph/schema
Response Status
200
Response Body
{
"propertykeys": [
{
"id": 7,
"name": "price",
"data_type": "DOUBLE",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:05.316"
}
},
{
"id": 6,
"name": "date",
"data_type": "TEXT",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:05.309"
}
},
{
"id": 3,
"name": "city",
"data_type": "TEXT",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:05.287"
}
},
{
"id": 2,
"name": "age",
"data_type": "INT",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:05.280"
}
},
{
"id": 5,
"name": "lang",
"data_type": "TEXT",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:05.301"
}
},
{
"id": 4,
"name": "weight",
"data_type": "DOUBLE",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:05.294"
}
},
{
"id": 1,
"name": "name",
"data_type": "TEXT",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:05.250"
}
}
],
"vertexlabels": [
{
"id": 1,
"name": "person",
"id_strategy": "PRIMARY_KEY",
"primary_keys": [
"name"
],
"nullable_keys": [
"age",
"city"
],
"index_labels": [
"personByAge",
"personByCity",
"personByAgeAndCity"
],
"properties": [
"name",
"age",
"city"
],
"status": "CREATED",
"ttl": 0,
"enable_label_index": true,
"user_data": {
"~create_time": "2023-05-08 17:49:05.336"
}
},
{
"id": 2,
"name": "software",
"id_strategy": "CUSTOMIZE_NUMBER",
"primary_keys": [],
"nullable_keys": [],
"index_labels": [
"softwareByPrice"
],
"properties": [
"name",
"lang",
"price"
],
"status": "CREATED",
"ttl": 0,
"enable_label_index": true,
"user_data": {
"~create_time": "2023-05-08 17:49:05.347"
}
}
],
"edgelabels": [
{
"id": 1,
"name": "knows",
"source_label": "person",
"target_label": "person",
"frequency": "SINGLE",
"sort_keys": [],
"nullable_keys": [],
"index_labels": [
"knowsByWeight"
],
"properties": [
"weight",
"date"
],
"status": "CREATED",
"ttl": 0,
"enable_label_index": true,
"user_data": {
"~create_time": "2023-05-08 17:49:08.437"
}
},
{
"id": 2,
"name": "created",
"source_label": "person",
"target_label": "software",
"frequency": "SINGLE",
"sort_keys": [],
"nullable_keys": [],
"index_labels": [
"createdByDate",
"createdByWeight"
],
"properties": [
"weight",
"date"
],
"status": "CREATED",
"ttl": 0,
"enable_label_index": true,
"user_data": {
"~create_time": "2023-05-08 17:49:08.446"
}
}
],
"indexlabels": [
{
"id": 1,
"name": "personByAge",
"base_type": "VERTEX_LABEL",
"base_value": "person",
"index_type": "RANGE_INT",
"fields": [
"age"
],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:05.375"
}
},
{
"id": 2,
"name": "personByCity",
"base_type": "VERTEX_LABEL",
"base_value": "person",
"index_type": "SECONDARY",
"fields": [
"city"
],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:06.898"
}
},
{
"id": 3,
"name": "personByAgeAndCity",
"base_type": "VERTEX_LABEL",
"base_value": "person",
"index_type": "SECONDARY",
"fields": [
"age",
"city"
],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:07.407"
}
},
{
"id": 4,
"name": "softwareByPrice",
"base_type": "VERTEX_LABEL",
"base_value": "software",
"index_type": "RANGE_DOUBLE",
"fields": [
"price"
],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:07.916"
}
},
{
"id": 5,
"name": "createdByDate",
"base_type": "EDGE_LABEL",
"base_value": "created",
"index_type": "SECONDARY",
"fields": [
"date"
],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:08.454"
}
},
{
"id": 6,
"name": "createdByWeight",
"base_type": "EDGE_LABEL",
"base_value": "created",
"index_type": "RANGE_DOUBLE",
"fields": [
"weight"
],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:08.963"
}
},
{
"id": 7,
"name": "knowsByWeight",
"base_type": "EDGE_LABEL",
"base_value": "knows",
"index_type": "RANGE_DOUBLE",
"fields": [
"weight"
],
"status": "CREATED",
"user_data": {
"~create_time": "2023-05-08 17:49:09.473"
}
}
]
}
2 - PropertyKey API
1.2 PropertyKey
Params说明：
- name：属性类型名称，必填
- data_type：属性类型数据类型，包括：bool、byte、int、long、float、double、string、date、uuid、blob，默认string类型
- cardinality：属性类型基数，包括：single、list、set，默认single
请求体字段说明：
- id：属性类型id值
- properties：属性的属性，对于属性而言，此项为空
- user_data：设置属性类型的通用信息，比如可设置age属性的取值范围，最小为0，最大为100；目前此项不做任何校验，只为后期拓展提供预留入口
1.2.1 创建一个 PropertyKey
Method & Url
POST http://localhost:8080/graphs/hugegraph/schema/propertykeys
Request Body
{
"name": "age",
"data_type": "INT",
"cardinality": "SINGLE"
}
Response Status
202
Response Body
{
"property_key": {
"id": 1,
"name": "age",
"data_type": "INT",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"~create_time": "2022-05-13 13:47:23.745"
}
},
"task_id": 0
}
1.2.2 为已存在的 PropertyKey 添加或移除 userdata
Params
- action: 表示当前行为是添加还是移除，取值为
append（添加）和eliminate（移除）
Method & Url
PUT http://localhost:8080/graphs/hugegraph/schema/propertykeys/age?action=append
Request Body
{
"name": "age",
"user_data": {
"min": 0,
"max": 100
}
}
Response Status
202
Response Body
{
"property_key": {
"id": 1,
"name": "age",
"data_type": "INT",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"min": 0,
"max": 100,
"~create_time": "2022-05-13 13:47:23.745"
}
},
"task_id": 0
}
1.2.3 获取所有的 PropertyKey
Method & Url
GET http://localhost:8080/graphs/hugegraph/schema/propertykeys
Response Status
200
Response Body
{
"propertykeys": [
{
"id": 3,
"name": "city",
"data_type": "TEXT",
"cardinality": "SINGLE",
"properties": [],
"user_data": {}
},
{
"id": 2,
"name": "age",
"data_type": "INT",
"cardinality": "SINGLE",
"properties": [],
"user_data": {}
},
{
"id": 5,
"name": "lang",
"data_type": "TEXT",
"cardinality": "SINGLE",
"properties": [],
"user_data": {}
},
{
"id": 4,
"name": "weight",
"data_type": "DOUBLE",
"cardinality": "SINGLE",
"properties": [],
"user_data": {}
},
{
"id": 6,
"name": "date",
"data_type": "TEXT",
"cardinality": "SINGLE",
"properties": [],
"user_data": {}
},
{
"id": 1,
"name": "name",
"data_type": "TEXT",
"cardinality": "SINGLE",
"properties": [],
"user_data": {}
},
{
"id": 7,
"name": "price",
"data_type": "INT",
"cardinality": "SINGLE",
"properties": [],
"user_data": {}
}
]
}
1.2.4 根据name获取PropertyKey
Method & Url
GET http://localhost:8080/graphs/hugegraph/schema/propertykeys/age
其中，age为要获取的 PropertyKey 的名称
Response Status
200
Response Body
{
"id": 1,
"name": "age",
"data_type": "INT",
"cardinality": "SINGLE",
"aggregate_type": "NONE",
"write_type": "OLTP",
"properties": [],
"status": "CREATED",
"user_data": {
"min": 0,
"max": 100,
"~create_time": "2022-05-13 13:47:23.745"
}
}
1.2.5 根据 name 删除 PropertyKey
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/schema/propertykeys/age
其中，age为要删除的 PropertyKey 的名称
Response Status
202
Response Body
{
"task_id" : 0
}
3 - VertexLabel API
1.3 VertexLabel
假设已经创建好了1.1.3中列出来的 PropertyKeys
Params说明
- id：顶点类型id值
- name：顶点类型名称，必填
- id_strategy: 顶点类型的ID策略，主键ID、自动生成、自定义字符串、自定义数字、自定义UUID，默认主键ID
- properties: 顶点类型关联的属性类型
- primary_keys: 主键属性，当ID策略为PRIMARY_KEY时必须有值，其他ID策略时必须为空；
- enable_label_index： 是否开启类型索引，默认关闭
- index_names：顶点类型创建的索引，详情见3.4
- nullable_keys：可为空的属性
- user_data：设置顶点类型的通用信息，作用同属性类型
1.3.1 创建一个VertexLabel
Method & Url
POST http://localhost:8080/graphs/hugegraph/schema/vertexlabels
Request Body
{
"name": "person",
"id_strategy": "DEFAULT",
"properties": [
"name",
"age"
],
"primary_keys": [
"name"
],
"nullable_keys": [],
"enable_label_index": true
}
Response Status
201
Response Body
{
"id": 1,
"primary_keys": [
"name"
],
"id_strategy": "PRIMARY_KEY",
"name": "person2",
"index_names": [
],
"properties": [
"name",
"age"
],
"nullable_keys": [
],
"enable_label_index": true,
"user_data": {}
}
从 hugegraph-server v0.11.2 版本开始支持顶点的 TTL 功能。顶点的 TTL 是通过 VertexLabel 来设置的。比如希望 person 类型的顶点存活时间为一天，需要在创建 person VertexLabel 的时候将 TTL 字段设置为 86400000，即单位为毫秒。
{
"name": "person",
"id_strategy": "DEFAULT",
"properties": [
"name",
"age"
],
"primary_keys": [
"name"
],
"nullable_keys": [],
"ttl": 86400000,
"enable_label_index": true
}
另外，当顶点中带有"创建时间"的属性且希望以"创建时间"属性作为计算顶点存活时间的起点时，可以设置 VertexLabel 中的 ttl_start_time 字段。比如 person VertexLabel 有 createdTime 属性，且 createdTime 是 Date 类型的参数，希望 person 类型的顶点从创建开始存活一天的时间，那么创建 person VertexLabel 的 Request Body 如下：
{
"name": "person",
"id_strategy": "DEFAULT",
"properties": [
"name",
"age",
"createdTime"
],
"primary_keys": [
"name"
],
"nullable_keys": [],
"ttl": 86400000,
"ttl_start_time": "createdTime",
"enable_label_index": true
}
1.3.2 为已存在的VertexLabel添加properties或userdata，或者移除userdata（目前不支持移除properties）
Params
- action: 表示当前行为是添加还是移除，取值为
append（添加）和eliminate（移除）
Method & Url
PUT http://localhost:8080/graphs/hugegraph/schema/vertexlabels/person?action=append
Request Body
{
"name": "person",
"properties": [
"city"
],
"nullable_keys": ["city"],
"user_data": {
"super": "animal"
}
}
Response Status
200
Response Body
{
"id": 1,
"primary_keys": [
"name"
],
"id_strategy": "PRIMARY_KEY",
"name": "person",
"index_names": [
],
"properties": [
"city",
"name",
"age"
],
"nullable_keys": [
"city"
],
"enable_label_index": true,
"user_data": {
"super": "animal"
}
}
1.3.3 获取所有的VertexLabel
Method & Url
GET http://localhost:8080/graphs/hugegraph/schema/vertexlabels
Response Status
200
Response Body
{
"vertexlabels": [
{
"id": 1,
"primary_keys": [
"name"
],
"id_strategy": "PRIMARY_KEY",
"name": "person",
"index_names": [
],
"properties": [
"city",
"name",
"age"
],
"nullable_keys": [
"city"
],
"enable_label_index": true,
"user_data": {
"super": "animal"
}
},
{
"id": 2,
"primary_keys": [
"name"
],
"id_strategy": "PRIMARY_KEY",
"name": "software",
"index_names": [
],
"properties": [
"price",
"name",
"lang"
],
"nullable_keys": [
"price"
],
"enable_label_index": false,
"user_data": {}
}
]
}
1.3.4 根据name获取VertexLabel
Method & Url
GET http://localhost:8080/graphs/hugegraph/schema/vertexlabels/person
Response Status
200
Response Body
{
"id": 1,
"primary_keys": [
"name"
],
"id_strategy": "PRIMARY_KEY",
"name": "person",
"index_names": [
],
"properties": [
"city",
"name",
"age"
],
"nullable_keys": [
"city"
],
"enable_label_index": true,
"user_data": {
"super": "animal"
}
}
1.3.5 根据name删除VertexLabel
删除 VertexLabel 会导致删除对应的顶点以及相关的索引数据，会产生一个异步任务
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/schema/vertexlabels/person
Response Status
202
Response Body
{
"task_id": 1
}
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
4 - EdgeLabel API
1.4 EdgeLabel
假设已经创建好了1.2.3中的 PropertyKeys 和 1.3.3中的 VertexLabels
Params说明
- name：顶点类型名称，必填
- source_label: 源顶点类型的名称，必填
- target_label: 目标顶点类型的名称，必填
- frequency：两个点之间是否可以有多条边，可以取值SINGLE和MULTIPLE，非必填，默认值SINGLE
- properties: 边类型关联的属性类型，选填
- sort_keys: 当允许关联多次时，指定区分键属性列表
- nullable_keys：可为空的属性，选填，默认可为空
- enable_label_index： 是否开启类型索引，默认关闭
1.4.1 创建一个EdgeLabel
Method & Url
POST http://localhost:8080/graphs/hugegraph/schema/edgelabels
Request Body
{
"name": "created",
"source_label": "person",
"target_label": "software",
"frequency": "SINGLE",
"properties": [
"date"
],
"sort_keys": [],
"nullable_keys": [],
"enable_label_index": true
}
Response Status
201
Response Body
{
"id": 1,
"sort_keys": [
],
"source_label": "person",
"name": "created",
"index_names": [
],
"properties": [
"date"
],
"target_label": "software",
"frequency": "SINGLE",
"nullable_keys": [
],
"enable_label_index": true,
"user_data": {}
}
从 hugegraph-server v0.11.2 版本开始支持边的 TTL 功能。边的 TTL 是通过 EdgeLabel 来设置的。比如希望 knows 类型的边存活时间为一天，需要在创建 knows EdgeLabel 的时候将 TTL 字段设置为 86400000，即单位为毫秒。
{
"id": 1,
"sort_keys": [
],
"source_label": "person",
"name": "knows",
"index_names": [
],
"properties": [
"date",
"createdTime"
],
"target_label": "person",
"frequency": "SINGLE",
"nullable_keys": [
],
"enable_label_index": true,
"ttl": 86400000,
"user_data": {}
}
另外，当边中带有"创建时间"的属性且希望以"创建时间"属性作为计算边存活时间的起点时，可以设置 EdgeLabel 中的 ttl_start_time 字段。比如 knows EdgeLabel 有 createdTime 属性，且 createdTime 是 Date 类型的参数，希望 knows 类型的边从创建开始存活一天的时间，那么创建 knows EdgeLabel 的 Request Body 如下：
{
"id": 1,
"sort_keys": [
],
"source_label": "person",
"name": "knows",
"index_names": [
],
"properties": [
"date",
"createdTime"
],
"target_label": "person",
"frequency": "SINGLE",
"nullable_keys": [
],
"enable_label_index": true,
"ttl": 86400000,
"ttl_start_time": "createdTime",
"user_data": {}
}
1.4.2 为已存在的EdgeLabel添加properties或userdata，或者移除userdata（目前不支持移除properties）
Params
- action: 表示当前行为是添加还是移除，取值为
append（添加）和eliminate（移除）
Method & Url
PUT http://localhost:8080/graphs/hugegraph/schema/edgelabels/created?action=append
Request Body
{
"name": "created",
"properties": [
"weight"
],
"nullable_keys": [
"weight"
]
}
Response Status
200
Response Body
{
"id": 2,
"sort_keys": [
],
"source_label": "person",
"name": "created",
"index_names": [
],
"properties": [
"date",
"weight"
],
"target_label": "software",
"frequency": "SINGLE",
"nullable_keys": [
"weight"
],
"enable_label_index": true,
"user_data": {}
}
1.4.3 获取所有的EdgeLabel
Method & Url
GET http://localhost:8080/graphs/hugegraph/schema/edgelabels
Response Status
200
Response Body
{
"edgelabels": [
{
"id": 1,
"sort_keys": [
],
"source_label": "person",
"name": "created",
"index_names": [
],
"properties": [
"date",
"weight"
],
"target_label": "software",
"frequency": "SINGLE",
"nullable_keys": [
"weight"
],
"enable_label_index": true,
"user_data": {}
},
{
"id": 2,
"sort_keys": [
],
"source_label": "person",
"name": "knows",
"index_names": [
],
"properties": [
"date",
"weight"
],
"target_label": "person",
"frequency": "SINGLE",
"nullable_keys": [
],
"enable_label_index": false,
"user_data": {}
}
]
}
1.4.4 根据name获取EdgeLabel
Method & Url
GET http://localhost:8080/graphs/hugegraph/schema/edgelabels/created
Response Status
200
Response Body
{
"id": 1,
"sort_keys": [
],
"source_label": "person",
"name": "created",
"index_names": [
],
"properties": [
"date",
"city",
"weight"
],
"target_label": "software",
"frequency": "SINGLE",
"nullable_keys": [
"city",
"weight"
],
"enable_label_index": true,
"user_data": {}
}
1.4.5 根据name删除EdgeLabel
删除 EdgeLabel 会导致删除对应的边以及相关的索引数据，会产生一个异步任务
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/schema/edgelabels/created
Response Status
202
Response Body
{
"task_id": 1
}
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
5 - IndexLabel API
1.5 IndexLabel
假设已经创建好了1.1.3中的 PropertyKeys 、1.2.3中的 VertexLabels 以及 1.3.3中的 EdgeLabels
1.5.1 创建一个IndexLabel
Method & Url
POST http://localhost:8080/graphs/hugegraph/schema/indexlabels
Request Body
{
"name": "personByCity",
"base_type": "VERTEX_LABEL",
"base_value": "person",
"index_type": "SECONDARY",
"fields": [
"city"
]
}
Response Status
202
Response Body
{
"index_label": {
"id": 1,
"base_type": "VERTEX_LABEL",
"base_value": "person",
"name": "personByCity",
"fields": [
"city"
],
"index_type": "SECONDARY"
},
"task_id": 2
}
1.5.2 获取所有的IndexLabel
Method & Url
GET http://localhost:8080/graphs/hugegraph/schema/indexlabels
Response Status
200
Response Body
{
"indexlabels": [
{
"id": 3,
"base_type": "VERTEX_LABEL",
"base_value": "software",
"name": "softwareByPrice",
"fields": [
"price"
],
"index_type": "RANGE"
},
{
"id": 4,
"base_type": "EDGE_LABEL",
"base_value": "created",
"name": "createdByDate",
"fields": [
"date"
],
"index_type": "SECONDARY"
},
{
"id": 1,
"base_type": "VERTEX_LABEL",
"base_value": "person",
"name": "personByCity",
"fields": [
"city"
],
"index_type": "SECONDARY"
},
{
"id": 3,
"base_type": "VERTEX_LABEL",
"base_value": "person",
"name": "personByAgeAndCity",
"fields": [
"age",
"city"
],
"index_type": "SECONDARY"
}
]
}
1.5.3 根据name获取IndexLabel
Method & Url
GET http://localhost:8080/graphs/hugegraph/schema/indexlabels/personByCity
Response Status
200
Response Body
{
"id": 1,
"base_type": "VERTEX_LABEL",
"base_value": "person",
"name": "personByCity",
"fields": [
"city"
],
"index_type": "SECONDARY"
}
1.5.4 根据name删除IndexLabel
删除 IndexLabel 会导致删除相关的索引数据，会产生一个异步任务
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/schema/indexlabels/personByCity
Response Status
202
Response Body
{
"task_id": 1
}
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
6 - Rebuild API
1.6 Rebuild
1.6.1 重建IndexLabel
Method & Url
PUT http://localhost:8080/graphs/hugegraph/jobs/rebuild/indexlabels/personByCity
Response Status
202
Response Body
{
"task_id": 1
}
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
1.6.2 VertexLabel对应的全部索引重建
Method & Url
PUT http://localhost:8080/graphs/hugegraph/jobs/rebuild/vertexlabels/person
Response Status
202
Response Body
{
"task_id": 2
