Cypher 查询语言（CQL）是一个为查询图数据库而设计的强大工具。与传统的关系数据库不同，图数据库擅长管理具有未定义关系的紧密连接的数据。 CQL 提供了既直观又强大的语法，使创建、读取、更新和删除存储在图数据库中的数据变得更加容易。在这份综合指南中，我们将探讨 CQL 的功能、约束、术语和命令，以及帮助您充分利用其潜力的实际示例。
CQL 的突出特点之一是它适用于高度关联的数据。与关系数据库的关系通常很复杂且管理起来很麻烦，图数据库则不同，它依赖于连接而蓬勃发展。 CQL 允许直观、高效地查询这些关系，使其成为社交网络、推荐引擎等的理想选择。
在CQL中，一个节点可以关联多个标签。这种灵活性可以更好地组织和分类数据。例如，代表一个人的节点可以具有诸如“Person”、“Employee”和“Customer”之类的标签，每个标签代表个人身份的不同方面。
虽然 CQL 很强大，但它也有一些限制。碎片仅适用于某些域。这意味着，在某些情况下，可能需要遍历整个数据才能检索明确的答案。
对于某些查询，尤其是涉及复杂关系的查询，可能需要遍历整个图以确保返回的数据准确完整。这可能是资源密集型且耗时的，具体取决于图的大小和复杂性。
节点代表图中的一个实体。节点可以具有存储有关实体的信息的属性，例如名称、年龄或任何其他相关属性。
标签允许对节点进行分组。它们取代了 SQL 中表的概念。例如，带有标签“人”的节点对代表人的所有节点进行分组。
关系是两个节点之间的具体化链接。这取代了 SQL 中的关系概念，从而实现实体之间的直接连接。
属性是节点或关系可以具有的属性。例如，“Person”节点可能具有诸如姓名和年龄之类的属性，而“LIKES”关系可能具有诸如“since”之类的属性。
CREATE命令用于创建节点和关系。这是构建图结构的基础。
MATCH 命令用于搜索图表中的模式。它是 CQL 查询的基石，允许您根据指定条件检索节点和关系。
在 CQL 中创建节点非常简单。使用 CREATE 命令，然后添加节点详细信息。
CREATE (:Person {name:\"John\", age:30})
CREATE (:Food {name:\"Pizza\"})
可以使用属性创建节点，属性是存储有关节点信息的键值对。
CREATE (:Person {name:\"Jane\", age:25, occupation:\"Engineer\"})
CREATE (:Food {name:\"Burger\", calories:500})
MATCH 命令允许您搜索图中的节点。
MATCH (p:Person) RETURN p
要进行更具体的搜索，请使用 WHERE 子句根据属性过滤节点。
MATCH (p:Person) WHERE p.age > 20 RETURN p.name, p.age
您可以在创建节点时创建节点之间的关系。
CREATE (p:Person {name:\"John\", age:30})-[:LIKES]->(f:Food {name:\"Pizza\"})
还可以使用 MATCH 命令在现有节点之间创建关系。
MATCH (p:Person {name:\"John\"})
MATCH (f:Food {name:\"Pizza\"})
CREATE (p)-[r:LIKES]->(f)
RETURN r
可以使用 SET 命令将属性添加到现有节点。
MATCH (p:Person {name:\"John\"})
SET p.occupation = \"Developer\"
RETURN p
要删除属性，请将其值设置为 NULL。
MATCH (p:Person {name:\"John\"})
SET p.age = NULL
RETURN p
Attributes can be modified by setting them to new values.
MATCH (p:Person {name:\"John\"})
SET p.age = 35
RETURN p
The COUNT function returns the number of nodes or relationships.
MATCH (n) RETURN count(n)
The AVG function calculates the average value of a numeric property.
MATCH (n) RETURN avg(n.age)
The SUM function calculates the total sum of a numeric property.
MATCH (n) RETURN sum(n.age)
To get the count of each type of relationship in the graph, use the type function.
