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HugeGraph-Server provides interfaces for clients to operate on graphs based on the HTTP protocol through the HugeGraph-API. These interfaces primarily include the ability to add, delete, modify, and query metadata and graph data, perform traversal algorithms, handle variables, and perform other graph-related operations.
Expect the doc below, you can also use swagger-ui to visit the RESTful API by localhost:8080/swagger-ui/index.html. Here is an example
HugeGraph provides a single interface to get all Schema information of a graph, including: PropertyKey, VertexLabel, EdgeLabel and IndexLabel.
GET http://localhost:8080/graphs/{graph_name}/schema
e.g: GET http://localhost:8080/graphs/hugegraph/schema
200
{
"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"
}
}
]
}
Params Description:
Request Body Field Description:
POST http://localhost:8080/graphs/hugegraph/schema/propertykeys
{
"name": "age",
"data_type": "INT",
"cardinality": "SINGLE"
}
202
{
"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
}
append (add) and eliminate (remove).PUT http://localhost:8080/graphs/hugegraph/schema/propertykeys/age?action=append
{
"name": "age",
"user_data": {
"min": 0,
"max": 100
}
}
202
{
"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
}
GET http://localhost:8080/graphs/hugegraph/schema/propertykeys
200
{
"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": {}
}
]
}
GET http://localhost:8080/graphs/hugegraph/schema/propertykeys/age
Where age is the name of the PropertyKey to be retrieved.
200
{
"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"
}
}
DELETE http://localhost:8080/graphs/hugegraph/schema/propertykeys/age
Where age is the name of the PropertyKey to be deleted.
202
{
"task_id" : 0
}
Assuming that the PropertyKeys listed in 1.1.3 have already been created.
Params Description:
POST http://localhost:8080/graphs/hugegraph/schema/vertexlabels
{
"name": "person",
"id_strategy": "DEFAULT",
"properties": [
"name",
"age"
],
"primary_keys": [
"name"
],
"nullable_keys": [],
"enable_label_index": true
}
201
{
"id": 1,
"primary_keys": [
"name"
],
"id_strategy": "PRIMARY_KEY",
"name": "person2",
"index_names": [
],
"properties": [
"name",
"age"
],
"nullable_keys": [
],
"enable_label_index": true,
"user_data": {}
}
Starting from version v0.11.2, hugegraph-server supports Time-to-Live (TTL) functionality for vertices. The TTL for vertices is set through VertexLabel. For example, if you want the vertices of type “person” to have a lifespan of one day, you need to set the TTL field to 86400000 (in milliseconds) when creating the “person” VertexLabel.
{
"name": "person",
"id_strategy": "DEFAULT",
"properties": [
"name",
"age"
],
"primary_keys": [
"name"
],
"nullable_keys": [],
"ttl": 86400000,
"enable_label_index": true
}
Additionally, if the vertex has a property called “createdTime” and you want to use it as the starting point for calculating the vertex’s lifespan, you can set the ttl_start_time field in the VertexLabel. For example, if the “person” VertexLabel has a property called “createdTime” of type Date, and you want the vertices of type “person” to live for one day starting from the creation time, the Request Body for creating the “person” VertexLabel would be as follows:
{
"name": "person",
"id_strategy": "DEFAULT",
"properties": [
"name",
"age",
"createdTime"
],
"primary_keys": [
"name"
],
"nullable_keys": [],
"ttl": 86400000,
"ttl_start_time": "createdTime",
"enable_label_index": true
}
append (add) and eliminate (remove).PUT http://localhost:8080/graphs/hugegraph/schema/vertexlabels/person?action=append
{
"name": "person",
"properties": [
"city"
],
"nullable_keys": ["city"],
"user_data": {
"super": "animal"
}
}
200
{
"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"
}
}
GET http://localhost:8080/graphs/hugegraph/schema/vertexlabels
200
{
"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": {}
}
]
}
GET http://localhost:8080/graphs/hugegraph/schema/vertexlabels/person
200
{
"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"
}
}
Deleting a VertexLabel will result in the removal of corresponding vertices and related index data. This operation will generate an asynchronous task.
DELETE http://localhost:8080/graphs/hugegraph/schema/vertexlabels/person
202
{
"task_id": 1
}
Note:
You can use
GET http://localhost:8080/graphs/hugegraph/tasks/1(where “1” is the task_id) to query the execution status of the asynchronous task. For more information, refer to the Asynchronous Task RESTful API.
Assuming PropertyKeys from version 1.2.3 and VertexLabels from version 1.3.3 have already been created.
Params Explanation
POST http://localhost:8080/graphs/hugegraph/schema/edgelabels
{
"name": "created",
"source_label": "person",
"target_label": "software",
"frequency": "SINGLE",
"properties": [
"date"
],
"sort_keys": [],
"nullable_keys": [],
"enable_label_index": true
}
201
{
"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": {}
}
Starting from version 0.11.2 of hugegraph-server, the TTL (Time to Live) feature for edges is supported. The TTL for edges is set through EdgeLabel. For example, if you want the “knows” type of edge to have a lifespan of one day, you need to set the TTL field to 86400000 when creating the “knows” EdgeLabel, where the unit is milliseconds.
{
"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": {}
}
Additionally, when the edge has a property called “createdTime” and you want to use the “createdTime” property as the starting point for calculating the edge’s lifespan, you can set the ttl_start_time field in the EdgeLabel. For example, if the knows EdgeLabel has a property called “createdTime” which is of type Date, and you want the “knows” type of edge to live for one day from the time of creation, the Request Body for creating the knows EdgeLabel would be as follows:
{
"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": {}
}
append (add) and eliminate (remove).PUT http://localhost:8080/graphs/hugegraph/schema/edgelabels/created?action=append
{
"name": "created",
"properties": [
"weight"
],
"nullable_keys": [
"weight"
]
}
200
{
"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": {}
}
GET http://localhost:8080/graphs/hugegraph/schema/edgelabels
200
{
"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": {}
}
]
}
GET http://localhost:8080/graphs/hugegraph/schema/edgelabels/created
200
{
"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": {}
}
Deleting an EdgeLabel will result in the deletion of corresponding edges and related index data. This operation will generate an asynchronous task.
DELETE http://localhost:8080/graphs/hugegraph/schema/edgelabels/created
202
{
"task_id": 1
}
Note:
You can query the execution status of an asynchronous task by using
GET http://localhost:8080/graphs/hugegraph/tasks/1(where “1” is the task_id). For more information, refer to the Asynchronous Task RESTful API.
Assuming PropertyKeys from version 1.1.3, VertexLabels from version 1.2.3, and EdgeLabels from version 1.3.3 have already been created.
