Neo4j

neo4j

Hey Hackers! Welcome back to our weekly HackerNoon Company of the Week series! Every week, we introduce a prominent tech enterprise from our tech company database, in an effort to recognize their impact on the industry. This comprehensive database ranks S&P 500 companies alongside the most innovative startups of the year, presenting a diverse range of industry excellence. This week, we present to you: Neo4j, the world’s leading graph database that revolutionizes how data is stored, processed, and utilized. With native graph storage and processing, Neo4j empowers developers to create GenAI applications anchored in knowledge graphs through its GraphRAG Ecosystem Tools. This cutting-edge ecosystem provides context-rich, deeply explainable AI solutions backed by the only knowledge graph with native vector search, delivering performance 1000X faster than SQL. With 60+ graph algorithms, 160M+ downloads, and a global community of over 230K developers, Neo4j is driving innovation and scalability across industries. Request your own Tech Company News Page on HackerNoon Meet Neo4j: Fun Facts Neo4j recently announced a major milestone, surpassing $200M in annual recurring revenue and doubling its ARR over the past three years. With a $2B+ valuation, Neo4j is driving the adoption of GenAI-driven graph technology, serving 84% of Fortune 100 companies. “This milestone is a testament to the growing recognition of graph technology as foundational to the new data stack. Neo4j empowers customers to transform data into knowledge, unlocking insights and possibilities that weren’t possible before. We’re now at the forefront of a seismic shift in how organizations achieve this with GenAI, with Neo4j uniquely positioned to enable this new wave of innovation.”
-Emil Eifrem, Co-Founder and CEO, Neo4j Their cloud offering has grown fivefold, and they lead the graph database market with a 44% share. As graph technologies become essential for AI systems, Neo4j continues to set the standard for innovation and scalability. Neo4j 🤝 HackerNoon Targeted Ads + Newsletter Campaign HackerNoon and Neo4j’s collaboration has grown over the years, evolving from business blogging to a pivotal partnership in November 2024 for Neo4j’s flagship event, NODES 2024. Celebrating its sixth year, NODES 2024 brought together thousands of developers and data scientists in a free, 24-hour conference to explore graph-powered applications and contextual AI. Last year’s event showcased cutting-edge sessions on hot topics like Retrieval-Augmented Generation (RAG) and AI orchestration frameworks, along with deep-dive workshops on graph solutions. With live presentations, interactive Q&As, and a global community spanning time zones, NODES 2024 solidified its place as the must-attend event for graph and AI enthusiasts. HackerNoon proudly supported the event through targeted ads and newsletter campaigns, amplifying Neo4j’s mission to lead the graph-powered AI revolution. Neo4j announces Neo4j 5.26 as their Long-Term Support (LTS) release. Learn more here. That’s all for this week, See you in the next one! -The HackerNoon Team Hey Hackers! Welcome back to our weekly HackerNoon Company of the Week series! Every week, we introduce a prominent tech enterprise from our tech company database , in an effort to recognize their impact on the industry. This comprehensive database ranks S&P 500 companies alongside the most innovative startups of the year , presenting a diverse range of industry excellence. Company of the Week tech company database S&P 500 companies most innovative startups of the year This week, we present to you : Neo4j, the world’s leading graph database that revolutionizes how data is stored, processed, and utilized. With native graph storage and processing, Neo4j empowers developers to create GenAI applications anchored in knowledge graphs through its GraphRAG Ecosystem Tools. This cutting-edge ecosystem provides context-rich, deeply explainable AI solutions backed by the only knowledge graph with native vector search, delivering performance 1000X faster than SQL. With 60+ graph algorithms, 160M+ downloads, and a global community of over 230K developers, Neo4j is driving innovation and scalability across industries. This week, we present to you Neo4j , Neo4j Request your own Tech Company News Page on HackerNoon Request your own Tech Company News Page on HackerNoon Request your own Tech Company News Page on HackerNoon Request your own Tech Company News Page on HackerNoon Request your own Tech Company News Page on HackerNoon Meet Neo4j: Fun Facts Neo4j recently announced a major milestone , surpassing $200M in annual recurring revenue and doubling its ARR over the past three years. With a $2B+ valuation, Neo4j is driving the adoption of GenAI-driven graph technology, serving 84% of Fortune 100 companies. Neo4j milestone “This milestone is a testament to the growing recognition of graph technology as foundational to the new data stack. Neo4j empowers customers to transform data into knowledge, unlocking insights and possibilities that weren’t possible before. We’re now at the forefront of a seismic shift in how organizations achieve this with GenAI, with Neo4j uniquely positioned to enable this new wave of innovation.” -Emil Eifrem, Co-Founder and CEO, Neo4j “This milestone is a testament to the growing recognition of graph technology as foundational to the new data stack. Neo4j empowers customers to transform data into knowledge, unlocking insights and possibilities that weren’t possible before. We’re now at the forefront of a seismic shift in how organizations achieve this with GenAI, with Neo4j uniquely positioned to enable this new wave of innovation.” -Emil Eifrem, Co-Founder and CEO, Neo4j -Emil Eifrem, Co-Founder and CEO, Neo4j -Emil Eifrem, Co-Founder and CEO, Neo4j Their cloud offering has grown fivefold, and they lead the graph database market with a 44% share. As graph technologies become essential for AI systems, Neo4j continues to set the standard for innovation and scalability. Neo4j 🤝 HackerNoon Targeted Ads + Newsletter Campaign HackerNoon and Neo4j’s collaboration has grown over the years, evolving from business blogging to a pivotal partnership in November 2024 for Neo4j’s flagship event, NODES 2024 . business blogging NODES 2024 NODES 2024 Celebrating its sixth year, NODES 2024 brought together thousands of developers and data scientists in a free, 24-hour conference to explore graph-powered applications and contextual AI. Last year’s event showcased cutting-edge sessions on hot topics like Retrieval-Augmented Generation (RAG) and AI orchestration frameworks, along with deep-dive workshops on graph solutions. With live presentations, interactive Q&As, and a global community spanning time zones, NODES 2024 solidified its place as the must-attend event for graph and AI enthusiasts. HackerNoon proudly supported the event through targeted ads and newsletter campaigns, amplifying Neo4j’s mission to lead the graph-powered AI revolution. targeted ads newsletter Neo4j announces Neo4j 5.26 as their Long-Term Support (LTS) release. Learn more here. Neo4j announces Neo4j 5.26 as their Long-Term Support (LTS) release. Learn more here . here That’s all for this week, That’s all for this week, See you in the next one! See you in the next one! -The HackerNoon Team

