Visualize Query Data with GraphQL and Neo4j

In this tutorial, you will learn how to use Neo4j AuraDB and GraphQL to visualize query data and what problems you can solve using them. 

As we continue to build and develop software applications, our requirements for building complex applications have become more diverse and unique. We have a variety of data and many ways to work with it. However, one way is sometimes better and more performant than the other.

This article will discuss how we can use Neo4j and GraphQL and what problems we can solve using them. Before starting this article, you should have a strong knowledge of creating projects and backend development with Node.js and some familiarity with GraphQL.

This is an introductory article for Neo4j, so it is absolutely fine if you are unfamiliar with Neo4j. Even if you have used Neo4j before, this article can help you introduce GraphQL and work on projects with Neo4j and GraphQL. So, without further ado, let’s get right into it!

Table of contents:

• What is GraphQL?
• What is Neo4j?
  • Why should we use Neo4j?
• Using Neo4j with GraphQL
• Building an example project
  • Setting up the project
  • Installing the GraphQL and Neo4j dependencies
  • Configuring the project
  • Viewing the visualization result in Neo4j

What is GraphQL?

GraphQL is a query language for implementing the API. According to the GraphQL website:

“GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful dev tools.”

In more simple terms, GraphQL is a query language that can be used as an API to communicate between the client and the server. For example, we can use queries in GraphQL to get properties and mutations for specific operations.

We make GraphQL requests using types and fields instead of endpoints and retrieve the data as a JSON object. This helps us get only the data we requested from the server. For example, a typical GraphQL query will look like this:

{
  products {
    productId
    title
    variant {
      price
      size
    }
  }
}

The response data will look like this:

{
  "data": {
    "products": [
      {
        "productId": "1",
        "title": "Blue Jeans",
        "variant": {
          "price": 35,
          "size": "XL"
        }
      },
      {
        "productId": "2",
        "title": "Armani Suit",
        "variant": {
          "price": 59,
          "size": "XXL"
        }
      }
    ]
  }
}

As you can see, we are getting the JSON response in the same structure as we sent the request. Please head over to the GraphQL documentation to learn more about GraphQL.

What is Neo4j?

Neo4j is a native graph database that differs from other data storage solutions. Neo4j uses the data storage facility and is highly optimized for storing and maintaining relational data. Neo4j stores the data in the database as a graph. In this graph, each node represents the data, and the relationships between them are defined by a direct edge between the two nodes. All the information to find the next node in a sequence is available in the node itself. According to their website:

“With a native graph database at the core, Neo4j stores and manages data in a natural, connected state. The graph database takes a property graph approach, which benefits traversal performance and operations runtime.”

Why should we use Neo4j?

So, why should we use Neo4j? Because Neo4j uses the connected graph to store data in the native storage layer. It is much more helpful in the relational database, where the data is extensively connected to the other nodes. The queries are similar to SQL. However, the execution time is much faster, especially when performing heavy query operations between multiple nodes.

Some of the most outstanding advantages of using Neo4j include the following:

• Ease of use: Very easy to represent the data
• Speed: The query of the data is much faster
• Learning curve: Neo4j uses CQL for query language, which is similar to SQL and easy for humans to understand
• Simplicity: It doesn’t require complex JOIN operations like SQL because the data is directly connected to the graph

Using Neo4j with GraphQL

Neo4j gives us the facility to work directly with GraphQL. This allows us to implement our backend project with Neo4j and GraphQL using the Neo4j GraphQL Library. It is a JavaScript library that can be used in any JavaScript GraphQL implementation, such as Apollo Server.

The Neo4j GraphQL Library automatically generates CRUD operations when we provide the GraphQL type definitions to the library. That means we don’t need to write queries or mutations explicitly to perform CRUD operations. The Neo4j GraphQL Library automatically handles all of that for us. It also provides complex pagination, sorting, filtering, and more.

Building an example project

Enough theory discussion — let’s build an example project. In this article, we will create a to-do application using GraphQL as the API and Neo4j AuraDB as the database. We will only focus on the backend part and not cover the frontend, as this is not in the scope of the article. Let’s get started!

Setting up the project

First, let’s initiate a new project. We will create our root project folder and create a package.json file for our Node.js backend project by running the following command:

npm i -y

We will get the following result:

Installing the GraphQL and Neo4j dependencies

Now, let’s run the following command to install the required files for our project:

npm install @neo4j/graphql neo4j-driver graphql apollo-server dotenv

The code above will install all the required packages that we need. Also, let’s install nodemon. It will make our life easier, and we won’t have to restart our server every time while making any changes. Install it with the npm install --save-dev nodemon command.

Next, we will create a new Neo4j AuraDB instance from the Neo4j Aura website. You will need to create an account for a new Neo4j AuraDB instance. First, let’s create an Empty instance from the website. You can also make other instances with existing data and play around:

After clicking the Create button, we will get the following modal containing the username and password:

Let’s download the env file containing the credentials by clicking the Download button and selecting Continue.

Configuring the project

Let’s create our first file and start writing some code. Let’s start by creating a new file named server.js and paste the following code:

const { ApolloServer } = require("apollo-server");
const { Neo4jGraphQL } = require("@neo4j/graphql");
const neo4j = require("neo4j-driver");
const { typeDefs } = require("./typedefs");
const Config = require("./config");

const driver = neo4j.driver(
  Config.NEO4J_URI,
  neo4j.auth.basic(Config.NEO4J_USERNAME, Config.NEO4J_PASSWORD)
);

const neoSchema = new Neo4jGraphQL({ typeDefs, driver });

neoSchema.getSchema().then((schema) => {
  const server = new ApolloServer({
    schema: schema,
  });
  server.listen().then(({ url }) => {
    console.log(`GraphQL server ready on ${url}`);
  });
});

Here, we can see that we will also need to create two additional files: config.js and typedefs.js. So, let’s create the config.js file and paste the following code:

require("dotenv").config();

module.exports = class Config {
  static NEO4J_URI = process.env.NEO4J_URI;
  static NEO4J_USERNAME = process.env.NEO4J_USERNAME;
  static NEO4J_PASSWORD = process.env.NEO4J_PASSWORD;
  static NEO4J_AURA_INSTANCENAME = process.env.AURA_INSTANCENAME;
};

Now, let’s paste the following code into the typedefs.js file:

const { gql } = require("apollo-server");

module.exports.typeDefs = gql`
  type Todo @node(label: "Todo") {
    title: String!
    status: String!
    category: Category! @relationship(type: "Category", direction: IN)
  }

  type Category @node(label: "Category") {
    title: String!
    todos: [Todo!]! @relationship(type: "Category", direction: OUT)
  }
`;

Here, the @relationship schema directive helps the Neo4j understand the relationships between the types in our type definition. Before running the server, we also must make the following changes to our package.json file:

{
  ...
  "scripts": {
    ...,
    "start": "node server.js",
    "start:dev": "nodemon server.js"
  },
  ...
}

Now, let’s run the server and see the changes by running npm run start:dev to the terminal. After running the command, we will see the following message:

If we click the link, we will see the following screen in our browser:

After clicking the Query your server button, we can see that the Neo4j GraphQL library provides some basic CRUD operations (queries and mutations). Now, because we started from an empty Neo4j AuraDB instance, we need to create some todos by running the following mutation in the playground (and, of course, changing the input for each entry):

mutation CreateTodos {
  createTodos(input: {
    category: {
      create: {
        node: {
          title: "Assignment"
        }
      }
    },
    status: "NEW",
    title: "Assignment on Fourier Transform"
  }) {
    info {
      bookmark
      nodesCreated
      relationshipsCreated
    }
    todos {
      category {
        title
      }
      status
      title
    }
  }
}

You can find all the information about the nodes in the Apollo Server playground. Here, note that we can create categories while also creating todo. However, if we want to use an existing category while running the createTodos mutation, we can rewrite the previous mutation, as shown below:

mutation CreateTodos {
  createTodos(input: {
    category: {
      connect: {
        where: {
          node: {
            title: "Assignment"
          }
        }
      }
    },
    status: "NEW",
    title: "Assignment on Fourier Transform"
  }) {
    info {
      bookmark
      nodesCreated
      relationshipsCreated
    }
    todos {
      category {
        title
      }
      status
      title
    }
  }
}

We can also query all the todo items in our database by running the following query in GraphQL:

query Todos {
  todos {
    title
    status
    category {
      title
    }
  }
}

After running the query, we will get the JSON output:

{
  "data": {
    "todos": [
      {
        "title": "Write an Article on ChatGPT",
        "status": "NEW",
        "category": {
          "title": "Writing"
        }
      },
      {
        "title": "Assignment on Fourier Transform",
        "status": "NEW",
        "category": {
          "title": "Assignment"
        }
      },
      {
        "title": "Assignment on Neural Network",
        "status": "NEW",
        "category": {
          "title": "Assignment"
        }
      }
    ]
  }
}

Viewing the visualization result in Neo4j

We can also visualize the data from the Neo4j Workspace. Let’s go to the Neo4j AuraDB dashboard and click the Open button of our instance. Then enter your password, and you will log in to the Neo4j Workspace:

Now, in the Neo4j Workspace, select Show me a graph and hit enter in the search bar. You will see all the nodes and relationships between them. In our example, we will see a graph like the image below:

Conclusion

In this article, we successfully performed the CRUD operation using GraphQL and then visualized the data in Neo4j AuraDB. Using the powerful technology of Neo4j, we can do complex relational queries without all the JOIN operations and get our results faster and more efficiently. We can also use the Neo4j GraphQL Library to work with GraphQL. We saw that we could quickly generate CRUD operations for our given GraphQL type definitions without explicitly writing any queries or mutations.

Source: https://blog.logrocket.com

#graphql #neo4j

Neo4j and GraphQL: Developer Guides

In this article, we will learn about Neo4j and GraphQL: Developer Guides. GraphQL enables an API developer to model application data as a graph, and API clients that request that data to easily traverse this data graph to retrieve it. These are powerful game-changing capabilities. But if your backend isn’t graph-ready, these capabilities could become liabilities by putting additional pressure on your database, consuming greater time and resources.

In this article, I’ll shed some light on ways you can mitigate these issues when you use a graph database as the backend for your next GraphQL API by taking advantage of the capabilities offered by the open-source Neo4j GraphQL Library.

What Graphs Are, And Why They Need A Database 

Fundamentally, a graph is a data structure composed of nodes (the entities in the data model) along with the relationships between nodes. Graphs are all about the connections in your data. For this reason, relationships are first-class citizens in the graph data model.

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Graphs are so important that an entire category of databases was created to work with graphs: graph databases. Unlike relational or document databases that use tables or documents, respectively, as their data models, the data model of a graph database is (you guessed it!) a graph.

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GraphQL is not and was never intended to be a database query language. It is indeed a query language, yet it lacks much of the semantics we would expect from a true database query language like SQL or Cypher. That’s on purpose. You don’t want to be exposing our entire database to all our client applications out there in the world.

