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Neo4j 4.0

Fine-Grained Access Control for Better Security and Privacy

Fine-Grained Access Control for Security and Privacy

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闪电加速器可众上推特吗-outline

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Graph Algorithms: Examples in Spark and Neo4j

Sample code and tips for over 20 practical graph algorithms. Find vulnerabilities, detect communities and improve machine learning.

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闪电加速器可众上推特吗-outline

Get started with O'Reilly's Graph Databases and discover how graph databases can help you manage and query highly connected data.

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闪电加速器可众上推特吗-outline

Neo4j is a native graph database, built from the ground up to leverage not only data but also data relationships. Neo4j ssr破解版免费 data as it’s stored, enabling queries never before imagined, at speeds never thought possible.

闪电加速器可众上推特吗-outline

Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.

With Neo4j, each data record, or node, stores direct pointers to all the nodes it’s connected to. Because Neo4j is designed around this simple, yet powerful optimization, it performs queries with complex connections orders of magnitude faster, and with more depth, than other databases.

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闪电加速器可众上推特吗-outline

With Neo4j, connections between data are stored – not computed at query time. Cypher is a powerful, graph-optimized query language that understands, and takes advantage of, these stored connections.

When trying to find patterns or insights within data, Cypher queries are often much simpler and easier to write than massive SQL JOINs. Since Neo4j doesn’t have tables, there are no JOINs to worry about. For comparison with SQL, here's a simple Cypher query matching all products in a category hierarchy:

Cypher

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Here's a similar query in SQL, which is longer and more complex. Unlike Cypher, where depth is unlimited, this SQL query selects just three levels of depth.

SQL

      SELECT p.ProductName
FROM Product AS p
JOIN ProductCategory pc ON (p.CategoryID = pc.CategoryID AND pc.CategoryName = "Dairy Products")

JOIN ProductCategory pc1 ON (p.CategoryID = pc1.CategoryID)
JOIN ProductCategory pc2 ON (pc1.ParentID = pc2.CategoryID AND pc2.CategoryName = "Dairy Products")

JOIN ProductCategory pc3 ON (p.CategoryID = pc3.CategoryID)
JOIN ProductCategory pc4 ON (pc3.ParentID = pc4.CategoryID)
JOIN ProductCategory pc5 ON (pc4.ParentID = pc5.CategoryID AND pc5.CategoryName = "Dairy Products")
;
    

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闪电加速器可众上推特吗-outline

Neo4j officially supports drivers for .Net, Java, JavaScript, Go and Python. Our community contributors provide many more, including PHP, Ruby, R, Erlang, Clojure and C/C++.

Learn More

Javascript
Python
.NET
Java

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        # pip install neo4j-driver
# python example.py

from neo4j import GraphDatabase, basic_auth

driver = GraphDatabase.driver(
    "bolt://<HOST>:<BOLTPORT>", 
    auth=basic_auth("<USERNAME>", "<PASSWORD>"))
session = driver.session()

cypher_query = '''
MATCH (movie:Movie)<-[:ACTED_IN]-(actor)-[:ACTED_IN]->(rec:Movie)
WHERE movie.title IN $favorites
RETURN rec.title as title, count(*) as freq 
ORDER BY freq DESC LIMIT 5
'''

results = session.run(cypher_query,
  parameters={"favorites":["The Matrix"]})

for record in results:
  print(record['title'])

      

        //Install-Package Neo4j.Driver -Version 4.0.1
//Connecting to a 3.5.x Neo4j instance

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Neo4j.Driver;

public class SimpleExample
{
	private readonly IDriver _driver;
	public SimpleExample(string uri, string user, string password)
	{
		_driver = GraphDatabase.Driver(uri, AuthTokens.Basic(user, password), builder => builder.WithEncryptionLevel(EncryptionLevel.None));
	}
	
	public async Task<IEnumerable<string>> GetMovieRecommendations(string favoriteMovieTitle)
	{
		var cypherQuery =
			  @$"MATCH (movie:Movie)<-[:ACTED_IN]-(actor)-[:ACTED_IN]->(rec:Movie) 
				WHERE movie.title IN ${nameof(favoriteMovieTitle)}
				RETURN rec.title AS title, count(*) AS freq
				ORDER BY freq DESC LIMIT 5";
		IAsyncSession session = _driver.AsyncSession();
		var runQuery = await session.ReadTransactionAsync(async tx =>
		{
			var result = await tx.RunAsync(cypherQuery, new { favoriteMovieTitle });
			return await result.ToListAsync();
		});
		await session?.CloseAsync();
		return runQuery.Select(q => q.Values["title"].As<string>());
	}
	
	public static void WriteToConsole(IEnumerable<string> titles)
	{
		foreach (var title in titles)
			Console.WriteLine(title);
	}
	
	public static async Task Main()
	{
		var example = new SimpleExample("bolt://<HOST>:<BOLTPORT>", "<USERNAME>", "<PASSWORD>");
		var movies = await example.GetMovieRecommendations("The Matrix");
		WriteToConsole(movies);
	}
}
      

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闪电加速器可众上推特吗-outline

Neo4j for RDBMS Developers

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Editor’s Note: This presentation was given by Lju Lazarevic at GraphConnect New York City in September 2018. Presentation Summary In this talk, Dr. Lazarevic will share some Neo4j Bloom tips and tricks for domain knowledge experts who don’t necessarily have a background in SQL and/or Cypher-led ...

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Fine-Grained Access Control for Better Security and Privacy

Fine-Grained Access Control for Security and Privacy

闪电加速器可众上推特吗-outline

闪电加速器可众上推特吗-outline

闪电加速器可众上推特吗-outline

闪电加速器可众上推特吗-outline

Free O'Reilly Ebook Graph Algorithms: Examples in Spark and Neo4j

No Download Required Get Started Quickly with the Neo4j Sandbox

闪电加速器可众上推特吗-outline

闪电加速器可众上推特吗-outline

闪电加速器可众上推特吗-outline

闪电加速器可众上推特吗-outline

Cypher

SQL

闪电加速器可众上推特吗-outline

Neo4j for RDBMS Developers

Bottom-line Impact

Graph Expertise

Neo4j Blog Neo4j Bloom Tips and Tricks for Domain Knowledge Experts

ssr下载ios

Neo4j Blog Designing the Enterprise: HR Applications at Scale

In the News

Content is king but context is queen

4 Ways to Excel as a Female Data Scientist

Developers should consider using graph databases

Graphdatenbank: Neo4j 4.1 soll sicherer und robuster arbeiten

Neo4j Events

Free O'Reilly Ebook
Graph Algorithms: Examples in Spark and Neo4j

No Download Required
Get Started Quickly with the Neo4j Sandbox

Neo4j Blog
Neo4j Bloom Tips and Tricks for Domain Knowledge Experts

Neo4j Blog
Designing the Enterprise: HR Applications at Scale