HugeGraph Python 客户端快速上手

 1from pyhugegraph.client import PyHugeClient
 2
 3client = PyHugeClient(
 4    url="http://127.0.0.1:8080",
 5    graph="hugegraph",
 6    user="admin",
 7    pwd="admin",
 8    graphspace=None,
 9)
10
11schema = client.schema()
12schema.property_key("name").as_text().if_not_exist().create()
13schema.property_key("birthDate").as_text().if_not_exist().create()
14schema.vertex_label("Person").properties("name", "birthDate") \
15      .use_primary_key_id().primary_keys("name").if_not_exist().create()
16schema.vertex_label("Movie").properties("name") \
17      .use_primary_key_id().primary_keys("name").if_not_exist().create()
18schema.edge_label("ActedIn").source_label("Person").target_label("Movie") \
19      .if_not_exist().create()
20
21graph = client.graph()
22person = graph.add_vertex(
23    "Person", {"name": "Al Pacino", "birthDate": "1940-04-25"}
24)
25movie = graph.add_vertex("Movie", {"name": "The Godfather"})
26edge = graph.add_edge("ActedIn", person.id, movie.id, {})
27
28print(graph.get_vertex_by_id(person.id))
29print(graph.get_edge_by_id(edge.id))
30
31client.close()

 1schema = client.schema()
 2
 3# 属性类型：as_text / as_int / as_long / as_float / as_double / as_bool / as_date
 4# 基数模式：value_single / value_list / value_set
 5schema.property_key("age").as_int().value_single().if_not_exist().create()
 6
 7# 顶点类型：use_automatic_id / use_customize_string_id / use_primary_key_id
 8schema.vertex_label("person").properties("name", "age", "city") \
 9      .primary_keys("name").nullable_keys("city").if_not_exist().create()
10
11# 边类型：link() 快捷指定起点与终点顶点类型
12schema.edge_label("knows").link("person", "person").multi_times() \
13      .properties("date", "city").sort_keys("date").nullable_keys("city") \
14      .if_not_exist().create()
15
16# 索引类型：on_v / on_e，支持 secondary / range / search / shard / unique
17schema.index_label("personByCity").on_v("person").by("city") \
18      .secondary().if_not_exist().create()

 1schema = client.schema()
 2print(schema.get_schema())            # 获取全图 Schema 结构字典
 3print(schema.get_property_keys())
 4print(schema.get_vertex_labels())
 5print(schema.get_edge_labels())
 6print(schema.get_index_labels())
 7
 8# 查询单个 Schema 定义
 9print(schema.get_property_key("name"))
10print(schema.get_vertex_label("person"))
11print(schema.get_edge_label("knows"))
12print(schema.get_index_label("personByCity"))
13
14# 边关联关系概览，格式形如 "Person--ActedIn-->Movie"
15print(schema.get_relations())

1graph = client.graph()
2graph.append_vertex(person.id, {"birthDate": "1940-04-25"})    # 追加属性
3graph.eliminate_vertex(person.id, {"birthDate": "1940-04-25"}) # 移除属性
4graph.append_edge(edge.id, {"city": "Beijing"})
5graph.eliminate_edge(edge.id, {"city": "Beijing"})
6graph.remove_edge_by_id(edge.id)
7graph.remove_vertex_by_id(person.id)

1graph = client.graph()
2vertices = graph.add_vertices([
3    ("person", {"name": "Alice", "age": 20}),
4    ("person", {"name": "Bob", "age": 23}),
5])
6edges = graph.add_edges([
7    ("knows", vertices[0].id, vertices[1].id, "person", "person", {"date": "2012-01-10"}),
8])

 1graph = client.graph()
 2
 3# 分页迭代查询：传入上一页返回的 next_page token 即可继续拉取下一页
 4vertices, next_page = graph.get_vertex_by_page("person", limit=10)
 5vertices, next_page = graph.get_vertex_by_page("person", limit=10, page=next_page)
 6
 7# 服务端属性条件过滤
 8older = graph.get_vertex_by_condition("person", properties={"age": "P.gt(29)"})
 9
10# 边分页查询（指定 vertex_id 时必须同时指定 direction 方向）
11edges, next_page = graph.get_edge_by_page(label="knows", limit=10)
12edges, next_page = graph.get_edge_by_page(vertex_id=person.id, direction="OUT", limit=10)
13
14# 根据 ID 列表批量查询顶点与边
15graph.get_vertices_by_id([v1.id, v2.id])
16graph.get_edges_by_id([e1.id, e2.id])