}
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/2（其中"2"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
1.6.3 EdgeLabel对应的全部索引重建
Method & Url
PUT http://localhost:8080/graphs/hugegraph/jobs/rebuild/edgelabels/created
Response Status
202
Response Body
{
"task_id": 3
}
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/3（其中"3"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
7 - Vertex API
2.1 Vertex
顶点类型中的 Id 策略决定了顶点的 Id 类型，其对应的 id 类型如下：
| Id_Strategy | id type |
|---|---|
| AUTOMATIC | number |
| PRIMARY_KEY | string |
| CUSTOMIZE_STRING | string |
| CUSTOMIZE_NUMBER | number |
| CUSTOMIZE_UUID | uuid |
顶点的 GET/PUT/DELETE API 中 url 的 id 部分应该传入带有类型信息的 id 值，这个类型信息通过 json 串是否带引号来表示，也就是说：
- 当 id 类型为
number时，url 中的 id 不带引号，例如xxx/vertices/123456 - 当 id 类型为
string时，url 中的 id 带引号，例如xxx/vertices/"123456"
接下来的示例需要先根据以下 groovy 脚本创建图 schema
schema.propertyKey("name").asText().ifNotExist().create();
schema.propertyKey("age").asInt().ifNotExist().create();
schema.propertyKey("city").asText().ifNotExist().create();
schema.propertyKey("weight").asDouble().ifNotExist().create();
schema.propertyKey("lang").asText().ifNotExist().create();
schema.propertyKey("price").asDouble().ifNotExist().create();
schema.propertyKey("hobby").asText().valueList().ifNotExist().create();
schema.vertexLabel("person").properties("name", "age", "city", "weight", "hobby").primaryKeys("name").nullableKeys("age", "city", "weight", "hobby").ifNotExist().create();
schema.vertexLabel("software").properties("name", "lang", "price").primaryKeys("name").nullableKeys("lang", "price").ifNotExist().create();
schema.indexLabel("personByAge").onV("person").by("age").range().ifNotExist().create();
2.1.1 创建一个顶点
Method & Url
POST http://localhost:8080/graphs/hugegraph/graph/vertices
Request Body
{
"label": "person",
"properties": {
"name": "marko",
"age": 29
}
}
Response Status
201
Response Body
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 29
}
}
2.1.2 创建多个顶点
Method & Url
POST http://localhost:8080/graphs/hugegraph/graph/vertices/batch
Request Body
[
{
"label": "person",
"properties": {
"name": "marko",
"age": 29
}
},
{
"label": "software",
"properties": {
"name": "ripple",
"lang": "java",
"price": 199
}
}
]
Response Status
201
Response Body
[
"1:marko",
"2:ripple"
]
2.1.3 更新顶点属性
Method & Url
PUT http://127.0.0.1:8080/graphs/hugegraph/graph/vertices/"1:marko"?action=append
Request Body
{
"label": "person",
"properties": {
"age": 30,
"city": "Beijing"
}
}
注意：属性的取值有三种类别，分别为single、set和list。single表示增加或更新属性值，set或list表示追加属性值。
Response Status
200
Response Body
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 30,
"city": "Beijing"
}
}
2.1.4 批量更新顶点属性
功能说明
批量更新顶点的属性时，可以选择多种更新策略，如下：
- SUM: 数值累加
- BIGGER: 原值和新值(数字、日期)取更大的
- SMALLER: 原值和新值(数字、日期)取更小的
- UNION: Set属性取并集
- INTERSECTION: Set属性取交集
- APPEND: List属性追加元素
- ELIMINATE: List/Set属性删除元素
- OVERRIDE: 覆盖已有属性，如果新属性为null，则仍然使用旧属性
假设原顶点的属性如下：
{
"vertices": [
{
"id": "2:lop",
"label": "software",
"type": "vertex",
"properties": {
"name": "lop",
"lang": "java",
"price": 328
}
},
{
"id": "1:josh",
"label": "person",
"type": "vertex",
"properties": {
"name": "josh",
"age": 32,
"city": "Beijing",
"weight": 0.1,
"hobby": [
"reading",
"football"
]
}
}
]
}
通过以下命令新增顶点：
curl -H "Content-Type: application/json" -d '[{"label":"person","properties":{"name":"josh","age":32,"city":"Beijing","weight":0.1,"hobby":["reading","football"]}},{"label":"software","properties":{"name":"lop","lang":"java","price":328}}]' http:///127.0.0.1:8080/graphs/hugegraph/graph/vertices/batch
Method & Url
PUT http://127.0.0.1:8080/graphs/hugegraph/graph/vertices/batch
Request Body
{
"vertices": [
{
"label": "software",
"type": "vertex",
"properties": {
"name": "lop",
"lang": "c++",
"price": 299
}
},
{
"label": "person",
"type": "vertex",
"properties": {
"name": "josh",
"city": "Shanghai",
"weight": 0.2,
"hobby": [
"swimming"
]
}
}
],
"update_strategies": {
"price": "BIGGER",
"age": "OVERRIDE",
"city": "OVERRIDE",
"weight": "SUM",
"hobby": "UNION"
},
"create_if_not_exist": true
}
Response Status
200
Response Body
{
"vertices": [
{
"id": "2:lop",
"label": "software",
"type": "vertex",
"properties": {
"name": "lop",
"lang": "c++",
"price": 328
}
},
{
"id": "1:josh",
"label": "person",
"type": "vertex",
"properties": {
"name": "josh",
"age": 32,
"city": "Shanghai",
"weight": 0.3,
"hobby": [
"reading",
"football",
"swimming"
]
}
}
]
}
结果分析如下：
- lang 属性未指定更新策略，直接用新值覆盖旧值，无论新值是否为null；
- price 属性指定 BIGGER 的更新策略，旧属性值为328，新属性值为299，所以仍然保留了旧属性值328；
- age 属性指定 OVERRIDE 更新策略，而新属性值中未传入age，相当于age为null，所以仍然保留了原属性值32；
- city 属性也指定了 OVERRIDE 更新策略，且新属性值不为null，所以覆盖了旧值；
- weight 属性指定了 SUM 更新策略，旧属性值为0.1，新属性值为0.2，最后的值为0.3；
- hobby 属性（基数为Set）指定了 UNION 更新策略，所以新值与旧值取了并集；
其他更新策略的使用方式与此类似，此处不再详述。
2.1.5 删除顶点属性
Method & Url
PUT http://127.0.0.1:8080/graphs/hugegraph/graph/vertices/"1:marko"?action=eliminate
Request Body
{
"label": "person",
"properties": {
"city": "Beijing"
}
}
注意：这里会直接删除属性（删除key和所有value），无论其属性的取值是single、set或list。
Response Status
200
Response Body
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 30
}
}
2.1.6 获取符合条件的顶点
Params
- label: 顶点的类型
- properties: 属性键值对（查询属性的前提是该属性已经建立了索引）
- limit: 查询结果的最大数目
- page: 分页的页号
以上参数都是可选的，但如果提供了page参数，就必须同时提供limit参数，并且不能再提供其他参数。label, properties和limit之间可以任意组合。
属性键值对由属性名称和属性值组成JSON格式的对象，可以使用多个属性键值对作为查询条件，属性值支持精确匹配和范围匹配，精确匹配的形式如properties={"age":29}，范围匹配的形式如properties={"age":"P.gt(29)"}，范围匹配支持以下表达式：
| 表达式 | 说明 |
|---|---|
| P.eq(number) | 属性值等于number的顶点 |
| P.neq(number) | 属性值不等于number的顶点 |
| P.lt(number) | 属性值小于number的顶点 |
| P.lte(number) | 属性值小于等于number的顶点 |
| P.gt(number) | 属性值大于number的顶点 |
| P.gte(number) | 属性值大于等于number的顶点 |
| P.between(number1,number2) | 属性值大于等于number1且小于number2的顶点 |
| P.inside(number1,number2) | 属性值大于number1且小于number2的顶点 |
| P.outside(number1,number2) | 属性值小于number1且大于number2的顶点 |
| P.within(value1,value2,value3,…) | 属性值等于任何一个给定value的顶点 |
查询所有 age 为 29 且 label 为 person 的顶点
Method & Url
GET http://localhost:8080/graphs/hugegraph/graph/vertices?label=person&properties={"age":29}&limit=1
Response Status
200
Response Body
{
"vertices": [
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 30
}
}
]
}
分页查询所有顶点，获取第一页（page不带参数值），限定3条
通过以下命令新增顶点：
curl -H "Content-Type: application/json" -d '[{"label":"person","properties":{"name":"peter","age":29,"city":"Shanghai"}},{"label":"person","properties":{"name":"vadas","age":27,"city":"Hongkong"}}]' http://localhost:8080/graphs/hugegraph/graph/vertices/batch
Method & Url
GET http://localhost:8080/graphs/hugegraph/graph/vertices?page&limit=3
Response Status
200
Response Body
{
"vertices": [
{
"id": "2:lop",
"label": "software",
"type": "vertex",
"properties": {
"name": "lop",
"lang": "c++",
"price": 328
}
},
{
"id": "1:josh",
"label": "person",
"type": "vertex",
"properties": {
"name": "josh",
"age": 32,
"city": "Shanghai",
"weight": 0.3,
"hobby": [
"reading",
"football",
"swimming"
]
}
},
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 30
}
}
],
"page": "CIYxOnBldGVyAAAAAAAAAAM="
}
返回的 body 里面是带有下一页的页号信息的，"page": "CIYxOnBldGVyAAAAAAAAAAM="，在查询下一页的时候将该值赋给 page 参数。
分页查询所有顶点，获取下一页（page带上上一页返回的page值），限定3条
Method & Url
GET http://localhost:8080/graphs/hugegraph/graph/vertices?page=CIYxOnBldGVyAAAAAAAAAAM=&limit=3
Response Status
200
Response Body
{
"vertices": [
{
"id": "1:peter",
"label": "person",
"type": "vertex",
"properties": {
"name": "peter",
"age": 29,
"city": "Shanghai"
}
},
{
"id": "1:vadas",
"label": "person",
"type": "vertex",
"properties": {
"name": "vadas",
"age": 27,
"city": "Hongkong"
}
},
{
"id": "2:ripple",
"label": "software",
"type": "vertex",
"properties": {
"name": "ripple",
"lang": "java",
"price": 199
}
}
],
"page": null
}
当"page": null时，表示已经没有下一页了（注：如果后端使用的是 Cassandra ，为了提高性能，当返回的页数刚好是最后一页时，返回的 page 值可能不为空，但是如果用这个 page 值再请求下一页数据时，就会返回 空数据 和 page = null，其他情况也类似）
2.1.7 根据Id获取顶点
Method & Url
GET http://localhost:8080/graphs/hugegraph/graph/vertices/"1:marko"
Response Status
200
Response Body
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 30
}
}
2.1.8 根据Id删除顶点
Params
- label: 顶点类型，可选参数
仅根据Id删除顶点
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/graph/vertices/"1:marko"
Response Status
204
根据Label+Id删除顶点
通过指定Label参数和Id来删除顶点时，一般来说其性能比仅根据Id删除会更好。
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/graph/vertices/"1:marko"?label=person
Response Status
204
8 - Edge API
2.2 Edge
顶点 id 格式的修改也影响到了边的 id 以及源顶点和目标顶点 id 的格式
EdgeId 是由 src-vertex-id + direction + label + sort-values + tgt-vertex-id 拼接而成，但是这里的顶点 id 类型不是通过引号区分的，而是根据前缀区分：
- 当 id 类型为 number 时，EdgeId 的顶点 id 前有一个前缀
L，形如 “L123456>1»L987654” - 当 id 类型为 string 时，EdgeId 的顶点 id 前有一个前缀
S，形如 “S1:peter>1»S2:lop”
接下来的示例需要先根据以下 groovy 脚本创建图 schema
import org.apache.hugegraph.HugeFactory
import org.apache.tinkerpop.gremlin.structure.T
conf = "conf/graphs/hugegraph.properties"
graph = HugeFactory.open(conf)
schema = graph.schema()
schema.propertyKey("name").asText().ifNotExist().create()
schema.propertyKey("age").asInt().ifNotExist().create()
schema.propertyKey("city").asText().ifNotExist().create()
schema.propertyKey("weight").asDouble().ifNotExist().create()
schema.propertyKey("lang").asText().ifNotExist().create()
schema.propertyKey("date").asText().ifNotExist().create()
schema.propertyKey("price").asInt().ifNotExist().create()
schema.vertexLabel("person").properties("name", "age", "city").primaryKeys("name").ifNotExist().create()
schema.vertexLabel("software").properties("name", "lang", "price").primaryKeys("name").ifNotExist().create()
schema.indexLabel("personByCity").onV("person").by("city").secondary().ifNotExist().create()
schema.indexLabel("personByAgeAndCity").onV("person").by("age", "city").secondary().ifNotExist().create()
schema.indexLabel("softwareByPrice").onV("software").by("price").range().ifNotExist().create()
schema.edgeLabel("knows").sourceLabel("person").targetLabel("person").properties("date", "weight").ifNotExist().create()
schema.edgeLabel("created").sourceLabel("person").targetLabel("software").properties("date", "weight").ifNotExist().create()
schema.indexLabel("createdByDate").onE("created").by("date").secondary().ifNotExist().create()
schema.indexLabel("createdByWeight").onE("created").by("weight").range().ifNotExist().create()
schema.indexLabel("knowsByWeight").onE("knows").by("weight").range().ifNotExist().create()
marko = graph.addVertex(T.label, "person", "name", "marko", "age", 29, "city", "Beijing")
vadas = graph.addVertex(T.label, "person", "name", "vadas", "age", 27, "city", "Hongkong")
lop = graph.addVertex(T.label, "software", "name", "lop", "lang", "java", "price", 328)
josh = graph.addVertex(T.label, "person", "name", "josh", "age", 32, "city", "Beijing")
ripple = graph.addVertex(T.label, "software", "name", "ripple", "lang", "java", "price", 199)
peter = graph.addVertex(T.label, "person", "name", "peter", "age", 35, "city", "Shanghai")
graph.tx().commit()
g = graph.traversal()
2.2.1 创建一条边
Params
路径参数说明：
- graph：待操作的图
请求体说明：
- label：边类型名称，必填
- outV：源顶点 id，必填
- inV：目标顶点 id，必填
- outVLabel：源顶点类型，必填
- inVLabel：目标顶点类型，必填
- properties: 边关联的属性，对象内部结构为：
- name：属性名称
- value：属性值
Method & Url
POST http://localhost:8080/graphs/hugegraph/graph/edges
Request Body
{
"label": "created",
"outV": "1:marko",
"inV": "2:lop",
"outVLabel": "person",
"inVLabel": "software",
"properties": {
"date": "20171210",
"weight": 0.4
}
}
Response Status
201
Response Body
{
"id": "S1:marko>2>>S2:lop",
"label": "created",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "2:lop",
"inVLabel": "software",
"properties": {
"weight": 0.4,
"date": "20171210"
}
}
2.2.2 创建多条边
Params
路径参数说明：
- graph：待操作的图
请求参数说明：
- check_vertex：是否检查顶点存在 (true | false)，当设置为 true 而待插入边的源顶点或目标顶点不存在时会报错，默认为 true
请求体说明：
- 边信息的列表
Method & Url
POST http://localhost:8080/graphs/hugegraph/graph/edges/batch
Request Body
[
{
"label": "knows",
"outV": "1:marko",
"inV": "1:vadas",
"outVLabel": "person",
"inVLabel": "person",
"properties": {
"date": "20160110",
"weight": 0.5
}
},
{
"label": "knows",
"outV": "1:marko",
"inV": "1:josh",
"outVLabel": "person",
"inVLabel": "person",
"properties": {
"date": "20130220",
"weight": 1.0
}
}
]
Response Status
201
Response Body
[
"S1:marko>1>>S1:vadas",
"S1:marko>1>>S1:josh"
]
2.2.3 更新边属性
Params
路径参数说明：
- graph：待操作的图
- id：待操作的边 id
请求参数说明：
- action：append 操作
请求体说明：
- 边信息
Method & Url
PUT http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>2>>S2:lop?action=append
Request Body
{
"properties": {
"weight": 1.0
}
}
注意：属性的取值是有三种类别的，分别是 single、set 和 list。如果是 single，表示增加或更新属性值；如果是 set 或 list，则表示追加属性值
Response Status
200
Response Body
{
"id": "S1:marko>2>>S2:lop",
"label": "created",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "2:lop",
"inVLabel": "software",
"properties": {
"weight": 1.0,
"date": "20171210"
}
}
2.2.4 批量更新边属性
Params
路径参数说明：
- graph：待操作的图
请求体说明：
- edges：边信息的列表
- update_strategies：对于每个属性，可以单独设置其更新策略，包括：
- SUM：仅支持 number 类型
- BIGGER/SMALLER：仅支持 date/number 类型
- UNION/INTERSECTION：仅支持 set 类型
- APPEND/ELIMINATE：仅支持 collection 类型
- OVERRIDE
- check_vertex：是否检查顶点存在 (true | false)，当设置为 true 而待插入边的源顶点或目标顶点不存在时会报错，默认为 true
- create_if_not_exist：目前只支持设定为 true
Method & Url
PUT http://127.0.0.1:8080/graphs/hugegraph/graph/edges/batch
Request Body
{
"edges": [
{
"label": "knows",
"outV": "1:marko",
"inV": "1:vadas",
"outVLabel": "person",
"inVLabel": "person",
"properties": {
"date": "20160111",
"weight": 1.0
}
},
{
"label": "knows",
"outV": "1:marko",
"inV": "1:josh",
"outVLabel": "person",
"inVLabel": "person",
"properties": {
"date": "20130221",
"weight": 0.5
}
}
],
"update_strategies": {
"weight": "SUM",
"date": "OVERRIDE"
},
"check_vertex": false,
"create_if_not_exist": true
}
Response Status
200
Response Body
{
"edges": [
{
"id": "S1:marko>1>>S1:vadas",
"label": "knows",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "1:vadas",
"inVLabel": "person",
"properties": {
"weight": 1.5,
"date": "20160111"
}
},
{
"id": "S1:marko>1>>S1:josh",
"label": "knows",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "1:josh",
"inVLabel": "person",
"properties": {
"weight": 1.5,
"date": "20130221"
}
}
]
}
2.2.5 删除边属性
Params
路径参数说明：
- graph：待操作的图
- id：待操作的边 id
请求参数说明：
- action：eliminate 操作
请求体说明：
- 边信息
Method & Url
PUT http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>2>>S2:lop?action=eliminate
Request Body
{
"properties": {
"weight": 1.0
}
}
注意：这里会直接删除属性（删除 key 和所有 value），无论其属性的取值是 single、set 或 list
Response Status
400
Response Body
无法删除未设置为 nullable 的属性
{
"exception": "class java.lang.IllegalArgumentException",
"message": "Can't remove non-null edge property 'p[weight->1.0]'",
"cause": ""
}
2.2.6 获取符合条件的边
Params
路径参数说明：
- graph：待操作的图
请求参数说明：
- vertex_id: 顶点 id
- direction: 边的方向 (OUT | IN | BOTH)，默认为 BOTH
- label: 边的标签
- properties: 属性键值对 (根据属性查询的前提是预先建立了索引)
- keep_start_p: 默认为 false，当设置为 true 后，不会自动转义范围匹配输入的表达式，例如此时
properties={"age":"P.gt(0.8)"}会被理解为精确匹配，即 age 属性等于 “P.gt(0.8)” - offset：偏移，默认为 0
- limit: 查询数目，默认为 100
- page: 页号
属性键值对由 JSON 格式的属性名称和属性值组成，允许多个属性键值对作为查询条件，属性值支持精确匹配和范围匹配，精确匹配时形如 properties={"weight":0.8}，范围匹配时形如 properties={"age":"P.gt(0.8)"}，范围匹配支持的表达式如下：
| 表达式 | 说明 |
|---|---|
| P.eq(number) | 属性值等于 number 的边 |
| P.neq(number) | 属性值不等于 number 的边 |
| P.lt(number) | 属性值小于 number 的边 |
| P.lte(number) | 属性值小于等于 number 的边 |
| P.gt(number) | 属性值大于 number 的边 |
| P.gte(number) | 属性值大于等于 number 的边 |
| P.between(number1,number2) | 属性值大于等于 number1 且小于 number2 的边 |
| P.inside(number1,number2) | 属性值大于 number1 且小于 number2 的边 |
| P.outside(number1,number2) | 属性值小于 number1 且大于 number2 的边 |
| P.within(value1,value2,value3,…) | 属性值等于任何一个给定 value 的边 |
| P.textcontains(value) | 属性值包含给定 value 的边 (string 类型) |
| P.contains(value) | 属性值包含给定 value 的边 (collection 类型) |
查询与顶点 person:marko(vertex_id=“1:marko”) 相连且 label 为 knows 的且 date 属性等于 “20160111” 的边
Method & Url
GET http://127.0.0.1:8080/graphs/hugegraph/graph/edges?vertex_id="1:marko"&label=knows&properties={"date":"P.within(\"20160111\")"}
Response Status
200
Response Body
{
"edges": [
{
"id": "S1:marko>1>>S1:vadas",
"label": "knows",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "1:vadas",
"inVLabel": "person",
"properties": {
"weight": 1.5,
"date": "20160111"