POST http://localhost:8080/graphs/hugegraph/schema/indexlabels
{
"name": "personByCity",
"base_type": "VERTEX_LABEL",
"base_value": "person",
"index_type": "SECONDARY",
"fields": [
"city"
]
}
202
{
"index_label": {
"id": 1,
"base_type": "VERTEX_LABEL",
"base_value": "person",
"name": "personByCity",
"fields": [
"city"
],
"index_type": "SECONDARY"
},
"task_id": 2
}
GET http://localhost:8080/graphs/hugegraph/schema/indexlabels
200
{
"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"
}
]
}
GET http://localhost:8080/graphs/hugegraph/schema/indexlabels/personByCity
200
{
"id": 1,
"base_type": "VERTEX_LABEL",
"base_value": "person",
"name": "personByCity",
"fields": [
"city"
],
"index_type": "SECONDARY"
}
Deleting an IndexLabel will result in the deletion of related index data. This operation will generate an asynchronous task.
DELETE http://localhost:8080/graphs/hugegraph/schema/indexlabels/personByCity
202
{
"task_id": 1
}
Note:
You can query the execution status of an asynchronous task by using
GET http://localhost:8080/graphs/hugegraph/tasks/1(where “1” is the task_id). For more information, refer to the Asynchronous Task RESTful API.
PUT http://localhost:8080/graphs/hugegraph/jobs/rebuild/indexlabels/personByCity
202
{
"task_id": 1
}
Note:
You can get the asynchronous job status by
GET http://localhost:8080/graphs/hugegraph/tasks/${task_id}(the task_id here should be 1). See More AsyncJob RESTfull API
PUT http://localhost:8080/graphs/hugegraph/jobs/rebuild/vertexlabels/person
202
{
"task_id": 2
}
Note:
You can get the asynchronous job status by
GET http://localhost:8080/graphs/hugegraph/tasks/${task_id}(the task_id here should be 2). See More AsyncJob RESTfull API
PUT http://localhost:8080/graphs/hugegraph/jobs/rebuild/edgelabels/created
202
{
"task_id": 3
}
Note:
You can get the asynchronous job status by
GET http://localhost:8080/graphs/hugegraph/tasks/${task_id}(the task_id here should be 3). See More AsyncJob RESTfull API
In vertex types, the Id strategy determines the type of the vertex Id, with the corresponding relationships as follows:
| Id_Strategy | id type |
|---|---|
| AUTOMATIC | number |
| PRIMARY_KEY | string |
| CUSTOMIZE_STRING | string |
| CUSTOMIZE_NUMBER | number |
| CUSTOMIZE_UUID | uuid |
For the GET/PUT/DELETE API of a vertex, the id part in the URL should be passed as the id value with type information. This type information is indicated by whether the JSON string is enclosed in quotes, meaning:
number, the id in the URL is without quotes, for example: xxx/vertices/123456.string, the id in the URL is enclosed in quotes, for example: xxx/vertices/"123456".The next example requires first creating the graph schema from the following groovy script
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();
POST http://localhost:8080/graphs/hugegraph/graph/vertices
{
"label": "person",
"properties": {
"name": "marko",
"age": 29
}
}
201
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 29
}
}
POST http://localhost:8080/graphs/hugegraph/graph/vertices/batch
[
{
"label": "person",
"properties": {
"name": "marko",
"age": 29
}
},
{
"label": "software",
"properties": {
"name": "ripple",
"lang": "java",
"price": 199
}
}
]
201
[
"1:marko",
"2:ripple"
]
PUT http://127.0.0.1:8080/graphs/hugegraph/graph/vertices/"1:marko"?action=append
{
"label": "person",
"properties": {
"age": 30,
"city": "Beijing"
}
}
Note: There are three categories for property values: single, set, and list. If it is single, it means adding or updating the property value. If it is set or list, it means appending the property value.
200
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 30,
"city": "Beijing"
}
}
Batch update properties of vertices and support various update strategies, including:
Assuming the original vertex and properties are:
{
"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"
]
}
}
]
}
Add vertices with the following command:
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
PUT http://127.0.0.1:8080/graphs/hugegraph/graph/vertices/batch
{
"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
}
200
{
"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"
]
}
}
]
}
Result Analysis:
The usage of other update strategies can be inferred in a similar manner and will not be further elaborated.
PUT http://127.0.0.1:8080/graphs/hugegraph/graph/vertices/"1:marko"?action=eliminate
{
"label": "person",
"properties": {
"city": "Beijing"
}
}
Note: Here, the properties (keys and all values) will be directly deleted, regardless of whether the property values are single, set, or list.
200
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 30
}
}
All of the above parameters are optional. If the page parameter is provided, the limit parameter must also be provided, and no other parameters are allowed. label, properties, and limit can be combined in any way.
Property key-value pairs consist of the property name and value in JSON format. Multiple property key-value pairs are allowed as query conditions. The property value supports exact matching, range matching, and fuzzy matching. For exact matching, use the format properties={"age":29}, for range matching, use the format properties={"age":"P.gt(29)"}, and for fuzzy matching, use the format properties={"city": "P.textcontains("ChengDu China")}. The following expressions are supported for range matching:
| Expression | Explanation |
|---|---|
| P.eq(number) | Vertices with property value equal to number |
| P.neq(number) | Vertices with property value not equal to number |
| P.lt(number) | Vertices with property value less than number |
| P.lte(number) | Vertices with property value less than or equal to number |
| P.gt(number) | Vertices with property value greater than number |
| P.gte(number) | Vertices with property value greater than or equal to number |
| P.between(number1,number2) | Vertices with property value greater than or equal to number1 and less than number2 |
| P.inside(number1,number2) | Vertices with property value greater than number1 and less than number2 |
| P.outside(number1,number2) | Vertices with property value less than number1 and greater than number2 |
| P.within(value1,value2,value3,…) | Vertices with property value equal to any of the given values |
Query all vertices with age 29 and label person
GET http://localhost:8080/graphs/hugegraph/graph/vertices?label=person&properties={"age":29}&limit=1
200
{
"vertices": [
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 30
}
}
]
}
Paginate through all vertices, retrieve the first page (page without parameter value), limited to 3 records
Add vertices with the following command:
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
GET http://localhost:8080/graphs/hugegraph/graph/vertices?page&limit=3
200
{
"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="
}
The returned body contains information about the page number of the next page, "page": "CIYxOnBldGVyAAAAAAAAAAM". When querying the next page, assign this value to the page parameter.
Paginate and retrieve all vertices, including the next page (passing the page value returned from the previous page), limited to 3 items.
GET http://localhost:8080/graphs/hugegraph/graph/vertices?page=CIYxOnBldGVyAAAAAAAAAAM=&limit=3
200
{
"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
}
At this point, "page": null indicates that there are no more pages available. (Note: When using Cassandra as the backend for performance reasons, if the returned page happens to be the last page, the page value may not be empty. When requesting the next page using that page value, it will return empty data and page = null. The same applies to other similar situations.)
