AuraDB 的无服务器图分析
1. 先决条件
此 Notebook 要求您拥有一个可用的 AuraDB 实例，并且为您的项目启用了无服务器图分析功能。
您还需要安装 graphdatascience Python 库，版本 1.15 或更高。
%pip install "graphdatascience>=1.15"2. Aura API 凭证
管理 GDS 会话的入口点是 GdsSessions 对象，它需要创建Aura API 凭证。
import os
from graphdatascience.session import AuraAPICredentials, GdsSessions
client_id = os.environ["AURA_API_CLIENT_ID"]
client_secret = os.environ["AURA_API_CLIENT_SECRET"]
# If your account is a member of several project, you must also specify the project ID to use
project_id = os.environ.get("AURA_API_PROJECT_ID", None)
sessions = GdsSessions(api_credentials=AuraAPICredentials(client_id, client_secret, project_id=project_id))3. 创建新会话
通过调用 sessions.get_or_create() 并传入以下参数来创建新会话：
-
会话名称，通过再次调用
get_or_create，您可以使用此名称重新连接到现有会话。 -
包含 AuraDB 实例的地址、用户名和密码的
DbmsConnectionInfo。 -
会话内存。
-
云位置。
-
生存时间 (TTL)，它确保会话在闲置达到设定时间后自动删除，以避免产生费用。
有关参数的更多详细信息，请参阅 API 参考文档或手册。
from graphdatascience.session import AlgorithmCategory, SessionMemory
# Explicitly define the size of the session
memory = SessionMemory.m_8GB
# Estimate the memory needed for the GDS session
memory = sessions.estimate(
node_count=20,
relationship_count=50,
algorithm_categories=[AlgorithmCategory.CENTRALITY, AlgorithmCategory.NODE_EMBEDDING],
)from datetime import timedelta
from graphdatascience.session import DbmsConnectionInfo
# Identify the AuraDB instance
db_connection = DbmsConnectionInfo(
uri=os.environ["AURA_DB_ADDRESS"], username=os.environ["AURA_DB_USER"], password=os.environ["AURA_DB_PW"]
)
# Create a GDS session!
gds = sessions.get_or_create(
# we give it a representative name
session_name="people_and_fruits",
memory=SessionMemory.m_4GB,
db_connection=db_connection,
ttl=timedelta(minutes=2),
)4. 列出会话
您可以使用 sessions.list() 来查看每个已创建会话的详细信息。
from pandas import DataFrame
gds_sessions = sessions.list()
# for better visualization
DataFrame(gds_sessions)5. 添加数据集
我们假设已配置的 AuraDB 实例为空。我们将使用标准 Cypher 添加我们的数据集。
在更真实的场景中，此步骤已经完成，我们只需连接到现有数据库。
data_query = """
CREATE
(dan:Person {name: 'Dan', age: 18, experience: 63, hipster: 0}),
(annie:Person {name: 'Annie', age: 12, experience: 5, hipster: 0}),
(matt:Person {name: 'Matt', age: 22, experience: 42, hipster: 0}),
(jeff:Person {name: 'Jeff', age: 51, experience: 12, hipster: 0}),
(brie:Person {name: 'Brie', age: 31, experience: 6, hipster: 0}),
(elsa:Person {name: 'Elsa', age: 65, experience: 23, hipster: 1}),
(john:Person {name: 'John', age: 4, experience: 100, hipster: 0}),
(apple:Fruit {name: 'Apple', tropical: 0, sourness: 0.3, sweetness: 0.6}),
(banana:Fruit {name: 'Banana', tropical: 1, sourness: 0.1, sweetness: 0.9}),
(mango:Fruit {name: 'Mango', tropical: 1, sourness: 0.3, sweetness: 1.0}),
(plum:Fruit {name: 'Plum', tropical: 0, sourness: 0.5, sweetness: 0.8})
CREATE
(dan)-[:LIKES]->(apple),
(annie)-[:LIKES]->(banana),
(matt)-[:LIKES]->(mango),
(jeff)-[:LIKES]->(mango),
(brie)-[:LIKES]->(banana),
(elsa)-[:LIKES]->(plum),
(john)-[:LIKES]->(plum),
(dan)-[:KNOWS]->(annie),
(dan)-[:KNOWS]->(matt),
(annie)-[:KNOWS]->(matt),
(annie)-[:KNOWS]->(jeff),
(annie)-[:KNOWS]->(brie),
(matt)-[:KNOWS]->(brie),
(brie)-[:KNOWS]->(elsa),
(brie)-[:KNOWS]->(jeff),
(john)-[:KNOWS]->(jeff);
"""
# making sure the database is actually empty
assert gds.run_cypher("MATCH (n) RETURN count(n)").squeeze() == 0, "Database is not empty!"