}
}
]
}
分页查询所有边，获取第一页（page 不带参数值），限定 2 条
Method & Url
GET http://127.0.0.1:8080/graphs/hugegraph/graph/edges?page&limit=2
Response Status
200
Response Body
{
"edges": [
{
"id": "S1:marko>1>>S1:josh",
"label": "knows",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "1:josh",
"inVLabel": "person",
"properties": {
"weight": 1.5,
"date": "20130221"
}
},
{
"id": "S1:marko>1>>S1:vadas",
"label": "knows",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "1:vadas",
"inVLabel": "person",
"properties": {
"weight": 1.5,
"date": "20160111"
}
}
],
"page": "EoYxOm1hcmtvgggCAIQyOmxvcAAAAAAAAAAC"
}
返回的 body 里面是带有下一页的页号信息的，"page": "EoYxOm1hcmtvgggCAIQyOmxvcAAAAAAAAAAC"，在查询下一页的时候将该值赋给 page 参数
分页查询所有边，获取下一页（page 带上上一页返回的 page 值），限定 2 条
Method & Url
GET http://127.0.0.1:8080/graphs/hugegraph/graph/edges?page=EoYxOm1hcmtvgggCAIQyOmxvcAAAAAAAAAAC&limit=2
Response Status
200
Response Body
{
"edges": [
{
"id": "S1:marko>2>>S2:lop",
"label": "created",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "2:lop",
"inVLabel": "software",
"properties": {
"weight": 1.0,
"date": "20171210"
}
}
],
"page": null
}
此时 "page": null 表示已经没有下一页了
注：后端为 Cassandra 时，为了性能考虑，返回页恰好为最后一页时，返回
page值可能非空，通过该page再请求下一页数据时则返回空数据及page = null，其他情况类似
2.2.7 根据 id 获取边
Params
路径参数说明：
- graph：待操作的图
- id：待操作的边 id
Method & Url
GET http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>2>>S2:lop
Response Status
200
Response Body
{
"id": "S1:marko>2>>S2:lop",
"label": "created",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "2:lop",
"inVLabel": "software",
"properties": {
"weight": 1.0,
"date": "20171210"
}
}
2.2.8 根据 id 删除边
Params
路径参数说明：
- graph：待操作的图
- id：待操作的边 id
请求参数说明：
- label: 边的标签
仅根据 id 删除边
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>2>>S2:lop
Response Status
204
根据 label + id 删除边
通过指定 label 参数和 id 来删除边时，一般来说其性能比仅根据 id 删除会更好
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>1>>S1:vadas?label=knows
Response Status
204
9 - Traverser API
3.1 traverser API概述
HugeGraphServer为HugeGraph图数据库提供了RESTful API接口。除了顶点和边的CRUD基本操作以外，还提供了一些遍历（traverser）方法，我们称为traverser API。这些遍历方法实现了一些复杂的图算法，方便用户对图进行分析和挖掘。
HugeGraph支持的Traverser API包括：
- K-out API，根据起始顶点，查找恰好N步可达的邻居，分为基础版和高级版：
- 基础版使用GET方法，根据起始顶点，查找恰好N步可达的邻居
- 高级版使用POST方法，根据起始顶点，查找恰好N步可达的邻居，与基础版的不同在于：
- 支持只统计邻居数量
- 支持顶点和边属性过滤
- 支持返回到达邻居的最短路径
- K-neighbor API，根据起始顶点，查找N步以内可达的所有邻居，分为基础版和高级版：
- 基础版使用GET方法，根据起始顶点，查找N步以内可达的所有邻居
- 高级版使用POST方法，根据起始顶点，查找N步以内可达的所有邻居，与基础版的不同在于：
- 支持只统计邻居数量
- 支持顶点和边属性过滤
- 支持返回到达邻居的最短路径
- Same Neighbors, 查询两个顶点的共同邻居
- Jaccard Similarity API，计算jaccard相似度，包括两种：
- 一种是使用GET方法，计算两个顶点的邻居的相似度（交并比）
- 一种是使用POST方法，在全图中查找与起点的jaccard similarity最高的N个点
- Shortest Path API，查找两个顶点之间的最短路径
- All Shortest Paths，查找两个顶点间的全部最短路径
- Weighted Shortest Path，查找起点到目标点的带权最短路径
- Single Source Shortest Path，查找一个点到其他各个点的加权最短路径
- Multi Node Shortest Path，查找指定顶点集之间两两最短路径
- Paths API，查找两个顶点间的全部路径，分为基础版和高级版：
- 基础版使用GET方法，根据起点和终点，查找两个顶点间的全部路径
- 高级版使用POST方法，根据一组起点和一组终点，查找两个集合间符合条件的全部路径
- Customized Paths API，从一批顶点出发，按（一种）模式遍历经过的全部路径
- Template Path API，指定起点和终点以及起点和终点间路径信息，查找符合的路径
- Crosspoints API，查找两个顶点的交点（共同祖先或者共同子孙）
- Customized Crosspoints API，从一批顶点出发，按多种模式遍历，最后一步到达的顶点的交点
- Rings API，从起始顶点出发，可到达的环路路径
- Rays API，从起始顶点出发，可到达边界的路径（即无环路径）
- Fusiform Similarity API，查找一个顶点的梭形相似点
- Vertices API
- 按ID批量查询顶点；
- 获取顶点的分区；
- 按分区查询顶点；
- Edges API
- 按ID批量查询边；
- 获取边的分区；
- 按分区查询边；
3.2. traverser API详解
使用方法中的例子，都是基于TinkerPop官网给出的图：
数据导入程序如下：
public class Loader {
public static void main(String[] args) {
HugeClient client = new HugeClient("http://127.0.0.1:8080", "hugegraph");
SchemaManager schema = client.schema();
schema.propertyKey("name").asText().ifNotExist().create();
schema.propertyKey("age").asInt().ifNotExist().create();
schema.propertyKey("city").asText().ifNotExist().create();
schema.propertyKey("weight").asDouble().ifNotExist().create();
schema.propertyKey("lang").asText().ifNotExist().create();
schema.propertyKey("date").asText().ifNotExist().create();
schema.propertyKey("price").asInt().ifNotExist().create();
schema.vertexLabel("person")
.properties("name", "age", "city")
.primaryKeys("name")
.nullableKeys("age")
.ifNotExist()
.create();
schema.vertexLabel("software")
.properties("name", "lang", "price")
.primaryKeys("name")
.nullableKeys("price")
.ifNotExist()
.create();
schema.indexLabel("personByCity")
.onV("person")
.by("city")
.secondary()
.ifNotExist()
.create();
schema.indexLabel("personByAgeAndCity")
.onV("person")
.by("age", "city")
.secondary()
.ifNotExist()
.create();
schema.indexLabel("softwareByPrice")
.onV("software")
.by("price")
.range()
.ifNotExist()
.create();
schema.edgeLabel("knows")
.multiTimes()
.sourceLabel("person")
.targetLabel("person")
.properties("date", "weight")
.sortKeys("date")
.nullableKeys("weight")
.ifNotExist()
.create();
schema.edgeLabel("created")
.sourceLabel("person").targetLabel("software")
.properties("date", "weight")
.nullableKeys("weight")
.ifNotExist()
.create();
schema.indexLabel("createdByDate")
.onE("created")
.by("date")
.secondary()
.ifNotExist()
.create();
schema.indexLabel("createdByWeight")
.onE("created")
.by("weight")
.range()
.ifNotExist()
.create();
schema.indexLabel("knowsByWeight")
.onE("knows")
.by("weight")
.range()
.ifNotExist()
.create();
GraphManager graph = client.graph();
Vertex marko = graph.addVertex(T.label, "person", "name", "marko",
"age", 29, "city", "Beijing");
Vertex vadas = graph.addVertex(T.label, "person", "name", "vadas",
"age", 27, "city", "Hongkong");
Vertex lop = graph.addVertex(T.label, "software", "name", "lop",
"lang", "java", "price", 328);
Vertex josh = graph.addVertex(T.label, "person", "name", "josh",
"age", 32, "city", "Beijing");
Vertex ripple = graph.addVertex(T.label, "software", "name", "ripple",
"lang", "java", "price", 199);
Vertex peter = graph.addVertex(T.label, "person", "name", "peter",
"age", 35, "city", "Shanghai");
marko.addEdge("knows", vadas, "date", "20160110", "weight", 0.5);
marko.addEdge("knows", josh, "date", "20130220", "weight", 1.0);
marko.addEdge("created", lop, "date", "20171210", "weight", 0.4);
josh.addEdge("created", lop, "date", "20091111", "weight", 0.4);
josh.addEdge("created", ripple, "date", "20171210", "weight", 1.0);
peter.addEdge("created", lop, "date", "20170324", "weight", 0.2);
}
}
顶点ID为：
"2:ripple",
"1:vadas",
"1:peter",
"1:josh",
"1:marko",
"2:lop"
边ID为：
"S1:peter>2>>S2:lop",
"S1:josh>2>>S2:lop",
"S1:josh>2>>S2:ripple",
"S1:marko>1>20130220>S1:josh",
"S1:marko>1>20160110>S1:vadas",
"S1:marko>2>>S2:lop"
3.2.1 K-out API（GET，基础版）
3.2.1.1 功能介绍
根据起始顶点、方向、边的类型（可选）和深度depth，查找从起始顶点出发恰好depth步可达的顶点
Params
- source：起始顶点id，必填项
- direction：起始顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
- max_depth：步数，必填项
- label：边的类型，选填项，默认代表所有edge label
- nearest：nearest为true时，代表起始顶点到达结果顶点的最短路径长度为depth，不存在更短的路径；nearest为false时，代表起始顶点到结果顶点有一条长度为depth的路径（未必最短且可以有环），选填项，默认为true
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：返回的顶点的最大数目，选填项，默认为10000000
3.2.1.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/kout?source="1:marko"&max_depth=2
Response Status
200
Response Body
{
"vertices":[
"2:ripple",
"1:peter"
]
}
3.2.1.3 适用场景
查找恰好N步关系可达的顶点。两个例子：
- 家族关系中，查找一个人的所有孙子，person A通过连续的两条“儿子”边到达的顶点集合。
- 社交关系中发现潜在好友，例如：与目标用户相隔两层朋友关系的用户，可以通过连续两条“朋友”边到达的顶点。
3.2.2 K-out API（POST，高级版）
3.2.2.1 功能介绍
根据起始顶点、步骤（包括方向、边类型和过滤属性）和深度depth，查找从起始顶点出发恰好depth步可达的顶点。
与K-out基础版的不同在于：
- 支持只统计邻居数量
- 支持边属性过滤
- 支持返回到达邻居的最短路径
Params
- source：起始顶点id，必填项
- steps: 从起始点出发的Steps，必填项，结构如下：
- direction：表示边的方向（OUT,IN,BOTH），默认是BOTH
- edge_steps：边Step集合，支持对单边的类型和属性过滤，如果为空，则不过滤
- label：边类型
- properties：边属性
- vertex_steps：顶点Step集合，支持对单点的类型和属性过滤，如果为空，则不过滤
- label：顶点类型
- properties：顶点属性
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，默认为 10000 (注: 0.12版之前 step 内仅支持 degree 作为参数名, 0.12开始统一使用 max_degree, 并向下兼容 degree 写法)
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启)
- max_depth：步数，必填项
- nearest：nearest为true时，代表起始顶点到达结果顶点的最短路径长度为depth，不存在更短的路径；nearest为false时，代表起始顶点到结果顶点有一条长度为depth的路径（未必最短且可以有环），选填项，默认为true
- count_only：Boolean值，true表示只统计结果的数目，不返回具体结果；false表示返回具体的结果，默认为false
- with_path：true表示返回起始点到每个邻居的最短路径，false表示不返回起始点到每个邻居的最短路径，选填项，默认为false
- with_edge，选填项，默认为false：
- 如果设置为true，则结果将包含所有边的完整信息，即路径中的所有边
- 当with_path为true时，将返回所有路径中的边的完整信息
- 当with_path为false时，不返回任何信息
- 如果设置为false，则仅返回边的id
- 如果设置为true，则结果将包含所有边的完整信息，即路径中的所有边
- with_vertex，选填项，默认为false：
- 如果设置为true，则结果将包含所有顶点的完整信息，即路径中的所有顶点
- 当with_path为true时，将返回所有路径中的顶点的完整信息
- 当with_path为false时，返回所有邻居顶点的完整信息
- 如果设置为false，则仅返回顶点的id
- 如果设置为true，则结果将包含所有顶点的完整信息，即路径中的所有顶点
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：返回的顶点的最大数目，选填项，默认为10000000
- traverse_mode: 遍历方式，可选择“breadth_first_search”或“depth_first_search”作为参数，默认为“breadth_first_search”
3.2.2.2 使用方法
Method & Url
POST http://localhost:8080/graphs/{graph}/traversers/kout
Request Body
{
"source": "1:marko",
"steps": {
"direction": "BOTH",
"edge_steps": [
{
"label": "knows",
"properties": {
"weight": "P.gt(0.1)"
}
},
{
"label": "created",
"properties": {
"weight": "P.gt(0.1)"
}
}
],
"vertex_steps": [
{
"label": "person",
"properties": {
"age": "P.lt(32)"
}
},
{
"label": "software",
"properties": {}
}
],
"max_degree": 10000,
"skip_degree": 100000
},
"max_depth": 1,
"nearest": true,
"limit": 10000,
"with_vertex": true,
"with_path": true,
"with_edge": true
}
Response Status
200
Response Body
{
"size": 2,
"kout": [
"1:vadas",
"2:lop"
],
"paths": [
{
"objects": [
"1:marko",
"2:lop"
]
},
{
"objects": [
"1:marko",
"1:vadas"
]
}
],
"vertices": [
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 29,
"city": "Beijing"
}
},
{
"id": "1:vadas",
"label": "person",
"type": "vertex",
"properties": {
"name": "vadas",
"age": 27,
"city": "Hongkong"
}
},
{
"id": "2:lop",
"label": "software",
"type": "vertex",
"properties": {
"name": "lop",
"lang": "java",
"price": 328
}
}
],
"edges": [
{
"id": "S1:marko>1>20160110>S1:vadas",
"label": "knows",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "1:vadas",
"inVLabel": "person",
"properties": {
"weight": 0.5,
"date": "20160110"
}
},
{
"id": "S1:marko>2>>S2:lop",
"label": "created",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "2:lop",
"inVLabel": "software",
"properties": {
"weight": 0.4,
"date": "20171210"
}
}
]
}
3.2.2.3 适用场景
参见3.2.1.3
3.2.3 K-neighbor（GET，基础版）
3.2.3.1 功能介绍
根据起始顶点、方向、边的类型（可选）和深度depth，查找包括起始顶点在内、depth步之内可达的所有顶点
相当于：起始顶点、K-out(1)、K-out(2)、… 、K-out(max_depth)的并集
Params
- source: 起始顶点id，必填项
- direction：起始顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
- max_depth：步数，必填项
- label：边的类型，选填项，默认代表所有edge label
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- limit：返回的顶点的最大数目，也即遍历过程中最大的访问的顶点数目，选填项，默认为10000000
3.2.3.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/kneighbor?source=“1:marko”&max_depth=2
Response Status
200
Response Body
{
"vertices":[
"2:ripple",
"1:marko",
"1:josh",
"1:vadas",
"1:peter",
"2:lop"
]
}
3.2.3.3 适用场景
查找N步以内可达的所有顶点，例如：
- 家族关系中，查找一个人五服以内所有子孙，person A通过连续的5条“亲子”边到达的顶点集合。
- 社交关系中发现好友圈子，例如目标用户通过1条、2条、3条“朋友”边可到达的用户可以组成目标用户的朋友圈子
3.2.4 K-neighbor API（POST，高级版）
3.2.4.1 功能介绍
根据起始顶点、步骤（包括方向、边类型和过滤属性）和深度depth，查找从起始顶点出发depth步内可达的所有顶点。
与K-neighbor基础版的不同在于：
- 支持只统计邻居数量
- 支持边属性过滤
- 支持返回到达邻居的最短路径
Params
- source：起始顶点id，必填项
- steps: 从起始点出发的Steps，必填项，结构如下：
- direction：表示边的方向（OUT,IN,BOTH），默认是BOTH
- 从起始点出发的Steps，必填项，结构如下：
- direction：表示边的方向（OUT,IN,BOTH），默认是BOTH
- edge_steps：边Step集合，支持对单边的类型和属性过滤，如果为空，则不过滤
- label：边类型
- properties：边属性
- vertex_steps：顶点Step集合，支持对单点的类型和属性过滤，如果为空，则不过滤
- label：顶点类型
- properties：顶点属性
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，默认为 10000 (注: 0.12版之前 step 内仅支持 degree 作为参数名, 0.12开始统一使用 max_degree, 并向下兼容 degree 写法)
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启)
- max_depth：步数，必填项
- count_only：Boolean值，true表示只统计结果的数目，不返回具体结果；false表示返回具体的结果，默认为false
- with_path：true表示返回起始点到每个邻居的最短路径，false表示不返回起始点到每个邻居的最短路径，选填项，默认为false
- with_edge，选填项，默认为false：
- 如果设置为true，则结果将包含所有边的完整信息，即路径中的所有边
- 当with_path为true时，将返回所有路径中的边的完整信息
- 当with_path为false时，不返回任何信息
- 如果设置为false，则仅返回边的id
- 如果设置为true，则结果将包含所有边的完整信息，即路径中的所有边
- with_vertex，选填项，默认为false：
- 如果设置为true，则结果将包含所有顶点的完整信息，即路径中的所有顶点
- 当with_path为true时，将返回所有路径中的顶点的完整信息
- 当with_path为false时，返回所有邻居顶点的完整信息
- 如果设置为false，则仅返回顶点的id
- 如果设置为true，则结果将包含所有顶点的完整信息，即路径中的所有顶点
- limit：返回的顶点的最大数目，选填项，默认为10000000
3.2.4.2 使用方法
Method & Url
POST http://localhost:8080/graphs/{graph}/traversers/kneighbor
Request Body
{
"source": "1:marko",
"steps": {
"direction": "BOTH",
"edge_steps": [
{
"label": "knows",
"properties": {}
},
{
"label": "created",
"properties": {}
}
],
"vertex_steps": [
{
"label": "person",
"properties": {
"age": "P.gt(28)"
}
},
{
"label": "software",
"properties": {}
}
],
"max_degree": 10000,
"skip_degree": 100000
},
"max_depth": 3,
"limit": 10000,
"with_vertex": true,
"with_path": true,
"with_edge": true
}
Response Status
200
Response Body
{
"size": 4,
"kneighbor": [
"1:josh",
"2:lop",
"1:peter",
"2:ripple"
],
"paths": [
{
"objects": [
"1:marko",
"2:lop"
]
},
{
"objects": [
"1:marko",
"2:lop",
"1:peter"
]
},
{
"objects": [
"1:marko",
"1:josh"
]
},
{
"objects": [
"1:marko",
"1:josh",
"2:ripple"
]
}
],
"vertices": [
{
"id": "2:ripple",
"label": "software",
"type": "vertex",
"properties": {
"name": "ripple",
"lang": "java",
"price": 199
}
},
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 29,
"city": "Beijing"
}
},
{
"id": "1:josh",
"label": "person",
"type": "vertex",
"properties": {
"name": "josh",
"age": 32,
"city": "Beijing"
}
},
{
"id": "1:peter",
"label": "person",
"type": "vertex",
"properties": {
"name": "peter",
"age": 35,
"city": "Shanghai"
}
},
{
"id": "2:lop",
"label": "software",
"type": "vertex",
"properties": {
"name": "lop",
"lang": "java",
"price": 328
}
}
],
"edges": [
{
"id": "S1:josh>2>>S2:ripple",
"label": "created",
"type": "edge",
"outV": "1:josh",
"outVLabel": "person",
"inV": "2:ripple",
"inVLabel": "software",
"properties": {
"weight": 1.0,
"date": "20171210"
}
},
{
"id": "S1:marko>2>>S2:lop",
"label": "created",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "2:lop",
"inVLabel": "software",
"properties": {
"weight": 0.4,
"date": "20171210"
}
},
{
"id": "S1:marko>1>20130220>S1:josh",
"label": "knows",
"type": "edge",
"outV": "1:marko",
"outVLabel": "person",
"inV": "1:josh",
"inVLabel": "person",
"properties": {
"weight": 1.0,
"date": "20130220"
}
},
{
"id": "S1:peter>2>>S2:lop",
"label": "created",
"type": "edge",
"outV": "1:peter",
"outVLabel": "person",
"inV": "2:lop",
"inVLabel": "software",
"properties": {
"weight": 0.2,
"date": "20170324"
}
}
]
}
3.2.4.3 适用场景
参见3.2.3.3
3.2.5 Same Neighbors
3.2.5.1 功能介绍
查询两个点的共同邻居
Params
- vertex：一个顶点id，必填项
- other：另一个顶点id，必填项
- direction：顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
- label：边的类型，选填项，默认代表所有edge label
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- limit：返回的共同邻居的最大数目，选填项，默认为10000000
3.2.5.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/sameneighbors?vertex=“1:marko”&other="1:josh"
Response Status
200
Response Body
{
"same_neighbors":[
"2:lop"
]
}
3.2.5.3 适用场景
查找两个顶点的共同邻居：
- 社交关系中发现两个用户的共同粉丝或者共同关注用户
3.2.6 Jaccard Similarity（GET）
3.2.6.1 功能介绍
计算两个顶点的jaccard similarity（两个顶点邻居的交集比上两个顶点邻居的并集）
Params
- vertex：一个顶点id，必填项
- other：另一个顶点id，必填项
- direction：顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
- label：边的类型，选填项，默认代表所有edge label
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
3.2.6.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/jaccardsimilarity?vertex="1:marko"&other="1:josh"
Response Status
200
Response Body
{
"jaccard_similarity": 0.2
}
3.2.6.3 适用场景
用于评估两个点的相似性或者紧密度
3.2.7 Jaccard Similarity（POST）
3.2.7.1 功能介绍
计算与指定顶点的jaccard similarity最大的N个点
jaccard similarity的计算方式为：两个顶点邻居的交集比上两个顶点邻居的并集
Params
- vertex：一个顶点id，必填项
- 从起始点出发的Step，必填项，结构如下：
- direction：表示边的方向（OUT,IN,BOTH），默认是BOTH
- labels：边的类型列表
- properties：通过属性的值过滤边
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，默认为 10000 (注: 0.12版之前 step 内仅支持 degree 作为参数名, 0.12开始统一使用 max_degree, 并向下兼容 degree 写法)
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启)
- top：返回一个起点的jaccard similarity中最大的top个，选填项，默认为100
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
3.2.7.2 使用方法
Method & Url
POST http://localhost:8080/graphs/{graph}/traversers/jaccardsimilarity