GET http://localhost:8080/graphs/hugegraph/graph/vertices/"1:marko"
200
{
"id": "1:marko",
"label": "person",
"type": "vertex",
"properties": {
"name": "marko",
"age": 30
}
}
Delete the vertex based on ID only.
DELETE http://localhost:8080/graphs/hugegraph/graph/vertices/"1:marko"
204
Delete Vertex by Label+ID
When deleting a vertex by specifying both the Label parameter and the ID, it generally offers better performance compared to deleting by ID alone.
DELETE http://localhost:8080/graphs/hugegraph/graph/vertices/"1:marko"?label=person
204
The modification of the vertex ID format also affects the ID of the edge, as well as the formats of the source vertex and target vertex IDs.
The EdgeId is formed by concatenating src-vertex-id + direction + label + sort-values + tgt-vertex-id, but the vertex ID types are not distinguished by quotation marks here. Instead, they are distinguished by prefixes:
L, like “L123456>1»L987654”.S, like “S1:peter>1»S2:lop”.The following example requires creating a graph schema based on the following groovy script:
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()
Path Parameter Description:
Request Body Description:
POST http://localhost:8080/graphs/hugegraph/graph/edges
{
"label": "created",
"outV": "1:marko",
"inV": "2:lop",
"outVLabel": "person",
"inVLabel": "software",
"properties": {
"date": "20171210",
"weight": 0.4
}
}
201
{
"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"
}
}
Path Parameter Description:
Request Parameter Description:
Request Body Description:
POST http://localhost:8080/graphs/hugegraph/graph/edges/batch
[
{
"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
}
}
]
201
[
"S1:marko>1>>S1:vadas",
"S1:marko>1>>S1:josh"
]
Path Parameter Description:
Request Parameter Description:
Request Body Description:
PUT http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>2>>S2:lop?action=append
{
"properties": {
"weight": 1.0
}
}
NOTE: There are three categories of property values: single, set, and list. If it is single, it means adding or updating the property value. If it is set or list, it means appending the property value.
200
{
"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"
}
}
Path Parameter Description:
Request Body Description:
PUT http://127.0.0.1:8080/graphs/hugegraph/graph/edges/batch
{
"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
}
200
{
"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"
}
}
]
}
Path Parameter Description:
Request Parameter Description:
Request Body Description:
PUT http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>2>>S2:lop?action=eliminate
{
"properties": {
"weight": 1.0
}
}
NOTE: This will directly delete the properties (removing the key and all values), regardless of whether the property values are single, set, or list.
400
It is not possible to delete an attribute that is not set as nullable.
{
"exception": "class java.lang.IllegalArgumentException",
"message": "Can't remove non-null edge property 'p[weight->1.0]'",
"cause": ""
}
Path Parameter:
Request Parameters:
properties={"age":"P.gt(0.8)"} will be interpreted as an exact match, i.e., the age property is equal to “P.gt(0.8)”Key-value pairs of properties consist of the property name and value in JSON format. Multiple key-value pairs are allowed as query conditions. Property values support exact matching and range matching. For exact matching, it is in the form properties={"weight":0.8}. For range matching, it is in the form properties={"age":"P.gt(0.8)"}. The expressions supported by range matching are as follows:
| Expression | Description |
|---|---|
| P.eq(number) | Edges with property value equal to number |
| P.neq(number) | Edges with property value not equal to number |
| P.lt(number) | Edges with property value less than number |
| P.lte(number) | Edges with property value less than or equal to number |
| P.gt(number) | Edges with property value greater than number |
| P.gte(number) | Edges with property value greater than or equal to number |
| P.between(number1,number2) | Edges with property value greater than or equal to number1 and less than number2 |
| P.inside(number1,number2) | Edges with property value greater than number1 and less than number2 |
| P.outside(number1,number2) | Edges with property value less than number1 and greater than number2 |
| P.within(value1,value2,value3,…) | Edges with property value equal to any of the given values |
| P.textcontains(value) | Edges with property value containing the given value (string type) |
| P.contains(value) | Edges with property value containing the given value (collection type) |
Edges connected to the vertex person:marko(vertex_id=“1:marko”) with label knows and date property equal to “20160111”
GET http://127.0.0.1:8080/graphs/hugegraph/graph/edges?vertex_id="1:marko"&label=knows&properties={"date":"P.within(\"20160111\")"}
200
{
"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"
}
}
]
}
Paginate and retrieve all edges, get the first page (page without parameter value), limit to 2 entries
GET http://127.0.0.1:8080/graphs/hugegraph/graph/edges?page&limit=2
200
{
"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"
}
The returned body contains the page number information for the next page, "page": "EoYxOm1hcmtvgggCAIQyOmxvcAAAAAAAAAAC". When querying the next page, assign this value to the page parameter.
Paginate and retrieve all edges, get the next page (include the page value returned from the previous page), limit to 2 entries
GET http://127.0.0.1:8080/graphs/hugegraph/graph/edges?page=EoYxOm1hcmtvgggCAIQyOmxvcAAAAAAAAAAC&limit=2
200
{
"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
}
When "page": null is returned, it indicates that there are no more pages available.
NOTE: When the backend is Cassandra, for performance considerations, if the returned page happens to be the last page, the
pagevalue may not be empty. When requesting the next page data using thatpagevalue, it will returnempty dataandpage = null. Similar situations apply for other cases.
Path parameter description:
GET http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>2>>S2:lop
200
{
"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"
}
}
Path parameter description:
Request parameter description:
Deleting Edge by ID only
DELETE http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>2>>S2:lop
204
Deleting Edge by Label + ID
In general, specifying the Label parameter along with the ID to delete an edge will provide better performance compared to deleting by ID only.
DELETE http://localhost:8080/graphs/hugegraph/graph/edges/S1:marko>1>>S1:vadas?label=knows
204
HugeGraphServer provides a RESTful API interface for the HugeGraph graph database. In addition to the basic CRUD operations for vertices and edges, it also offers several traversal methods, which we refer to as the traverser API. These traversal methods implement various complex graph algorithms, making it convenient for users to analyze and explore the graph.
The Traverser API supported by HugeGraph includes:
In the following, we provide a detailed explanation of the Traverser API:
The usage examples provided in this section are based on the graph presented on the TinkerPop official website:
The data import program is as follows:
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);
}
}
The vertex IDs are:
"2:ripple",
"1:vadas",
"1:peter",
"1:josh",
"1:marko",
"2:lop"
The edge IDs are:
"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"
The K-out API allows you to find vertices that are exactly “depth” steps away from a given starting vertex, considering the specified direction, edge type (optional), and depth.