Instead, GraphQL is an API query language, modeling application data as a graph and purpose-built for exposing and querying that data graph, just as SQL and Cypher were purpose-built for working with relational and graph databases, respectively. Since one of the primary functions of an API application is to interact with a database, it makes sense that GraphQL database integrations should help build GraphQL APIs that are backed by a database. That’s exactly what the Neo4j GraphQL Library does — makes it easier to build GraphQL APIs backed by Neo4j.

One of GraphQL’s most powerful capabilities enables the API designer to express the entire data domain as a graph using nodes and relationships. This way, API clients can traverse the data graph to find the relevant data. This makes better sense because most API interactions are done in the context of relationships. For example, if we want to fetch all orders placed by a specific customer or all the products in a given order, we’re traversing the pattern of relationships to find those connections in our data.

Soon after GraphQL was open-sourced by Facebook in 2015, a crop of GraphQL database integrations sprung up, evidently in an effort to address the n+1 conundrum and similar problems. Neo4j GraphQL Library was one of these integrations.

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Common GraphQL Implementation Problems 

Building a GraphQL API service requires you to perform two steps:

1. Define the schema and type definitions.
2. Create resolver functions for each type and field in the schema that will be responsible for fetching or updating data in our data layer.

Combining these schema and resolver functions gives you an executable GraphQL schema object. You may then attach the schema object to a networking layer, such as a Node.js web server or lambda function, to expose the GraphQL API to clients. Often developers will use tools like Apollo Server or GraphQL Yoga to help with this process, but it’s still up to them to handle the first two steps.

If you’ve ever written resolver functions, you’ll recall they can be a bit tedious, as they’re typically filled with boilerplate data fetching code. But even worse than lost developer productivity is the dreaded n+1 query problem. Because of the nested way that GraphQL resolver functions are called, a single GraphQL request can result in multiple round-trip requests to the database. Addressing this typically involves a batching and caching strategy, adding additional complexity to your GraphQL application.

Doubling Down On GraphQL-First Development 

Originally, the term GraphQL-First Development described a collaborative process. Frontend and backend teams would agree on a GraphQL schema, then go to work independently building their respective pieces of the codebase. Database integrations extend the idea of GraphQL-First development by applying this concept to the database as well. GraphQL-type definitions can now drive the database.

You can find the full code examples presented here on GitHub.

Let’s say you’re building a business reviews application where you want to keep track of businesses, users, and user reviews. GraphQL-type definitions to describe this API might look something like this:

type Business {
  businessId: ID!
  name: String!
  city: String!
  state: String!
  address: String!
  location: Point!
  reviews: [Review!]! @relationship(type: "REVIEWS", direction: IN)
  categories: [Category!]!
    @relationship(type: "IN_CATEGORY", direction: OUT)
}

type User {
  userID: ID!
  name: String!
  reviews: [Review!]! @relationship(type: "WROTE", direction: OUT)
}

type Review {
  reviewId: ID!
  stars: Float!
  date: Date!
  text: String
  user: User! @relationship(type: "WROTE", direction: IN)
  business: Business! @relationship(type: "REVIEWS", direction: OUT)
}

type Category {
  name: String!
  businesses: [Business!]!
    @relationship(type: "IN_CATEGORY", direction: IN)
}

Note the use of the GraphQL schema directive @relationship in our type definitions. GraphQL schema directives are the language’s built-in extension mechanism and key components for extending and configuring GraphQL APIs — especially with database integrations like Neo4j GraphQL Library. In this case, the @relationship directive encodes the relationship type and direction (in or out) for pairs of nodes in the database.

Type definitions are then used to define the property graph data model in Neo4j. Instead of maintaining two schemas (one for our database and another for our API), you can now use type definitions to define both the API and the database’s data model. Furthermore, since Neo4j is schema-optional, using GraphQL to drive the database adds a layer of type safety to your application.

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From GraphQL Type Definitions To Complete API Schemas 

In GraphQL, you use fields on special types (Query, Mutation, and Subscription) to define the entry points for the API. In addition, you may want to define field arguments that can be passed at query time, for example, for sorting or filtering. Neo4j GraphQL Library handles this by creating entry points in the GraphQL API for create, read, update, and delete operations for each type, as well as field arguments for sorting and filtering.

Let’s look at some examples. For our business reviews application, suppose you want to show a list of businesses sorted alphabetically by name. Neo4j GraphQL Library automatically adds field arguments to accomplish just this.

{
  businesses(options: { limit: 10, sort: { name: ASC } }) {
    name
  }
}

Perhaps you want to allow the users to filter this list of businesses by searching for companies by name or keyword. The where argument handles this kind of filtering:

{
  businesses(where: { name_CONTAINS: "Brew" }) {
    name
    address
  }

You can then combine these filter arguments to express very complex operations. Say you want to find businesses that are in either the Coffee or Breakfast category and filter for reviews containing the keyword “breakfast sandwich:”

{
  businesses(
    where: {
      OR: [
        { categories_SOME: { name: "Coffee" } }
        { categories_SOME: { name: "Breakfast" } }
      ]
    }
  ) {
    name
    address
    reviews(where: { text_CONTAINS: "breakfast sandwich" }) {
    stars
    text
  }
 }
}

Using location data, for example, you can even search for businesses within 1 km of our current location:

{
  businesses(
    where: {
      location_LT: {
        distance: 1000
        point: { latitude: 37.563675, longitude: -122.322243 }
      }
    }
  ) {
  name
  address
  city
  state
  }
}

As you can see, this functionality is extremely powerful, and the generated API can be configured through the use of GraphQL schema directives.

We Don’t Need No Stinking Resolvers 

As we noted earlier, GraphQL server implementations require resolver functions where the logic for interacting with the data layer lives. With database integrations such as Neo4j GraphQL Library, resolvers are generated for you at query time for translating arbitrary GraphQL requests into singular, encapsulated database queries. This is a huge developer productivity win (we don’t have to write boilerplate data fetching code — yay!). It also addresses the n+1 query problem by making a single round-trip request to the database.

Moreover, graph databases like Neo4j are optimized for exactly the kind of nested graph data traversals commonly expressed in GraphQL. Let’s see this in action. Once you’ve defined your GraphQL type definitions, here’s all the code necessary to spin up your fully functional GraphQL API:

const { ApolloServer } = require("apollo-server");
const neo4j = require("neo4j-driver");
const { Neo4jGraphQL } = require("@neo4j/graphql");

// Connect to your Neo4j instance.
const driver = neo4j.driver(
  "neo4j+s://my-neo4j-db.com",
  neo4j.auth.basic("neo4j", "letmein")
);

// Pass our GraphQL type definitions and Neo4j driver instance.
const neoSchema = new Neo4jGraphQL({ typeDefs, driver });

// Generate an executable GraphQL schema object and start
// Apollo Server.
neoSchema.getSchema().then((schema) => {
  const server = new ApolloServer({
    schema,
  });
  server.listen().then(({ url }) => {
    console.log(`GraphQL server ready at ${url}`);
  });
});

That’s it! No resolvers.

Extend GraphQL With The Power Of Cypher 

So far, we’ve only been talking about basic create, read, update, and delete operations. How can you handle custom logic with Neo4j GraphQL Library?

Let’s say you want to show recommended businesses to your users based on their search or review history. One way would be to implement your own resolver function with the logic for generating those personalized recommendations built in. Yet there’s a better way to maintain the one-to-one, GraphQL-to-database operation performance guarantee: You can leverage the power of the Cypher query language using the @cypher GraphQL schema directive to define your custom logic within your GraphQL type definitions.

Cypher is an extremely powerful language that enables you to express complex graph patterns using ASCII-art-like declarative syntax. I won’t go into detail about Cypher in this article, but let’s see how you could accomplish our personalized recommendation task by adding a new field to your GraphQL-type definitions:

extend type Business {
  recommended(first: Int = 1): [Business!]!
    @cypher(
      statement: """
        MATCH (this)<-[:REVIEWS]-(:Review)<-[:WROTE]-(u:User)
        MATCH (u)-[:WROTE]->(:Review)-[:REVIEWS]->(rec:Business)
        WITH rec, COUNT(*) AS score
        RETURN rec ORDER BY score DESC LIMIT $first
      """
    )
}

Here, the Business type has a recommended field, which uses the Cypher query defined above to show recommended businesses whenever requested in the GraphQL query. You didn’t need to write a custom resolver to accomplish this. Neo4j GraphQL Library is still able to generate a single database request even when using a custom recommended field.

GraphQL Database Integrations Under The Hood #

GraphQL database integrations like Neo4j GraphQL Library are powered by the GraphQLResolveInfo object. This object is passed to all resolvers, including the ones generated for us by Neo4j GraphQL Library. It contains information about both the GraphQL schema and GraphQL operation being resolved. By closely inspecting this object, GraphQL database integrations can generate database queries at the time queries are placed.

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If you’re interested, I recently gave a talk at GraphQL Summit that goes into much more detail.

Powering Low-Code, Open Source-Powered GraphQL Tools 

An open-source library that works with any JavaScript GraphQL implementation can conceivably power an entire ecosystem of low-code GraphQL tools. Collectively, these tools leverage the functionality of Neo4j GraphQL Library to help make it easier for you to build, test, and deploy GraphQL APIs backed by a real graph database.

For example, GraphQL Mesh uses Neo4j GraphQL Library to enable Neo4j as a data source for data federation. Don’t want to write the code necessary to build a GraphQL API for testing and development? The Neo4j GraphQL Toolbox is an open-source, low-code web UI that wraps Neo4j GraphQL Library. This way, it can generate a GraphQL API from an existing Neo4j database with a single click.

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Where From Here #

If building a GraphQL API backed by a native graph database sounds interesting or at all helpful for the problems you’re trying to solve as a developer, I would encourage you to give the Neo4j GraphQL Library a try. Also, the Neo4j GraphQL Library landing page is a good starting point for documentation, further examples, and comprehensive workshops.

I’ve also written a book Full Stack GraphQL Applications, published by Manning, that covers this topic in much more depth. My book covers handling authorization, working with the frontend application, and using cloud services like Auth0, Netlify, AWS Lambda, and Neo4j Aura to deploy a full-stack GraphQL application. In fact, I’ve built out the very business reviews application from this article as an example in the book! Thanks to Neo4j, this book is now available as a free download.

Last but not least, I will be presenting a live session entitled “Making Sense of Geospatial Data with Knowledge Graphs” during the NODES 2022 virtual conference on Wednesday, November 16, produced by Neo4j. Registration is free to all attendees.

Original article sourced at: https://www.smashingmagazine.com

#graphql #neo4j 

Popoto: Visual Query Builder for Neo4j Graph Database

Popoto.js is a JavaScript library built with D3.js designed to create interactive and customizable visual query builder for Neo4j graph databases.

The graph queries are translated into Cypher and run on the database. Popoto also helps to display and customize the results.