Request Body
{
"vertex": "1:marko",
"step": {
"direction": "BOTH",
"labels": [],
"max_degree": 10000,
"skip_degree": 100000
},
"top": 3
}
Response Status
200
Response Body
{
"2:ripple": 0.3333333333333333,
"1:peter": 0.3333333333333333,
"1:josh": 0.2
}
3.2.7.3 适用场景
用于在图中找出与指定顶点相似性最高的顶点
3.2.8 Shortest Path
3.2.8.1 功能介绍
根据起始顶点、目的顶点、方向、边的类型（可选）和最大深度，查找一条最短路径
Params
- source：起始顶点id，必填项
- target：目的顶点id，必填项
- direction：起始顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
- max_depth：最大步数，必填项
- label：边的类型，选填项，默认代表所有edge label
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启) - capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
3.2.8.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/shortestpath?source="1:marko"&target="2:ripple"&max_depth=3
Response Status
200
Response Body
{
"path":[
"1:marko",
"1:josh",
"2:ripple"
]
}
3.2.8.3 适用场景
查找两个顶点间的最短路径，例如：
- 社交关系网中，查找两个用户有关系的最短路径，即最近的朋友关系链
- 设备关联网络中，查找两个设备最短的关联关系
3.2.9 All Shortest Paths
3.2.9.1 功能介绍
根据起始顶点、目的顶点、方向、边的类型（可选）和最大深度，查找两点间所有的最短路径
Params
- source：起始顶点id，必填项
- target：目的顶点id，必填项
- direction：起始顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
- max_depth：最大步数，必填项
- label：边的类型，选填项，默认代表所有edge label
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启) - capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
3.2.9.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/allshortestpaths?source="A"&target="Z"&max_depth=10
Response Status
200
Response Body
{
"paths":[
{
"objects": [
"A",
"B",
"C",
"Z"
]
},
{
"objects": [
"A",
"M",
"N",
"Z"
]
}
]
}
3.2.9.3 适用场景
查找两个顶点间的所有最短路径，例如：
- 社交关系网中，查找两个用户有关系的全部最短路径，即最近的朋友关系链
- 设备关联网络中，查找两个设备全部的最短关联关系
3.2.10 Weighted Shortest Path
3.2.10.1 功能介绍
根据起始顶点、目的顶点、方向、边的类型（可选）和最大深度，查找一条带权最短路径
Params
- source：起始顶点id，必填项
- target：目的顶点id，必填项
- direction：起始顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
- label：边的类型，选填项，默认代表所有edge label
- weight：边的权重属性，必填项，必须是数字类型的属性
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启) - capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- with_vertex：true表示返回结果包含完整的顶点信息（路径中的全部顶点），false时表示只返回顶点id，选填项，默认为false
3.2.10.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/weightedshortestpath?source="1:marko"&target="2:ripple"&weight="weight"&with_vertex=true
Response Status
200
Response Body
{
"path": {
"weight": 2.0,
"vertices": [
"1:marko",
"1:josh",
"2:ripple"
]
},
"vertices": [
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 29,
"city": "Beijing"
}
},
{
"id": "1:josh",
"label": "person",
"type": "vertex",
"properties": {
"name": "josh",
"age": 32,
"city": "Beijing"
}
},
{
"id": "2:ripple",
"label": "software",
"type": "vertex",
"properties": {
"name": "ripple",
"lang": "java",
"price": 199
}
}
]
}
3.2.10.3 适用场景
查找两个顶点间的带权最短路径，例如：
- 交通线路中查找从A城市到B城市花钱最少的交通方式
3.2.11 Single Source Shortest Path
3.2.11.1 功能介绍
从一个顶点出发，查找该点到图中其他顶点的最短路径（可选是否带权重）
Params
- source：起始顶点id，必填项
- direction：起始顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
- label：边的类型，选填项，默认代表所有edge label
- weight：边的权重属性，选填项，必须是数字类型的属性，如果不填或者虽然填了但是边没有该属性，则权重为1.0
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启) - capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：查询到的目标顶点个数，也是返回的最短路径的条数，选填项，默认为10
- with_vertex：true表示返回结果包含完整的顶点信息（路径中的全部顶点），false时表示只返回顶点id，选填项，默认为false
3.2.11.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/singlesourceshortestpath?source="1:marko"&with_vertex=true
Response Status
200
Response Body
{
"paths": {
"2:ripple": {
"weight": 2.0,
"vertices": [
"1:marko",
"1:josh",
"2:ripple"
]
},
"1:josh": {
"weight": 1.0,
"vertices": [
"1:marko",
"1:josh"
]
},
"1:vadas": {
"weight": 1.0,
"vertices": [
"1:marko",
"1:vadas"
]
},
"1:peter": {
"weight": 2.0,
"vertices": [
"1:marko",
"2:lop",
"1:peter"
]
},
"2:lop": {
"weight": 1.0,
"vertices": [
"1:marko",
"2:lop"
]
}
},
"vertices": [
{
"id": "2:ripple",
"label": "software",
"type": "vertex",
"properties": {
"name": "ripple",
"lang": "java",
"price": 199
}
},
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 29,
"city": "Beijing"
}
},
{
"id": "1:josh",
"label": "person",
"type": "vertex",
"properties": {
"name": "josh",
"age": 32,
"city": "Beijing"
}
},
{
"id": "1:vadas",
"label": "person",
"type": "vertex",
"properties": {
"name": "vadas",
"age": 27,
"city": "Hongkong"
}
},
{
"id": "1:peter",
"label": "person",
"type": "vertex",
"properties": {
"name": "peter",
"age": 35,
"city": "Shanghai"
}
},
{
"id": "2:lop",
"label": "software",
"type": "vertex",
"properties": {
"name": "lop",
"lang": "java",
"price": 328
}
}
]
}
3.2.11.3 适用场景
查找从一个点出发到其他顶点的带权最短路径，比如：
- 查找从北京出发到全国其他所有城市的耗时最短的乘车方案
3.2.12 Multi Node Shortest Path
3.2.12.1 功能介绍
查找指定顶点集两两之间的最短路径
Params
- vertices：定义起始顶点，必填项，指定方式包括：
- ids：通过顶点id列表提供起始顶点
- label和properties：如果没有指定ids，则使用label和properties的联合条件查询起始顶点
- label：顶点的类型
- properties：通过属性的值查询起始顶点
注意：properties中的属性值可以是列表，表示只要key对应的value在列表中就可以
- step：表示从起始顶点到终止顶点走过的路径，必填项，Step的结构如下：
- direction：表示边的方向（OUT,IN,BOTH），默认是BOTH
- labels：边的类型列表
- properties：通过属性的值过滤边
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，默认为 10000 (注: 0.12版之前 step 内仅支持 degree 作为参数名, 0.12开始统一使用 max_degree, 并向下兼容 degree 写法)
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启)
- max_depth：步数，必填项
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- with_vertex：true表示返回结果包含完整的顶点信息（路径中的全部顶点），false时表示只返回顶点id，选填项，默认为false
3.2.12.2 使用方法
Method & Url
POST http://localhost:8080/graphs/{graph}/traversers/multinodeshortestpath
Request Body
{
"vertices": {
"ids": ["382:marko", "382:josh", "382:vadas", "382:peter", "383:lop", "383:ripple"]
},
"step": {
"direction": "BOTH",
"properties": {
}
},
"max_depth": 10,
"capacity": 100000000,
"with_vertex": true
}
Response Status
200
Response Body
{
"paths": [
{
"objects": [
"382:peter",
"383:lop"
]
},
{
"objects": [
"382:peter",
"383:lop",
"382:marko"
]
},
{
"objects": [
"382:peter",
"383:lop",
"382:josh"
]
},
{
"objects": [
"382:peter",
"383:lop",
"382:marko",
"382:vadas"
]
},
{
"objects": [
"383:lop",
"382:marko"
]
},
{
"objects": [
"383:lop",
"382:josh"
]
},
{
"objects": [
"383:lop",
"382:marko",
"382:vadas"
]
},
{
"objects": [
"382:peter",
"383:lop",
"382:josh",
"383:ripple"
]
},
{
"objects": [
"382:marko",
"382:josh"
]
},
{
"objects": [
"383:lop",
"382:josh",
"383:ripple"
]
},
{
"objects": [
"382:marko",
"382:vadas"
]
},
{
"objects": [
"382:marko",
"382:josh",
"383:ripple"
]
},
{
"objects": [
"382:josh",
"383:ripple"
]
},
{
"objects": [
"382:josh",
"382:marko",
"382:vadas"
]
},
{
"objects": [
"382:vadas",
"382:marko",
"382:josh",
"383:ripple"
]
}
],
"vertices": [
{
"id": "382:peter",
"label": "person",
"type": "vertex",
"properties": {
"name": "peter",
"age": 29,
"city": "Shanghai"
}
},
{
"id": "383:lop",
"label": "software",
"type": "vertex",
"properties": {
"name": "lop",
"lang": "java",
"price": 328
}
},
{
"id": "382:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 29,
"city": "Beijing"
}
},
{
"id": "382:josh",
"label": "person",
"type": "vertex",
"properties": {
"name": "josh",
"age": 32,
"city": "Beijing"
}
},
{
"id": "382:vadas",
"label": "person",
"type": "vertex",
"properties": {
"name": "vadas",
"age": 27,
"city": "Hongkong"
}
},
{
"id": "383:ripple",
"label": "software",
"type": "vertex",
"properties": {
"name": "ripple",
"lang": "java",
"price": 199
}
}
]
}
3.2.12.3 适用场景
查找多个点之间的最短路径，比如：
- 查找多个公司和法人之间的最短路径
3.2.13 Paths （GET，基础版）
3.2.13.1 功能介绍
根据起始顶点、目的顶点、方向、边的类型（可选）和最大深度等条件查找所有路径
Params
- source：起始顶点id，必填项
- target：目的顶点id，必填项
- direction：起始顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
- label：边的类型，选填项，默认代表所有edge label
- max_depth：步数，必填项
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：返回的路径的最大数目，选填项，默认为10
3.2.13.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/paths?source="1:marko"&target="1:josh"&max_depth=5
Response Status
200
Response Body
{
"paths":[
{
"objects":[
"1:marko",
"1:josh"
]
},
{
"objects":[
"1:marko",
"2:lop",
"1:josh"
]
}
]
}
3.2.13.3 适用场景
查找两个顶点间的所有路径，例如：
- 社交网络中，查找两个用户所有可能的关系路径
- 设备关联网络中，查找两个设备之间所有的关联路径
3.2.14 Paths （POST，高级版）
3.2.14.1 功能介绍
根据起始顶点、目的顶点、步骤（step）和最大深度等条件查找所有路径
Params
- sources：定义起始顶点，必填项，指定方式包括：
- ids：通过顶点id列表提供起始顶点
- label和properties：如果没有指定ids，则使用label和properties的联合条件查询起始顶点
- label：顶点的类型
- properties：通过属性的值查询起始顶点
注意：properties中的属性值可以是列表，表示只要key对应的value在列表中就可以
- targets：定义终止顶点，必填项，指定方式包括：
- ids：通过顶点id列表提供终止顶点
- label和properties：如果没有指定ids，则使用label和properties的联合条件查询终止顶点
- label：顶点的类型
- properties：通过属性的值查询终止顶点
注意：properties中的属性值可以是列表，表示只要key对应的value在列表中就可以
- step：表示从起始顶点到终止顶点走过的路径，必填项，Step的结构如下：
- direction：表示边的方向（OUT,IN,BOTH），默认是BOTH
- labels：边的类型列表
- properties：通过属性的值过滤边
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，默认为 10000 (注: 0.12版之前 step 内仅支持 degree 作为参数名, 0.12开始统一使用 max_degree, 并向下兼容 degree 写法)
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启)
- max_depth：步数，必填项
- nearest：nearest为true时，代表起始顶点到达结果顶点的最短路径长度为depth，不存在更短的路径；nearest为false时，代表起始顶点到结果顶点有一条长度为depth的路径（未必最短且可以有环），选填项，默认为true
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：返回的路径的最大数目，选填项，默认为10
- with_vertex：true表示返回结果包含完整的顶点信息（路径中的全部顶点），false时表示只返回顶点id，选填项，默认为false
3.2.14.2 使用方法
Method & Url
POST http://localhost:8080/graphs/{graph}/traversers/paths
Request Body
{
"sources": {
"ids": ["1:marko"]
},
"targets": {
"ids": ["1:peter"]
},
"step": {
"direction": "BOTH",
"properties": {
"weight": "P.gt(0.01)"
}
},
"max_depth": 10,
"capacity": 100000000,
"limit": 10000000,
"with_vertex": false
}
Response Status
200
Response Body
{
"paths": [
{
"objects": [
"1:marko",
"1:josh",
"2:lop",
"1:peter"
]
},
{
"objects": [
"1:marko",
"2:lop",
"1:peter"
]
}
]
}
3.2.14.3 适用场景
查找两个顶点间的所有路径，例如：
- 社交网络中，查找两个用户所有可能的关系路径
- 设备关联网络中，查找两个设备之间所有的关联路径
3.2.15 Customized Paths
3.2.15.1 功能介绍
根据一批起始顶点、边规则（包括方向、边的类型和属性过滤）和最大深度等条件查找符合条件的所有的路径
Params
- sources：定义起始顶点，必填项，指定方式包括：
- ids：通过顶点id列表提供起始顶点
- label和properties：如果没有指定ids，则使用label和properties的联合条件查询起始顶点
- label：顶点的类型
- properties：通过属性的值查询起始顶点
注意：properties中的属性值可以是列表，表示只要key对应的value在列表中就可以
- steps：表示从起始顶点走过的路径规则，是一组Step的列表。必填项。每个Step的结构如下：
- direction：表示边的方向（OUT,IN,BOTH），默认是BOTH
- labels：边的类型列表
- properties：通过属性的值过滤边
- weight_by：根据指定的属性计算边的权重，sort_by不为NONE时有效，与default_weight互斥
- default_weight：当边没有属性作为权重计算值时，采取的默认权重，sort_by不为NONE时有效，与weight_by互斥
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，默认为 10000 (注: 0.12版之前 step 内仅支持 degree 作为参数名, 0.12开始统一使用 max_degree, 并向下兼容 degree 写法)
- sample：当需要对某个step的符合条件的边进行采样时设置，-1表示不采样，默认为采样100
- sort_by：根据路径的权重排序，选填项，默认为NONE：
- NONE表示不排序，默认值
- INCR表示按照路径权重的升序排序
- DECR表示按照路径权重的降序排序
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：返回的路径的最大数目，选填项，默认为10
- with_vertex：true表示返回结果包含完整的顶点信息（路径中的全部顶点），false时表示只返回顶点id，选填项，默认为false
3.2.15.2 使用方法
Method & Url
POST http://localhost:8080/graphs/{graph}/traversers/customizedpaths
Request Body
{
"sources":{
"ids":[
],
"label":"person",
"properties":{
"name":"marko"
}
},
"steps":[
{
"direction":"OUT",
"labels":[
"knows"
],
"weight_by":"weight",
"max_degree":-1
},
{
"direction":"OUT",
"labels":[
"created"
],
"default_weight":8,
"max_degree":-1,
"sample":1
}
],
"sort_by":"INCR",
"with_vertex":true,
"capacity":-1,
"limit":-1
}
Response Status
200
Response Body
{
"paths":[
{
"objects":[
"1:marko",
"1:josh",
"2:lop"
],
"weights":[
1,
8
]
}
],
"vertices":[
{
"id":"1:marko",
"label":"person",
"type":"vertex",
"properties":{
"city":[
{
"id":"1:marko>city",
"value":"Beijing"
}
],
"name":[
{
"id":"1:marko>name",
"value":"marko"
}
],
"age":[
{
"id":"1:marko>age",
"value":29
}
]
}
},
{
"id":"1:josh",
"label":"person",
"type":"vertex",
"properties":{
"city":[
{
"id":"1:josh>city",
"value":"Beijing"
}
],
"name":[
{
"id":"1:josh>name",
"value":"josh"
}
],
"age":[
{
"id":"1:josh>age",
"value":32
}
]
}
},
{
"id":"2:lop",
"label":"software",
"type":"vertex",
"properties":{
"price":[
{
"id":"2:lop>price",
"value":328
}
],
"name":[
{
"id":"2:lop>name",
"value":"lop"
}
],
"lang":[
{
"id":"2:lop>lang",
"value":"java"
}
]
}
}
]
}
3.2.15.3 适用场景
适合查找各种复杂的路径集合，例如：
- 社交网络中，查找看过张艺谋所导演的电影的用户关注的大V的路径（张艺谋—>电影—->用户—>大V）
- 风控网络中，查找多个高风险用户的直系亲属的朋友的路径（高风险用户—>直系亲属—>朋友）
3.2.16 Template Paths
3.2.16.1 功能介绍
根据一批起始顶点、边规则（包括方向、边的类型和属性过滤）和最大深度等条件查找符合条件的所有的路径
Params
- sources：定义起始顶点，必填项，指定方式包括：
- ids：通过顶点id列表提供起始顶点
- label和properties：如果没有指定ids，则使用label和properties的联合条件查询起始顶点
- label：顶点的类型
- properties：通过属性的值查询起始顶点
注意：properties中的属性值可以是列表，表示只要key对应的value在列表中就可以
- targets：定义终止顶点，必填项，指定方式包括：
- ids：通过顶点id列表提供终止顶点
- label和properties：如果没有指定ids，则使用label和properties的联合条件查询终止顶点
- label：顶点的类型
- properties：通过属性的值查询终止顶点
注意：properties中的属性值可以是列表，表示只要key对应的value在列表中就可以
- steps：表示从起始顶点走过的路径规则，是一组Step的列表。必填项。每个Step的结构如下：
- direction：表示边的方向（OUT,IN,BOTH），默认是BOTH
- labels：边的类型列表
- properties：通过属性的值过滤边
- max_times：当前step可以重复的次数，当为N时，表示从起始顶点可以经过当前step 1-N 次
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，默认为 10000 (注: 0.12版之前 step 内仅支持 degree 作为参数名, 0.12开始统一使用 max_degree, 并向下兼容 degree 写法)
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启)
- with_ring：Boolean值，true表示包含环路；false表示不包含环路，默认为false
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：返回的路径的最大数目，选填项，默认为10
- with_vertex：true表示返回结果包含完整的顶点信息（路径中的全部顶点），false时表示只返回顶点id，选填项，默认为false
3.2.16.2 使用方法
Method & Url
POST http://localhost:8080/graphs/{graph}/traversers/templatepaths
Request Body
{
"sources": {
"ids": [],
"label": "person",
"properties": {
"name": "vadas"
}
},
"targets": {
"ids": [],
"label": "software",
"properties": {
"name": "ripple"
}
},
"steps": [
{
"direction": "IN",
"labels": ["knows"],
"properties": {
},
"max_degree": 10000,
"skip_degree": 100000
},
{
"direction": "OUT",
"labels": ["created"],
"properties": {
},
"max_degree": 10000,
"skip_degree": 100000
},
{
"direction": "IN",
"labels": ["created"],
"properties": {
},
"max_degree": 10000,
"skip_degree": 100000
},
{
"direction": "OUT",
"labels": ["created"],
"properties": {
},
"max_degree": 10000,
"skip_degree": 100000
}
],
"capacity": 10000,
"limit": 10,
"with_vertex": true
}
Response Status
200
Response Body
{
"paths": [
{
"objects": [
"1:vadas",
"1:marko",
"2:lop",
"1:josh",
"2:ripple"
]
}
],
"vertices": [
{
"id": "2:ripple",
"label": "software",
"type": "vertex",
"properties": {
"name": "ripple",
"lang": "java",
"price": 199
}
},
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 29,
"city": "Beijing"
}
},
{
"id": "1:josh",
"label": "person",
"type": "vertex",
"properties": {
"name": "josh",
"age": 32,
"city": "Beijing"
}
},
{
"id": "1:vadas",
"label": "person",
"type": "vertex",
"properties": {
"name": "vadas",
"age": 27,
"city": "Hongkong"
}
},
{
"id": "2:lop",
"label": "software",
"type": "vertex",
"properties": {
"name": "lop",
"lang": "java",
"price": 328
}
}
]
}
3.2.16.3 适用场景
适合查找各种复杂的模板路径，比如personA -(朋友)-> personB -(同学)-> personC，其中"朋友"和"同学"边可以分别是最多3层和4层的情况
3.2.17 Crosspoints
3.2.17.1 功能介绍
根据起始顶点、目的顶点、方向、边的类型（可选）和最大深度等条件查找相交点
Params
- source：起始顶点id，必填项
- target：目的顶点id，必填项
- direction：起始顶点到目的顶点的方向, 目的点到起始点是反方向，BOTH时不考虑方向（OUT,IN,BOTH），选填项，默认是BOTH
- label：边的类型，选填项，默认代表所有edge label
- max_depth：步数，必填项
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：返回的交点的最大数目，选填项，默认为10
3.2.17.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/crosspoints?source="2:lop"&target="2:ripple"&max_depth=5&direction=IN
Response Status
200
Response Body
{
"crosspoints":[
{
"crosspoint":"1:josh",
"objects":[
"2:lop",
"1:josh",
"2:ripple"
]
}
]
}
3.2.17.3 适用场景
查找两个顶点的交点及其路径，例如：
- 社交网络中，查找两个用户共同关注的话题或者大V
- 家族关系中，查找共同的祖先
3.2.18 Customized Crosspoints
3.2.18.1 功能介绍
根据一批起始顶点、多种边规则（包括方向、边的类型和属性过滤）和最大深度等条件查找符合条件的所有的路径终点的交集
Params
sources：定义起始顶点，必填项，指定方式包括：
- ids：通过顶点id列表提供起始顶点
- label和properties：如果没有指定ids，则使用label和properties的联合条件查询起始顶点
- label：顶点的类型
- properties：通过属性的值查询起始顶点
注意：properties中的属性值可以是列表，表示只要key对应的value在列表中就可以
path_patterns：表示从起始顶点走过的路径规则，是一组规则的列表。必填项。每个规则是一个PathPattern
- 每个PathPattern是一组Step列表，每个Step结构如下：
- direction：表示边的方向（OUT,IN,BOTH），默认是BOTH
- labels：边的类型列表
- properties：通过属性的值过滤边
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，默认为 10000 (注: 0.12版之前 step 内仅支持 degree 作为参数名, 0.12开始统一使用 max_degree, 并向下兼容 degree 写法)
- skip_degree：用于设置查询过程中舍弃超级顶点的最小边数，即当某个顶点的邻接边数目大于 skip_degree 时，完全舍弃该顶点。选填项，如果开启时，需满足
skip_degree >= max_degree约束，默认为0 (不启用)，表示不跳过任何点 (注意: 开启此配置后，遍历时会尝试访问一个顶点的 skip_degree 条边，而不仅仅是 max_degree 条边，这样有额外的遍历开销，对查询性能影响可能有较大影响，请确认理解后再开启)