GET http://localhost:8080/graphs/{graph}/traversers/kout?source="1:marko"&max_depth=2
200
{
"vertices":[
"2:ripple",
"1:peter"
]
}
Finding vertices that are exactly N steps away in a relationship. Two examples:
The K-out API allows you to find vertices that are exactly “depth” steps away from a given starting vertex, considering the specified steps (including direction, edge type, and attribute filtering).
The advanced version differs from the basic version of K-out API in the following aspects:
- Supports counting the number of neighbors only
- Supports edge attribute filtering
- Supports returning the shortest path to the neighbor
skip_degree >= max_degree. Default is 0 (not enabled), indicating no skipping of any vertices (Note: Enabling this configuration means that during traversal, an attempt will be made to access skip_degree edges of a vertex, not just max_degree edges. This incurs additional traversal overhead and may have a significant impact on query performance. Please enable it only after understanding the implications).POST http://localhost:8080/graphs/{graph}/traversers/kout
{
"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
}
200
{
"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"
}
}
]
}
Refer to 3.2.1.3.
Find all vertices that are reachable within depth steps, including the starting vertex, based on the starting vertex, direction, edge type (optional), and depth.
Equivalent to the union of: starting vertex, K-out(1), K-out(2), …, K-out(max_depth).
GET http://localhost:8080/graphs/{graph}/traversers/kneighbor?source=“1:marko”&max_depth=2
200
{
"vertices":[
"2:ripple",
"1:marko",
"1:josh",
"1:vadas",
"1:peter",
"2:lop"
]
}
Find all vertices reachable within N steps, for example:
Find all vertices that are reachable within depth steps from the starting vertex, based on the starting vertex, steps (including direction, edge type, and filter properties), and depth.
The difference from the Basic Version of K-neighbor API is that:
- It supports counting the number of neighbors only.
- It supports filtering edges based on their properties.
- It supports returning the shortest path to reach the neighbors.
skip_degree >= max_degree. Default is 0 (not enabled), which means no vertices are skipped. (Note: When this configuration is enabled, the traversal will attempt to access skip_degree edges for each vertex, not just max_degree edges. This incurs additional traversal overhead and may significantly impact query performance. Please make sure to understand this before enabling.)POST http://localhost:8080/graphs/{graph}/traversers/kneighbor
{
"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
}
200
{
"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"
}
}
]
}
See 3.2.3.3
Retrieve the common neighbors of two vertices.
GET http://localhost:8080/graphs/{graph}/traversers/sameneighbors?vertex=“1:marko”&other="1:josh"
200
{
"same_neighbors":[
"2:lop"
]
}
Find the common neighbors of two vertices:
Compute the Jaccard similarity between two vertices (the intersection of the neighbors of the two vertices divided by the union of the neighbors of the two vertices).
GET http://localhost:8080/graphs/{graph}/traversers/jaccardsimilarity?vertex="1:marko"&other="1:josh"
200
{
"jaccard_similarity": 0.2
}
Used to evaluate the similarity or closeness between two vertices.
Compute the N vertices with the highest Jaccard similarity to a specified vertex.
The Jaccard similarity is calculated as the intersection of the neighbors of the two vertices divided by the union of the neighbors of the two vertices.
POST http://localhost:8080/graphs/{graph}/traversers/jaccardsimilarity
{
"vertex": "1:marko",
"step": {
"direction": "BOTH",
"labels": [],
"max_degree": 10000,
"skip_degree": 100000
},
"top": 3
}
200
{
"2:ripple": 0.3333333333333333,
"1:peter": 0.3333333333333333,
"1:josh": 0.2
}
Used to find the vertices in the graph that have the highest similarity to a specified vertex.
Find the shortest path between a starting vertex and a target vertex based on the direction, edge type (optional), and maximum depth.
GET http://localhost:8080/graphs/{graph}/traversers/shortestpath?source="1:marko"&target="2:ripple"&max_depth=3
200
{
"path":[
"1:marko",
"1:josh",
"2:ripple"
]
}
Used to find the shortest path between two vertices, for example:
Find all shortest paths between a starting vertex and a target vertex based on the direction, edge type (optional), and maximum depth.
GET http://localhost:8080/graphs/{graph}/traversers/allshortestpaths?source="A"&target="Z"&max_depth=10
200
{
"paths":[
{
"objects": [
"A",
"B",
"C",
"Z"
]
},
{
"objects": [
"A",
"M",
"N",
"Z"
]
}
]
}
Used to find all shortest paths between two vertices, for example:
Find a weighted shortest path between a starting vertex and a target vertex based on the direction, edge type (optional), maximum depth, and edge weight property.
GET http://localhost:8080/graphs/{graph}/traversers/weightedshortestpath?source="1:marko"&target="2:ripple"&weight="weight"&with_vertex=true
200
{
"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
}
}
]
}
Used to find the weighted shortest path between two vertices, for example:
Starting from a vertex, find the shortest paths from that vertex to other vertices in the graph (optional with weight).
GET http://localhost:8080/graphs/{graph}/traversers/singlesourceshortestpath?source="1:marko"&with_vertex=true
200
{
"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
}
}
]
}
Used to find the weighted shortest path from one vertex to other vertices, for example:
Finds the shortest paths between pairs of specified vertices.
Note: Property values in properties can be a list, indicating that the value of the key can be any value in the list.
POST http://localhost:8080/graphs/{graph}/traversers/multinodeshortestpath
{
"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
}
200
{
"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
}
}
]
}
Used to find the shortest paths between multiple vertices, for example:
Finds all paths based on conditions such as the starting vertex, destination vertex, direction, edge types (optional), and maximum depth.
GET http://localhost:8080/graphs/{graph}/traversers/paths?source="1:marko"&target="1:josh"&max_depth=5
200
{
"paths":[
{
"objects":[
"1:marko",
"1:josh"
]
},
{
"objects":[
"1:marko",
"2:lop",
"1:josh"
]
}
]
}
Used to find all paths between two vertices, for example:
Finds all paths based on conditions such as the starting vertex, destination vertex, steps (step), and maximum depth.
Note: The property values in properties can be a list, indicating that any value corresponding to the key is acceptable.
Note: The property values in properties can be a list, indicating that any value corresponding to the key is acceptable.
skip_degree >= max_degree. Default is 0 (not enabled), which means no points are skipped. (Note: When this configuration is enabled, the traversal will attempt to visit skip_degree edges of a vertex, not just max_degree edges. This incurs additional traversal overhead and may have a significant impact on query performance. Please make sure to understand before enabling it.)POST http://localhost:8080/graphs/{graph}/traversers/paths
{
"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
}
200
{
"paths": [
{
"objects": [
"1:marko",
"1:josh",
"2:lop",
"1:peter"
]
},
{
"objects": [
"1:marko",
"2:lop",
"1:peter"
]
}
]
}
Used to find all paths between two vertices, for example:
Finds all paths that meet the specified conditions based on a batch of starting vertices, edge rules (including direction, edge types, and property filters), and maximum depth.
