程序代写代做代考 database SQL Microsoft PowerPoint - 25- Neo4J_Part2

Microsoft PowerPoint – 25- Neo4J_Part2

Neo4j: Graph Database

Abdu Alawini
University of Illinois at Urbana-Champaign

CS411: Database Systems

December 5, 2018

Announcements

• HW 5 is due on 12/9

• 2nd Midterm review session: Mon. 12/10

• 2nd Midterm Exam on Wednesday 12/12
•1404 Siebel Center
•1024 Chemistry Annex

Last lecture!

•Map-Reduce
•Introduction to Neo4J
•Cypher: A Graph Query Language

• Querying Nodes and Relationships using Patterns

•Cypher: A Graph Query Language
•Manipulating Graph Data
•Aggregation
•Composing Statements
•Indexing and Constraints

•From Relational to Graph Databases

Today’s Lecture

Manipulating Graph Data

Create statement creates nodes and relationships specified in the
pattern

FOREACH Statement

6
https://neo4j.com/docs/cypher-manual/current/clauses/foreach/

Connecting Nodes

DELETE, REMOVE and SET Commands

To delete a node:

To delete a node and
all its relationships:

To remove a property:

To update or add
a property:

MATCH p =(begin)-[*]->(END )
WHERE begin.name = ‘A’ AND END .name = ‘D’
FOREACH (n IN nodes(p)| SET n.marked = TRUE )

Update a property
using FOREACH

•acts like a combination of MATCH or CREATE
•checks for the existence of data first before creating it

Completing patterns: Merge

ON CREATE, ON MATCH

Running the above command for the first time

Running the same command for the second time

Source: https://neo4j.com/developer/cypher-query-language/

•Cypher: A Graph Query Language
Manipulating Graph Data
•Aggregation
•Composing Statements
•Indexing and Constraints

•From Relational to Graph Databases

Outline

Aggregation

common aggregation functions are supported:
count, sum, avg, min, and max

• Collect() function collects all aggregated values into a list
COLLECT

•Cypher: A Graph Query Language
Manipulating Graph Data
Aggregation
•Composing Statements
•Indexing and Constraints

•From Relational to Graph Databases

Outline

•UNION combines the results of two statements that
have the same result structure

Composing Statements: UNION

Equivalent
Query

Source: https://neo4j.com/developer/cypher-query-language/

•WITH clause combines individual parts of a query and
declare which data flows from one to the other.

•WITH is like RETURN with the difference that it
doesn’t finish a query but prepares the input for the
next part.

Composing Statements: WITH

WITH Example

Source: https://neo4j.com/developer/cypher-query-language/

•Cypher: A Graph Query Language
Manipulating Graph Data
Aggregation
Composing Statements
•Indexing and Constraints

•From Relational to Graph Databases

Outline

Indexing and Constraints

•Goal of indexing: find the starting point in the graph
as fast as possible

•Unique constraints guarantee uniqueness of a certain
property on nodes with a specific label.

Interested in DB Tuning?
http://neo4j.com/docs/developer-manual/current/cypher/query-tuning/using/

•Listing database indexes

•Dropping an Index

Index Management

Cypher: A Graph Query Language
Manipulating Graph Data
Aggregation
Composing Statements
Indexing and Constraints

•From Relational to Graph Databases

Outline

•Graph databases store relationships and connections as
first-class entities: “Property Graph Model”

From Relational to Graph Databases

RDBMS Graph Databases

Tables Set of Nodes/Relationships

Rows Nodes

Columns and data Data properties and values

Constraints Relationships

Joins Traversals

From Relational to Graph Databases

Relational
Model

Graph Model

Source: https://neo4j.com/developer/graph-db-vs-rdbms/#_from_relational_to_graph_databases

DB=>Graph Data Model Transformation

26
Source: https://neo4j.com/developer/graph-db-vs-rdbms/#_from_relational_to_graph_databases

Northwind Example

Source: https://neo4j.com/developer/cypher-query-language/

Northwind: Graph Model

Source: https://neo4j.com/developer/cypher-query-language/

Customer
PURCHASED

•Select everything from the products table

Querying Northwind DB: SQL vs. Neo4J

SELECT p.*
FROM products as p;

SQL

MATCH (p:Product)
RETURN p;

Cypher

Source: https://neo4j.com/developer/cypher-query-language/

•Select Product Name and Price from products table

SQL vs. Neo4J: Projection

SELECT p.ProductName, p.UnitPrice
FROM products as p
ORDER BY p.UnitPrice DESC
LIMIT 10;

SQL

Cypher MATCH (p:Product)
RETURN p.productName, p.unitPrice
ORDER BY p.unitPrice DESC
LIMIT 10;

Source: https://neo4j.com/developer/cypher-query-language/

•Select Product Name and Price for ”Chocolate”

SQL vs. Neo4J: Filtering

SELECT p.ProductName, p.UnitPrice
FROM products AS p
WHERE p.ProductName = ‘Chocolate’;

SQL

Cypher

MATCH (p:Product {productName:”Chocolate”})
RETURN p.productName, p.unitPrice;

MATCH (p:Product)
WHERE p.productName = “Chocolate”
RETURN p.productName, p.unitPrice;

Source: https://neo4j.com/developer/cypher-query-language/

•List expensive products that starts with C.

SQL vs. Neo4J: Filtering

SELECT p.ProductName, p.UnitPrice
FROM products AS p
WHERE p.ProductName LIKE ‘C%’
AND p.UnitPrice > 100;

SQL

Cypher MATCH (p:Product)
WHERE p.productName STARTS WITH “C”
AND p.unitPrice > 100
RETURN p.productName, p.unitPrice;

Source: https://neo4j.com/developer/cypher-query-language/

•Who bought Chocolate?

SQL vs. Neo4J: Joining vs. Traversing

SQL

Cypher MATCH (p:Product {productName:”Chocolade”})<- [:PRODUCT]-(:Order)<-[:PURCHASED]-(c:Customer) RETURN distinct c.companyName; SELECT DISTINCT c.CompanyName FROM customers AS c JOIN orders AS o ON (c.CustomerID = o.CustomerID) JOIN order_details AS od ON (o.OrderID = od.OrderID) JOIN products AS p ON (od.ProductID = p.ProductID) WHERE p.ProductName = 'Chocolade'; Source: https://neo4j.com/developer/cypher-query-language/ © 2018 A. Alawini •Find top-selling employees SQL vs. Neo4J: Aggregation 34 SELECT e.EmployeeID, count(*) AS Count FROM Employee AS e JOIN Order AS o ON (o.EmployeeID = e.EmployeeID) GROUP BY e.EmployeeID ORDER BY Count DESC; SQL Cypher MATCH (:Order)<-[:SOLD]-(e:Employee) RETURN e.name, count(*) AS cnt ORDER BY cnt DESC; Source: https://neo4j.com/developer/cypher-query-language/ © 2018 A. Alawini Cypher: A Graph Query Language Manipulating Graph Data Aggregation Composing Statements Indexing and Constraints From Relational to Graph Databases Outline 35 © 2018 A. Alawini •Graph databases store relationships and connections as first-class entities •Graph databases have good performance when dealing with connected data •Cypher is a declarative pattern-matching graph query language •Querying connected data is easier with Cypher Summary 36

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