- 每个PathPattern是一组Step列表，每个Step结构如下：
capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
limit：返回的路径的最大数目，选填项，默认为10
with_path：true表示返回交点所在的路径，false表示不返回交点所在的路径，选填项，默认为false
with_vertex，选填项，默认为false：
- true表示返回结果包含完整的顶点信息（路径中的全部顶点）
- with_path为true时，返回所有路径中的顶点的完整信息
- with_path为false时，返回所有交点的完整信息
- false时表示只返回顶点id
- true表示返回结果包含完整的顶点信息（路径中的全部顶点）
3.2.18.2 使用方法
Method & Url
POST http://localhost:8080/graphs/{graph}/traversers/customizedcrosspoints
Request Body
{
"sources":{
"ids":[
"2:lop",
"2:ripple"
]
},
"path_patterns":[
{
"steps":[
{
"direction":"IN",
"labels":[
"created"
],
"max_degree":-1
}
]
}
],
"with_path":true,
"with_vertex":true,
"capacity":-1,
"limit":-1
}
Response Status
200
Response Body
{
"crosspoints":[
"1:josh"
],
"paths":[
{
"objects":[
"2:ripple",
"1:josh"
]
},
{
"objects":[
"2:lop",
"1:josh"
]
}
],
"vertices":[
{
"id":"2:ripple",
"label":"software",
"type":"vertex",
"properties":{
"price":[
{
"id":"2:ripple>price",
"value":199
}
],
"name":[
{
"id":"2:ripple>name",
"value":"ripple"
}
],
"lang":[
{
"id":"2:ripple>lang",
"value":"java"
}
]
}
},
{
"id":"1:josh",
"label":"person",
"type":"vertex",
"properties":{
"city":[
{
"id":"1:josh>city",
"value":"Beijing"
}
],
"name":[
{
"id":"1:josh>name",
"value":"josh"
}
],
"age":[
{
"id":"1:josh>age",
"value":32
}
]
}
},
{
"id":"2:lop",
"label":"software",
"type":"vertex",
"properties":{
"price":[
{
"id":"2:lop>price",
"value":328
}
],
"name":[
{
"id":"2:lop>name",
"value":"lop"
}
],
"lang":[
{
"id":"2:lop>lang",
"value":"java"
}
]
}
}
]
}
3.2.18.3 适用场景
查询一组顶点通过多种路径在终点有交集的情况。例如：
- 在商品图谱中，多款手机、学习机、游戏机通过不同的低级别的类目路径，最终都属于一级类目的电子设备
3.2.19 Rings
3.2.19.1 功能介绍
根据起始顶点、方向、边的类型（可选）和最大深度等条件查找可达的环路
例如：1 -> 25 -> 775 -> 14690 -> 25, 其中环路为 25 -> 775 -> 14690 -> 25
Params
- source：起始顶点id，必填项
- direction：起始顶点发出的边的方向（OUT,IN,BOTH），选填项，默认是BOTH
- label：边的类型，选填项，默认代表所有edge label
- max_depth：步数，必填项
- source_in_ring：环路是否包含起点，选填项，默认为true
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：返回的可达环路的最大数目，选填项，默认为10
3.2.19.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/rings?source="1:marko"&max_depth=2
Response Status
200
Response Body
{
"rings":[
{
"objects":[
"1:marko",
"1:josh",
"1:marko"
]
},
{
"objects":[
"1:marko",
"1:vadas",
"1:marko"
]
},
{
"objects":[
"1:marko",
"2:lop",
"1:marko"
]
}
]
}
3.2.19.3 适用场景
查询起始顶点可达的环路，例如：
- 风控项目中，查询一个用户可达的循环担保的人或者设备
- 设备关联网络中，发现一个设备周围的循环引用的设备
3.2.20 Rays
3.2.20.1 功能介绍
根据起始顶点、方向、边的类型（可选）和最大深度等条件查找发散到边界顶点的路径
例如：1 -> 25 -> 775 -> 14690 -> 2289 -> 18379, 其中 18379 为边界顶点，即没有从 18379 发出的边
Params
- source：起始顶点id，必填项
- direction：起始顶点发出的边的方向（OUT,IN,BOTH），选填项，默认是BOTH
- label：边的类型，选填项，默认代表所有edge label
- max_depth：步数，必填项
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
- capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
- limit：返回的非环路的最大数目，选填项，默认为10
3.2.20.2 使用方法
Method & Url
GET http://localhost:8080/graphs/{graph}/traversers/rays?source="1:marko"&max_depth=2&direction=OUT
Response Status
200
Response Body
{
"rays":[
{
"objects":[
"1:marko",
"1:vadas"
]
},
{
"objects":[
"1:marko",
"2:lop"
]
},
{
"objects":[
"1:marko",
"1:josh",
"2:ripple"
]
},
{
"objects":[
"1:marko",
"1:josh",
"2:lop"
]
}
]
}
3.2.20.3 适用场景
查找起始顶点到某种关系的边界顶点的路径，例如：
- 家族关系中，查找一个人到所有还没有孩子的子孙的路径
- 设备关联网络中，找到某个设备到终端设备的路径
3.2.21 Fusiform Similarity
3.2.21.1 功能介绍
按照条件查询一批顶点对应的"梭形相似点"。当两个顶点跟很多共同的顶点之间有某种关系的时候，我们认为这两个点为"梭形相似点"。举个例子说明"梭形相似点"：“读者A"读了100本书，可以定义读过这100本书中的80本以上的读者，是"读者A"的"梭形相似点”
Params
sources：定义起始顶点，必填项，指定方式包括：
- ids：通过顶点id列表提供起始顶点
- label和properties：如果没有指定ids，则使用label和properties的联合条件查询起始顶点
- label：顶点的类型
- properties：通过属性的值查询起始顶点
注意：properties中的属性值可以是列表，表示只要key对应的value在列表中就可以
label：边的类型，选填项，默认代表所有edge label
direction：起始顶点向外发散的方向（OUT,IN,BOTH），选填项，默认是BOTH
min_neighbors：最少邻居数目，邻居数目少于这个阈值时，认为起点不具备"梭形相似点"。比如想要找一个"读者A"读过的书的"梭形相似点"，那么
min_neighbors为100时，表示"读者A"至少要读过100本书才可以有"梭形相似点"，必填项alpha：相似度，代表：起点与"梭形相似点"的共同邻居数目占起点的全部邻居数目的比例，必填项
min_similars：“梭形相似点"的最少个数，只有当起点的"梭形相似点"数目大于或等于该值时，才会返回起点及其"梭形相似点”，选填项，默认值为1
top：返回一个起点的"梭形相似点"中相似度最高的top个，必填项，0表示全部
group_property：与
min_groups一起使用，当起点跟其所有的"梭形相似点"某个属性的值有至少min_groups个不同值时，才会返回该起点及其"梭形相似点"。比如为"读者A"推荐"异地"书友时，需要设置group_property为读者的"城市"属性，min_group至少为2，选填项，不填代表不需要根据属性过滤min_groups：与
group_property一起使用，只有group_property设置时才有意义max_degree：查询过程中，单个顶点遍历的最大邻接边数目，选填项，默认为10000
capacity：遍历过程中最大的访问的顶点数目，选填项，默认为10000000
limit：返回的结果数目上限（一个起点及其"梭形相似点"算一个结果），选填项，默认为10
with_intermediary：是否返回起点及其"梭形相似点"共同关联的中间点，默认为false
with_vertex，选填项，默认为false：
- true表示返回结果包含完整的顶点信息
- false时表示只返回顶点id
3.2.21.2 使用方法
Method & Url
POST http://localhost:8080/graphs/hugegraph/traversers/fusiformsimilarity
Request Body
{
"sources":{
"ids":[],
"label": "person",
"properties": {
"name":"p1"
}
},
"label":"read",
"direction":"OUT",
"min_neighbors":8,
"alpha":0.75,
"min_similars":1,
"top":0,
"group_property":"city",
"min_group":2,
"max_degree": 10000,
"capacity": -1,
"limit": -1,
"with_intermediary": false,
"with_vertex":true
}
Response Status
200
Response Body
{
"similars": {
"3:p1": [
{
"id": "3:p2",
"score": 0.8888888888888888,
"intermediaries": [
]
},
{
"id": "3:p3",
"score": 0.7777777777777778,
"intermediaries": [
]
}
]
},
"vertices": [
{
"id": "3:p1",
"label": "person",
"type": "vertex",
"properties": {
"name": "p1",
"city": "Beijing"
}
},
{
"id": "3:p2",
"label": "person",
"type": "vertex",
"properties": {
"name": "p2",
"city": "Shanghai"
}
},
{
"id": "3:p3",
"label": "person",
"type": "vertex",
"properties": {
"name": "p3",
"city": "Beijing"
}
}
]
}
3.2.21.3 适用场景
查询一组顶点相似度很高的顶点。例如：
- 跟一个读者有类似书单的读者
- 跟一个玩家玩类似游戏的玩家
3.2.22 Vertices
3.2.22.1 根据顶点的id列表，批量查询顶点
Params
- ids：要查询的顶点id列表
Method & Url
GET http://localhost:8080/graphs/hugegraph/traversers/vertices?ids="1:marko"&ids="2:lop"
Response Status
200
Response Body
{
"vertices":[
{
"id":"1:marko",
"label":"person",
"type":"vertex",
"properties":{
"city":[
{
"id":"1:marko>city",
"value":"Beijing"
}
],
"name":[
{
"id":"1:marko>name",
"value":"marko"
}
],
"age":[
{
"id":"1:marko>age",
"value":29
}
]
}
},
{
"id":"2:lop",
"label":"software",
"type":"vertex",
"properties":{
"price":[
{
"id":"2:lop>price",
"value":328
}
],
"name":[
{
"id":"2:lop>name",
"value":"lop"
}
],
"lang":[
{
"id":"2:lop>lang",
"value":"java"
}
]
}
}
]
}
3.2.22.2 获取顶点 Shard 信息
通过指定的分片大小split_size，获取顶点分片信息（可以与 3.2.21.3 中的 Scan 配合使用来获取顶点）。
Params
- split_size：分片大小，必填项
Method & Url
GET http://localhost:8080/graphs/hugegraph/traversers/vertices/shards?split_size=67108864
Response Status
200
Response Body
{
"shards":[
{
"start": "0",
"end": "2165893",
"length": 0
},
{
"start": "2165893",
"end": "4331786",
"length": 0
},
{
"start": "4331786",
"end": "6497679",
"length": 0
},
{
"start": "6497679",
"end": "8663572",
"length": 0
},
......
]
}
3.2.22.3 根据Shard信息批量获取顶点
通过指定的分片信息批量查询顶点（Shard信息的获取参见 3.2.21.2 Shard）。
Params
- start：分片起始位置，必填项
- end：分片结束位置，必填项
- page：分页位置，选填项，默认为null，不分页；当page为“”时表示分页的第一页，从start指示的位置开始
- page_limit：分页获取顶点时，一页中顶点数目的上限，选填项，默认为100000
Method & Url
GET http://localhost:8080/graphs/hugegraph/traversers/vertices/scan?start=0&end=4294967295
Response Status
200
Response Body
{
"vertices":[
{
"id":"2:ripple",
"label":"software",
"type":"vertex",
"properties":{
"price":[
{
"id":"2:ripple>price",
"value":199
}
],
"name":[
{
"id":"2:ripple>name",
"value":"ripple"
}
],
"lang":[
{
"id":"2:ripple>lang",
"value":"java"
}
]
}
},
{
"id":"1:vadas",
"label":"person",
"type":"vertex",
"properties":{
"city":[
{
"id":"1:vadas>city",
"value":"Hongkong"
}
],
"name":[
{
"id":"1:vadas>name",
"value":"vadas"
}
],
"age":[
{
"id":"1:vadas>age",
"value":27
}
]
}
},
{
"id":"1:peter",
"label":"person",
"type":"vertex",
"properties":{
"city":[
{
"id":"1:peter>city",
"value":"Shanghai"
}
],
"name":[
{
"id":"1:peter>name",
"value":"peter"
}
],
"age":[
{
"id":"1:peter>age",
"value":35
}
]
}
},
{
"id":"1:josh",
"label":"person",
"type":"vertex",
"properties":{
"city":[
{
"id":"1:josh>city",
"value":"Beijing"
}
],
"name":[
{
"id":"1:josh>name",
"value":"josh"
}
],
"age":[
{
"id":"1:josh>age",
"value":32
}
]
}
},
{
"id":"1:marko",
"label":"person",
"type":"vertex",
"properties":{
"city":[
{
"id":"1:marko>city",
"value":"Beijing"
}
],
"name":[
{
"id":"1:marko>name",
"value":"marko"
}
],
"age":[
{
"id":"1:marko>age",
"value":29
}
]
}
},
{
"id":"2:lop",
"label":"software",
"type":"vertex",
"properties":{
"price":[
{
"id":"2:lop>price",
"value":328
}
],
"name":[
{
"id":"2:lop>name",
"value":"lop"
}
],
"lang":[
{
"id":"2:lop>lang",
"value":"java"
}
]
}
}
]
}
3.2.22.4 适用场景
- 按id列表查询顶点，可用于批量查询顶点，比如在path查询到多条路径之后，可以进一步查询某条路径的所有顶点属性。
- 获取分片和按分片查询顶点，可以用来遍历全部顶点
3.2.23 Edges
3.2.23.1 根据边的id列表，批量查询边
Params
- ids：要查询的边id列表
Method & Url
GET http://localhost:8080/graphs/hugegraph/traversers/edges?ids="S1:josh>1>>S2:lop"&ids="S1:josh>1>>S2:ripple"
Response Status
200
Response Body
{
"edges": [
{
"id": "S1:josh>1>>S2:lop",
"label": "created",
"type": "edge",
"inVLabel": "software",
"outVLabel": "person",
"inV": "2:lop",
"outV": "1:josh",
"properties": {
"date": "20091111",
"weight": 0.4
}
},
{
"id": "S1:josh>1>>S2:ripple",
"label": "created",
"type": "edge",
"inVLabel": "software",
"outVLabel": "person",
"inV": "2:ripple",
"outV": "1:josh",
"properties": {
"date": "20171210",
"weight": 1
}
}
]
}
3.2.23.2 获取边 Shard 信息
通过指定的分片大小split_size，获取边分片信息（可以与 3.2.22.3 中的 Scan 配合使用来获取边）。
Params
- split_size：分片大小，必填项
Method & Url
GET http://localhost:8080/graphs/hugegraph/traversers/edges/shards?split_size=4294967295
Response Status
200
Response Body
{
"shards":[
{
"start": "0",
"end": "1073741823",
"length": 0
},
{
"start": "1073741823",
"end": "2147483646",
"length": 0
},
{
"start": "2147483646",
"end": "3221225469",
"length": 0
},
{
"start": "3221225469",
"end": "4294967292",
"length": 0
},
{
"start": "4294967292",
"end": "4294967295",
"length": 0
}
]
}
3.2.23.3 根据 Shard 信息批量获取边
通过指定的分片信息批量查询边（Shard信息的获取参见 3.2.22.2）。
Params
- start：分片起始位置，必填项
- end：分片结束位置，必填项
- page：分页位置，选填项，默认为null，不分页；当page为“”时表示分页的第一页，从start指示的位置开始
- page_limit：分页获取边时，一页中边数目的上限，选填项，默认为100000
Method & Url
GET http://localhost:8080/graphs/hugegraph/traversers/edges/scan?start=0&end=3221225469
Response Status
200
Response Body
{
"edges":[
{
"id":"S1:peter>2>>S2:lop",
"label":"created",
"type":"edge",
"inVLabel":"software",
"outVLabel":"person",
"inV":"2:lop",
"outV":"1:peter",
"properties":{
"weight":0.2,
"date":"20170324"
}
},
{
"id":"S1:josh>2>>S2:lop",
"label":"created",
"type":"edge",
"inVLabel":"software",
"outVLabel":"person",
"inV":"2:lop",
"outV":"1:josh",
"properties":{
"weight":0.4,
"date":"20091111"
}
},
{
"id":"S1:josh>2>>S2:ripple",
"label":"created",
"type":"edge",
"inVLabel":"software",
"outVLabel":"person",
"inV":"2:ripple",
"outV":"1:josh",
"properties":{
"weight":1,
"date":"20171210"
}
},
{
"id":"S1:marko>1>20130220>S1:josh",
"label":"knows",
"type":"edge",
"inVLabel":"person",
"outVLabel":"person",
"inV":"1:josh",
"outV":"1:marko",
"properties":{
"weight":1,
"date":"20130220"
}
},
{
"id":"S1:marko>1>20160110>S1:vadas",
"label":"knows",
"type":"edge",
"inVLabel":"person",
"outVLabel":"person",
"inV":"1:vadas",
"outV":"1:marko",
"properties":{
"weight":0.5,
"date":"20160110"
}
},
{
"id":"S1:marko>2>>S2:lop",
"label":"created",
"type":"edge",
"inVLabel":"software",
"outVLabel":"person",
"inV":"2:lop",
"outV":"1:marko",
"properties":{
"weight":0.4,
"date":"20171210"
}
}
]
}
3.2.23.4 适用场景
- 按id列表查询边，可用于批量查询边
- 获取分片和按分片查询边，可以用来遍历全部边
10 - Rank API
4.1 rank API 概述
HugeGraphServer 除了上一节提到的遍历（traverser）方法，还提供了一类专门做推荐的方法，我们称为rank API，
可在图中为一个点推荐与其关系密切的其它点。
4.2 rank API 详解
4.2.1 Personal Rank API
Personal Rank 算法典型场景是用于推荐应用中, 根据某个点现有的出边, 推荐具有相近 / 相同关系的其他点, 比如根据某个人的阅读记录 / 习惯, 向它推荐其他可能感兴趣的书, 或潜在的书友, 举例如下:
- 假设给定 1个 Person 点 是 tom, 它喜欢
a,b,c,d,e5本书, 我们的想给 tom 推荐一些书友, 以及一些书, 最容易的想法就是看看还有哪些人喜欢过这些书 (共同兴趣) - 那么此时, 需要有其它的 Person 点比如 neo, 他喜欢
b,d,f3本书, 以及 jay, 它喜欢c,d,e,g4本书, lee 它喜欢a,d,e,f4本书 - 由于 tom 已经看过的书不需要重复推荐, 所以返回结果里应该期望推荐有共同喜好的其他书友看过, 但 tom 没看过的书, 比如推荐 “f” 和 “g” 书, 且优先级 f > g
- 此时再计算 tom 的个性化 rank 值, 就会返回排序后 TopN 推荐的 书友 + 书 的结果了 (如果只需要推荐的书, 选择 OTHER_LABEL 即可)
4.2.1.0 数据准备
上面是一个简单的例子, 这里再提供一个公开的 1MB 测试数据集 MovieLens 为例， 用户需下载该数据集，然后使用 HugeGraph-Loader 导入到 HugeGraph 中，简单起见，数据中顶点 user 和 movie 的属性都忽略，仅使用 id 字段即可，边 rating 的具体评分值也忽略。loader 使用的元数据 文件和输入源映射文件内容如下：
////////////////////////////////////////////////////////////
// UserID::Gender::Age::Occupation::Zip-code
// MovieID::Title::Genres
// UserID::MovieID::Rating::Timestamp
////////////////////////////////////////////////////////////
// Define schema
schema.propertyKey("id").asInt().ifNotExist().create();
schema.propertyKey("rate").asInt().ifNotExist().create();
schema.vertexLabel("user")
.properties("id")
.primaryKeys("id")
.ifNotExist()
.create();
schema.vertexLabel("movie")
.properties("id")
.primaryKeys("id")
.ifNotExist()
.create();
schema.edgeLabel("rating")
.sourceLabel("user")
.targetLabel("movie")
.properties("rate")
.ifNotExist()
.create();
{
"vertices": [
{
"label": "user",
"input": {
"type": "file",
"path": "users.dat",
"format": "TEXT",
"delimiter": "::",
"header": ["UserID", "Gender", "Age", "Occupation", "Zip-code"]
},
"ignored": ["Gender", "Age", "Occupation", "Zip-code"],
"mapping": {
"UserID": "id"
}
},
{
"label": "movie",
"input": {
"type": "file",
"path": "movies.dat",
"format": "TEXT",
"delimiter": "::",
"header": ["MovieID", "Title", "Genres"]
},
"ignored": ["Title", "Genres"],
"mapping": {
"MovieID": "id"
}
}
],
"edges": [
{
"label": "rating",
"source": ["UserID"],
"target": ["MovieID"],
"input": {
"type": "file",
"path": "ratings.dat",
"format": "TEXT",
"delimiter": "::",
"header": ["UserID", "MovieID", "Rating", "Timestamp"]
},
"ignored": ["Timestamp"],
"mapping": {
"UserID": "id",
"MovieID": "id",
"Rating": "rate"
}
}
]
}
注意将映射文件中
input.path的值修改为自己本地的路径。
4.2.1.1 功能介绍
适用于二分图，给出所有源顶点相关的其他顶点及其相关性组成的列表。
二分图：也称二部图，是图论里的一种特殊模型，也是一种特殊的网络流。其最大的特点在于，可以将图里的顶点分为两个集合，两个集合之间的点有边相连，但集合内的点之间没有直接关联。
假设有一个用户和物品的二分图，基于随机游走的 PersonalRank 算法步骤如下：
- 选定一个起点用户 u，其初始权重为 1.0，从 Vu 开始游走（有 alpha 的概率走到邻居点，1 - alpha 的概率停留）；
- 如果决定向外游走, 那么会选取某一个类型的出边, 例如
rating来查找共同的打分人：- 那就从当前节点的邻居节点中按照均匀分布随机选择一个，并且按照均匀分布划分权重值；
- 给源顶点补偿权重 1 - alpha；
- 重复步骤2；
- 达到一定步数或达到精度后收敛，得到推荐列表。
Params
必填项:
- source: 源顶点 id
- label: 源点出发的某类边 label，须连接两类不同顶点
选填项:
- alpha：每轮迭代时从某个点往外走的概率，与 PageRank 算法中的 alpha 类似，取值区间为 (0, 1], 默认值
0.85 - max_degree: 查询过程中，单个顶点遍历的最大邻接边数目，默认为
10000 - max_depth: 迭代次数，取值区间为 [2, 50], 默认值
5 - with_label：筛选结果中保留哪些结果，可选以下三类, 默认为
BOTH_LABEL- SAME_LABEL：仅保留与源顶点相同类别的顶点
- OTHER_LABEL：仅保留与源顶点不同类别（二分图的另一端）的顶点
- BOTH_LABEL：同时保留与源顶点相同和相反类别的顶点
- limit: 返回的顶点的最大数目，默认为
100 - max_diff: 提前收敛的精度差, 默认为
0.0001(后续实现) - sorted：返回的结果是否根据 rank 排序，为 true 时降序排列，反之不排序，默认为
true
4.2.1.2 使用方法
Method & Url
POST http://localhost:8080/graphs/hugegraph/traversers/personalrank
Request Body
{
"source": "1:1",
"label": "rating",
"alpha": 0.6,
"max_depth": 15,
"with_label": "OTHER_LABEL",
"sorted": true,
"limit": 10
}
Response Status
200
Response Body
{
"2:2858": 0.0005014026017816927,
"2:1196": 0.0004336708357653617,
"2:1210": 0.0004128083140214213,
"2:593": 0.00038117341069881513,
"2:480": 0.00037005373269728036,
"2:1198": 0.000366641614652057,
"2:2396": 0.0003622362410538888,
"2:2571": 0.0003593312457300953,
"2:589": 0.00035922123055598566,
"2:110": 0.0003466135844390885
}
4.2.1.3 适用场景
两类不同顶点连接形成的二分图中，给某个点推荐相关性最高的其他顶点，例如：
- 阅读推荐: 找出优先给某人推荐的其他书籍, 也可以同时推荐共同喜好最高的书友 (例: 微信 “你的好友也在看 xx 文章” 功能)
- 社交推荐: 找出拥有相同关注话题的其他博主, 也可以推荐可能感兴趣的新闻/消息 (例: Weibo 中的 “热点推荐” 功能)
- 商品推荐: 通过某人现在的购物习惯, 找出应优先推给它的商品列表, 也可以给它推荐带货播主 (例: TaoBao 的 “猜你喜欢” 功能)
4.2.2 Neighbor Rank API
4.2.2.0 数据准备
public class Loader {
public static void main(String[] args) {
HugeClient client = new HugeClient("http://127.0.0.1:8080", "hugegraph");
SchemaManager schema = client.schema();
schema.propertyKey("name").asText().ifNotExist().create();
schema.vertexLabel("person")
.properties("name")
.useCustomizeStringId()
.ifNotExist()
.create();
schema.vertexLabel("movie")
.properties("name")
.useCustomizeStringId()
.ifNotExist()
.create();
schema.edgeLabel("follow")
.sourceLabel("person")
.targetLabel("person")
.ifNotExist()
.create();
schema.edgeLabel("like")
.sourceLabel("person")
.targetLabel("movie")
.ifNotExist()
.create();
schema.edgeLabel("directedBy")
.sourceLabel("movie")
.targetLabel("person")
.ifNotExist()