Note: The property values in properties can be a list, indicating that any value corresponding to the key is acceptable.
POST http://localhost:8080/graphs/{graph}/traversers/customizedpaths
{
"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
}
200
{
"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"
}
]
}
}
]
}
Suitable for finding various complex sets of paths, for example:
Finds all paths that meet the specified conditions based on a batch of starting vertices, edge rules (including direction, edge types, and property filters), and maximum depth.
Note: The property values in properties can be a list, indicating that any value corresponding to the key is acceptable.
Note: The property values in properties can be a list, indicating that any value corresponding to the key is acceptable.
skip_degree >= max_degree constraint. Default is 0 (not enabled), which means no points are skipped. (Note: After enabling this configuration, traversing will attempt to access a vertex’s skip_degree edges, not just max_degree edges. This incurs additional traversal overhead and may have a significant impact on query performance. Please ensure understanding before enabling.)false.
POST http://localhost:8080/graphs/{graph}/traversers/templatepaths
{
"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
}
200
{
"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
}
}
]
}
Suitable for finding various complex template paths, such as personA -(Friend)-> personB -(Classmate)-> personC, where the “Friend” and “Classmate” edges can have a maximum depth of 3 and 4 layers, respectively.
Finds the intersection points based on the specified conditions, including starting vertices, destination vertices, direction, edge types (optional), and maximum depth.
GET http://localhost:8080/graphs/{graph}/traversers/crosspoints?source="2:lop"&target="2:ripple"&max_depth=5&direction=IN
200
{
"crosspoints":[
{
"crosspoint":"1:josh",
"objects":[
"2:lop",
"1:josh",
"2:ripple"
]
}
]
}
Used to find the intersection points and their paths between two vertices, such as:
Finds the intersection of destination vertices that satisfy the specified conditions, including starting vertices, multiple edge rules (including direction, edge type, and property filters), and maximum depth.
sources: Defines the starting vertices, required. The specified options include:
Note: Property values in properties can be a list, indicating that the value of the key can be any item in the list.
path_patterns: Represents the path rules to be followed from the starting vertices. It is a list of rules. Required. Each rule is a PathPattern.
skip_degree >= max_degree. Default is 0 (not enabled), which means no vertices are skipped. Note: When this configuration is enabled, the traversal process will attempt to visit skip_degree edges of a vertex, not just max_degree edges. This incurs additional traversal overhead and may significantly impact query performance. Please make sure you understand it before enabling.capacity: Maximum number of vertices to be visited during the traversal process. Optional. Default is 10000000.
limit: Maximum number of paths to be returned. Optional. Default is 10.
with_path: When set to true, returns the paths where the intersection points are located. When set to false, does not return the paths. Optional. Default is false.
with_vertex: Optional. Default is false.
POST http://localhost:8080/graphs/{graph}/traversers/customizedcrosspoints
{
"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
}
200
{
"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"
}
]
}
}
]
}
Used to query a group of vertices that have intersections at the destination through multiple paths. For example:
Finds reachable cycles based on the specified conditions, including starting vertices, direction, edge types (optional), and maximum depth.
For example: 1 -> 25 -> 775 -> 14690 -> 25, where the cycle is 25 -> 775 -> 14690 -> 25.
GET http://localhost:8080/graphs/{graph}/traversers/rings?source="1:marko"&max_depth=2
200
{
"rings":[
{
"objects":[
"1:marko",
"1:josh",
"1:marko"
]
},
{
"objects":[
"1:marko",
"1:vadas",
"1:marko"
]
},
{
"objects":[
"1:marko",
"2:lop",
"1:marko"
]
}
]
}
Used to query cycles reachable from the starting vertex, for example:
Finds paths that diverge from the starting vertex and reach boundary vertices based on the specified conditions, including starting vertices, direction, edge types (optional), and maximum depth.
For example: 1 -> 25 -> 775 -> 14690 -> 2289 -> 18379, where 18379 is the boundary vertex, meaning there are no edges emitted from 18379.
GET http://localhost:8080/graphs/{graph}/traversers/rays?source="1:marko"&max_depth=2&direction=OUT
200
{
"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"
]
}
]
}
Used to find paths from the starting vertex to boundary vertices based on a specific relationship, for example:
Queries a batch of “fusiform similar vertices” based on specified conditions. When two vertices share a certain relationship with many common vertices, they are considered “fusiform similar vertices.” For example, if “Reader A” has read 100 books, readers who have read 80 or more of these 100 books can be defined as “fusiform similar vertices” of “Reader A.”
sources: Starting vertices, required. Specify using:
Note: Property values in properties can be a list, indicating that the value of the key can be any value in the list.
label: Edge type. Optional. Default represents all edge labels.
direction: Direction in which the starting vertex diverges (OUT, IN, BOTH). Optional. Default is BOTH.
min_neighbors: Minimum number of neighbors. If the number of neighbors is less than this threshold, the starting vertex is not considered a “fusiform similar vertex.” For example, if you want to find “fusiform similar vertices” of books read by “Reader A,” and min_neighbors is set to 100, it means that “Reader A” must have read at least 100 books to have “fusiform similar vertices.” Required.
alpha: Similarity, representing the proportion of common neighbors between the starting vertex and “fusiform similar vertices” to all neighbors of the starting vertex. Required.
min_similars: Minimum number of “fusiform similar vertices.” Only when the number of “fusiform similar vertices” of the starting vertex is greater than or equal to this value, the starting vertex and its “fusiform similar vertices” will be returned. Optional. Default is 1.
top: Returns the top highest similarity “fusiform similar vertices” of a starting vertex. Required. 0 means all.
group_property: Used together with min_groups. Returns the starting vertex and its “fusiform similar vertices” only if there are at least min_groups different values for a certain attribute of the starting vertex and its “fusiform similar vertices.” For example, when recommending “out-of-town” book buddies for “Reader A,” set group_property to the “city” attribute of readers and min_group to at least 2. Optional. If not specified, no filtering based on attributes is needed.
min_groups: Used together with group_property. Only meaningful when group_property is set.
max_degree: Maximum number of adjacent edges to traverse for each vertex during the query process. Optional. Default is 10000.
capacity: Maximum number of vertices to be visited during the traversal process. Optional. Default is 10000000.
limit: Maximum number of results to be returned (one starting vertex and its “fusiform similar vertices” count as one result). Optional. Default is 10.
with_intermediary: Whether to return the starting vertex and the intermediate vertices that are commonly related to the “fusiform
similar vertices.” Default is false.
