Cypher 查詢語言（CQL）是一個為查詢圖資料庫設計的強大工具。與傳統的關聯式資料庫不同，圖資料庫擅長管理具有未定義關係的緊密連接的資料。 CQL 提供了直覺又強大的語法，讓建立、讀取、更新和刪除儲存在圖資料庫中的資料變得更加容易。在這份綜合指南中，我們將探討 CQL 的功能、限制、術語和命令，以及幫助您充分利用其潛力的實際範例。
CQL 的突出特點之一是它適用於高度關聯的資料。與關聯式資料庫的關係通常很複雜且管理起來很麻煩，圖資料庫則不同，它依賴連結而蓬勃發展。 CQL 允許直觀、有效率地查詢這些關係，使其成為社交網路、推薦引擎等的理想選擇。
在CQL中，一個節點可以關聯多個標籤。這種靈活性可以更好地組織和分類資料。例如，代表一個人的節點可以具有諸如“Person”、“Employee”和“Customer”之類的標籤，每個標籤代表個人身份的不同方面。
雖然 CQL 很強大，但它也有一些限制。碎片僅適用於某些網域。這意味著，在某些情況下，可能需要遍歷整個資料才能檢索明確的答案。
對於某些查詢，尤其是涉及複雜關係的查詢，可能需要遍歷整個圖以確保傳回的資料準確且完整。這可能是資源密集型且耗時的，具體取決於圖的大小和複雜性。
節點代表圖中的一個實體。節點可以具有儲存有關實體的資訊的屬性，例如名稱、年齡或任何其他相關屬性。
標籤允許對節點進行分組。它們取代了 SQL 中表格的概念。例如，帶有標籤「人」的節點會將代表人的所有節點進行分組。
關係是兩個節點之間的具體化連結。這取代了 SQL 中的關係概念，從而實現實體之間的直接連結。
屬性是節點或關係可以具有的屬性。例如，「Person」節點可能具有諸如姓名和年齡之類的屬性，而「LIKES」關係可能具有諸如「since」之類的屬性。
CREATE指令用於建立節點和關係。這是建構圖結構的基礎。
MATCH 指令用於搜尋圖表中的模式。它是 CQL 查詢的基石，可讓您根據指定條件檢索節點和關係。
在 CQL 中建立節點非常簡單。使用 CREATE 命令，然後新增節點詳細資訊。
CREATE (:Person {name:\"John\", age:30})
CREATE (:Food {name:\"Pizza\"})
可以使用屬性建立節點，屬性是儲存有關節點資訊的鍵值對。
CREATE (:Person {name:\"Jane\", age:25, occupation:\"Engineer\"})
CREATE (:Food {name:\"Burger\", calories:500})
MATCH 指令可讓您搜尋圖中的節點。
MATCH (p:Person) RETURN p
要進行更具體的搜索，請使用 WHERE 子句根據屬性過濾節點。
MATCH (p:Person) WHERE p.age > 20 RETURN p.name, p.age
您可以在建立節點時建立節點之間的關係。
CREATE (p:Person {name:\"John\", age:30})-[:LIKES]->(f:Food {name:\"Pizza\"})
也可以使用 MATCH 指令在現有節點之間建立關係。
MATCH (p:Person {name:\"John\"})
MATCH (f:Food {name:\"Pizza\"})
CREATE (p)-[r:LIKES]->(f)
RETURN r
可以使用 SET 指令將屬性加入現有節點。
MATCH (p:Person {name:\"John\"})
SET p.occupation = \"Developer\"
RETURN p
要刪除屬性，請將其值設為 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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