.create();
GraphManager graph = client.graph();
Vertex O = graph.addVertex(T.label, "person", T.id, "O", "name", "O");
Vertex A = graph.addVertex(T.label, "person", T.id, "A", "name", "A");
Vertex B = graph.addVertex(T.label, "person", T.id, "B", "name", "B");
Vertex C = graph.addVertex(T.label, "person", T.id, "C", "name", "C");
Vertex D = graph.addVertex(T.label, "person", T.id, "D", "name", "D");
Vertex E = graph.addVertex(T.label, "movie", T.id, "E", "name", "E");
Vertex F = graph.addVertex(T.label, "movie", T.id, "F", "name", "F");
Vertex G = graph.addVertex(T.label, "movie", T.id, "G", "name", "G");
Vertex H = graph.addVertex(T.label, "movie", T.id, "H", "name", "H");
Vertex I = graph.addVertex(T.label, "movie", T.id, "I", "name", "I");
Vertex J = graph.addVertex(T.label, "movie", T.id, "J", "name", "J");
Vertex K = graph.addVertex(T.label, "person", T.id, "K", "name", "K");
Vertex L = graph.addVertex(T.label, "person", T.id, "L", "name", "L");
Vertex M = graph.addVertex(T.label, "person", T.id, "M", "name", "M");
O.addEdge("follow", A);
O.addEdge("follow", B);
O.addEdge("follow", C);
D.addEdge("follow", O);
A.addEdge("follow", B);
A.addEdge("like", E);
A.addEdge("like", F);
B.addEdge("like", G);
B.addEdge("like", H);
C.addEdge("like", I);
C.addEdge("like", J);
E.addEdge("directedBy", K);
F.addEdge("directedBy", B);
F.addEdge("directedBy", L);
G.addEdge("directedBy", M);
}
}
4.2.2.1 功能介绍
在一般图结构中，找出每一层与给定起点相关性最高的前 N 个顶点及其相关度，用图的语义理解就是：从起点往外走， 走到各层各个顶点的概率。
Params
- source: 源顶点 id，必填项
- alpha：每轮迭代时从某个点往外走的概率，与 PageRank 算法中的 alpha 类似，必填项，取值区间为 (0, 1]
- steps: 表示从起始顶点走过的路径规则，是一组 Step 的列表，每个 Step 对应结果中的一层，必填项。每个 Step 的结构如下：
- direction：表示边的方向（OUT, IN, BOTH），默认是 BOTH
- labels：边的类型列表，多个边类型取并集
- max_degree：查询过程中，单个顶点遍历的最大邻接边数目，默认为 10000 (注: 0.12版之前 step 内仅支持 degree 作为参数名, 0.12开始统一使用 max_degree, 并向下兼容 degree 写法)
- top：在结果中每一层只保留权重最高的前 N 个结果，默认为 100，最大值为 1000
- capacity: 遍历过程中最大的访问的顶点数目，选填项，默认为10000000
4.2.2.2 使用方法
Method & Url
POST http://localhost:8080/graphs/hugegraph/traversers/neighborrank
Request Body
{
"source":"O",
"steps":[
{
"direction":"OUT",
"labels":[
"follow"
],
"max_degree":-1,
"top":100
},
{
"direction":"OUT",
"labels":[
"follow",
"like"
],
"max_degree":-1,
"top":100
},
{
"direction":"OUT",
"labels":[
"directedBy"
],
"max_degree":-1,
"top":100
}
],
"alpha":0.9,
"capacity":-1
}
Response Status
200
Response Body
{
"ranks": [
{
"O": 1
},
{
"B": 0.4305,
"A": 0.3,
"C": 0.3
},
{
"G": 0.17550000000000002,
"H": 0.17550000000000002,
"I": 0.135,
"J": 0.135,
"E": 0.09000000000000001,
"F": 0.09000000000000001
},
{
"M": 0.15795,
"K": 0.08100000000000002,
"L": 0.04050000000000001
}
]
}
4.2.2.3 适用场景
为给定的起点在不同的层中找到最应该推荐的顶点。
- 比如：在观众、朋友、电影、导演的四层图结构中，根据某个观众的朋友们喜欢的电影，为这个观众推荐电影；或者根据这些电影是谁拍的，为其推荐导演。
11 - Variable API
5.1 Variables
Variables可以用来存储有关整个图的数据，数据按照键值对的方式存取
5.1.1 创建或者更新某个键值对
Method & Url
PUT http://localhost:8080/graphs/hugegraph/variables/name
Request Body
{
"data": "tom"
}
Response Status
200
Response Body
{
"name": "tom"
}
5.1.2 列出全部键值对
Method & Url
GET http://localhost:8080/graphs/hugegraph/variables
Response Status
200
Response Body
{
"name": "tom"
}
5.1.3 列出某个键值对
Method & Url
GET http://localhost:8080/graphs/hugegraph/variables/name
Response Status
200
Response Body
{
"name": "tom"
}
5.1.4 删除某个键值对
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/variables/name
Response Status
204
12 - Graphs API
6.1 Graphs
6.1.1 列出数据库中全部的图
Method & Url
GET http://localhost:8080/graphs
Response Status
200
Response Body
{
"graphs": [
"hugegraph",
"hugegraph1"
]
}
6.1.2 查看某个图的信息
Method & Url
GET http://localhost:8080/graphs/hugegraph
Response Status
200
Response Body
{
"name": "hugegraph",
"backend": "cassandra"
}
6.1.3 清空某个图的全部数据，包括 schema、vertex、edge 和 index 等，该操作需要管理员权限
Params
由于清空图是一个比较危险的操作，为避免用户误调用，我们给 API 添加了用于确认的参数：
- confirm_message: 默认为
I'm sure to delete all data
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/clear?confirm_message=I%27m+sure+to+delete+all+data
Response Status
204
6.1.4 克隆一个图，该操作需要管理员权限
Params
- clone_graph_name: 已有图的名称；从已有的图来克隆，用户可选择传递配置文件，传递时将替换已有图中的配置；
Method & Url
POST http://localhost:8080/graphs/hugegraph_clone?clone_graph_name=hugegraph
Request Body【可选】
gremlin.graph=org.apache.hugegraph.auth.HugeFactoryAuthProxy
backend=rocksdb
serializer=binary
store=hugegraph_clone
rocksdb.data_path=./rks-data-xx
rocksdb.wal_path=./rks-data-xx
Note: 存储路径不能与现有图相同（使用不同的目录）
Response Status
200
Response Body
{
"name": "hugegraph_clone",
"backend": "rocksdb"
}
6.1.5 创建一个图，该操作需要管理员权限
Method & Url
POST http://localhost:8080/graphs/hugegraph-xx
Request Body
gremlin.graph=org.apache.hugegraph.auth.HugeFactoryAuthProxy
backend=rocksdb
serializer=binary
store=hugegraph2
rocksdb.data_path=./rks-data-xx
rocksdb.wal_path=./rks-data-xx
Note: 存储路径不能与现有图相同（使用不同的目录）
Response Status
200
Response Body
{
"name": "hugegraph2",
"backend": "rocksdb"
}
6.1.6 删除某个图及其全部数据
Params
由于删除图是一个比较危险的操作，为避免用户误调用，我们给 API 添加了用于确认的参数：
- confirm_message: 默认为
I'm sure to drop the graph
Method & Url
DELETE http://localhost:8080/graphs/hugegraph_clone?confirm_message=I%27m%20sure%20to%20drop%20the%20graph
Response Status
204
6.2 Conf
6.2.1 查看某个图的配置，该操作需要管理员权限
Method & Url
GET http://localhost:8080/graphs/hugegraph/conf
Response Status
200
Response Body
# gremlin entrence to create graph
gremlin.graph=org.apache.hugegraph.HugeFactory
# cache config
#schema.cache_capacity=1048576
#graph.cache_capacity=10485760
#graph.cache_expire=600
# schema illegal name template
#schema.illegal_name_regex=\s+|~.*
#vertex.default_label=vertex
backend=cassandra
serializer=cassandra
store=hugegraph
...=
6.3 Mode
合法的图模式包括：NONE，RESTORING，MERGING，LOADING
- None 模式（默认），元数据和图数据的写入属于正常状态。特别的：
- 元数据（schema）创建时不允许指定 ID
- 图数据（vertex）在 id strategy 为 Automatic 时，不允许指定 ID
- LOADING：批量导入数据时自动启用，特别的：
- 添加顶点/边时，不会检查必填属性是否传入
Restore 时存在两种不同的模式：Restoring 和 Merging
- Restoring 模式，恢复到一个新图中，特别的：
- 元数据（schema）创建时允许指定 ID
- 图数据（vertex）在 id strategy 为 Automatic 时，允许指定 ID
- Merging 模式，合并到一个已存在元数据和图数据的图中，特别的：
- 元数据（schema）创建时不允许指定 ID
- 图数据（vertex）在 id strategy 为 Automatic 时，允许指定 ID
正常情况下，图模式为 None，当需要 Restore 图时，需要根据需要临时修改图模式为 Restoring 模式或者 Merging 模式，并在完成 Restore 时，恢复图模式为 None。
6.3.1 查看某个图的模式。
Method & Url
GET http://localhost:8080/graphs/hugegraph/mode
Response Status
200
Response Body
{
"mode": "NONE"
}
合法的图模式包括：NONE，RESTORING，MERGING
6.3.2 设置某个图的模式。该操作需要管理员权限
Method & Url
PUT http://localhost:8080/graphs/hugegraph/mode
Request Body
"RESTORING"
合法的图模式包括：NONE，RESTORING，MERGING
Response Status
200
Response Body
{
"mode": "RESTORING"
}
6.3.3 查看某个图的读模式。
Params
- name: 图的名称
Method & Url
GET http://localhost:8080/graphs/hugegraph/graph_read_mode
Response Status
200
Response Body
{
"graph_read_mode": "ALL"
}
6.3.4 设置某个图的读模式。该操作需要管理员权限
Params
- name: 图的名称
Method & Url
PUT http://localhost:8080/graphs/hugegraph/graph_read_mode
Request Body
"OLTP_ONLY"
合法的图模式包括：ALL，OLTP_ONLY，OLAP_ONLY
Response Status
200
Response Body
{
"graph_read_mode": "OLTP_ONLY"
}
6.4 Snapshot
6.4.1 创建快照
Params
- name: 图的名称
Method & Url
PUT http://localhost:8080/graphs/hugegraph/snapshot_create
Response Status
200
Response Body
{
"hugegraph": "snapshot_created"
}
6.4.2 快照恢复
Params
- name: 图的名称
Method & Url
PUT http://localhost:8080/graphs/hugegraph/snapshot_resume
Response Status
200
Response Body
{
"hugegraph": "snapshot_resumed"
}
6.5 Compact
6.5.1 手动压缩图，该操作需要管理员权限
Params
- name: 图的名称
Method & Url
PUT http://localhost:8080/graphs/hugegraph/compact
Response Status
200
Response Body
{
"nodes": 1,
"cluster_id": "local",
"servers": {
"local": "OK"
}
}
13 - Task API
7.1 Task
7.1.1 列出某个图中全部的异步任务
Params
- status: 异步任务的状态
- limit：返回异步任务数目上限
Method & Url
GET http://localhost:8080/graphs/hugegraph/tasks?status=success
Response Status
200
Response Body
{
"tasks": [{
"task_name": "hugegraph.traversal().V()",
"task_progress": 0,
"task_create": 1532943976585,
"task_status": "success",
"task_update": 1532943976736,
"task_result": "0",
"task_retries": 0,
"id": 2,
"task_type": "gremlin",
"task_callable": "org.apache.hugegraph.api.job.GremlinAPI$GremlinJob",
"task_input": "{\"gremlin\":\"hugegraph.traversal().V()\",\"bindings\":{},\"language\":\"gremlin-groovy\",\"aliases\":{\"hugegraph\":\"graph\"}}"
}]
}
7.1.2 查看某个异步任务的信息
Method & Url
GET http://localhost:8080/graphs/hugegraph/tasks/2
Response Status
200
Response Body
{
"task_name": "hugegraph.traversal().V()",
"task_progress": 0,
"task_create": 1532943976585,
"task_status": "success",
"task_update": 1532943976736,
"task_result": "0",
"task_retries": 0,
"id": 2,
"task_type": "gremlin",
"task_callable": "org.apache.hugegraph.api.job.GremlinAPI$GremlinJob",
"task_input": "{\"gremlin\":\"hugegraph.traversal().V()\",\"bindings\":{},\"language\":\"gremlin-groovy\",\"aliases\":{\"hugegraph\":\"graph\"}}"
}
7.1.3 删除某个异步任务信息，不删除异步任务本身
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/tasks/2
Response Status
204
7.1.4 取消某个异步任务，该异步任务必须具有处理中断的能力
假设已经通过Gremlin API创建了一个异步任务如下：
"for (int i = 0; i < 10; i++) {" +
"hugegraph.addVertex(T.label, 'man');" +
"hugegraph.tx().commit();" +
"try {" +
"sleep(1000);" +
"} catch (InterruptedException e) {" +
"break;" +
"}" +
"}"
Method & Url
PUT http://localhost:8080/graphs/hugegraph/tasks/2?action=cancel
请保证在 10 秒内发送该请求，如果超过 10 秒发送，任务可能已经执行完成，无法取消。
Response Status
202
Response Body
{
"cancelled": true
}
此时查询 label 为 man 的顶点数目，一定是小于 10 的。
14 - Gremlin API
8.1 Gremlin
8.1.1 向HugeGraphServer发送gremlin语句（GET），同步执行
Params
- gremlin: 要发送给
HugeGraphServer执行的gremlin语句 - bindings: 用来绑定参数，key是字符串，value是绑定的值（只能是字符串或者数字），功能类似于MySQL的 Prepared Statement，用于加速语句执行
- language: 发送语句的语言类型，默认为
gremlin-groovy - aliases: 为存在于图空间的已有变量添加别名
查询顶点
Method & Url
GET http://127.0.0.1:8080/gremlin?gremlin=hugegraph.traversal().V('1:marko')
Response Status
200
Response Body
{
"requestId": "c6ef47a8-b634-4b07-9d38-6b3b69a3a556",
"status": {
"message": "",
"code": 200,
"attributes": {}
},
"result": {
"data": [{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"city": [{
"id": "1:marko>city",
"value": "Beijing"
}],
"name": [{
"id": "1:marko>name",
"value": "marko"
}],
"age": [{
"id": "1:marko>age",
"value": 29
}]
}
}],
"meta": {}
}
}
8.1.2 向HugeGraphServer发送gremlin语句（POST），同步执行
Method & Url
POST http://localhost:8080/gremlin
查询顶点
Request Body
{
"gremlin": "hugegraph.traversal().V('1:marko')",
"bindings": {},
"language": "gremlin-groovy",
"aliases": {}
}
Response Status
200
Response Body
{
"requestId": "c6ef47a8-b634-4b07-9d38-6b3b69a3a556",
"status": {
"message": "",
"code": 200,
"attributes": {}
},
"result": {
"data": [{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"city": [{
"id": "1:marko>city",
"value": "Beijing"
}],
"name": [{
"id": "1:marko>name",
"value": "marko"
}],
"age": [{
"id": "1:marko>age",
"value": 29
}]
}
}],
"meta": {}
}
}
注意：
这里是直接使用图对象（hugegraph），先获取其遍历器（traversal()），再获取顶点。 不能直接写成
graph.traversal().V()或g.V()，可以通过"aliases": {"graph": "hugegraph", "g": "__g_hugegraph"}为图和遍历器添加别名后使用别名操作。其中，hugegraph是原生存在的变量，__g_hugegraph是HugeGraphServer额外添加的变量， 每个图都会存在一个对应的这样格式（_g${graph}）的遍历器对象。
响应体的结构与其他 Vertex 或 Edge 的 RESTful API的结构有区别，用户可能需要自行解析。
查询边
Request Body
{
"gremlin": "g.E('S1:marko>2>>S2:lop')",
"bindings": {},
"language": "gremlin-groovy",
"aliases": {
"graph": "hugegraph",
"g": "__g_hugegraph"
}
}
Response Status
200
Response Body
{
"requestId": "3f117cd4-eedc-4e08-a106-ee01d7bb8249",
"status": {
"message": "",
"code": 200,
"attributes": {}
},
"result": {
"data": [{
"id": "S1:marko>2>>S2:lop",
"label": "created",
"type": "edge",
"inVLabel": "software",
"outVLabel": "person",
"inV": "2:lop",
"outV": "1:marko",
"properties": {
"weight": 0.4,
"date": "20171210"
}
}],
"meta": {}
}
}
8.1.3 向HugeGraphServer发送gremlin语句（POST），异步执行
Method & Url
POST http://localhost:8080/graphs/hugegraph/jobs/gremlin
查询顶点
Request Body
{
"gremlin": "g.V('1:marko')",
"bindings": {},
"language": "gremlin-groovy",
"aliases": {}
}
注意：
异步执行Gremlin语句暂不支持aliases，可以使用
graph代表要操作的图，也可以直接使用图的名字， 例如hugegraph; 另外g代表 traversal，等价于graph.traversal()或者hugegraph.traversal()
Response Status
201
Response Body
{
"task_id": 1
}
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
查询边
Request Body
{
"gremlin": "g.E('S1:marko>2>>S2:lop')",
"bindings": {},
"language": "gremlin-groovy",
"aliases": {}
}
Response Status
201
Response Body
{
"task_id": 2
}
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/2（其中"2"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
15 - Cypher API
9.1 Cypher
9.1.1 向HugeGraphServer发送Cypher语句（GET），同步执行
Method & Url
GET /graphs/{graph}/cypher?cypher={cypher}
Params
- graph: 图名称
- cypher: cypher语句
使用示例
GET http://localhost:8080/graphs/hugecypher1/cypher?cypher=match(n:person) return n.name as name order by n.name limit 1
Response Status
200
Response Body
{
"requestId": "766b9f48-2f10-40d9-951a-3027d0748ab7",
"status": {
"message": "",
"code": 200,
"attributes": {
}
},
"result": {
"data": [
{
"name": "hello"
}
],
"meta": {
}
}
}
9.1.2 向HugeGraphServer发送Cypher语句（POST），同步执行
Method & Url
POST /graphs/{graph}/cypher
Params
- graph: 图名称
Body
{cypher}
- cypher: cypher语句
注意：
不是JSON格式，是纯文本的Cypher语句
使用示例
POST http://localhost:8080/graphs/hugecypher1/cypher
Request Body
match(n:person) return n.name as name order by n.name limit 1
Response Status
200
Response Body
{
"requestId": "f096bee0-e249-498f-b5a3-ea684fc84f57",
"status": {
"message": "",
"code": 200,
"attributes": {
}
},
"result": {
"data": [
{
"name": "hello"
}
],
"meta": {
}
}
}
16 - Authentication API
10.1 用户认证与权限控制
开启权限及相关配置请先参考 权限配置 文档
用户认证与权限控制概述：
HugeGraph 支持多用户认证、以及细粒度的权限访问控制，采用基于“用户 - 用户组 - 操作 - 资源”的 4 层设计，灵活控制用户角色与权限。 资源描述了图数据库中的数据，比如符合某一类条件的顶点，每一个资源包括 type、label、properties 三个要素，共有 18 种 type、 任意 label、任意 properties 的组合形成的资源，一个资源的内部条件是且关系，多个资源之间的条件是或关系。用户可以属于一个或多个用户组， 每个用户组可以拥有对任意个资源的操作权限，操作类型包括：读、写、删除、执行等种类。HugeGraph 支持动态创建用户、用户组、资源， 支持动态分配或取消权限。初始化数据库时超级管理员用户被创建，后续可通过超级管理员创建各类角色用户，新创建的用户如果被分配足够权限后，可以由其创建或管理更多的用户。
举例说明：
user(name=boss) -belong-> group(name=all) -access(read)-> target(graph=graph1, resource={label: person,
city: Beijing})
描述：用户’boss’拥有对’graph1’图中北京人的读权限。
接口说明：
用户认证与权限控制接口包括 5 类：UserAPI、GroupAPI、TargetAPI、BelongAPI、AccessAPI。
10.2 用户（User）API
用户接口包括：创建用户，删除用户，修改用户，和查询用户相关信息接口。
10.2.1 创建用户
Params
- user_name: 用户名称
- user_password: 用户密码
- user_phone: 用户手机号
- user_email: 用户邮箱
其中 user_name 和 user_password 为必填。
Request Body
{
"user_name": "boss",
"user_password": "******",
"user_phone": "182****9088",
"user_email": "123@xx.com"
}
Method & Url
POST http://localhost:8080/graphs/hugegraph/auth/users
Response Status
201
Response Body
返回报文中，密码为加密后的密文
{
"user_password": "******",
"user_email": "123@xx.com",
"user_update": "2020-11-17 14:31:07.833",
"user_name": "boss",
"user_creator": "admin",
"user_phone": "182****9088",
"id": "-63:boss",
"user_create": "2020-11-17 14:31:07.833"
}
10.2.2 删除用户
Params
- id: 需要删除的用户 Id
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/auth/users/-63:test
Response Status
204
Response Body
1
10.2.3 修改用户
Params
- id: 需要修改的用户 Id
Method & Url
PUT http://localhost:8080/graphs/hugegraph/auth/users/-63:test
Request Body
修改 user_name、user_password 和 user_phone
{
"user_name": "test",
"user_password": "******",
"user_phone": "183****9266"
}
Response Status
200
Response Body
返回结果是包含修改过的内容在内的整个用户组对象
{
"user_password": "******",
"user_update": "2020-11-12 10:29:30.455",
"user_name": "test",
"user_creator": "admin",
"user_phone": "183****9266",
"id": "-63:test",
"user_create": "2020-11-12 10:27:13.601"
}
10.2.4 查询用户列表
Params
- limit: 返回结果条数的上限
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/users
Response Status
200
Response Body
{
"users": [
{
"user_password": "******",
"user_update": "2020-11-11 11:41:12.254",
"user_name": "admin",
"user_creator": "system",
"id": "-63:admin",
"user_create": "2020-11-11 11:41:12.254"
}
]
}
10.2.5 查询某个用户
Params
- id: 需要查询的用户 Id
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/users/-63:admin
Response Status
200
Response Body
{
"users": [
{
"user_password": "******",
"user_update": "2020-11-11 11:41:12.254",
"user_name": "admin",
"user_creator": "system",
"id": "-63:admin",
"user_create": "2020-11-11 11:41:12.254"
}
]
}
10.2.6 查询某个用户的角色
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/users/-63:boss/role