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Building a Hybrid RAG Agent with Neo4j Graphs and Milvus Vector Search

ዲቃላ RAG ወኪል በNeo4j Graphs እና Milvus Vector ፍለጋ መገንባት

Neo4j গ্রাফ এবং মিলভাস ভেক্টর অনুসন্ধান সহ একটি হাইব্রিড RAG এজেন্ট তৈরি করা

Construcció d'un agent RAG híbrid amb Neo4j Graphs i Milvus Vector Search

Creación de un agente RAG híbrido con gráficos Neo4j y búsqueda vectorial Milvus

Construíndo un axente RAG híbrido con Neo4j Graphs e Milvus Vector Search

Bati yon Ajan Hybrid RAG ak graf Neo4j ak rechèch vektè Milvus

Neo4j グラフと Milvus ベクトル検索を使用したハイブリッド RAG エージェントの構築

Dhisidda Wakiilka Isku-dhafka ah ee RAG oo wata Sawirro Neo4j ah iyo Raadinta Milvus Vector

Прављење хибридног РАГ агента са Нео4ј графовима и Милвус векторском претрагом

Neo4j வரைபடங்கள் மற்றும் மில்வஸ் வெக்டர் தேடலுடன் ஒரு கலப்பின RAG முகவரை உருவாக்குதல்

Сохтани агенти гибридии RAG бо графикҳои Neo4j ва ҷустуҷӯи Milvus

Neo4j Grafikleri ve Milvus Vektör Araması ile Hibrit Bir RAG Aracısı Oluşturma

Building Knowledge Graphs for RAG: Exploring GraphRAG with Neo4j and LangChain

Unlocking Precision in RAG Applications: Harnessing Knowledge Graphs with Neo4j and LangChain

Ontsluit presisie in JOOL-toepassings: benut kennisgrafieke met Neo4j en LangChain

Cómo desbloquear la precisión en aplicaciones RAG: aprovechar los gráficos de conocimiento con Neo4j y LangChain

RAG アプリケーションの精度を解き放つ: Neo4j と LangChain によるナレッジ グラフの活用

RAG колдонмолорунда тактыктын кулпусун ачуу: Neo4j жана LangChain менен билим графиктерин колдонуу