An application is composed of various components, each one can be included independently anywhere in a web application. It just need to be bound to a container ID in an HTML page and the content will be generated automatically.

A common example application contains the following components:

	Graph component is an interactive interface designed to build queries for non technical users, the graph is made of selectable nodes connected to each other by links.
	Toolbar is a list of actions available in the graph container.
	Taxonomy container contains the list of searchable labels in the database.
	Query viewers container shows different representation of the corresponding query defined in the Graph component.
	Result container displays the results matching the graph query.

Resources

• Examples
• Wiki

Install

For NPM, npm install popoto For Yarn, yarn add popoto.

Otherwise, download the latest release.

You can also load directly from unpkg or jsDelivr

Example:

<!-- Add default CSS reference -->
<link rel="stylesheet" href="https://unpkg.com/popoto/dist/popoto.min.css">
<!-- Or -->
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/popoto/dist/popoto.min.css">

<!-- Add Popoto script reference, will default to popoto.min.js -->
<script src="https://unpkg.com/popoto"></script>
<!-- Or -->
<script src="https://cdn.jsdelivr.net/npm/popoto/dist/popoto.min.js"></script>

For source version:

<!-- Add Popoto script reference -->
<script src="https://unpkg.com/popoto/dist/popoto.js"></script>
<!-- Or -->
<script src="https://cdn.jsdelivr.net/npm/popoto/dist/popoto.js"></script>

Quick start guide:

• Edit the "index.html" file, by default this application is based on Neo4j movie graph example.
• Create your driver instance following Neo4j developer guide: https://neo4j.com/developer/javascript/

const driver = neo4j.driver(
    "neo4j://dff437fa.databases.neo4j.io", // Unencrypted 
    //"neo4j+s://dff437fa.databases.neo4j.io", //Encrypted with Full Certificate
    neo4j.auth.basic("popoto", "popotopassword"),
    //{disableLosslessIntegers: true} // Enabling native numbers
);

• Change the value of popoto.runner.DRIVER = driver to your running server driver instance.
• If needed you can change the default session creation to add parameters:

popoto.runner.createSession = function () {
    return runner.DRIVER.session({defaultAccessMode: "READ"})
};

• Update the list of labels defined in "popoto.provider.node.Provider" definition. All node labels to display in the graph should be added in this list.
• Add any other customization you need in this file. See Nhogs/popoto-examples for detailed configuration examples.
• Open index.html file in your preferred web browser to see the result.
• Click on a node to display and select a value, click on relationship arcs around nodes to navigate in relations uses right click to remove a value and ctrl+click to negate a node. See all Basic actions for details

See an explained example page source in Getting started.

Download Details:

Author: Nhogs
Source Code: https://github.com/Nhogs/popoto 
License: GPL-3.0 license

#javascript #neo4j #graph #database 

Rneo4j: Neo4j Driver for R

RNeo4j

RNeo4j is Neo4j's R driver. It allows you to read and write data from / to Neo4j directly from your R environment.

Install

Neo4j

First and foremost, download Neo4j!

Windows

If you're on Windows, download the .exe and follow the instructions. You'll get a GUI where you simply press "Start" to start Neo4j.

OS X

If you're on OS X, you can download either the .dmg or the .tar.gz. The .dmg will give you a GUI where you simply press "Start" to start Neo4j. Otherwise, download the .tar.gz, unzip, navigate to the directory and execute ./bin/neo4j start.

Linux

If you're on Linux, you have to use the .tar.gz. Download the .tar.gz, unzip, navigate to the directory and execute ./bin/neo4j start.

You may also find neo4j in your distribution's package manager.

Bolt dependencies

These depencies are are only required if you want to use the Bolt interface. They must be present at build time, and libneo4j-client must also be present at runtime.

Windows

• Rust: https://rustup.rs
• Clang: binary releases are available at http://releases.llvm.org/download.html
• libneo4j-client:
  • Make sure you have RTools installed (necessary for building R packages on Windows)
  • Open your MinGW shell (check in start menu, C:\RTools\MinGW\bin, and C:\MinGW\bin)
  • See "Installing libneo4j-from source" section

OS X (with Homebrew installed)

• Rust: brew install rust (or https://rustup.rs but see "Rust Path" section)
• Clang: brew install llvm
• libneo4j-client: brew install cleishm/neo4j/libneo4j-client

Linux

• Rust:
  • Debian based (e.g. Mint, Ubuntu): sudo apt-get install cargo
  • Arch Linux: sudo pacman -S rust
  • Building from source: https://rustup.rs but see "Rust Path" section
• Clang: get it from your package manager
  • Debian based (e.g. Mint, Ubuntu): sudo apt-get install clang libclang-dev
  • Arch Linux: sudo pacman -S clang
  • Other: your package manager almost certainly has clang. It may be called llvm.
• libneo4j-client:
  • Debian based: sudo apt-get install libneo4j-client-dev
  • Other: See "Installing libneo4j-client from source"

Rust Path

By default, on *nix systems (such as Linux and OS X), rustup only sets the PATH in your shell. That means that if you try to build RNeo4j in a GUI application like RStudio, it may fail. To work around this issue, simply build RNeo4j in a terminal.

Installing libneo4j-client from source

Newer versions of GCC require removing the -Werror from GCC_CFLAGS in configure.ac.

Run these commands in your shell:

git clone https://github.com/cleishm/libneo4j-client
cd libneo4j-client
./autogen.sh
./configure --disable-tools
sudo make install

See https://github.com/cleishm/libneo4j-client for more details

RNeo4j

From CRAN

install.packages("RNeo4j")

From GitHub

devtools::install_github("nicolewhite/RNeo4j")

From Source

Go to the latest release and download the source code. You can then install with install.packages.

install.packages("/path/to/file.tar.gz", repos=NULL, type="source")

Load the Package

library(RNeo4j)

Connect

graph = startGraph("http://localhost:7474/db/data/")

If you have authentication enabled, pass your username and password.

graph = startGraph("http://localhost:7474/db/data/", username="neo4j", password="password")

Nodes

nicole = createNode(graph, "Person", name="Nicole", age=24)
greta = createNode(graph, "Person", name="Greta", age=24)
kenny = createNode(graph, "Person", name="Kenny", age=27)
shannon = createNode(graph, "Person", name="Shannon", age=23)

Relationships

r1 = createRel(greta, "LIKES", nicole, weight=7)
r2 = createRel(nicole, "LIKES", kenny, weight=1)
r3 = createRel(kenny, "LIKES", shannon, weight=3)
r4 = createRel(nicole, "LIKES", shannon, weight=5)

Cypher

If you're returning tabular results, use cypher, which will give you a data.frame.

query = "
MATCH (nicole:Person)-[r:LIKES]->(p:Person)
WHERE nicole.name = 'Nicole'
RETURN nicole.name, r.weight, p.name
"

cypher(graph, query)

##   nicole.name r.weight  p.name
## 1      Nicole        5 Shannon
## 2      Nicole        1   Kenny

For anything more complicated, use cypherToList, which will give you a list.

query = "
MATCH (nicole:Person)-[:LIKES]->(p:Person)
WHERE nicole.name = 'Nicole'
RETURN nicole, COLLECT(p.name) AS friends
"

cypherToList(graph, query)

## [[1]]
## [[1]]$nicole
## < Node > 
## Person
## 
## $name
## [1] "Nicole"
## 
## $age
## [1] 24
## 
## 
## [[1]]$friends
## [[1]]$friends[[1]]
## [1] "Shannon"
## 
## [[1]]$friends[[2]]
## [1] "Kenny"

Both cypher and cypherToList accept parameters. These parameters can be passed individually or as a list.

query = "
MATCH (p1:Person)-[r:LIKES]->(p2:Person)
WHERE p1.name = {name1} AND p2.name = {name2}
RETURN p1.name, r.weight, p2.name
"

cypher(graph, query, name1="Nicole", name2="Shannon")

##   p1.name r.weight p2.name
## 1  Nicole        5 Shannon

cypher(graph, query, list(name1="Nicole", name2="Shannon"))

##   p1.name r.weight p2.name
## 1  Nicole        5 Shannon

Shortest Paths

p = shortestPath(greta, "LIKES", shannon, max_depth=4)
n = nodes(p)
sapply(n, "[[", "name")

## [1] "Greta"   "Nicole"  "Shannon"

Weighted Shortest Paths

p = shortestPath(greta, "LIKES", shannon, max_depth=4, cost_property="weight")
n = nodes(p)
sapply(n, "[[", "name")

## [1] "Greta"   "Nicole"  "Kenny"   "Shannon"

p$weight

## [1] 11

Graph Algorithms

library(igraph)

query = "
MATCH (n)-->(m)
RETURN n.name, m.name
"

edgelist = cypher(graph, query)
ig = graph.data.frame(edgelist, directed=F)

betweenness(ig)

##  Nicole   Greta   Kenny Shannon 
##       2       0       0       0

closeness(ig)

##    Nicole     Greta     Kenny   Shannon 
## 0.3333333 0.2000000 0.2500000 0.2500000

Visualizations

igraph

plot(ig)

ggnet

library(network)
library(GGally)

net = network(edgelist)
ggnet(net, label.nodes=TRUE)

visNetwork

Read this blog post and check out this slide deck.

Import

library(hflights)
hflights = hflights[sample(nrow(hflights), 1000), ]
row.names(hflights) = NULL

head(hflights)

##   Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum
## 1 2011     1         15         6     927    1038            XE      2885
## 2 2011    10         10         1    2001    2322            XE      4243
## 3 2011     6         15         3    1853    2108            CO       670
## 4 2011     4         10         7    2100     102            CO       410
## 5 2011     1         25         2     739    1016            XE      3083
## 6 2011     9         13         2    1745    1841            CO      1204
##   TailNum ActualElapsedTime AirTime ArrDelay DepDelay Origin Dest Distance
## 1  N34110               131     113      -10       -3    IAH  COS      809
## 2  N13970               141     127        2       19    IAH  CMH      986
## 3  N36207               255     231       15       -2    IAH  SFO     1635
## 4  N76517               182     162      -18        5    IAH  EWR     1400
## 5  N12922               157     128        0       -6    IAH  MKE      984
## 6  N35271                56      34       -7       -5    IAH  SAT      191
##   TaxiIn TaxiOut Cancelled CancellationCode Diverted
## 1      6      12         0                         0
## 2      4      10         0                         0
## 3      5      19         0                         0
## 4      7      13         0                         0
## 5      4      25         0                         0
## 6      3      19         0                         0

addConstraint(graph, "Carrier", "name")
addConstraint(graph, "Airport", "name")

query = "
CREATE (flight:Flight {number: {FlightNum} })
SET flight.year = TOINT({Year}),
    flight.month = TOINT({DayofMonth}),
    flight.day = TOINT({DayOfWeek})

MERGE (carrier:Carrier {name: {UniqueCarrier} })
CREATE (flight)-[:OPERATED_BY]->(carrier)

MERGE (origin:Airport {name: {Origin} })
MERGE (dest:Airport {name: {Dest} })

CREATE (flight)-[o:ORIGIN]->(origin)
CREATE (flight)-[d:DESTINATION]->(dest)

SET o.delay = TOINT({DepDelay}),
    o.taxi_time = TOINT({TaxiOut})

SET d.delay = TOINT({ArrDelay}),
    d.taxi_time = TOINT({TaxiIn})
"

tx = newTransaction(graph)

for(i in 1:nrow(hflights)) {
  row = hflights[i, ]
  
  appendCypher(tx, query,
               FlightNum=row$FlightNum,
               Year=row$Year,
               DayofMonth=row$DayofMonth,
               DayOfWeek=row$DayOfWeek,
               UniqueCarrier=row$UniqueCarrier,
               Origin=row$Origin,
               Dest=row$Dest,
               DepDelay=row$DepDelay,
               TaxiOut=row$TaxiOut,
               ArrDelay=row$ArrDelay,
               TaxiIn=row$TaxiIn)
}

commit(tx)

summary(graph)

##     This          To    That
## 1 Flight OPERATED_BY Carrier
## 2 Flight      ORIGIN Airport
## 3 Flight DESTINATION Airport

Connection Issues

Couldn't connect to server

Error in curl::curl_fetch_memory(url, handle = handle) : 
  Couldn't connect to server

Neo4j probably isn't running. Make sure Neo4j is running first. It's also possible you have localhost resolution issues; try connecting to http://127.0.0.1:7474/db/data/ instead.