Response Status
200
Response Body
{
"roles": {
"hugegraph": {
"READ": [
{
"type": "ALL",
"label": "*",
"properties": null
}
]
}
}
}
10.3 用户组（Group）API
用户组会赋予相应的资源权限，用户会被分配不同的用户组，即可拥有不同的资源权限。
用户组接口包括：创建用户组，删除用户组，修改用户组，和查询用户组相关信息接口。
10.3.1 创建用户组
Params
- group_name: 用户组名称
- group_description: 用户组描述
Request Body
{
"group_name": "all",
"group_description": "group can do anything"
}
Method & Url
POST http://localhost:8080/graphs/hugegraph/auth/groups
Response Status
201
Response Body
{
"group_creator": "admin",
"group_name": "all",
"group_create": "2020-11-11 15:46:08.791",
"group_update": "2020-11-11 15:46:08.791",
"id": "-69:all",
"group_description": "group can do anything"
}
10.3.2 删除用户组
Params
- id: 需要删除的用户组 Id
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/auth/groups/-69:grant
Response Status
204
Response Body
1
10.3.3 修改用户组
Params
- id: 需要修改的用户组 Id
Method & Url
PUT http://localhost:8080/graphs/hugegraph/auth/groups/-69:grant
Request Body
修改 group_description
{
"group_name": "grant",
"group_description": "grant"
}
Response Status
200
Response Body
返回结果是包含修改过的内容在内的整个用户组对象
{
"group_creator": "admin",
"group_name": "grant",
"group_create": "2020-11-12 09:50:58.458",
"group_update": "2020-11-12 09:57:58.155",
"id": "-69:grant",
"group_description": "grant"
}
10.3.4 查询用户组列表
Params
- limit: 返回结果条数的上限
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/groups
Response Status
200
Response Body
{
"groups": [
{
"group_creator": "admin",
"group_name": "all",
"group_create": "2020-11-11 15:46:08.791",
"group_update": "2020-11-11 15:46:08.791",
"id": "-69:all",
"group_description": "group can do anything"
}
]
}
10.3.5 查询某个用户组
Params
- id: 需要查询的用户组 Id
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/groups/-69:all
Response Status
200
Response Body
{
"group_creator": "admin",
"group_name": "all",
"group_create": "2020-11-11 15:46:08.791",
"group_update": "2020-11-11 15:46:08.791",
"id": "-69:all",
"group_description": "group can do anything"
}
10.4 资源（Target）API
资源描述了图数据库中的数据，比如符合某一类条件的顶点，每一个资源包括 type、label、properties 三个要素，共有 18 种 type、
任意 label、任意 properties 的组合形成的资源，一个资源的内部条件是且关系，多个资源之间的条件是或关系。
资源接口包括：资源的创建、删除、修改和查询。
10.4.1 创建资源
Params
- target_name: 资源名称
- target_graph: 资源图
- target_url: 资源地址
- target_resources: 资源定义 (列表)
target_resources 可以包括多个 target_resource，以列表的形式存储。
每个 target_resource 包含：
- type：可选值 VERTEX, EDGE 等，可填 ALL，则表示可以是顶点或边；
- label：可选值，⼀个顶点或边类型的名称，可填*，则表示任意类型；
- properties：map 类型，可包含多个属性的键值对，必须匹配所有属性值，属性值⽀持填条件范围（age: P.gte(18)），properties 如果为 null 表示任意属性均可，如果属性名和属性值均为‘*ʼ也表示任意属性均可。
如精细资源：“target_resources”: [{“type”:“VERTEX”,“label”:“person”,“properties”:{“city”:“Beijing”,“age”:“P.gte(20)”}}]**
资源定义含义：类型是’person’的顶点，且城市属性是’Beijing’，年龄属性大于等于 20。
Request Body
{
"target_name": "all",
"target_graph": "hugegraph",
"target_url": "127.0.0.1:8080",
"target_resources": [
{
"type": "ALL"
}
]
}
Method & Url
POST http://localhost:8080/graphs/hugegraph/auth/targets
Response Status
201
Response Body
{
"target_creator": "admin",
"target_name": "all",
"target_url": "127.0.0.1:8080",
"target_graph": "hugegraph",
"target_create": "2020-11-11 15:32:01.192",
"target_resources": [
{
"type": "ALL",
"label": "*",
"properties": null
}
],
"id": "-77:all",
"target_update": "2020-11-11 15:32:01.192"
}
10.4.2 删除资源
Params
- id: 需要删除的资源 Id
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/auth/targets/-77:gremlin
Response Status
204
Response Body
1
10.4.3 修改资源
Params
- id: 需要修改的资源 Id
Method & Url
PUT http://localhost:8080/graphs/hugegraph/auth/targets/-77:gremlin
Request Body
修改资源定义中的 type
{
"target_name": "gremlin",
"target_graph": "hugegraph",
"target_url": "127.0.0.1:8080",
"target_resources": [
{
"type": "NONE"
}
]
}
Response Status
200
Response Body
返回结果是包含修改过的内容在内的整个用户组对象
{
"target_creator": "admin",
"target_name": "gremlin",
"target_url": "127.0.0.1:8080",
"target_graph": "hugegraph",
"target_create": "2020-11-12 09:34:13.848",
"target_resources": [
{
"type": "NONE",
"label": "*",
"properties": null
}
],
"id": "-77:gremlin",
"target_update": "2020-11-12 09:37:12.780"
}
10.4.4 查询资源列表
Params
- limit: 返回结果条数的上限
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/targets
Response Status
200
Response Body
{
"targets": [
{
"target_creator": "admin",
"target_name": "all",
"target_url": "127.0.0.1:8080",
"target_graph": "hugegraph",
"target_create": "2020-11-11 15:32:01.192",
"target_resources": [
{
"type": "ALL",
"label": "*",
"properties": null
}
],
"id": "-77:all",
"target_update": "2020-11-11 15:32:01.192"
},
{
"target_creator": "admin",
"target_name": "grant",
"target_url": "127.0.0.1:8080",
"target_graph": "hugegraph",
"target_create": "2020-11-11 15:43:24.841",
"target_resources": [
{
"type": "GRANT",
"label": "*",
"properties": null
}
],
"id": "-77:grant",
"target_update": "2020-11-11 15:43:24.841"
}
]
}
10.4.5 查询某个资源
Params
- id: 需要查询的资源 Id
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/targets/-77:grant
Response Status
200
Response Body
{
"target_creator": "admin",
"target_name": "grant",
"target_url": "127.0.0.1:8080",
"target_graph": "hugegraph",
"target_create": "2020-11-11 15:43:24.841",
"target_resources": [
{
"type": "GRANT",
"label": "*",
"properties": null
}
],
"id": "-77:grant",
"target_update": "2020-11-11 15:43:24.841"
}
10.5 关联角色（Belong）API
关联用户和用户组的关系，一个用户可以关联一个或者多个用户组。用户组拥有相关资源的权限，不同用户组的资源权限可以理解为不同的角色。即给用户关联角色。
关联角色接口包括：用户关联角色的创建、删除、修改和查询。
10.5.1 创建用户的关联角色
Params
- user: 用户 Id
- group: 用户组 Id
- belong_description: 描述
Request Body
{
"user": "-63:boss",
"group": "-69:all"
}
Method & Url
POST http://localhost:8080/graphs/hugegraph/auth/belongs
Response Status
201
Response Body
{
"belong_create": "2020-11-11 16:19:35.422",
"belong_creator": "admin",
"belong_update": "2020-11-11 16:19:35.422",
"id": "S-63:boss>-82>>S-69:all",
"user": "-63:boss",
"group": "-69:all"
}
10.5.2 删除关联角色
Params
- id: 需要删除的关联角色 Id
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/auth/belongs/S-63:boss>-82>>S-69:grant
Response Status
204
Response Body
1
10.5.3 修改关联角色
关联角色只能修改描述，不能修改 user 和 group 属性，如果需要修改关联角色，需要删除原来关联关系，新增关联角色。
Params
- id: 需要修改的关联角色 Id
Method & Url
PUT http://localhost:8080/graphs/hugegraph/auth/belongs/S-63:boss>-82>>S-69:grant
Request Body
修改 belong_description
{
"belong_description": "update test"
}
Response Status
200
Response Body
返回结果是包含修改过的内容在内的整个用户组对象
{
"belong_description": "update test",
"belong_create": "2020-11-12 10:40:21.720",
"belong_creator": "admin",
"belong_update": "2020-11-12 10:42:47.265",
"id": "S-63:boss>-82>>S-69:grant",
"user": "-63:boss",
"group": "-69:grant"
}
10.5.4 查询关联角色列表
Params
- limit: 返回结果条数的上限
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/belongs
Response Status
200
Response Body
{
"belongs": [
{
"belong_create": "2020-11-11 16:19:35.422",
"belong_creator": "admin",
"belong_update": "2020-11-11 16:19:35.422",
"id": "S-63:boss>-82>>S-69:all",
"user": "-63:boss",
"group": "-69:all"
}
]
}
10.5.5 查看某个关联角色
Params
- id: 需要查询的关联角色 Id
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/belongs/S-63:boss>-82>>S-69:all
Response Status
200
Response Body
{
"belong_create": "2020-11-11 16:19:35.422",
"belong_creator": "admin",
"belong_update": "2020-11-11 16:19:35.422",
"id": "S-63:boss>-82>>S-69:all",
"user": "-63:boss",
"group": "-69:all"
}
10.6 赋权（Access）API
给用户组赋予资源的权限，主要包含：读操作 (READ)、写操作 (WRITE)、删除操作 (DELETE)、执行操作 (EXECUTE) 等。
赋权接口包括：赋权的创建、删除、修改和查询。
10.6.1 创建赋权 (用户组赋予资源的权限)
Params
- group: 用户组 Id
- target: 资源 Id
- access_permission: 权限许可
- access_description: 赋权描述
access_permission：
- READ：读操作，所有的查询，包括查询 Schema、查顶点/边，查询顶点和边的数量 VERTEX_AGGR/EDGE_AGGR，也包括读图的状态 STATUS、变量 VAR、任务 TASK 等；
- WRITE：写操作，所有的创建、更新操作，包括给 Schema 增加 property key，给顶点增加或更新属性等；
- DELETE：删除操作，包括删除元数据、删除顶点/边；
- EXECUTE：执⾏操作，包括执⾏ Gremlin 语句、执⾏ Task、执⾏ metadata 函数；
Request Body
{
"group": "-69:all",
"target": "-77:all",
"access_permission": "READ"
}
Method & Url
POST http://localhost:8080/graphs/hugegraph/auth/accesses
Response Status
201
Response Body
{
"access_permission": "READ",
"access_create": "2020-11-11 15:54:54.008",
"id": "S-69:all>-88>11>S-77:all",
"access_update": "2020-11-11 15:54:54.008",
"access_creator": "admin",
"group": "-69:all",
"target": "-77:all"
}
10.6.2 删除赋权
Params
- id: 需要删除的赋权 Id
Method & Url
DELETE http://localhost:8080/graphs/hugegraph/auth/accesses/S-69:all>-88>12>S-77:all
Response Status
204
Response Body
1
10.6.3 修改赋权
赋权只能修改描述，不能修改用户组、资源和权限许可，如果需要修改赋权的关系，可以删除原来的赋权关系，新增赋权。
Params
- id: 需要修改的赋权 Id
Method & Url
PUT http://localhost:8080/graphs/hugegraph/auth/accesses/S-69:all>-88>12>S-77:all
Request Body
修改 access_description
{
"access_description": "test"
}
Response Status
200
Response Body
返回结果是包含修改过的内容在内的整个用户组对象
{
"access_description": "test",
"access_permission": "WRITE",
"access_create": "2020-11-12 10:12:03.074",
"id": "S-69:all>-88>12>S-77:all",
"access_update": "2020-11-12 10:16:18.637",
"access_creator": "admin",
"group": "-69:all",
"target": "-77:all"
}
10.6.4 查询赋权列表
Params
- limit: 返回结果条数的上限
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/accesses
Response Status
200
Response Body
{
"accesses": [
{
"access_permission": "READ",
"access_create": "2020-11-11 15:54:54.008",
"id": "S-69:all>-88>11>S-77:all",
"access_update": "2020-11-11 15:54:54.008",
"access_creator": "admin",
"group": "-69:all",
"target": "-77:all"
}
]
}
10.6.5 查询某个赋权
Params
- id: 需要查询的赋权 Id
Method & Url
GET http://localhost:8080/graphs/hugegraph/auth/accesses/S-69:all>-88>11>S-77:all
Response Status
200
Response Body
{
"access_permission": "READ",
"access_create": "2020-11-11 15:54:54.008",
"id": "S-69:all>-88>11>S-77:all",
"access_update": "2020-11-11 15:54:54.008",
"access_creator": "admin",
"group": "-69:all",
"target": "-77:all"
}
17 - Metrics API
HugeGraph 提供了获取监控信息的 Metrics 接口，比如各个 Gremlin 执行时间的统计、缓存的占用大小等。Metrics 接口包括如下几类：基础指标、统计指标、系统指标、后端存储指标。
1. 基础指标
1.1 获取所有基础指标
Params
- type：如果传值为
json，则以 json 格式返回，否则以 Promethaus 格式返回。
1.1.1 Method & Url
http://localhost:8080/metrics/?type=json
Response Status
200
Response Body
{
"gauges": {
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.capacity": {
"value": 1000000
},
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.instances": {
"value": 7
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.capacity": {
"value": 10000
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.expire": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.size": {
"value": 17
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.capacity": {
"value": 10000
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.expire": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.size": {
"value": 17
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.capacity": {
"value": 10240
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.capacity": {
"value": 10240
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.capacity": {
"value": 10240
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.capacity": {
"value": 10000000
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.server.RestServer.max-write-threads": {
"value": 0
},
"org.apache.hugegraph.task.TaskManager.pending-tasks": {
"value": 0
},
"org.apache.hugegraph.task.TaskManager.workers": {
"value": 4
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.average-load-penalty": {
"value": 922769200
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.estimated-size": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.eviction-count": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.eviction-weight": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.hit-count": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.hit-rate": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.load-count": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.load-failure-count": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.load-failure-rate": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.load-success-count": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.long-run-compilation-count": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.miss-count": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.miss-rate": {
"value": 1
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.request-count": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.total-load-time": {
"value": 1845538400
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.sessions": {
"value": 0
}
},
"counters": {
"favicon.ico/GET/FAILED_COUNTER": {
"count": 1
},
"favicon.ico/GET/TOTAL_COUNTER": {
"count": 1
},
"metrics/POST/FAILED_COUNTER": {
"count": 1
},
"metrics/POST/TOTAL_COUNTER": {
"count": 1
},
"metrics/backend/GET/SUCCESS_COUNTER": {
"count": 2
},
"metrics/backend/GET/TOTAL_COUNTER": {
"count": 2
},
"metrics/gauges/GET/SUCCESS_COUNTER": {
"count": 1
},
"metrics/gauges/GET/TOTAL_COUNTER": {
"count": 1
},
"metrics/system/GET/SUCCESS_COUNTER": {
"count": 2
},
"metrics/system/GET/TOTAL_COUNTER": {
"count": 2
},
"system/GET/FAILED_COUNTER": {
"count": 1
},
"system/GET/TOTAL_COUNTER": {
"count": 1
}
},
"histograms": {
"favicon.ico/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 1,
"min": 1,
"mean": 1,
"max": 1,
"stddev": 0,
"p50": 1,
"p75": 1,
"p95": 1,
"p98": 1,
"p99": 1,
"p999": 1
},
"metrics/POST/RESPONSE_TIME_HISTOGRAM": {
"count": 1,
"min": 21,
"mean": 21,
"max": 21,
"stddev": 0,
"p50": 21,
"p75": 21,
"p95": 21,
"p98": 21,
"p99": 21,
"p999": 21
},
"metrics/backend/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 2,
"min": 6,
"mean": 12.6852124529148,
"max": 20,
"stddev": 6.992918475157571,
"p50": 6,
"p75": 20,
"p95": 20,
"p98": 20,
"p99": 20,
"p999": 20
},
"metrics/gauges/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 1,
"min": 7,
"mean": 7,
"max": 7,
"stddev": 0,
"p50": 7,
"p75": 7,
"p95": 7,
"p98": 7,
"p99": 7,
"p999": 7
},
"metrics/system/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 2,
"min": 0,
"mean": 8.942674506664073,
"max": 40,
"stddev": 16.665399873223066,
"p50": 0,
"p75": 0,
"p95": 40,
"p98": 40,
"p99": 40,
"p999": 40
},
"system/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 1,
"min": 2,
"mean": 2,
"max": 2,
"stddev": 0,
"p50": 2,
"p75": 2,
"p95": 2,
"p98": 2,
"p99": 2,
"p999": 2
}
},
"meters": {
"org.apache.hugegraph.api.API.commit-succeed": {
"count": 0,
"mean_rate": 0,
"m15_rate": 0,
"m5_rate": 0,
"m1_rate": 0,
"rate_unit": "events/second"
},
"org.apache.hugegraph.api.API.expected-error": {
"count": 0,
"mean_rate": 0,
"m15_rate": 0,
"m5_rate": 0,
"m1_rate": 0,
"rate_unit": "events/second"
},
"org.apache.hugegraph.api.API.illegal-arg": {
"count": 0,
"mean_rate": 0,
"m15_rate": 0,
"m5_rate": 0,
"m1_rate": 0,
"rate_unit": "events/second"
},
"org.apache.hugegraph.api.API.unknown-error": {
"count": 0,
"mean_rate": 0,
"m15_rate": 0,
"m5_rate": 0,
"m1_rate": 0,
"rate_unit": "events/second"
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.errors": {
"count": 0,
"mean_rate": 0,
"m15_rate": 0,
"m5_rate": 0,
"m1_rate": 0,
"rate_unit": "events/second"
}
},
"timers": {
"org.apache.hugegraph.api.auth.AccessAPI.create": {
"count": 0,
"min": 0,
"mean": 0,
"max": 0,
"stddev": 0,
"p50": 0,
"p75": 0,
"p95": 0,
"p98": 0,
"p99": 0,
"p999": 0,
"duration_unit": "milliseconds",
"mean_rate": 0,
"m15_rate": 0,
"m5_rate": 0,
"m1_rate": 0,
"rate_unit": "calls/second"
},
"org.apache.hugegraph.api.auth.AccessAPI.delete": {
"count": 0,
"min": 0,
"mean": 0,
"max": 0,
"stddev": 0,
"p50": 0,
"p75": 0,
"p95": 0,
"p98": 0,
"p99": 0,
"p999": 0,
"duration_unit": "milliseconds",
"mean_rate": 0,
"m15_rate": 0,
"m5_rate": 0,
"m1_rate": 0,
"rate_unit": "calls/second"
},
"org.apache.hugegraph.api.auth.AccessAPI.get": {
"count": 0,
"min": 0,
"mean": 0,
"max": 0,
"stddev": 0,
"p50": 0,
"p75": 0,
"p95": 0,
"p98": 0,
"p99": 0,
"p999": 0,
"duration_unit": "milliseconds",
"mean_rate": 0,
"m15_rate": 0,
"m5_rate": 0,
"m1_rate": 0,
"rate_unit": "calls/second"
},
"org.apache.hugegraph.api.auth.AccessAPI.list": {
"count": 0,
"min": 0,
"mean": 0,
"max": 0,
"stddev": 0,
"p50": 0,
"p75": 0,
"p95": 0,
"p98": 0,
"p99": 0,
"p999": 0,
"duration_unit": "milliseconds",
"mean_rate": 0,
"m15_rate": 0,
"m5_rate": 0,
"m1_rate": 0,
"rate_unit": "calls/second"
},
...