Desbloqueando a precisão em aplicações RAG: aproveitando gráficos de conhecimento com Neo4j e LangChain

RAG Aplicacionkunapi Precisión kichay: Neo4j chaymanta LangChain kaqwan Yachaypa Gráficos kaqninta aprovechay

Odomknutie presnosti v aplikáciách RAG: Využitie znalostných grafov s Neo4j a LangChain

Furitaanka saxda ah ee Codsiyada RAG: Ka faa'iidaysiga Garaafyada Aqoonta ee Neo4j iyo LangChain

การปลดล็อกความแม่นยำในแอปพลิเคชัน RAG: การใช้ประโยชน์จากกราฟความรู้ด้วย Neo4j และ LangChain

RAG Uygulamalarında Hassasiyetin Kilidini Açma: Neo4j ve LangChain ile Bilgi Grafiklerinden Yararlanma

Ku Pfula Vukheta eka Switirhisiwa swa RAG: Ku tirhisa Tigirafu ta Vutivi hi Neo4j na LangChain

RAG ایپلی کیشنز میں درستگی کو غیر مقفل کرنا: Neo4j اور LangChain کے ساتھ نالج گرافس کا استعمال

Databases: the warehouse of our data. if you have used databases before, it will likely be a relational database, or a NoSQL database. Every use case has the data structure and database most suited for it. Today I would like to talk about the graph database. NEO4J to be precise. CONCEPTS I have always heard about graph databases and assumed it was a complex system. That made me postpone learning it. Thanks to an interview requirement, I am forced to learn and apply it. It is a different paradigm, but not as complex as I thought it would. Let's discuss some core concepts about NEO4J: NODES Everything is a node. Well not everything. The way rows are added in relational database, or documents in NoSQL database, we add nodes to graph databases. a node represents a single entity of a Neo4j database. a way to recognize a node in a query is with “()”. anything between the “(“ and “)“ is to classify the node. let write a query that helps create a node in the database. CREATE (n) The “n”  in the query like a variable name for that node to use in the query. we not current using it. so we can also do CREATE () LABELS The same way we have tables in a relational database, we also have LABELS. We use the labels to categories nodes to a group. This grouping enable us structure our data, and fetch nodes belonging only to a particular group. Let see a snippet to create a labelled node. CREATE (:School) With the above snippet, we created a node and labeled it “School”. we can also use the variable names which can result in the following query CREATE (s:School) RETURN s This query is the same as the one above it. the only difference is that it uses the variable name to return the node after it's created. Another special thing about the Neo4j, is that we can assign many label to a node, making it polymorphic. let take an example. CREATE (s:School :Building) RETURN s This query will create one node and assign it with two labels “School” and “Building”. PROPERTIES Each node in Neo4j has properties, the same way relational database has columns. You can set the properties or leave them empty. The query below shows how to create an node with properties CREATE (s:School { name: 'University Of Nigeria', location: 'Enugu' }) RETURN s The following nodes has two properties, “name” and “location“. you can add more properties as you choose. RELATIONSHIP Relationship is how each node relates to each other. It’s kinda like the way tables have relationships in a relational database. But in graph database, relationships are with each nodes. “[]” is a way to recognize relationship in a query. Let denote a relationship below [:GOES_TO] The above denotes a relationship with a label of “GOES TO“. Now lets write a query that create a relationship between two nodes. To make the query complete, let create two nodes and then create a relationship between them. CREATE (school:SCHOOL { name: 'UNILAG' })
CREATE (student:STUDENT { name: 'John' })

CREATE (student) -[:GOES TO] -> (school) The query above, creates two nodes with labels “SCHOOL“ and “STUDENT“ . And then goes ahead to create a relationship with label “GOES_TO“. One thing to also remember is that, relationships are always directed. this brings us to the usage of “-“ and “→” in the query. I hope from the query it seems self explanatory, but if it is not, the relationship is from the student node to the school node. CONCLUSION The graph database is an interesting data paradigm where there is no table or collection. each node is an independent entity that can be classified to as many label as you want. it gives the freedom to structure that suites your design.