No authorization header supplied

Error: client error: (401) Unauthorized
Neo.ClientError.Security.AuthorizationFailed
No authorization header supplied.

You have auth enabled on Neo4j and either didn't provide your username and password or they were invalid. You can pass a username and password to startGraph.

graph = startGraph("http://localhost:7474/db/data/", username="neo4j", password="password")

You can also disable auth by editing the following line in conf/neo4j-server.properties.

# Require (or disable the requirement of) auth to access Neo4j
dbms.security.auth_enabled=false

Contribute

Check out the contributing doc if you'd like to contribute!

Download Details:

Author: Nicolewhite
Source Code: https://github.com/nicolewhite/Rneo4j 
License: View license

#r #neo4j #driver 

neovis.js 2.0: Neo4j + vis.js == Graph Visualizations in The Browser

neovis.js

Graph visualizations powered by vis.js with data from Neo4j.

Features

•  Connect to Neo4j instance to get live data
•  User specified labels and property to be displayed
•  User specified Cypher query to populate
•  Specify node property for url of image for node
•  Specify edge property for edge thickness
•  Specify node property for community / clustering
•  Specify node property for node size
•  Configure popover

Install

Neovis.js can be installed via npm:

npm install --save neovis.js

you can also obtain neovis.js via CDN:

CDN

For ease of use Neovis.js can be obtained from Neo4jLabs CDN:

Most recent release

<script src="https://unpkg.com/neovis.js@2.0.2"></script>

Version without neo4j-driver dependency

<script src="https://unpkg.com/neovis.js@2.0.2/dist/neovis-without-dependencies.js"></script>

Quickstart Example

Let's go through the steps to reproduce this visualization:

Prepare Neo4j

Start with a blank Neo4j instance, or spin up a blank Neo4j Sandbox. We'll load the Game of Thrones dataset, run:

LOAD CSV WITH HEADERS FROM 'https://raw.githubusercontent.com/mathbeveridge/asoiaf/master/data/asoiaf-all-edges.csv'
AS row
MERGE (src:Character {name: row.Source})
MERGE (tgt:Character {name: row.Target})
MERGE (src)-[r:INTERACTS]->(tgt)
  ON CREATE SET r.weight = toInteger(row.weight)

We've pre-calculated PageRank and ran a community detection algorithm to assign community ids for each Character. Let's load those next:

LOAD CSV WITH HEADERS FROM 'https://raw.githubusercontent.com/johnymontana/neovis.js/master/examples/data/got-centralities.csv'
AS row
MATCH (c:Character {name: row.name})
SET c.community = toInteger(row.community),
c.pagerank = toFloat(row.pagerank)

Our graph now consists of Character nodes that are connected by an INTERACTS relationships. We can visualize the whole graph in Neo4j Browser by running:

MATCH p = (:Character)-[:INTERACTS]->(:Character)
RETURN p

We can see characters that are connected and with the help of the force directed layout we can begin to see clusters in the graph. However, we want to visualize the centralities (PageRank) and community detection results that we also imported.

Specifically we would like:

• Node size to be proportional to the Character's pagerank score. This will allow us to quickly identify important nodes in the network.
• Node color to determined by the community property. This will allow us to visualize clusters.
• Relationship thickeness should be proportional to the weight property on the INTERACTS relationship.

Neovis.js, by combining the JavaScript driver for Neo4j and the vis.js visualization library will allow us to build this visualization.

index.html

Create a new html file:

<!doctype html>
<html>
<head>
    <title>Neovis.js Simple Example</title>
    <style type="text/css">
        html, body {
            font: 16pt arial;
        }

        #viz {
            width: 900px;
            height: 700px;
            border: 1px solid lightgray;
            font: 22pt arial;
        }
    </style>
</head>
<body onload="draw()">
<div id="viz"></div>
</body>
</html>

We define some basic CSS to specify the boundaries of a div and then create a single div in the body. We also specify onload="draw()" so that the draw() function is called as soon as the body is loaded.

We need to pull in neovis.js:

<script src="https://unpkg.com/neovis.js@2.0.2"></script>

And define our draw() function:

<script type="text/javascript">

    let neoViz;

    function draw() {
        const config = {
            containerId: "viz",
            neo4j: {
                serverUrl: "bolt://localhost:7687",
                serverUser: "neo4j",
                serverPassword: "sorts-swims-burglaries",
            }
            labels: {
                Character: {
                    label: "name",
                    value: "pagerank",
                    group: "community",
                    [NeoVis.NEOVIS_ADVANCED_CONFIG]: {
                        function: {
                            title: (node) => viz.nodeToHtml(node, [
                                "name",
                                "pagerank"
                            ])
                        }
                    }
                }
            },
            relationships: {
                INTERACTS: {
                    value: "weight"
                }
            },
            initialCypher: "MATCH (n)-[r:INTERACTS]->(m) RETURN *"
        };

        neoViz = new NeoVis.default(config);
        neoViz.render();
    }
</script>

This function creates a config object that specifies how to connect to Neo4j, what data to fetch, and how to configure the visualization.

See simple-example.html for the full code.

module usage

you can also use it as module, but it would require you have a way to import css files

import NeoVis from 'neovis.js';

or you can import the version with bundled dependency

import NeoVis from 'neovis.js/dist/neovis.js';

Api Reference

Api Reference

Build

This project uses git submodules to include the dependencies for neo4j-driver and vis.js. This project uses webpack to build a bundle that includes all project dependencies. webpack.config.js contains the configuration for webpack. After cloning the repo:

npm install
npm run build
npm run typedoc

will build dist/neovis.js and dist/neovis-without-dependencies.js

Download Details: 
Author: neo4j-contrib
Source Code: https://github.com/neo4j-contrib/neovis.js 
License: Apache-2.0 license
#datavisualization #javascript #neo4j #graphvisualization #neo4j #visjs

Py2neo: Comprehensive Neo4j Driver Library and Toolkit For Python

Py2neo

Py2neo is a client library and toolkit for working with Neo4j from within Python applications. The library supports both Bolt and HTTP and provides a high level API, an OGM, admin tools, a Cypher lexer for Pygments, and many other bells and whistles.

Command line tooling has been removed from the library in py2neo 2021.2. This functionality now exists in the separate ipy2neo project.

As of version 2021.1, py2neo contains full support for routing, as exposed by a Neo4j cluster. This can be enabled using a neo4j://... URI or by passing routing=True to a Graph constructor.

Installation & Compatibility

To install the latest release of py2neo, simply use:

$ pip install py2neo

The following versions of Python and Neo4j (all editions) are supported:

Neo4j	Python 3.5+	Python 2.7
4.4
4.3
4.2
4.1
4.0
3.5
3.4

Note that py2neo is developed and tested under Linux using standard CPython distributions. While other operating systems and Python distributions may work, support for these is not available.

Quick Example

To run a query against a local database is straightforward:

>>> from py2neo import Graph
>>> graph = Graph("bolt://localhost:7687", auth=("neo4j", "password"))
>>> graph.run("UNWIND range(1, 3) AS n RETURN n, n * n as n_sq")
   n | n_sq
-----|------
   1 |    1
   2 |    4
   3 |    9

Releases & Versioning

As of 2020, py2neo has switched to Calendar Versioning, using a scheme of YYYY.N.M. Here, N is an incrementing zero-based number for each year, and M is a revision within that version (also zero-based).

No compatibility guarantees are given between versions, but as a general rule, a change in M should require little-to-no work within client applications, whereas a change in N may require some work. A change to the year is likely to require a more significant amount of work to upgrade.

Note that py2neo is developed on a rolling basis, so patches are not made to old versions. Users will instead need to install the latest release to adopt bug fixes.

More

For more information, read the handbook.

Author: py2neo-org
Source Code: https://github.com/py2neo-org/py2neo
License: Apache-2.0 License

#python #neo4j 

How to Access Neo4j Data with REST in Spring

Accessing Neo4j Data with REST

This guide walks you through the process of creating an application that accesses graph-based data through a hypermedia-based RESTful front end.

What You Will Build

You will build a Spring application that lets you create and retrieve Person objects that are stored in a Neo4j NoSQL database by using Spring Data REST. Spring Data REST takes the features of Spring HATEOAS and Spring Data Neo4j and automatically combines them together.

How to complete this guide

Like most Spring Getting Started guides, you can start from scratch and complete each step or you can bypass basic setup steps that are already familiar to you. Either way, you end up with working code.

To start from scratch, move on to [scratch].

To skip the basics, do the following:

• Download and unzip the source repository for this guide, or clone it using Git: git clone https://github.com/spring-guides/gs-accessing-neo4j-data-rest.git
• cd into gs-accessing-neo4j-data-rest/initial
• Jump ahead to Permissions to Access Neo4j.

When you finish, you can check your results against the code in gs-accessing-neo4j-data-rest/complete.

Standing up a Neo4j Server

Before you can build a this application, you need to set up a Neo4j server.

Neo4j has an open source server that you can install for free.

On a Mac with Homebrew installed, you can type the following in a terminal window:

$ brew install neo4j

For other options, see https://neo4j.com/download/community-edition/

Once you have installed Neo4j, you can launch it with its default settings by running the following command:

$ neo4j start

You should see a message similar to the following:

Starting Neo4j.
Started neo4j (pid 96416). By default, it is available at http://localhost:7474/
There may be a short delay until the server is ready.
See /usr/local/Cellar/neo4j/3.0.6/libexec/logs/neo4j.log for current status.