MATCH ()-[r]->() RETURN type(r), count(*)
The COLLECT function creates a list of all values for a given property.
MATCH (p:Product)-[:BELONGS_TO]->(o:Order) RETURN id(o) as orderId, collect(p)
To delete all nodes and relationships, use the DELETE command.
MATCH (a)-[r]->(b) DELETE a, r, b
Visualize the database schema to understand the structure of your graph.
CALL db.schema.visualization YIELD nodes, relationships
Here are three ways to find a node representing a person named Lana Wachowski.
// Solution 1
MATCH (p:Person {name: \"Lana Wachowski\"})
RETURN p
// Solution 2
MATCH (p:Person)
WHERE p.name = \"Lana Wachowski\"
RETURN p
// Solution 3
MATCH (p:Person)
WHERE p.name =~ \".*Lana Wachowski.*\"
RETURN p
Display the name and role of people born after 1960 who acted in movies released in the 1980s.
MATCH (p:Person)-[a:ACTED_IN]->(m:Movie) WHERE p.born > 1960 AND m.released >= 1980 AND m.released < 1990 RETURN p.name, a.roles
Add the label Actor to people who have acted in at least one movie.
MATCH (p:Person)-[:ACTED_IN]->(:Movie) WHERE NOT (p:Actor) SET p:Actor
Consider a database for an online store where you need to manage products, clients, orders, and shipping addresses. Here's how you might model this in CQL.
Let's create some example nodes and relationships for an online store scenario:
CREATE (p1:Product {id: 1, name: \"Laptop\", price: 1000})
CREATE (p2:Product {id: 2, name: \"Phone\", price: 500})
CREATE (c:Client {id: 1, name: \"John Doe\"})
CREATE (o:Order {id: 1, date: \"2023-06-01\"})
CREATE (adr:Address {id: 1, street: \"123 Main St\", city: \"Anytown\", country: \"USA\"})
Now, let's create the relationships between these nodes:
CREATE (p1)-[:BELONGS_TO]->(o) CREATE (p2)-[:BELONGS_TO]->(o) CREATE (c)-[:MADE]->(o) CREATE (o)-[:SHIPPED_TO]->(adr)
To find out the products ordered in each order, including their quantity and unit price, use the following query:
MATCH (p:Product)-[:BELONGS_TO]->(o:Order) RETURN id(o) as orderId, collect(p)
To determine which client made each order and where each order was shipped, use this query:
MATCH (c:Client)-[:MADE]->(o:Order)-[:SHIPPED_TO]->(adr:Address) RETURN c.name as client, id(o) as orderId, adr.street, adr.city, adr.country
What is Cypher Query Language (CQL)?
Cypher Query Language (CQL) is a powerful query language designed specifically for querying and updating graph databases. It allows you to interact with data in a way that emphasizes the relationships between data points.
How does CQL differ from SQL?
While SQL is designed for querying relational databases, CQL is designed for graph databases. This means that CQL excels at handling complex, highly connected data, whereas SQL is better suited for tabular data structures.
Can I use CQL with any database?
CQL is primarily used with Neo4j, a popular graph database management system. However, other graph databases may have their own query languages with similar capabilities.
What are the benefits of using CQL?
CQL allows for intuitive querying of graph databases, making it easier to manage and analyze data with complex relationships. It supports a rich set of commands for creating, updating, and deleting nodes and relationships, as well as powerful query capabilities.
Is CQL difficult to learn?
CQL is designed to be user-friendly and intuitive. If you are familiar with SQL, you will find many similarities in CQL. The main difference lies in how data relationships are handled.
How can I optimize my CQL queries?
Optimizing CQL queries involves understanding your graph's structure and using efficient query patterns. Indexing frequently searched properties and avoiding unnecessary full graph traversals can significantly improve performance.
Cypher Query Language (CQL) is a robust tool for managing graph databases, offering powerful capabilities for querying and updating complex, highly connected data. By mastering CQL, you can leverage the full potential of graph databases, making it easier to handle intricate data relationships and perform sophisticated analyses.
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