Exploring the Neo4J Graph Database

Introduction In the previous parts ( , and ), the setting up of GraphQL APIs and querying Neo4j were covered. In this article, you will be applying the Kong API gateway and making it the single point of entry into the AWS EKS cluster. part 1 part 2 part 3 Prerequisite You need to have a running EKS cluster and created and applied a .kubeconfig file for your cluster. If not already in place, refer to the for creating a cluster and configuring EKS. official AWS link Kong Kong is an open-source API gateway, built on top of Nginx. It helps to manage the control traffic, in terms of authentication, rate limiting, Caching, Monitoring, and so on. To start using Kong in AWS EKS, you first need to install the ingress controller in the cluster. To install the Kong ingress controller, you can use Helm charts, which requires the installation of Helm. After installation, you just need to execute the following commands: helm repo kong http //charts.konghq. helm repo helm install kong/kong add s: com update , you can implement all kinds of configurations. Here are some samples of routing and rate-limiting: After the controller is installed The above sample is a YAML file, which is used as an ingress resource to configure the ingress controller. In this sample, the traffic is rerouted from the KONG URL, which is exposed to different microservices based on the path URL. Check the configuration through the following command: kubectl describe ingress < > ingress_name After ingress routing is configured, you can apply different Kong plugins. In this example, the rate limit plugin is applied to the microservices. In the following sample, the access to any microservice is being limited; that is an API cannot be called more than two times in a second. You can also place a limit, based on the number of requests executed in a minute, hour, day, month, and year. A limit based on request per customer, IP, and microservice can be placed as well. Kong will provide you with a URL that will be exposed to the world for incoming traffic. From there on, the controller will check ingress resource configurations and plugins to perform re-routing and rate limiting. The architecture diagram is as follows: Finally… In this article, you have learnt about the installation and configuration of the Kong API gateway at a very high level. The blog series will provide you with a good start to develop GraphQL API using GraphDB and deploying an API gateway for the application. References https://github.com/Kong/kubernetes-ingress-controller https://konghq.com/kong/

How To Build Graphql API with Spring Boot, Neo4j and Kong [Part 4]

In and , the setting up and testing of GraphQL APIs was covered. In this article, the focus is on the querying of the graph DB, which is Neo4j in this case. For querying, the Neo4j OGM library and Spring Data’s Neo4jRepository will be used. part 1 part 2 Neo4j OGM An OGM (Object Graph Mapper) maps nodes and relationships in the graph to objects and references in a domain model. Much like JPA, Neo4j-OGM allows you to annotate POJOs in order to map them to nodes, relationships, and properties in the graph. In the previous blog, we used annotations, such as NodeEntity, Relationship, RelationshipEntity, StartNode, and EndNode. All these annotations are part of the OGM library. It supports multiple drivers – Bolt, HTTP, and embedded. In this case, the Bolt driver has been used, as it is a fast native driver that communicates over the Bolt protocol. The default port to connect to Neo4J through the Bolt protocol is . The following dependencies are required to be included in pom.xml: 7687 This will include Neo4j OGM and its dependent libraries, as well as Spring Data capabilities, which are built on top of the Neo4J OGM library. Session Factory and Session On the successful startup of the server, the SessionFactory object is created at the backend. Using this object, you can acquire session class objects to fire custom queries at runtime. In one of the use cases, the DB query couldn’t be determined beforehand, like a search member. In search member, you will not be aware of all the parameters that can come in the request; it could range from 1 to 5, which makes it hard to create queries for every combination. The code sample is as follows: So the solution is to create a query at runtime dynamically and then execute it using the Session object. Spring Data Neo4j Repository Spring Data Neo4j uses Neo4-OGM to help in the rapid and easy development of spring applications. It is very useful in querying the DB with features similar to any other ORM framework, such as Hibernate or JPA. Two core components that are used in development are Neo4jRepository and Neo4jTransactionManager. As the name suggests, extending the Neo4jRepository comes with all kinds of CRUD, pagination, sorting, operations, etc. Neo4jTransactionManager is used to manage transactions. The above image shows an example of the @Repository class, which is extending the Neo4jRepository. The 2 most common ways for querying the DB are shown in the example. The first 2 methods, that is line number 14 & 16 are an example of querying through the methods. These are very powerful, as you can find through properties or use a combination of AND and OR between the properties. find Also, you can use various keywords, such as . For all supported keywords, refer to the following link: GreaterThan, LessThan, NotNull, Like, and so on https://docs.spring.io/spring-data/data-neo4j/docs/current/reference/html/#neo4j.repositories In the second method, the members with experience greater than the specified param are fetched. All the find methods execute Cypher queries at the backend. The last 2 methods, that is line number 19 & 22, are a way of defining your own Cypher queries. Queries using the Cypher graph query language can be provided with the @Query annotation. Pagination and Sorting SDN supports sorting and pagination while querying. To implement this you need to provide Sort or Pageable class objects to repository methods. The following excerpt is an example: In the above example, the member records that possess a skill with page 0 and size 10 are fetched. Also, it is sorted in descending order to list the most experienced person in some specific skill on top. Part 4 deals with…. In this article, you have learned how to query Neo4j DB using 2 different libraries – Noe4j OGM and Spring Data for Neo4j (SDN). You also had a sneak peek of Session factory, session, pagination, and sorting. In the next article, we will be looking into using Kong as an API gateway for the application. References https://neo4j.com/docs/ogm-manual/current/ https://docs.spring.io/spring-data/data-neo4j/docs/current/reference/html/#reference https://github.com/preetpramati/SpringBootNeo4JAPI