By default, Neo4j has a username and password of neo4j and neo4j. However, it requires that the new account password be changed. To do so, run the following command:

$ curl -v -u neo4j:neo4j POST localhost:7474/user/neo4j/password -H "Content-type:application/json" -d "{\"password\":\"secret\"}"

This changes the password from neo4j to secret (something to NOT DO in production!) With that completed, you should be ready to run this guide.

Starting with Spring Initializr

You can use this pre-initialized project and click Generate to download a ZIP file. This project is configured to fit the examples in this tutorial.

To manually initialize the project:

1. Navigate to https://start.spring.io. This service pulls in all the dependencies you need for an application and does most of the setup for you.
2. Choose either Gradle or Maven and the language you want to use. This guide assumes that you chose Java.
3. Click Dependencies and select Rest Repositories and Spring Data Neo4j.
4. Click Generate.
5. Download the resulting ZIP file, which is an archive of a web application that is configured with your choices.

Permissions to Access Neo4j

Neo4j Community Edition requires credentials to access it. You can configure the credentials by setting properties in src/main/resources/application.properties, as follows:

spring.neo4j.uri=bolt://localhost:7687
spring.data.neo4j.username=neo4j
spring.data.neo4j.password=secret

This includes the default username (neo4j) and the newly set password (secret) that you set earlier.

Create a Domain Object

You need to create a new domain object to present a person, as the following example (in src/main/java/com/example/accessingneo4jdatarest/Person.java) shows:

package com.example.accessingneo4jdatarest;

import org.springframework.data.neo4j.core.schema.Id;
import org.springframework.data.neo4j.core.schema.Node;
import org.springframework.data.neo4j.core.schema.GeneratedValue;

@Node
public class Person {

  @Id @GeneratedValue private Long id;

  private String firstName;
  private String lastName;

  public String getFirstName() {
    return firstName;
  }

  public void setFirstName(String firstName) {
    this.firstName = firstName;
  }

  public String getLastName() {
    return lastName;
  }

  public void setLastName(String lastName) {
    this.lastName = lastName;
  }
}

The Person object has a first name and a last name. There is also an ID object that is configured to be automatically generated so that you need not do so.

Create a Person Repository

Next, you need to create a simple repository, as the following example (in src/main/java/com/example/accessingneo4jdatarest/PersonRepository.java) shows:

package com.example.accessingneo4jdatarest;

import java.util.List;

import org.springframework.data.repository.PagingAndSortingRepository;
import org.springframework.data.repository.query.Param;
import org.springframework.data.rest.core.annotation.RepositoryRestResource;

@RepositoryRestResource(collectionResourceRel = "people", path = "people")
public interface PersonRepository extends PagingAndSortingRepository<Person, Long> {

  List<Person> findByLastName(@Param("name") String name);

}

This repository is an interface and lets you perform various operations that involve Person objects. It gets these operations by extending the PagingAndSortingRepositry interface defined in Spring Data Commons.

At runtime, Spring Data REST automatically creates an implementation of this interface. Then it uses the @RepositoryRestResource annotation to direct Spring MVC to create RESTful endpoints at /people.

Here you have also defined a custom query to retrieve a list of Person objects based on the lastName value. You can see how to invoke it later in this guide.

Finding the Application Class

The Spring Initializr creates an application class when you use it to create a project. You can find that in src/main/java/com/example/accessingneo4jdatarest/Application.java. Note that the Spring Initializr concatenates (and properly changes the case of) the package name and adds it to Application to create the application case name. In this case, we get AccessingNeo4jDataRestApplication, as the following listing shows:

package com.example.accessingneo4jdatarest;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.data.neo4j.repository.config.EnableNeo4jRepositories;
import org.springframework.transaction.annotation.EnableTransactionManagement;

@EnableTransactionManagement
@EnableNeo4jRepositories
@SpringBootApplication
public class AccessingNeo4jDataRestApplication {

  public static void main(String[] args) {
    SpringApplication.run(AccessingNeo4jDataRestApplication.class, args);
  }
}

You need not make any changes to this application class for this example

@SpringBootApplication is a convenience annotation that adds all of the following:

• @Configuration: Tags the class as a source of bean definitions for the application context.
• @EnableAutoConfiguration: Tells Spring Boot to start adding beans based on classpath settings, other beans, and various property settings. For example, if spring-webmvc is on the classpath, this annotation flags the application as a web application and activates key behaviors, such as setting up a DispatcherServlet.
• @ComponentScan: Tells Spring to look for other components, configurations, and services in the com/example package, letting it find the controllers.

The main() method uses Spring Boot’s SpringApplication.run() method to launch an application. Did you notice that there was not a single line of XML? There is no web.xml file, either. This web application is 100% pure Java and you did not have to deal with configuring any plumbing or infrastructure.

The @EnableNeo4jRepositories annotation activates Spring Data Neo4j. Spring Data Neo4j creates a concrete implementation of the PersonRepository and configures it to talk to an embedded Neo4j database by using the Cypher query language.

Build an executable JAR

You can run the application from the command line with Gradle or Maven. You can also build a single executable JAR file that contains all the necessary dependencies, classes, and resources and run that. Building an executable jar makes it easy to ship, version, and deploy the service as an application throughout the development lifecycle, across different environments, and so forth.

If you use Gradle, you can run the application by using ./gradlew bootRun. Alternatively, you can build the JAR file by using ./gradlew build and then run the JAR file, as follows:

java -jar build/libs/gs-accessing-neo4j-data-rest-0.1.0.jar

If you use Maven, you can run the application by using ./mvnw spring-boot:run. Alternatively, you can build the JAR file with ./mvnw clean package and then run the JAR file, as follows:

java -jar target/gs-accessing-neo4j-data-rest-0.1.0.jar

Logging output is displayed. The service should be up and running within a few seconds.

Test the Application

Now that the application is running, you can test it. You can use any REST client you wish. The following examples use the *nix tool called curl.

First, you want to see the top level service. The following example (with output) shows how to do so:

$ curl http://localhost:8080
{
  "_links" : {
    "people" : {
      "href" : "http://localhost:8080/people{?page,size,sort}",
      "templated" : true
    }
  }
}

Here you get a first glimpse of what this server has to offer. There is a people link located at http://localhost:8080/people. It has some options such as ?page, ?size, and ?sort.

$ curl http://localhost:8080/people
{
  "_links" : {
    "self" : {
      "href" : "http://localhost:8080/people{?page,size,sort}",
      "templated" : true
    },
    "search" : {
      "href" : "http://localhost:8080/people/search"
    }
  },
  "page" : {
    "size" : 20,
    "totalElements" : 0,
    "totalPages" : 0,
    "number" : 0
  }
}

There are currently no elements and, consequently, no pages, so it is time to create a new Person! To do so, run the following command (shown with its output):

$ curl -i -X POST -H "Content-Type:application/json" -d '{  "firstName" : "Frodo",  "lastName" : "Baggins" }' http://localhost:8080/people
HTTP/1.1 201 Created
Server: Apache-Coyote/1.1
Location: http://localhost:8080/people/0
Content-Length: 0
Date: Wed, 26 Feb 2014 20:26:55 GMT

• -i ensures you can see the response message including the headers. The URI of the newly created Person is shown
• -X POST signals this a POST used to create a new entry
• -H "Content-Type:application/json" sets the content type so the application knows the payload contains a JSON object
• -d '{ "firstName" : "Frodo", "lastName" : "Baggins" }' is the data being sent

From this you can query for all people by running the following command (shown with its output):

$ curl http://localhost:8080/people
{
  "_links" : {
    "self" : {
      "href" : "http://localhost:8080/people{?page,size,sort}",
      "templated" : true
    },
    "search" : {
      "href" : "http://localhost:8080/people/search"
    }
  },
  "_embedded" : {
    "people" : [ {
      "firstName" : "Frodo",
      "lastName" : "Baggins",
      "_links" : {
        "self" : {
          "href" : "http://localhost:8080/people/0"
        }
      }
    } ]
  },
  "page" : {
    "size" : 20,
    "totalElements" : 1,
    "totalPages" : 1,
    "number" : 0
  }
}

The people object contains a list with Frodo. Notice how it includes a self link. Spring Data REST also uses the Evo Inflector library to pluralize the name of the entity for groupings.

You can query directly for the individual record by running the following command (shown with its output):

$ curl http://localhost:8080/people/0
{
  "firstName" : "Frodo",
  "lastName" : "Baggins",
  "_links" : {
    "self" : {
      "href" : "http://localhost:8080/people/0"
    }
  }
}

In this guide, there is only one domain object. With a more complex system, where domain objects are related to each other, Spring Data REST renders additional links to help navigate to connected records.

You can find all the custom queries by running the following command (shown with its output):

$ curl http://localhost:8080/people/search
{
  "_links" : {
    "findByLastName" : {
      "href" : "http://localhost:8080/people/search/findByLastName{?name}",
      "templated" : true
    }
  }
}

You can see the URL for the query, including the HTTP query parameter: name. Note that this matches the @Param("name") annotation embedded in the interface.

To use the findByLastName query, run the following command (shown with its output):

$ curl http://localhost:8080/people/search/findByLastName?name=Baggins
{
  "_embedded" : {
    "people" : [ {
      "firstName" : "Frodo",
      "lastName" : "Baggins",
      "_links" : {
        "self" : {
          "href" : "http://localhost:8080/people/0"
        },
        "person" : {
          "href" : "http://localhost:8080/people/0"
        }
      }
    } ]
  },
  "_links" : {
    "self" : {
      "href" : "http://localhost:8080/people/search/findByLastName?name=Baggins"
    }
  }
}

Because you defined it to return List<Person> in the code, it returns all of the results. If you had defined it to return only Person, it would pick one of the Person objects to return. Since this can be unpredictable, you probably do not want to do that for queries that can return multiple entries.

You can also issue PUT, PATCH, and DELETE REST calls to either replace, update, or delete existing records. The following example (shown with its output) shows a PUT call:

$ curl -X PUT -H "Content-Type:application/json" -d '{ "firstName": "Bilbo", "lastName": "Baggins" }' http://localhost:8080/people/0
$ curl http://localhost:8080/people/0
{
  "firstName" : "Bilbo",
  "lastName" : "Baggins",
  "_links" : {
    "self" : {
      "href" : "http://localhost:8080/people/0"
    }
  }
}

The following example (shown with its output) shows a PATCH call:

$ curl -X PATCH -H "Content-Type:application/json" -d '{ "firstName": "Bilbo Jr." }' http://localhost:8080/people/0
$ curl http://localhost:8080/people/0
{
  "firstName" : "Bilbo Jr.",
  "lastName" : "Baggins",
  "_links" : {
    "self" : {
      "href" : "http://localhost:8080/people/0"
    }
  }
}

You can also delete records, as the following example (shown with its output) shows:

$ curl -X DELETE http://localhost:8080/people/0
$ curl http://localhost:8080/people
{
  "_links" : {
    "self" : {
      "href" : "http://localhost:8080/people{?page,size,sort}",
      "templated" : true
    },
    "search" : {
      "href" : "http://localhost:8080/people/search"
    }
  },
  "page" : {
    "size" : 20,
    "totalElements" : 0,
    "totalPages" : 0,
    "number" : 0
  }
}

A convenient aspect of this hypermedia-driven interface is how you can discover all the RESTful endpoints by using curl (or whatever REST client you like). You need not exchange a formal contract or interface document with your customers.