POST http://localhost:8080/graphs/hugegraph/traversers/fusiformsimilarity
{
"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
}
200
{
"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"
}
}
]
}
Used to query vertices that have high similarity with a group of vertices. For example:
GET http://localhost:8080/graphs/hugegraph/traversers/vertices?ids="1:marko"&ids="2:lop"
200
{
"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"
}
]
}
}
]
}
Obtain vertex shard information by specifying the shard size split_size (can be used in conjunction with Scan in 3.2.21.3 to retrieve vertices).
GET http://localhost:8080/graphs/hugegraph/traversers/vertices/shards?split_size=67108864
200
{
"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
},
......
]
}
Retrieve vertices in batches based on the specified shard information (refer to 3.2.21.2 Shard for obtaining shard information).
GET http://localhost:8080/graphs/hugegraph/traversers/vertices/scan?start=0&end=4294967295
200
{
"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"
}
]
}
}
]
}
GET http://localhost:8080/graphs/hugegraph/traversers/edges?ids="S1:josh>1>>S2:lop"&ids="S1:josh>1>>S2:ripple"
200
{
"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
}
}
]
}
Retrieve shard information for edges by specifying the shard size (split_size). This can be used in conjunction with the Scan operation described in section 3.2.22.3 to retrieve edges.
GET http://localhost:8080/graphs/hugegraph/traversers/edges/shards?split_size=4294967295
200
{
"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
}
]
}
Batch retrieve edges by specifying shard information (refer to section 3.2.22.2 for shard retrieval).
page is empty, it indicates the first page of pagination starting from the position indicated by start.GET http://localhost:8080/graphs/hugegraph/traversers/edges/scan?start=0&end=3221225469
200
{
"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"
}
}
]
}
Not only the Graph iteration （traverser) method, HugeGraph-Server also provide Rank API for recommendation purpose.
You can use it to recommend some vertexes much closer to a vertex.
A typical scenario for Personal Rank algorithm is in recommendation application. According to the out edges of a vertex,
recommend some other vertices that having the same or similar edges.
Here is a use case: According to someone’s reading habit or reading history, we can recommend some books he may be interested or some book pal.
For Example:
a,b,c,d,e. If we want to recommend some book pal and books for tom, an easier idea is let’s check whoever also liked these books (common hobby based).b,d,f. And Jay, he like 4 books c,d,e,g, and Lee, he also like 4 books a,d,e,f.The case above is simple. Here we also provide a public test dataset MovieLens for use case. You should download the dataset. The load it into HugeGraph with HugeGraph-Loader. To make it simple, we ignore all properties data of user and move. only field id is enough. we also ignore the value of edge rating.
The metadata for input file and mapping file as follows:
////////////////////////////////////////////////////////////
// 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"
}
}
]
}
Note: modify the
input.pathto your local path.
suitable for bipartite graph, will return all vertex or a list of its correlation which related to all source vertex.
Bipartite Graph is a special model in Graph Theory, as well as a special flow in network. The strongest feature is, it split all vertex in graph into two sets. The vertex in the set is not connected. However,the vertex in two sets may connect with each other.
Suppose we have one bipartite graph based on user and things. A random walk based PersonalRank algorithm should be likes this:
rating, to find a common judge.Required:
Optional:
100005BOTH_LABEL,optional list as follows:1000.0001 (will implement soon)truePOST http://localhost:8080/graphs/hugegraph/traversers/personalrank
{
"source": "1:1",
"label": "rating",
"alpha": 0.6,
"max_depth": 15,
"with_label": "OTHER_LABEL",
"sorted": true,
"limit": 10
}
200
{
"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
}
In a bipartite graph build by two different type of vertex, recommend other most related vertex to one vertex. for example:
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);
}
}
In a general graph structure,find the first N vertices of each layer with the highest correlation with a given starting point and their relevance.
In graph words: to go out from the starting point, get the probability of going to each vertex of each layer.
10000
(Note: before v0.12 step only support degree as parameter name, from v0.12, use max_degree, compatible with degree)POST http://localhost:8080/graphs/hugegraph/traversers/neighborrank
{
"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
}
200
{
"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
}
]
}
Find the vertices in different layers for a given start point that should be most recommended
Variables can be used to store data about the entire graph. The data is accessed and stored in the form of key-value pairs.
PUT http://localhost:8080/graphs/hugegraph/variables/name
{
"data": "tom"
}
200
{
"name": "tom"
}
GET http://localhost:8080/graphs/hugegraph/variables
200
{
"name": "tom"
}
GET http://localhost:8080/graphs/hugegraph/variables/name
200
{
"name": "tom"
}
DELETE http://localhost:8080/graphs/hugegraph/variables/name
204
GET http://localhost:8080/graphs
200
{
"graphs": [
"hugegraph",
"hugegraph1"
]
}
GET http://localhost:8080/graphs/hugegraph
200
{
"name": "hugegraph",
"backend": "cassandra"
}
requires administrator privileges**
Since emptying the graph is a dangerous operation, we have added parameters for confirmation to the API to avoid false calls by users:
I'm sure to delete all dataDELETE http://localhost:8080/graphs/hugegraph/clear?confirm_message=I%27m+sure+to+delete+all+data
204
POST http://localhost:8080/graphs/hugegraph_clone?clone_graph_name=hugegraph
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: the data/wal_path can’t be the same as the existing graph (use separate directories)
200
{
"name": "hugegraph_clone",
"backend": "rocksdb"
}
POST http://localhost:8080/graphs/hugegraph2
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: the data/wal_path can’t be the same as the existing graph (use separate directories)
200
{
"name": "hugegraph2",
"backend": "rocksdb"
}
Since deleting a graph is a dangerous operation, we have added parameters for confirmation to the API to avoid false calls by users:
I'm sure to drop the graphDELETE http://localhost:8080/graphs/hugegraph_clone?confirm_message=I%27m%20sure%20to%20drop%20the%20graph
204
GET http://localhost:8080/graphs/hugegraph/conf
200
# 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
...=
Allowed graph mode values are: NONE, RESTORING, MERGING, LOADING
Restore has two different modes: Restoring and Merging
Under normal circumstances, the graph mode is None. When you need to restore the graph, you need to temporarily modify the graph mode to Restoring or Merging as needed. When you complete the restore, change the graph mode to None.