}
}
1.1.2 Method & Url
http://localhost:8080/metrics/
Response Status
200
Response Body
# HELP hugegraph_info
# TYPE hugegraph_info untyped
hugegraph_info{version="0.69",
} 1.0
# HELP org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_capacity
# TYPE org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_capacity gauge
org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_capacity 1000000
# HELP org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_expire
# TYPE org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_expire gauge
org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_expire 600000
# HELP org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_hits
# TYPE org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_hits gauge
org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_hits 0
# HELP org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_miss
# TYPE org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_miss gauge
org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_miss 0
# HELP org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_size
# TYPE org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_size gauge
org_apache_hugegraph_backend_cache_Cache_edge_hugegraph_size 0
# HELP org_apache_hugegraph_backend_cache_Cache_instances
# TYPE org_apache_hugegraph_backend_cache_Cache_instances gauge
org_apache_hugegraph_backend_cache_Cache_instances 7
# HELP org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_capacity
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_capacity gauge
org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_capacity 10000
# HELP org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_expire
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_expire gauge
org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_expire 0
# HELP org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_hits
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_hits gauge
org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_hits 0
# HELP org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_miss
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_miss gauge
org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_miss 0
# HELP org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_size
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_size gauge
org_apache_hugegraph_backend_cache_Cache_schema_id_hugegraph_size 17
# HELP org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_capacity
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_capacity gauge
org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_capacity 10000
# HELP org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_expire
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_expire gauge
org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_expire 0
# HELP org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_hits
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_hits gauge
org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_hits 0
# HELP org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_miss
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_miss gauge
org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_miss 0
# HELP org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_size
# TYPE org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_size gauge
org_apache_hugegraph_backend_cache_Cache_schema_name_hugegraph_size 17
...
1.2 获取 Gauges 指标
Method & Url
http://localhost:8080/metrics/gauges
Response Status
200
Response Body
{
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.capacity": {
"value": 1000000
},
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.edge-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.instances": {
"value": 7
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.capacity": {
"value": 10000
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.expire": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-id-hugegraph.size": {
"value": 17
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.capacity": {
"value": 10000
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.expire": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.schema-name-hugegraph.size": {
"value": 17
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.capacity": {
"value": 10240
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.token-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.capacity": {
"value": 10240
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.capacity": {
"value": 10240
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.users_pwd-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.capacity": {
"value": 10000000
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.expire": {
"value": 600000
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.hits": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.miss": {
"value": 0
},
"org.apache.hugegraph.backend.cache.Cache.vertex-hugegraph.size": {
"value": 0
},
"org.apache.hugegraph.server.RestServer.max-write-threads": {
"value": 0
},
"org.apache.hugegraph.task.TaskManager.pending-tasks": {
"value": 0
},
"org.apache.hugegraph.task.TaskManager.workers": {
"value": 4
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.average-load-penalty": {
"value": 9.227692E8
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.estimated-size": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.eviction-count": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.eviction-weight": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.hit-count": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.hit-rate": {
"value": 0.0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.load-count": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.load-failure-count": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.load-failure-rate": {
"value": 0.0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.load-success-count": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.long-run-compilation-count": {
"value": 0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.miss-count": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.miss-rate": {
"value": 1.0
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.request-count": {
"value": 2
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.gremlin-groovy.sessionless.class-cache.total-load-time": {
"value": 1845538400
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.sessions": {
"value": 0
}
}
1.3 获取 Counters 指标
Method & Url
GET http://localhost:8080/metrics/counters
Response Status
200
Response Body
{
"favicon.ico/GET/FAILED_COUNTER": {
"count": 1
},
"favicon.ico/GET/TOTAL_COUNTER": {
"count": 1
},
"metrics//GET/SUCCESS_COUNTER": {
"count": 2
},
"metrics//GET/TOTAL_COUNTER": {
"count": 2
},
"metrics/POST/FAILED_COUNTER": {
"count": 1
},
"metrics/POST/TOTAL_COUNTER": {
"count": 1
},
"metrics/backend/GET/SUCCESS_COUNTER": {
"count": 2
},
"metrics/backend/GET/TOTAL_COUNTER": {
"count": 2
},
"metrics/gauges/GET/SUCCESS_COUNTER": {
"count": 1
},
"metrics/gauges/GET/TOTAL_COUNTER": {
"count": 1
},
"metrics/statistics/GET/SUCCESS_COUNTER": {
"count": 2
},
"metrics/statistics/GET/TOTAL_COUNTER": {
"count": 2
},
"metrics/system/GET/SUCCESS_COUNTER": {
"count": 2
},
"metrics/system/GET/TOTAL_COUNTER": {
"count": 2
},
"metrics/timers/GET/SUCCESS_COUNTER": {
"count": 1
},
"metrics/timers/GET/TOTAL_COUNTER": {
"count": 1
},
"system/GET/FAILED_COUNTER": {
"count": 1
},
"system/GET/TOTAL_COUNTER": {
"count": 1
}
}
1.4 获取 histograms 指标
Method & Url
GET http://localhost:8080/metrics/gauges
Response Status
200
Response Body
{
"favicon.ico/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 1,
"min": 1,
"mean": 1.0,
"max": 1,
"stddev": 0.0,
"p50": 1.0,
"p75": 1.0,
"p95": 1.0,
"p98": 1.0,
"p99": 1.0,
"p999": 1.0
},
"metrics//GET/RESPONSE_TIME_HISTOGRAM": {
"count": 2,
"min": 10,
"mean": 10.0,
"max": 10,
"stddev": 0.0,
"p50": 10.0,
"p75": 10.0,
"p95": 10.0,
"p98": 10.0,
"p99": 10.0,
"p999": 10.0
},
"metrics/POST/RESPONSE_TIME_HISTOGRAM": {
"count": 1,
"min": 21,
"mean": 21.0,
"max": 21,
"stddev": 0.0,
"p50": 21.0,
"p75": 21.0,
"p95": 21.0,
"p98": 21.0,
"p99": 21.0,
"p999": 21.0
},
"metrics/backend/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 2,
"min": 6,
"mean": 12.6852124529148,
"max": 20,
"stddev": 6.992918475157571,
"p50": 6.0,
"p75": 20.0,
"p95": 20.0,
"p98": 20.0,
"p99": 20.0,
"p999": 20.0
},
"metrics/gauges/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 1,
"min": 7,
"mean": 7.0,
"max": 7,
"stddev": 0.0,
"p50": 7.0,
"p75": 7.0,
"p95": 7.0,
"p98": 7.0,
"p99": 7.0,
"p999": 7.0
},
"metrics/statistics/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 2,
"min": 1,
"mean": 1.4551211076264199,
"max": 2,
"stddev": 0.49798181193626,
"p50": 1.0,
"p75": 2.0,
"p95": 2.0,
"p98": 2.0,
"p99": 2.0,
"p999": 2.0
},
"metrics/system/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 2,
"min": 0,
"mean": 8.942674506664073,
"max": 40,
"stddev": 16.665399873223066,
"p50": 0.0,
"p75": 0.0,
"p95": 40.0,
"p98": 40.0,
"p99": 40.0,
"p999": 40.0
},
"metrics/timers/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 1,
"min": 3,
"mean": 3.0,
"max": 3,
"stddev": 0.0,
"p50": 3.0,
"p75": 3.0,
"p95": 3.0,
"p98": 3.0,
"p99": 3.0,
"p999": 3.0
},
"system/GET/RESPONSE_TIME_HISTOGRAM": {
"count": 1,
"min": 2,
"mean": 2.0,
"max": 2,
"stddev": 0.0,
"p50": 2.0,
"p75": 2.0,
"p95": 2.0,
"p98": 2.0,
"p99": 2.0,
"p999": 2.0
}
}
1.5 获取 meters 指标
Method & Url
GET http://localhost:8080/metrics/meters
Response Status
200
Response Body
{
"org.apache.hugegraph.api.API.commit-succeed": {
"count": 0,
"mean_rate": 0.0,
"m15_rate": 0.0,
"m5_rate": 0.0,
"m1_rate": 0.0,
"rate_unit": "events/second"
},
"org.apache.hugegraph.api.API.expected-error": {
"count": 0,
"mean_rate": 0.0,
"m15_rate": 0.0,
"m5_rate": 0.0,
"m1_rate": 0.0,
"rate_unit": "events/second"
},
"org.apache.hugegraph.api.API.illegal-arg": {
"count": 0,
"mean_rate": 0.0,
"m15_rate": 0.0,
"m5_rate": 0.0,
"m1_rate": 0.0,
"rate_unit": "events/second"
},
"org.apache.hugegraph.api.API.unknown-error": {
"count": 0,
"mean_rate": 0.0,
"m15_rate": 0.0,
"m5_rate": 0.0,
"m1_rate": 0.0,
"rate_unit": "events/second"
},
"org.apache.tinkerpop.gremlin.server.GremlinServer.errors": {
"count": 0,
"mean_rate": 0.0,
"m15_rate": 0.0,
"m5_rate": 0.0,
"m1_rate": 0.0,
"rate_unit": "events/second"
}
}
1.6 获取 timers 指标
Method & Url
GET http://localhost:8080/metrics/timers
Response Status
200
Response Body
{
"org.apache.hugegraph.api.auth.AccessAPI.create": {
"count": 0,
"min": 0.0,
"mean": 0.0,
"max": 0.0,
"stddev": 0.0,
"p50": 0.0,
"p75": 0.0,
"p95": 0.0,
"p98": 0.0,
"p99": 0.0,
"p999": 0.0,
"duration_unit": "milliseconds",
"mean_rate": 0.0,
"m15_rate": 0.0,
"m5_rate": 0.0,
"m1_rate": 0.0,
"rate_unit": "calls/second"
},
"org.apache.hugegraph.api.auth.AccessAPI.delete": {
"count": 0,
"min": 0.0,
"mean": 0.0,
"max": 0.0,
"stddev": 0.0,
"p50": 0.0,
"p75": 0.0,
"p95": 0.0,
"p98": 0.0,
"p99": 0.0,
"p999": 0.0,
"duration_unit": "milliseconds",
"mean_rate": 0.0,
"m15_rate": 0.0,
"m5_rate": 0.0,
"m1_rate": 0.0,
"rate_unit": "calls/second"
},
...
}
2.统计指标
Params
- type：如果传值为 json，则以 json 格式返回，否则以 Promethaus 格式返回。
2.1 Method & Url
GET http://localhost:8080/metrics/statistics
Response Status
# HELP hugegraph_info
# TYPE hugegraph_info untyped
hugegraph_info{version="0.69",
} 1.0
# HELP metrics_POST
# TYPE metrics_POST gauge
metrics_POST{name=FAILED_REQUEST,} 1
metrics_POST{name=MEAN_RESPONSE_TIME,} 21.0
metrics_POST{
name=MAX_RESPONSE_TIME,
} 21
metrics_POST{name=SUCCESS_REQUEST,
} 0
metrics_POST{
name=TOTAL_REQUEST,
} 1
# HELP metrics_backend_GET
# TYPE metrics_backend_GET gauge
metrics_backend_GET{name=FAILED_REQUEST,
} 0
metrics_backend_GET{
name=MEAN_RESPONSE_TIME,
} 12.6852124529148
metrics_backend_GET{
name=MAX_RESPONSE_TIME,
} 20
metrics_backend_GET{
name=SUCCESS_REQUEST,
} 2
metrics_backend_GET{name=TOTAL_REQUEST,} 2
# HELP system_GET
# TYPE system_GET gauge
system_GET{name=FAILED_REQUEST,} 1
system_GET{name=MEAN_RESPONSE_TIME,} 2.0
system_GET{name=MAX_RESPONSE_TIME,} 2
system_GET{
name=SUCCESS_REQUEST,
} 0
system_GET{name=TOTAL_REQUEST,
} 1
# HELP metrics_gauges_GET
# TYPE metrics_gauges_GET gauge
metrics_gauges_GET{name=FAILED_REQUEST,} 0
metrics_gauges_GET{name=MEAN_RESPONSE_TIME,
} 7.0
metrics_gauges_GET{
name=MAX_RESPONSE_TIME,
} 7
metrics_gauges_GET{
name=SUCCESS_REQUEST,
} 1
metrics_gauges_GET{
name=TOTAL_REQUEST,
} 1
# HELP favicon.ico_GET
# TYPE favicon.ico_GET gauge
favicon.ico_GET{name=FAILED_REQUEST,
} 1
favicon.ico_GET{
name=MEAN_RESPONSE_TIME,
} 1.0
favicon.ico_GET{name=MAX_RESPONSE_TIME,} 1
favicon.ico_GET{name=SUCCESS_REQUEST,} 0
favicon.ico_GET{
name=TOTAL_REQUEST,
} 1
# HELP metrics__GET
# TYPE metrics__GET gauge
metrics__GET{name=FAILED_REQUEST,} 0
metrics__GET{name=MEAN_RESPONSE_TIME,} 10.0
metrics__GET{name=MAX_RESPONSE_TIME,
} 10
metrics__GET{
name=SUCCESS_REQUEST,
} 2
metrics__GET{
name=TOTAL_REQUEST,
} 2
# HELP metrics_system_GET
# TYPE metrics_system_GET gauge
metrics_system_GET{name=FAILED_REQUEST,} 0
metrics_system_GET{name=MEAN_RESPONSE_TIME,
} 8.942674506664073
metrics_system_GET{
name=MAX_RESPONSE_TIME,
} 40
metrics_system_GET{name=SUCCESS_REQUEST,} 2
metrics_system_GET{name=TOTAL_REQUEST,
} 2
Response Body
200
2.2 Method & Url
GET http://localhost:8080/metrics/statistics?type=json
Response Status
200
Response Body
{
"metrics/POST": {
"FAILED_REQUEST": 1,
"MEAN_RESPONSE_TIME": 21,
"MAX_RESPONSE_TIME": 21,
"SUCCESS_REQUEST": 0,
"TOTAL_REQUEST": 1
},
"metrics/backend/GET": {
"FAILED_REQUEST": 0,
"MEAN_RESPONSE_TIME": 12.6852124529148,
"MAX_RESPONSE_TIME": 20,
"SUCCESS_REQUEST": 2,
"TOTAL_REQUEST": 2
},
"system/GET": {
"FAILED_REQUEST": 1,
"MEAN_RESPONSE_TIME": 2,
"MAX_RESPONSE_TIME": 2,
"SUCCESS_REQUEST": 0,
"TOTAL_REQUEST": 1
},
"metrics/gauges/GET": {
"FAILED_REQUEST": 0,
"MEAN_RESPONSE_TIME": 7,
"MAX_RESPONSE_TIME": 7,
"SUCCESS_REQUEST": 1,
"TOTAL_REQUEST": 1
},
"favicon.ico/GET": {
"FAILED_REQUEST": 1,
"MEAN_RESPONSE_TIME": 1,
"MAX_RESPONSE_TIME": 1,
"SUCCESS_REQUEST": 0,
"TOTAL_REQUEST": 1
},
"metrics//GET": {
"FAILED_REQUEST": 0,
"MEAN_RESPONSE_TIME": 10,
"MAX_RESPONSE_TIME": 10,
"SUCCESS_REQUEST": 2,
"TOTAL_REQUEST": 2
},
"metrics/system/GET": {
"FAILED_REQUEST": 0,
"MEAN_RESPONSE_TIME": 8.942674506664073,
"MAX_RESPONSE_TIME": 40,
"SUCCESS_REQUEST": 2,
"TOTAL_REQUEST": 2
}
}
3.系统指标
系统指标主要返回机器运行指标，如内存、线程等信息。
Method & Url
GET http://localhost:8080/metrics/system
Response Status
200
Response Body
{
"basic": {
"mem": 1010,
"mem_total": 911,
"mem_used": 239,
"mem_free": 671,
"mem_unit": "MB",
"processors": 20,
"uptime": 137503,
"systemload_average": -1.0
},
"heap": {
"committed": 911,
"init": 254,
"used": 239,
"max": 3596
},
"nonheap": {
"committed": 98,
"init": 2,
"used": 95,
"max": 0
},
"thread": {
"peak": 82,
"daemon": 34,
"total_started": 108,
"count": 82
},
"class_loading": {
"count": 11495,
"loaded": 11495,
"unloaded": 0
},
"garbage_collector": {
"ps_scavenge_count": 16,
"ps_scavenge_time": 155,
"ps_marksweep_count": 3,
"ps_marksweep_time": 494,
"time_unit": "ms"
}
}
4.后端指标
hugeGraph 支持多种后端存储，后端指标包括内存、磁盘等信息。
Method & Url
GET http://localhost:8080/metrics/backend
Response Status
200
Response Body
{
"hugegraph": {
"backend": "rocksdb",
"nodes": 1,
"cluster_id": "local",
"servers": {
"local": {
"mem_unit": "MB",
"disk_unit": "GB",
"mem_used": 0.1,
"mem_used_readable": "103.53 KB",
"disk_usage": 0.03,
"disk_usage_readable": "29.03 KB",
"block_cache_usage": 0.00359344482421875,
"block_cache_pinned_usage": 0.00359344482421875,
"block_cache_capacity": 304.0,
"estimate_table_readers_mem": 0.019697189331054688,
"size_all_mem_tables": 0.07421875,
"cur_size_all_mem_tables": 0.07421875,
"estimate_live_data_size": 5.536526441574097E-5,
"total_sst_files_size": 5.536526441574097E-5,
"live_sst_files_size": 5.536526441574097E-5,
"estimate_pending_compaction_bytes": 0.0,
"estimate_num_keys": 0,
"num_entries_active_mem_table": 0,
"num_entries_imm_mem_tables": 0,
"num_deletes_active_mem_table": 0,
"num_deletes_imm_mem_tables": 0,
"num_running_flushes": 0,
"mem_table_flush_pending": 0,
"num_running_compactions": 0,
"compaction_pending": 0,
"num_immutable_mem_table": 0,
"num_snapshots": 0,
"oldest_snapshot_time": 0,
"num_live_versions": 38,
"current_super_version_number": 38
}
}
}
}
18 - Other API
11.1 Other
11.1.1 查看HugeGraph的版本信息
Method & Url
GET http://localhost:8080/versions
Response Status
200
Response Body
{
"versions": {
"version": "v1",
"core": "0.4.5.1",
"gremlin": "3.2.5",
"api": "0.13.2.0"
}
}