Summary

Congratulations! You have just developed an application with a hypermedia-based RESTful front end and a Neo4j-based back end.

Link: https://spring.io/guides/gs/accessing-neo4j-data-rest/

#neo4j #spring #rest 

Py2neo: A Comprehensive Neo4j Driver Library and toolkit for Python

Py2neo

Py2neo is a client library and toolkit for working with Neo4j from within Python applications. The library supports both Bolt and HTTP and provides a high level API, an OGM, admin tools, a Cypher lexer for Pygments, and many other bells and whistles.

Command line tooling has been removed from the library in py2neo 2021.2. This functionality now exists in the separate ipy2neo project.

As of version 2021.1, py2neo contains full support for routing, as exposed by a Neo4j cluster. This can be enabled using a neo4j://... URI or by passing routing=True to a Graph constructor.

Installation & Compatibility

To install the latest release of py2neo, simply use:

$ pip install py2neo

The following versions of Python and Neo4j (all editions) are supported:

Neo4j	Python 3.5+	Python 2.7
4.4
4.3
4.2
4.1
4.0
3.5
3.4

Note that py2neo is developed and tested under Linux using standard CPython distributions. While other operating systems and Python distributions may work, support for these is not available.

Quick Example

To run a query against a local database is straightforward:

>>> from py2neo import Graph
>>> graph = Graph("bolt://localhost:7687", auth=("neo4j", "password"))
>>> graph.run("UNWIND range(1, 3) AS n RETURN n, n * n as n_sq")
   n | n_sq
-----|------
   1 |    1
   2 |    4
   3 |    9

Releases & Versioning

As of 2020, py2neo has switched to Calendar Versioning, using a scheme of YYYY.N.M. Here, N is an incrementing zero-based number for each year, and M is a revision within that version (also zero-based).

No compatibility guarantees are given between versions, but as a general rule, a change in M should require little-to-no work within client applications, whereas a change in N may require some work. A change to the year is likely to require a more significant amount of work to upgrade.

Note that py2neo is developed on a rolling basis, so patches are not made to old versions. Users will instead need to install the latest release to adopt bug fixes.

More

For more information, read the handbook.

Author: Py2neo-org
Source Code: https://github.com/py2neo-org/py2neo 
License: Apache-2.0 License

#python #neo4j 

A Python Client For The Neo4j Graph Data Science (GDS) Library

Neo4j Graph Data Science Client

graphdatascience is a Python client for operating and working with the Neo4j Graph Data Science (GDS) library. It enables users to write pure Python code to project graphs, run algorithms, as well as define and use machine learning pipelines in GDS.

The API is designed to mimic the GDS Cypher procedure API in Python code. It abstracts the necessary operations of the Neo4j Python driver to offer a simpler surface.

Please leave any feedback as issues on the source repository. Happy coding!

NOTE

This is a work in progress and some GDS features are known to be missing or not working properly (see Known limitations below). Further, this library targets GDS versions 2.0+ (not yet released) and as such may not work with older versions.

Installation

To install the latest deployed version of graphdatascience, simply run:

pip install graphdatascience

Usage

What follows is a high level description of some of the operations supported by graphdatascience. For extensive documentation of all capabilities, please refer to the Python client chapter of the GDS Manual.

Extensive end-to-end examples in Jupyter ready-to-run notebooks can be found in the examples source directory:

• Computing similarities with kNN based on FastRP embeddings

Imports and setup

The library wraps the Neo4j Python driver with a GraphDataScience object through which most calls to GDS will be made.

from graphdatascience import GraphDataScience

# Use Neo4j URI and credentials according to your setup
gds = GraphDataScience("bolt://localhost:7687", auth=None)

There's also a method GraphDataScience.from_neo4j_driver for instantiating the gds object directly from a Neo4j driver object.

If we don't want to use the default database of our DBMS, we can specify which one to use:

gds.set_database("my-db")

AuraDS

If you are connecting the client to an AuraDS instance, you can get recommended non-default configuration settings of the Python Driver applied automatically. To achieve this, set the constructor argument aura_ds=True:

from graphdatascience import GraphDataScience

# Configures the driver with AuraDS-recommended settings
gds = GraphDataScience("neo4j+s://my-aura-ds.databases.neo4j.io:7687", auth=("neo4j", "my-password"), aura_ds=True)

Projecting a graph

Supposing that we have some graph data in our Neo4j database, we can project the graph into memory.

# Optionally we can estimate memory of the operation first
res = gds.graph.project.estimate("*", "*")
assert res["requiredMemory"] < 1e12

G = gds.graph.project("graph", "*", "*")

The G that is returned here is a Graph which on the client side represents the projection on the server side.

The analogous calls gds.graph.project.cypher{,.estimate} for Cypher based projection are also supported.

Running algorithms

We can take a projected graph, represented to us by a Graph object named G, and run algorithms on it.

# Optionally we can estimate memory of the operation first (if the algo supports it)
res = gds.pageRank.mutate.estimate(G, tolerance=0.5, mutateProperty="pagerank")
assert res["requiredMemory"] < 1e12

res = gds.pageRank.mutate(G, tolerance=0.5, mutateProperty="pagerank")
assert res["nodePropertiesWritten"] == G.node_count()

These calls take one positional argument and a number of keyword arguments depending on the algorithm. The first (positional) argument is a Graph, and the keyword arguments map directly to the algorithm's configuration map.

The other algorithm execution modes - stats, stream and write - are also supported via analogous calls. The stream mode call returns a list of dictionaries (with contents depending on the algorithm of course) - which we can think of as a table - as is also the case when using the Neo4j Python driver directly. The mutate, stats and write mode calls however return a dictionary with metadata about the algorithm execution.

Topological link prediction

The methods for doing topological link prediction are a bit different. Just like in the GDS procedure API they do not take a graph as an argument, but rather two node references as positional arguments. And they simply return the similarity score of the prediction just made as a float - not a list of dictionaries.

Similarity

Some of the methods for computing similarity are also different. The functions take two positional List[float] vectors as input and return a similarty score. The procedures that don't take a graph name as input (but only a configuration map) in the GDS API are represented by methods that only take keyword arguments mapping to the keys of their GDS configuration map.

The Graph object

In this library, graphs projected onto server-side memory are represented by Graph objects. There are convenience methods on the Graph object that let us extract information about our projected graph. Some examples are (where G is a Graph):

# Get the graph's node count
n = G.node_count()

# Get a list of all relationship properties present on
# relationships of the type "myRelType"
rel_props = G.relationship_properties("myRelType")

# Drop the projection represented by G
G.drop()

Machine learning models

In GDS, you can train machine learning models. When doing this using the graphdatascience, you can get a model object returned directly in the client. The model object allows for convenient access to details about the model via Python methods. It also offers the ability to directly compute predictions using the appropriate GDS procedure for that model. This includes support for models trained using pipelines (for Link Prediction and Node Classification) as well as GraphSAGE models.

Pipelines

There's native support for Link prediction pipelines and Node classification pipelines. Apart from the call to create a pipeline, the GDS native pipelines calls are represented by methods on pipeline Python objects. Additionally to the standard GDS calls, there are several methods to query the pipeline for information about it.

Below is a minimal example for node classification (supposing we have a graph G with a property "myClass"):

pipe = gds.alpha.ml.pipeline.nodeClassification.create("myPipe")
assert pipe.type() == "Node classification training pipeline"

pipe.addNodeProperty("degree", mutateProperty="rank")
pipe.selectFeatures("rank")
steps = pipe.feature_properties()
assert len(steps) == 1
assert steps[0]["feature"] == "rank"

trained_pipe = pipe.train(G, modelName="myModel", targetProperty="myClass", metrics=["ACCURACY"])
assert trained_pipe.metrics()["ACCURACY"]["test"] > 0

res = trained_pipe.predict_stream(G)
assert len(res) == G.node_count()

Link prediction works the same way, just with different method names for calls specific to that pipeline. Please see the GDS documentation for more on the pipelines' procedure APIs.

GraphSAGE

Assuming we have a graph G with node property x, we can do the following:

model = gds.beta.graphSage.train(G, modelName="myModel", featureProperties=["x"])
assert len(model.metrics()["epochLosses"]) == model.metrics()["ranEpochs"] 

res = model.predict_stream(G)
assert len(res) == G.node_count()

Note that with GraphSAGE we call the train method directly and supply all training configuration.

Graph catalog utils

All procedures from the GDS Graph catalog are supported with graphdatascience. Some examples are (where G is a Graph):

res = gds.graph.list()
assert len(res) == 1  # Exactly one graph is projected

res = gds.graph.streamNodeProperties(G, "rank")
assert len(res) == G.node_count()

Further, there's a new call named gds.graph.get (graphdatascience only) which takes a name as input and returns a Graph object if a graph projection of that name exists in the user's graph catalog. The idea is to have a way of creating Graphs for already projected graphs, without having to do a new projection.

Model catalog utils

All procedures from the GDS Model catalog are supported with graphdatascience. Some examples are (where model is a machine learning model object):

res = gds.beta.model.list()
assert len(res) == 1  # Exactly one model is loaded

res = gds.beta.model.drop(model)
assert res["modelInfo"]["modelName"] == model.name()

Further, there's a new call named gds.model.get (graphdatascience only) which takes a model name as input and returns a model object if a model of that name exists in the user's model catalog. The idea is to have a way of creating model objects for already loaded models, without having to create them again.

Node matching without Cypher

When calling path finding or topological link prediction algorithms one has to provide specific nodes as input arguments. When using the GDS procedure API directly to call such algorithms, typically Cypher MATCH statements are used in order to find valid representations of input nodes of interest, see eg. this example in the GDS docs. To simplify this, graphdatascience provides a utility function, gds.find_node_id, for letting one find nodes without using Cypher.

Below is an example of how this can be done (supposing G is a projected Graph with City nodes having name properties):

# gds.find_node_id takes a list of labels and a dictionary of
# property key-value pairs
source_id = gds.find_node_id(["City"], {"name": "New York"})
target_id = gds.find_node_id(["City"], {"name": "Philadelphia"})

res = gds.shortestPath.dijkstra.stream(G, sourceNode=source_id, targetNode=target_id)
assert res[0]["totalCost"] == 100

The nodes found by gds.find_node_id are those that have all labels specified and fully match all property key-value pairs given. Note that exactly one node per method call must be matched.

For more advanced filtering we recommend users do matching via Cypher's MATCH.