GET http://localhost:8080/graphs/hugegraph/mode
200
{
"mode": "NONE"
}
Allowed graph mode values are: NONE, RESTORING, MERGING
PUT http://localhost:8080/graphs/hugegraph/mode
"RESTORING"
Allowed graph mode values are: NONE, RESTORING, MERGING
200
{
"mode": "RESTORING"
}
GET http://localhost:8080/graphs/hugegraph/graph_read_mode
200
{
"graph_read_mode": "ALL"
}
PUT http://localhost:8080/graphs/hugegraph/graph_read_mode
"OLTP_ONLY"
Allowed read mode values are: ALL, OLTP_ONLY, OLAP_ONLY
200
{
"graph_read_mode": "OLTP_ONLY"
}
PUT http://localhost:8080/graphs/hugegraph/snapshot_create
200
{
"hugegraph": "snapshot_created"
}
PUT http://localhost:8080/graphs/hugegraph/snapshot_resume
200
{
"hugegraph": "snapshot_resumed"
}
PUT http://localhost:8080/graphs/hugegraph/compact
200
{
"nodes": 1,
"cluster_id": "local",
"servers": {
"local": "OK"
}
}
GET http://localhost:8080/graphs/hugegraph/tasks?status=success
200
{
"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\"}}"
}]
}
GET http://localhost:8080/graphs/hugegraph/tasks/2
200
{
"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\"}}"
}
DELETE http://localhost:8080/graphs/hugegraph/tasks/2
204
If you already created an async task via Gremlin API as follows:
"for (int i = 0; i < 10; i++) {" +
"hugegraph.addVertex(T.label, 'man');" +
"hugegraph.tx().commit();" +
"try {" +
"sleep(1000);" +
"} catch (InterruptedException e) {" +
"break;" +
"}" +
"}"
PUT http://localhost:8080/graphs/hugegraph/tasks/2?action=cancel
cancel it in 10s. if more than 10s, the task may already be finished, then can’t be cancelled.
202
{
"cancelled": true
}
At this point, the number of vertices whose label is man must be less than 10.
HugeGraphServer for executiongremlin-groovy.Querying vertices
GET http://127.0.0.1:8080/gremlin?gremlin=hugegraph.traversal().V('1:marko')
200
{
"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": {}
}
}
POST http://localhost:8080/gremlin
Querying vertices
{
"gremlin": "hugegraph.traversal().V('1:marko')",
"bindings": {},
"language": "gremlin-groovy",
"aliases": {}
}
200
{
"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": {}
}
}
Note:
Here we directly use the graph object (
hugegraph), first retrieve its traversal iterator (traversal()), and then retrieve the vertices. Instead of writinggraph.traversal().V()org.V(), you can use aliases to operate on the graph and traversal iterator. In this case,hugegraphis a native variable, and__g_hugegraphis an additional variable added by HugeGraphServer. Each graph will have a corresponding traversal iterator object in this format (__g_${graph}).
The structure of the response body is different from the RESTful API structure of other vertices or edges. Users may need to parse it manually.
Querying edges
{
"gremlin": "g.E('S1:marko>2>>S2:lop')",
"bindings": {},
"language": "gremlin-groovy",
"aliases": {
"graph": "hugegraph",
"g": "__g_hugegraph"
}
}
200
{
"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": {}
}
}
POST http://localhost:8080/graphs/hugegraph/jobs/gremlin
Querying vertices
{
"gremlin": "g.V('1:marko')",
"bindings": {},
"language": "gremlin-groovy",
"aliases": {}
}
Note:
Asynchronous execution of Gremlin statements does not currently support aliases. You can use
graphto represent the graph you want to operate on, or directly use the name of the graph, such ashugegraph. Additionally,grepresents the traversal, which is equivalent tograph.traversal()orhugegraph.traversal().
201
{
"task_id": 1
}
Note:
You can query the execution status of an asynchronous task by using
GET http://localhost:8080/graphs/hugegraph/tasks/1(where “1” is the task_id). For more information, refer to the Asynchronous Task RESTful API.
Querying edges
{
"gremlin": "g.E('S1:marko>2>>S2:lop')",
"bindings": {},
"language": "gremlin-groovy",
"aliases": {}
}
201
{
"task_id": 2
}
Note:
You can query the execution status of an asynchronous task by using
GET http://localhost:8080/graphs/hugegraph/tasks/2(where “2” is the task_id). For more information, refer to the Asynchronous Task RESTful API.
GET /graphs/{graph}/cypher?cypher={cypher}
GET http://localhost:8080/graphs/hugecypher1/cypher?cypher=match(n:person) return n.name as name order by n.name limit 1
200
{
"requestId": "766b9f48-2f10-40d9-951a-3027d0748ab7",
"status": {
"message": "",
"code": 200,
"attributes": {
}
},
"result": {
"data": [
{
"name": "hello"
}
],
"meta": {
}
}
}
POST /graphs/{graph}/cypher
{cypher}
Note:
It is not in JSON format, but a plain text Cypher statement.
POST http://localhost:8080/graphs/hugecypher1/cypher
match(n:person) return n.name as name order by n.name limit 1
200
{
"requestId": "f096bee0-e249-498f-b5a3-ea684fc84f57",
"status": {
"message": "",
"code": 200,
"attributes": {
}
},
"result": {
"data": [
{
"name": "hello"
}
],
"meta": {
}
}
}
To enable authentication and related configurations, please refer to the Authentication Configuration documentation.
HugeGraph supports multi-user authentication and fine-grained access control. It adopts a 4-tier design based on “User-User Group-Operation-Resource” to flexibly control user roles and permissions. Resources describe data in the graph database, such as vertices that meet certain conditions. Each resource consists of three elements: type, label, and properties. There are a total of 18 types and combinations of any label and properties to form resources. The internal condition of a resource is an “AND” relationship, while the condition between multiple resources is an “OR” relationship. Users can belong to one or more user groups, and each user group can have permissions for any number of resources. The types of operations include read, write, delete, execute, etc. HugeGraph supports dynamically creating users, user groups, and resources, and supports dynamically assigning or revoking permissions. During the initialization of the database, a super administrator user is created, and subsequently, various role users can be created by the super administrator. If a newly created user is assigned sufficient permissions, they can create or manage more users.
user(name=boss) -belong-> group(name=all) -access(read)-> target(graph=graph1, resource={label: person, city: Beijing})
Description: User ‘boss’ has read permission for people in the ‘graph1’ graph from Beijing.
The user authentication and access control interface includes 5 categories: UserAPI, GroupAPI, TargetAPI, BelongAPI, AccessAPI.
The user interface includes APIs for creating users, deleting users, modifying users, and querying user-related information.
Both user_name and user_password are required.
{
"user_name": "boss",
"user_password": "******",
"user_phone": "182****9088",
"user_email": "123@xx.com"
}
POST http://localhost:8080/graphs/hugegraph/auth/users
201
In the response message, the password is encrypted as ciphertext.
{
"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"
}
DELETE http://localhost:8080/graphs/hugegraph/auth/users/-63:test
204
1
PUT http://localhost:8080/graphs/hugegraph/auth/users/-63:test
Modify user_name, user_password, and user_phone.
{
"user_name": "test",
"user_password": "******",
"user_phone": "183****9266"
}
200
The returned result is the entire user object including the modified content.