# let's now write our graph!
gds.run_cypher(data_query)
gds.run_cypher("MATCH (n) RETURN count(n) AS nodeCount")6. 投影图
现在我们已将图导入数据库，可以将其投影到我们的 GDS 会话中。我们通过使用 gds.graph.project() 端点来完成此操作。
我们使用的远程投影查询会选择所有 Person 节点及其 LIKES 关系，以及所有 Fruit 节点及其 LIKES 关系。此外，我们出于演示目的投影节点属性。尽管我们在此 Notebook 中没有这样做，但我们可以将这些节点属性用作算法的输入。
G, result = gds.graph.project(
"people-and-fruits",
"""
CALL {
MATCH (p1:Person)
OPTIONAL MATCH (p1)-[r:KNOWS]->(p2:Person)
RETURN
p1 AS source, r AS rel, p2 AS target,
p1 {.age, .experience, .hipster } AS sourceNodeProperties,
p2 {.age, .experience, .hipster } AS targetNodeProperties
UNION
MATCH (f:Fruit)
OPTIONAL MATCH (f)<-[r:LIKES]-(p:Person)
RETURN
p AS source, r AS rel, f AS target,
p {.age, .experience, .hipster } AS sourceNodeProperties,
f { .tropical, .sourness, .sweetness } AS targetNodeProperties
}
RETURN gds.graph.project.remote(source, target, {
sourceNodeProperties: sourceNodeProperties,
targetNodeProperties: targetNodeProperties,
sourceNodeLabels: labels(source),
targetNodeLabels: labels(target),
relationshipType: type(rel)
})
""",
)
str(G)7. 运行算法
您可以使用标准 GDS Python 客户端 API 在构建的图上运行算法。有关更多示例，请参阅其他教程。
print("Running PageRank ...")
pr_result = gds.pageRank.mutate(G, mutateProperty="pagerank")
print(f"Compute millis: {pr_result['computeMillis']}")
print(f"Node properties written: {pr_result['nodePropertiesWritten']}")
print(f"Centrality distribution: {pr_result['centralityDistribution']}")
print("Running FastRP ...")
frp_result = gds.fastRP.mutate(
G,
mutateProperty="fastRP",
embeddingDimension=8,
featureProperties=["pagerank"],
propertyRatio=0.2,
nodeSelfInfluence=0.2,
)
print(f"Compute millis: {frp_result['computeMillis']}")
# stream back the results
gds.graph.nodeProperties.stream(G, ["pagerank", "fastRP"], separate_property_columns=True, db_node_properties=["name"])8. 写回 AuraDB
GDS 会话的内存图是从我们指定的 AuraDB 实例中的数据投影而来的。因此，写回操作会将数据持久化回同一个 AuraDB。让我们将 PageRank 和 FastRP 算法的结果写回 AuraDB 实例。
# if this fails once with some error like "unable to retrieve routing table"
# then run it again. this is a transient error with a stale server cache.
gds.graph.nodeProperties.write(G, ["pagerank", "fastRP"])当然，我们也可以直接使用 .write 模式。让我们以写入模式运行 Louvain 来演示
gds.louvain.write(G, writeProperty="louvain")现在我们可以使用 gds.run_cypher() 方法来查询更新后的图。请注意，run_cypher() 方法将在 AuraDB 实例上运行查询。
gds.run_cypher(
"""
MATCH (p:Person)
RETURN p.name, p.pagerank AS rank, p.louvain
ORDER BY rank DESC
"""
)9. 删除会话
现在我们已经完成了分析，可以删除会话。我们生成的结果已写回 AuraDB 实例，不会丢失。如果我们计算了其他未写回的内容，那些将会丢失。
删除会话将释放所有相关资源，并停止产生费用。
sessions.delete(session_name="people_and_fruits")
# or gds.delete()# let's also make sure the deleted session is truly gone:
sessions.list()# Lastly, let's clean up the database
gds.run_cypher("MATCH (n:Person|Fruit) DETACH DELETE n")