Known limitations

Operations known to not yet work with graphdatascience:

• Numeric utility functions (will never be supported)
• Cypher on GDS (might be supported in the future)

License

graphdatascience is licensed under the Apache Software License version 2.0. All content is copyright © Neo4j Sweden AB.

Acknowledgements

This work has been inspired by the great work done in the following libraries:

• pygds by stellasia
• gds-python by moxious

Author: neo4j
Source Code: https://github.com/neo4j/graph-data-science-client
License: Apache-2.0 License

#neo4j #python #data-science 

Create a New Project with the Neo4j GraphQL Library

Create a New Project with the Neo4j GraphQL Library

In this talk, William Lyon discusses the Neo4j GraphQL Library and how to get started with it, as well as issues that arise when building GraphQL APIs. The video covers how to translate business requirements to a graph data model and how to design our API using GraphQL type definitions, creating a CRUD GraphQL API using the Neo4j GraphQL Library, adding custom logic using the Cypher schema directive, and securing our API using authorization rules.

📚📚📚
Full Stack GraphQL Applications | http://mng.bz/NxO2
To save 40% off this book use discount code: watchtlyon40 
📚📚📚

About the author:
William Lyon is a software developer at Neo4j, working on integrations with other technologies and helping users build applications with Neo4j. He is the creator and maintainer of neo4j-graphql.js, a JavaScript library for creating GraphQL APIs, and is a contributor to GRANDstack.io. He serves as Neo4j’s representative on the GraphQL Foundation.

About the book:
Full Stack GraphQL Applications teaches you to leverage the power of GraphQL to create modern APIs that streamline data loads by allowing client applications to selectively fetch only the data required. GRANDstack.io contributor and GraphQL Foundation member William Lyon teaches you everything you need to know to design, deploy, and maintain a GraphQL API from scratch and create graph-aware fullstack web applications. In this project-driven book, you’ll build a complete business reviews application using the cutting-edge GRANDstack, learning how the different parts fit together. Chapter-by-chapter, you’ll master creating a GraphQL service with Apollo Server, modelling a GraphQL API with GraphQL type definitions, installing the Neo4j Database on different platforms, and more. By the time you’re done, you’ll be able to deploy all of the components of a serverless fullstack application in a secure and cost-effective way that takes full advantage of GraphQL’s performance capabilities. Along the way, you’ll also get tips for applying these techniques to other stacks.

#neo4j #graphql #api

A Container for The Neo4j Driver in Python

Neo4j Bolt Driver for Python

This repository contains the official Neo4j driver for Python. Each driver release (from 4.0 upwards) is built specifically to work with a corresponding Neo4j release, i.e. that with the same major.minor version number. These drivers will also be compatible with the previous Neo4j release, although new server features will not be available.

• Python 3.10 supported.
• Python 3.9 supported.
• Python 3.8 supported.
• Python 3.7 supported.

Python 2.7 support has been dropped as of the Neo4j 4.0 release.

Installation

To install the latest stable version, use:

pip install neo4j

Quick Example

from neo4j import GraphDatabase

driver = GraphDatabase.driver("neo4j://localhost:7687", auth=("neo4j", "password"))

def add_friend(tx, name, friend_name):
    tx.run("MERGE (a:Person {name: $name}) "
           "MERGE (a)-[:KNOWS]->(friend:Person {name: $friend_name})",
           name=name, friend_name=friend_name)

def print_friends(tx, name):
    for record in tx.run("MATCH (a:Person)-[:KNOWS]->(friend) WHERE a.name = $name "
                         "RETURN friend.name ORDER BY friend.name", name=name):
        print(record["friend.name"])

with driver.session() as session:
    session.write_transaction(add_friend, "Arthur", "Guinevere")
    session.write_transaction(add_friend, "Arthur", "Lancelot")
    session.write_transaction(add_friend, "Arthur", "Merlin")
    session.read_transaction(print_friends, "Arthur")

driver.close()

Connection Settings Breaking Change

• The driver’s default configuration for encrypted is now false (meaning that driver will only attempt plain text connections by default).
• Connections to encrypted services (such as Neo4j Aura) should now explicitly be set to encrypted.
• When encryption is explicitly enabled, the default trust mode is to trust the CAs that are trusted by operating system and use hostname verification.
• This means that encrypted connections to servers holding self-signed certificates will now fail on certificate verification by default.
• Using the new neo4j+ssc scheme will allow to connect to servers holding self-signed certificates and not use hostname verification.
• The neo4j:// scheme replaces bolt+routing:// and can be used for both clustered and single-instance configurations with Neo4j 4.0.

See, https://neo4j.com/docs/migration-guide/4.0/upgrade-driver/#upgrade-driver-breakingchanges

See, https://neo4j.com/docs/driver-manual/current/client-applications/#driver-connection-uris for changes in default security settings between 3.x and 4.x

Connecting with Python Driver 4.x to Neo4j 3.5

Using the Python Driver 4.x and connecting to Neo4j 3.5 with default connection settings for Neo4j 3.5.

# the preferred form

driver = GraphDatabase.driver("neo4j+ssc://localhost:7687", auth=("neo4j", "password"))

# is equivalent to

driver = GraphDatabase.driver("neo4j://localhost:7687", auth=("neo4j", "password"), encrypted=True, trust=False)

Connecting with Python Driver 1.7 to Neo4j 4.x

Using the Python Driver 1.7 and connecting to Neo4j 4.x with default connection settings for Neo4j 4.x.

driver = GraphDatabase.driver("neo4j://localhost:7687", auth=("neo4j", "password"), encrypted=False)

Other Information

• The Neo4j Operations Manual
• The Neo4j Drivers Manual
• Python Driver API Documentation
• Neo4j Cypher Refcard
• Example Project
• Driver Wiki (includes change logs)
• Neo4j 4.0 Migration Guide

Download Details:
Author: neo4j
Source Code: https://github.com/neo4j/neo4j-python-driver
License: Apache-2.0 License

#python #neo4j 

How To Combine Neo4j, Graphql and React Tor Graph Development

How To Combine Neo4j, Graphql and React Tor Graph Development

In this article, we learn how to combine Neo4j, Graphql and React to develop a graph discovery engine

#graphql #neo4j #react 

GRAND Stack Application using GraphQL, React, Apollo, Neo4j Database

GRANDstack Starter

npx create-grandstack-app myNewApp

This project is a starter for building a GRANDstack (GraphQL, React, Apollo, Neo4j Database) application. There are two components to the starter, the web frontend application (in React and Angular flavors) and the API app (GraphQL server).

The starter represents a business reviews dashboard. You need to adjust the GraphQL schema, the seed data, database index creation, and the UI components for your use-case.

Hands On With The GRANDstack Starter Video

Quickstart

The easiest way to get started with the GRANDstack Starter is to create a Neo4j Sandbox instance and use the create-grandstack-app command line tool.

(If you have a running Neo4j database on localhost via Neo4j Desktop or a Neo4j server installation, change the password in api/.env)

1. Create A Neo4j Instance

Option :one: - Sandbox

Neo4j Sandbox allows you to create a free hosted Neo4j instance private to you that can be used for development.

After signing in to Neo4j Sandbox, click the + New Project button and select the "Blank Sandbox" option. In the next step we'll use the connection credentials from the "Connection details" tab to connect our GraphQL API to this Neo4j instance.

Option :two: - Desktop

If you instead would like to use Neo4j Desktop. The process will be almost the same with a minor detour. Install Neo4j Desktop for your chosen OS and then make a new blank graph for your project. It will require you to put in a password and username. Remember those.

Next you need to go to open the manage screen from the options in the 3 dot stack menu

And install the apoc plugin, green button at the top of the list.

After that you can return to setting up your app with the credentials from the prior steps.

2. Run the create-grandstack-app CLI

npx create-grandstack-app myNewApp

or with Yarn

yarn create grandstack-app myNewApp

This will create a new directory myNewApp, download the latest release of the GRANDstack Starter, install dependencies and prompt for your connection credentials for Neo4j to connect to the GraphQL API.

3. Seed the database (optional)

Make sure your application is running locally with npm start or yarn start, open another terminal and run

npm run seedDb

or with Yarn

yarn run seedDb

4. Open In Browser

Overview

The GRANDstack Starter is a monorepo that includes a GraphQL API application and client web applications for React (default) and Angular for a business reviews dashboard.

/ - Project Root

The root directory contains some global configuration and scripts:

• npm run start and npm run build
• ESLint (.eslintrc.json) for code linting
• Prettier (.prettierrc.json) for code formatting
• Git hooks for applying formatting on commit

/api

This directory contains the GraphQL API application using Apollo Server and the Neo4j GraphQL Library.

• Change environment variable settings in .env:

# Use this file to set environment variables with credentials and configuration options
# This file is provided as an example and should be replaced with your own values
# You probably don't want to check this into version control!

NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=letmein

# Uncomment this line to enable encrypted driver connection for Neo4j
#NEO4J_ENCRYPTED=true

# Uncomment this line to specify a specific Neo4j database (v4.x+ only)
#NEO4J_DATABASE=neo4j

GRAPHQL_SERVER_HOST=0.0.0.0
GRAPHQL_SERVER_PORT=4001
GRAPHQL_SERVER_PATH=/graphql

/web-react

The frontend React web application is found in this directory.

It includes:

• Material UI
• React router
• Apollo Client / React Hooks
• Create React App

/web-angular

A UI built with Angular, Apollo and the Clarity Design System is also available.

Start the Angular UI server

cd ./web-angular && npm start

/mobile_client_flutter

A mobile client built with Flutter which supports Android, iOS, and web. See the README for detailed setup instructions.

cd ./mobile_client_flutter && flutter run

/web-react-ts

A UI built with CRA

Start the React dev server

cd ./web-react-ts && npm start

Deployment

Netlify

This monorepo can be deployed to Netlify. The frontend application will be served over Netlify's CDN and the GraphQL API will be provisioned as a serverless GraphQL API lambda function deployed to AWS (via Netlify). A netlify.toml file is included with the necessary build configurations. The following environment variables must be set in Netlify (either via the Netlify web UI or via the command line tool)

NEO4J_URI
NEO4J_USER
NEO4J_PASSWORD

See the "Hands On With The GRANDStack Starter" video linked at the beginning of this README for a walkthrough of deploying to Netlify.

Vercel

Vercel can be used with monorepos such as grand-stack-starter. vercel.json defines the configuration for deploying with Vercel.

1. get vercel cli
2. Set the vercel secrets for your Neo4j instance:

vercel secret add grand_stack_starter_neo4j_uri bolt://<YOUR_NEO4J_INSTANCE_HERE>
vercel secret add grand_stack_starter_neo4j_user <YOUR_DATABASE_USERNAME_HERE>
vercel secret add grand_stack_starter_neo4j_password <YOUR_DATABASE_USER_PASSWORD_HERE>

1. Run vercel

Docker Compose

You can quickly start via:

docker-compose up -d

If you want to load the example DB after the services have been started:

docker-compose run api npm run seedDb

See the project releases for the changelog.