Building GraphQL API with Spring Boot, Neo4j and Kong – Part 3

of this series provides detailed description on GraphQL server and how to start setting it up using springboot, Schema Definition Language (SDL) and its types. Part 1 Introduction This article will provide details on how to query GraphQL API using GraphiQL, the rich relationship between  type objects like member, skills, asset and nodes, and exception handling using the ‘GraphQLErrorHandler’ part of GraphQL servlet. GraphiQL GraphiQL is a GUI tool used for editing and testing GraphQL queries and mutations. It is useful during testing and development, but by default, should be disabled in production. To enable this in the application, you need to add the following in dependencies. After the dependency is added and your server is up, by default, you can access it through ‘http://localhost:/graphiql’. The following image illustrates how: The leftmost panel contains the query to be executed. In this case, the memberByName’ query with the ‘field’ argument field is executed and is followed by the request fields of the Member object. The center panel contains the response data of the query. The rightmost panel contains nodes of all types, relationships, and queries with their description and fields. You can also search for a specific node, query, or relationship in the panel. Relationships in GraphDB A relationship is a connection between two nodes. It is an edge of a graph that has a start node, end node, and direction. There is no limit to the number and kind of relationships that a node can have. To establish a relationship in GraphQL, you need to do it at the type level and the backend, where the POJO class of the type has been created in Java. In this use case, The Member object is related to Skill, Certifications, Reporting Manager(Member), Asset, Contribution, and Project. The following diagram is a sample use case followed by type and POJO objects: Member type object: POJO in Java sample: Rich Relationships There will always be a need for relationships to contain properties for querying purposes. These relationships are known as rich relationships. In the above example, there are 2 relationships which are part of the rich category, that is Project and certification relation. Rich relationships can provide answers to specific questions, such as when a certification was performed or when a member was associated with a project. You need to create a separate POJO class of the relationship with the ‘@RelationshipEntity’ annotation and add the fields to it with two mandatory annotations, which specify the start and end nodes. You can have any number of fields, depending upon the use case. The code sample is: Exception Handling GraphQL returns errors with descriptive messages for client requests that have syntax errors or in case of a requested field that does not exist without any configuration. It will indicate that an internal error has occurred in the server, if an exception is not handled properly or exception handling is not configured. To send meaningful messages or requirements or throw a custom business exception in response, you need to change the behavior of the ‘GraphQLErrorHandler’ class. You need to create an adapter class to send the desired custom error. The adapter must only implement the GraphQLError interface and not inherit the Exception class. You need to change the logic of the ‘getMessage()’ method to create a crisp and clear message, instead of a full error stack. After the adapter is created, an Exception class that extends the Exception class and also implements GraphQLError is required. You can create different business exception classes or 1 generic class to handle all the server exceptions. Or you can have both of them at the same time. The business exception class sample is as follows: To adapt to the above exception class, you need to change the behavior of the ‘GraphQLErrorHandler’ class, so that it transforms the exceptions to the GraphQLError class itself. This class is indicated when building the SimpleGraphQLServlet and ServletRegistrationBean object. Part 3 deals with…. In this article, we learned about GraphiQL and how we can use it for testing and development, Relationships and its properties, and Error handling for sending meaningful messages in response. In the , we will learn more about querying graph DB using cypher queries and also touch upon the Neo4j OGM library and Spring data Neo4j repository. next article References https://github.com/preetpramati/SpringBootNeo4JAPI https://grandstack.io/docs/graphql-relationship-types.html https://picodotdev.github.io/blog-bitix/2017/11/devolver-mensajes-de-error-descriptivos-en-graphql/

Building Graphql API with Spring Boot, Neo4j and Kong [Part 2]