{
"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"
}
GET http://localhost:8080/graphs/hugegraph/auth/users
200
{
"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"
}
]
}
GET http://localhost:8080/graphs/hugegraph/auth/users/-63:admin
200
{
"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"
}
]
}
GET http://localhost:8080/graphs/hugegraph/auth/users/-63:boss/role
200
{
"roles": {
"hugegraph": {
"READ": [
{
"type": "ALL",
"label": "*",
"properties": null
}
]
}
}
}
Groups grant corresponding resource permissions, and users are assigned to different groups, thereby having different resource permissions. The group interface includes APIs for creating groups, deleting groups, modifying groups, and querying group-related information.
{
"group_name": "all",
"group_description": "group can do anything"
}
POST http://localhost:8080/graphs/hugegraph/auth/groups
201
{
"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"
}
DELETE http://localhost:8080/graphs/hugegraph/auth/groups/-69:grant
204
1
PUT http://localhost:8080/graphs/hugegraph/auth/groups/-69:grant
Modify group_description
{
"group_name": "grant",
"group_description": "grant"
}
200
The returned result is the entire group object including the modified content.
{
"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"
}
GET http://localhost:8080/graphs/hugegraph/auth/groups
200
{
"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"
}
]
}
GET http://localhost:8080/graphs/hugegraph/auth/groups/-69:all
200
{
"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"
}
Resources describe data in the graph database, such as vertices that meet certain criteria. Each resource includes three elements: type, label, and properties. There are 18 types in total, and the combination of any label and any properties forms a resource. The internal conditions of a resource are based on the AND relationship, while the conditions between multiple resources are based on the OR relationship.
The resource API includes creating, deleting, modifying, and querying resources.
target_resources can include multiple target_resource, stored in the form of a list.
Each target_resource contains:
For example, a specific resource: “target_resources”: [{“type”:“VERTEX”,“label”:“person”,“properties”:{“city”:“Beijing”,“age”:“P.gte(20)”}}]
The resource definition means: a vertex of type ‘person’ with the city property set to ‘Beijing’ and the age property greater than or equal to 20.
{
"target_name": "all",
"target_graph": "hugegraph",
"target_url": "127.0.0.1:8080",
"target_resources": [
{
"type": "ALL"
}
]
}
POST http://localhost:8080/graphs/hugegraph/auth/targets
201
{
"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"
}
DELETE http://localhost:8080/graphs/hugegraph/auth/targets/-77:gremlin
204
1
PUT http://localhost:8080/graphs/hugegraph/auth/targets/-77:gremlin
Modify the ’type’ in the resource definition.
{
"target_name": "gremlin",
"target_graph": "hugegraph",
"target_url": "127.0.0.1:8080",
"target_resources": [
{
"type": "NONE"
}
]
}
200
The response contains the entire target group object, including the modified content.
{
"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"
}
GET http://localhost:8080/graphs/hugegraph/auth/targets
200
{
"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"
}
]
}
GET http://localhost:8080/graphs/hugegraph/auth/targets/-77:grant
200
{
"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"
}
The association between users and user groups allows a user to be associated with one or more user groups. User groups have permissions for related resources, and the permissions for different user groups can be understood as different roles. In other words, users are associated with roles.
The API for associating roles includes creating, deleting, modifying, and querying the association of roles for users.
{
"user": "-63:boss",
"group": "-69:all"
}
POST http://localhost:8080/graphs/hugegraph/auth/belongs
201
{
"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"
}
DELETE http://localhost:8080/graphs/hugegraph/auth/belongs/S-63:boss>-82>>S-69:grant
204
1
An association of roles can only be modified for its description. The user and group properties cannot be modified. If you need to modify an association of roles, you need to delete the existing association and create a new one.
PUT http://localhost:8080/graphs/hugegraph/auth/belongs/S-63:boss>-82>>S-69:grant
Modify the belong_description field
{
"belong_description": "update test"
}
200
The response includes the modified content as well as the entire association of roles object
{
"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"
}
GET http://localhost:8080/graphs/hugegraph/auth/belongs
200
{
"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"
}
]
}
GET http://localhost:8080/graphs/hugegraph/auth/belongs/S-63:boss>-82>>S-69:all
200
{
"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"
}
Grant permissions to user groups for resources, including operations such as READ, WRITE, DELETE, EXECUTE, etc. The authorization API includes: creating, deleting, modifying, and querying permissions.
Access permissions:
{
"group": "-69:all",
"target": "-77:all",
"access_permission": "READ"
}
POST http://localhost:8080/graphs/hugegraph/auth/accesses
201
{
"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"
}
DELETE http://localhost:8080/graphs/hugegraph/auth/accesses/S-69:all>-88>12>S-77:all
204
1
Authorization can only be modified for its description. User group, resource, and permission cannot be modified. If you need to modify the relationship of the authorization, you can delete the original authorization relationship and create a new one.
PUT http://localhost:8080/graphs/hugegraph/auth/accesses/S-69:all>-88>12>S-77:all
Modify access_description
{
"access_description": "test"
}
200
{
"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"
}
GET http://localhost:8080/graphs/hugegraph/auth/accesses
200
{
"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"
}
]
}
GET http://localhost:8080/graphs/hugegraph/auth/accesses/S-69:all>-88>11>S-77:all
200
{
"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"
}
HugeGraph provides a metrics interface for obtaining monitoring information, such as statistics on each Gremlin execution time, cache size, etc. The metrics interface includes the following categories: basic metrics, statistical metrics, system metrics, and backend storage metrics.
json, it is returned in json format, otherwise it is returned in
Promethaus format.http://localhost:8080/metrics/?type=json
200
{
"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"
},
...
}
}
http://localhost:8080/metrics/
200
# 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
...
http://localhost:8080/metrics/gauges
200
{
"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
}
}
GET http://localhost:8080/metrics/counters
200
{
"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
}
}
GET http://localhost:8080/metrics/gauges
200
{
"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
}
}
GET http://localhost:8080/metrics/meters
200
{
"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"
}
}
GET http://localhost:8080/metrics/timers
200
{
"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"
},
...
}
GET http://localhost:8080/metrics/statistics
# 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
200
GET http://localhost:8080/metrics/statistics?type=json
200
{
"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
}
}
System metrics mainly return the machine metrics, such as memory, threads, and other information.
GET http://localhost:8080/metrics/system
200
{
"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"
}
}
HugeGraph supports multiple backend storage, with backend metrics including memory, disk, and other information.
GET http://localhost:8080/metrics/backend
200
{
"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
}
}
}
}
GET http://localhost:8080/versions
200
{
"versions": {
"version": "v1",
"core": "0.4.5.1",
"gremlin": "3.2.5",
"api": "0.13.2.0"
}
}