You can find instructions for other ways to use Neo4j (Neo4j Desktop, Neo4j Aura, and other cloud services) in the Neo4j directory README.

This project is licensed under the Apache License v2. Copyright (c) 2020 Neo4j, Inc.

Download Details:
Author: kumarss20
Source Code: https://github.com/kumarss20/neo4j-gql
License: Apache-2.0 License

#graphql #react #apollo #neo4j #database #flutter #angular 

SpiderFoot Neo4j tools for Graph Database

SpiderFoot Neo4j Tools

Import, visualize, and analyze SpiderFoot OSINT data in Neo4j, a graph database

Step 1: Installation

NOTE: This installs the sfgraph command-line utility

$ pip install spiderfoot-neo4j

Step 2: Start Neo4j

NOTE: Docker must first be installed

$ docker run --rm --name sfgraph -v "$(pwd)/neo4j_database:/data" -e 'NEO4J_AUTH=neo4j/CHANGETHISIFYOURENOTZUCK' -e 'NEO4JLABS_PLUGINS=["apoc", "graph-data-science"]' -e 'NEO4J_dbms_security_procedures_unrestricted=apoc.*,gds.*' -p "7474:7474" -p "7687:7687" neo4j

Step 3: Import Scans

$ sfgraph path_to/spiderfoot.db -s <SCANID_1> <SCANID_2> ...

Step 4: Browse Spiderfoot Data in Neo4j

Visit http://127.0.0.1:7474 and log in with neo4j/CHANGETHISIFYOURENOTZUCK

Step 5 (Optional): Use cool algorithms to find new targets

The --suggest option will rank nodes based on their connectedness in the graph. This is perfect for finding closely-related affiliates (child companies, etc.) to scan and add to the graph. By default, Harmonic Centrality is used, but others such as PageRank can be specified with --closeness-algorithm

$ sfgraph --suggest DOMAIN_NAME

Example CYPHER Queries

# match all INTERNET_NAMEs
MATCH (n:INTERNET_NAME) RETURN n

# match multiple event types
MATCH (n) WHERE n:INTERNET_NAME OR n:DOMAIN_NAME OR n:EMAILADDR RETURN n

# match by attribute
MATCH (n {data: "evilcorp.com"}) RETURN n

# match by spiderfoot module (relationship)
MATCH p=()-[r:WHOIS]->() RETURN p

# shortest path to all INTERNET_NAMEs from seed domain
MATCH p=shortestPath((d:DOMAIN_NAME {data:"evilcorp.com"})-[*]-(n:INTERNET_NAME)) RETURN p

# match only primary targets (non-affiliates)
MATCH (n {scanned: true}) return n

# match only affiliates
MATCH (n {affiliate: true}) return n

CLI Help

sfgraph [-h] [-db SQLITEDB] [-s SCANS [SCANS ...]] [--uri URI] [-u USERNAME] [-p PASSWORD] [--clear] [--suggest SUGGEST]
               [--closeness-algorithm {pageRank,articleRank,closenessCentrality,harmonicCentrality,betweennessCentrality,eigenvectorCentrality}] [-v]

optional arguments:
  -h, --help            show this help message and exit
  -db SQLITEDB, --sqlitedb SQLITEDB
                        Spiderfoot sqlite database
  -s SCANS [SCANS ...], --scans SCANS [SCANS ...]
                        scan IDs to import
  --uri URI             Neo4j database URI (default: bolt://127.0.0.1:7687)
  -u USERNAME, --username USERNAME
                        Neo4j username (default: neo4j)
  -p PASSWORD, --password PASSWORD
                        Neo4j password
  --clear               Wipe the Neo4j database
  --suggest SUGGEST     Suggest targets of this type (e.g. DOMAIN_NAME) based on their connectedness in the graph
  --closeness-algorithm {pageRank,articleRank,closenessCentrality,harmonicCentrality,betweennessCentrality,eigenvectorCentrality}
                        Algorithm to use when suggesting targets
  -v, -d, --debug       Verbose / debug

Download Details:
Author: blacklanternsecurity
Source Code: https://github.com/blacklanternsecurity/spiderfoot-neo4j
License: GPL-3.0 License

#python #database #neo4j 

GraphKer: Open Source Tool - Cybersecurity Graph Database in Neo4j

GraphKer

Open Source Tool - Cybersecurity Graph Database in Neo4j

|G|r|a|p|h|K|e|r|

{ open source tool for a cybersecurity graph database in neo4j }

With GraphKer you can have the most recent update of cyber-security vulnerabilities, weaknesses, attack patterns and platforms from MITRE and NIST, in an very useful and user friendly way provided by Neo4j graph databases!

Prerequisites

3 + 1 Steps to run GraphKer Tool

1) Download and Install Neo4j Desktop

Windows Users: https://neo4j.com/download/

Create an account to get the license (totally free), download and install Neo4j Desktop.

Useful Video: https://tinyurl.com/yjjbn8jx

Linux Users:

sudo apt update
sudo apt install apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://debian.neo4j.com/neotechnology.gpg.key | sudo apt-key add -
sudo add-apt-repository "deb https://debian.neo4j.com stable 4.1"
sudo apt install neo4j
sudo systemctl enable neo4j.service
sudo systemctl status neo4j.service

You should have output that is similar to the following:

● neo4j.service - Neo4j Graph Database
Loaded: loaded (/lib/systemd/system/neo4j.service; enabled; vendor preset: enabled)
Active: active (running) since Fri 2020-08-07 01:43:00 UTC; 6min ago
Main PID: 21915 (java)
Tasks: 45 (limit: 1137)
Memory: 259.3M
CGroup: /system.slice/neo4j.service
. . .

Useful Video: https://tinyurl.com/vvpjf3dr

2) Create and Configure the Database

• Create Database:

Windows Users:

You can create databases in whatever version you want (latest version preferable) through GUI or Neo4j Terminal.

• Create a new database in GUI: Just click the (+), set DB Name, Username and Password. Useful Tutorial: https://www.sqlshack.com/getting-started-with-the-neo4j-graph-database/
• Through Neo4j Shell: https://neo4j.com/docs/cypher-manual/current/databases/

Linux Users: When you start neo4j through systemctl, type cypher-shell, then create database NAME;. Now you have to set this database, as default so when you start neo4j you start automatically this database. Go to /etc/neo4j/neo4j.conf and uncomment dbms.default_database=neo4j and change it with your new database name. Restart neo4j service and you are ready.

• Configure Database:

Install APOC Plugin:

• Windows Users: In Neo4j Desktop Main Page --> Choose your Database --> Click Plugins --> APOC --> Install
• Linux Users:

Download APOC jar File: https://github.com/neo4j-contrib/neo4j-apoc-procedures/releases (--all.jar file)

Place it in Plugins Folder --> check every folder path in Neo4j: https://neo4j.com/docs/operations-manual/current/configuration/file-locations/

Modify the Database Configuration File to approve apoc procedures.

Uncomment: dbms.directories.plugins=plugins

Uncomment and Modify:

 dbms.security.procedures.unrestricted=apoc.*
 dbms.security.procedures.whitelist=apoc.*,apoc.coll.*,apoc.load.*
 #loads unrestricted and white-listed procedures/plugins to the server

Restart Neo4j: systemctl restart neo4j

Configure Database Settings File:

Windows Users: In Neo4j Desktop Main Page --> Choose your Database --> ... (Three Dots) --> Settings --> Go to last line and set the commands below --> Apply and Restart the Database

apoc.export.file.enabled=true
apoc.import.file.enabled=true
apoc.import.file.user_neo4j_config=false
cypher.lenient_create_relationship = true

Linux Users: Same as above, in the neo4j.conf file --> check every folder path in Neo4j: https://neo4j.com/docs/operations-manual/current/configuration/file-locations/

Configure Memory Usage:

In Neo4j Configuration File (neo4j.conf): For 16GB RAM you can use 8G + 4G for heap. For 8GB RAM you can use 4G + 2G etc.

dbms.memory.heap.initial_size=4G
dbms.memory.heap.max_size=8G
dbms.memory.pagecache.size=4G

3) Install requirements.txt

• GraphKer Uses: xmltodict, neo4j, requests, beautifulsoup4
• pip install -r requirements.txt

4) Install Applications Created for Neo4j

• There are several applications created especially for Neo4j that you can use for better experience and work.
  • Neo4j Bloom: Application for better graph presentations. Free and Easy to use.
  • Graphlytic: Third-Party App, better graph presentations, but most important auto-analytics and statistics. Free and Paid Editions. We can do the most locally with free edition. Learn More: https://graphlytic.biz/
  • Neo4j Database Analyzer: Third-Party App, Free, provides great analysis tools for our Data and our Schema. Learn More: https://community.neo4j.com/t/introducing-the-neo4j-database-analyzer/6197

Run GraphKer

// Default
python main.py -u BOLT_URL -n USERNAME -p PASSWORD -d IMPORT_PATH
// Run and Open Neo4j Browser
python main.py -u BOLT_URL -n USERNAME -p PASSWORD -d IMPORT_PATH -b y
// Run and Open Graphlytic App
python main.py -u BOLT_URL -n USERNAME -p PASSWORD -d IMPORT_PATH -g y
// Default Run Example in Ubuntu
sudo python3 main.py -u BOLT_URL -n USERNAME -p PASSWORD -d /var/lib/neo4j/import/

Default Bolt URL for Neo4j: bolt://localhost:7687

Default Username in Neo4j Databases: neo4j

For Neo4j Import Folder check the link above with File Locations.

Estimated RunTime: 6-15 Minutes. Depends on hardware.

At least 8GB in your hard drive.

You can check out an existing example of the graph database that GraphKer creates. Just download the dump file from repo: https://github.com/amberzovitis/GraphKer-DBMS-Dump and import it to an existing or new graph database in Neo4j. This file consists of CVEs from 2021 (with related CPEs) and all CWEs and CAPECs.

You can access the CVE and CPE Datasets in National Vulnerability Database by NIST (https://nvd.nist.gov/vuln/data-feeds), CWE Dataset in MITRE (https://cwe.mitre.org/data/downloads.html) and CAPEC Dataset in MITRE (https://capec.mitre.org/data/downloads.html).

--Search, Export Data and Analytics, Enrich your Skills--

Created by Adamantios - Marios Berzovitis, Cybersecurity Expert MSc, BSc

Diploma Research - MSc @ Distributed Systems, Security and Emerging Information Technologies | University Of Piraeus --> https://www.cs.unipi.gr/distributed/

Co-Working with Cyber Security Research Lab | University Of Piraeus --> https://seclab.cs.unipi.gr/

Facebook: https://www.facebook.com/GraphKerTool/

LinkedIn: https://tinyurl.com/p57w4ntu

Github: https://github.com/amberzovitis

Enjoy! Provide Feedback!

Author: amberzovitis
Source Code: https://github.com/amberzovitis/GraphKer
#neo4j