1gremlin = client.gremlin()
2result = gremlin.exec("g.V().limit(5)")
3print(result)

 1traverser = client.traverser()
 2
 3traverser.k_out(marko_id, 2)
 4traverser.k_neighbor(marko_id, 2)
 5traverser.same_neighbors(marko_id, josh_id)
 6traverser.jaccard_similarity(marko_id, josh_id)
 7traverser.shortest_path(marko_id, ripple_id, 3)
 8traverser.all_shortest_paths(marko_id, ripple_id, 3)
 9traverser.weighted_shortest_path(marko_id, ripple_id, "weight", 3)
10traverser.single_source_shortest_path(marko_id, 2)
11traverser.multi_node_shortest_path([marko_id, josh_id], max_depth=2)
12traverser.paths(marko_id, josh_id, 2)
13traverser.crosspoints(marko_id, josh_id, 2)
14traverser.rings(marko_id, 3)
15traverser.rays(marko_id, 2)
16traverser.vertices(marko_id)
17traverser.edges(edge_id)

 1# 1. 提交异步计算任务
 2receipt = client.olap().count_vertex()
 3task_id = receipt["task_id"]
 4
 5# 2. 轮询等待任务完成并获取计算结果
 6task = client.task().wait(task_id, timeout=300, poll_interval=1)
 7result = client.task().result(task)
 8print(result)
 9
10# 也可以直接使用 run() 一步完成提交、等待与结果解析：
11result = client.olap().run("count_vertex", timeout=60)

 1task = client.task()
 2
 3# 查询任务历史列表
 4task.list_tasks(status="success", limit=10)
 5
 6# 轮询等待指定任务完成
 7record = task.wait(task_id, timeout=300, poll_interval=1)
 8
 9# 解码任务执行结果（只对序列化 JSON 解析一次）
10result = task.result(record)
11
12# 取消或删除任务
13task.cancel_task(task_id)
14task.delete_task(task_id, force=False)

 1# 图变量管理
 2variable = client.variable()
 3variable.set("owner", "mary")
 4print(variable.get("owner"))
 5print(variable.all())
 6variable.remove("owner")
 7
 8# 监控指标数据
 9metrics = client.metrics()
10metrics.get_all_basic_metrics()
11metrics.get_system_metrics()
12metrics.get_backend_metrics()
13
14# 图管理与版本
15graphs = client.graphs()
16print(graphs.list())
17print(client.version().version())

1# 将会话切换为 Bearer Token 认证
2client.use_token("your-jwt-token")
3
4# 恢复为初始化配置的 HTTP Basic Auth 凭据
5client.use_token(None)

 1auth = client.auth()
 2
 3user = auth.create_user("test_user", "password")
 4auth.modify_user(user["id"], user_email="hugegraph@apache.org")
 5auth.get_user(user["id"])
 6auth.list_users(limit=10)
 7
 8group = auth.create_group("test_group", "read only")
 9auth.modify_group(group["id"], group_description="updated")
10auth.list_groups()
11
12target = auth.create_target("target1", "hugegraph", "127.0.0.1:8080", [])
13auth.update_target(target["id"], "target1", "hugegraph", "127.0.0.1:8080", [])
14auth.list_targets()
15
16belong = auth.create_belong(user["id"], group["id"])
17auth.update_belong(belong["id"], "description")
18auth.list_belongs()
19
20access = auth.grant_accesses(group["id"], target["id"], "READ")
21auth.modify_accesses(access["id"], "description")
22auth.list_accesses()
23
24# 按照依赖反向顺序清理资源
25auth.revoke_accesses(access["id"])
26auth.delete_belong(belong["id"])
27auth.delete_target(target["id"])
28auth.delete_group(group["id"])
29auth.delete_user(user["id"])

1rest = client.rest()
2print(rest.vertex.list(label="person", offset=0, limit=20))
3print(rest.task.list(status="success", page="", limit=20))

1from pyhugegraph.utils.exceptions import NotFoundError
2
3try:
4    graph.get_vertex_by_id("missing-id")
5except NotFoundError:
6    print("未找到指定顶点")