Introduction This article will give a good understanding of the GraphQL server and setting it up using Spring Boot. GraphQL allows the client to specify exactly what data is desired, including multiple queries in a single request. GraphQL enables fetching data from nested data sets in a single request. Unlike REST, where there is an endpoint for every resource, an application using GraphQL exposes a single endpoint for all the resources. Since GraphQL APIs are organized in terms of types and fields and not endpoints, it provides far more flexibility than traditional REST APIs. GraphQL Spring Boot Starter The combined with the library makes it very easy to get a GraphQL server running in a very short time. Adding these few dependencies to pom.xml file is enough to start the GraphQL server. Spring Boot GraphQL Starter GraphQL Java Tools library parses the given GraphQL schema and allows you to BYOO (bring your own object) to fill in the implementations. It works extremely well if you already have domain POJOs (Plain old Java Object) that holds your data by allowing you to map these magically to GraphQL objects. GraphQL Java Tools Now that we have added the starter packages we are ready to set up query/mutation resolvers. Query/Mutation is an entry point for the application just like a controller in the case of REST API. This bean needs to be implemented in a markup interface GraphQLQueryResolver or GraphQLMutationResolver and contains public methods that are defined in a schema as shown below: { MemberService memberService; Logger logger = LogManager.getLogger(MemberQuery.class); { ( .memberService.getMemberByName(name).size() == ){ MemberException( );
 } { .memberService.getMemberByName(name);
 }
 }
 
} @Component public class MemberQuery implements GraphQLQueryResolver @Autowired public private static final List<Member> public getMemberByName ( String name) if this 0 throw new "no member data" else return this The name of the method should be equal to the method in type Query from the schema. It can return simple types, which will be mapped automatically to scalar values from the schema, or it can return more complex types. In order to understand the method name and its mapping, we need to understand schema writing in GraphQL which is the next topic. GraphQL Schema GraphQL comes with its own language to write GraphQL Schemas called Schema Definition Language (SDL). GraphQL employs a contract-first design approach and all operations supported by the API are defined here. These files are named with extension ".graphqls", and are required to be  in the resources folder in order to be in classpath. Please see below for the sample file. In the above schema, the ‘Member object’ is defined by type. The type system in GraphQL is the most basic component, and it represents the type of object that can be fetched from a service and the fields that are available within the object. Let us go through the Member object.: It has 20 fields comprising scalar and complex fields. ‘String’ and ‘Long’ are the scalar types in member object of the GraphQL . Other scalar types supported by SDL are Int, Float and Boolean and ID. The ID scalar type represents a unique identifier, often used to re-fetch an object or as the key for a cache. It contains complex types that are defined the same way as Member type and these are Assets, Skill, Contributions, MemberProjectRelation and MemberCertificationRelation. It also has self join to itself mentioned as "member: Member". This is because the member is reporting to a manager who is part of the member. ! is for the field which can not be null. Query and Mutation Type There are two types that are important for data fetching and manipulation within a schema i.e. Query and Mutation types. The Mutation type represents the queries that are used to perform write operations on the data. Every GraphQL service has a query type and may or may not have a mutation type. While query fields are executed in parallel, mutation fields run in series, one after the other. Therefore mutations are always preferred to manipulate the data in order to avoid a race condition. Below is the sample of the two: Both query and mutation contain entry point, request parameters and return type in their definition. Parameters followed by "!" can’t be null and the object type that is casted in square brackets "[ ]" is a type of list. To  Be Continued.... In this article we learned what is GraphQL, how we can start building it using springboot, Schema Definition Language (SDL) and its types. In the next article, we will know more about the relationships between type objects like member, skills, asset, etc. GraphiQL used for querying graphql API and exception handling using GraphQLErrorHandler. References https://github.com/preetpramati/SpringBootNeo4JAPI https://graphql.org/learn/schema/

How To Build Graphql API with Spring Boot, Neo4j and Kong [Part 1]

Leveraging the Neo4j Community Graph For Social Discovery Recently the Neo4j Developer Relations team decided to devote a few hours to work together, style, to launch a . One feature we decided was needed for launch was a trending activity feed that showed blog posts and projects that are currently trending in the community. hackathon new Neo4j community forum Fortunately, my colleague has built out the , a series of serverless functions that import data from APIs like , Twitter, Meetup, and Stackoverflow into a Neo4j instance. We use Neo4j Community Graph to keep track of what’s going on in the Neo4j community. In fact if you’ve seen Mark’s newsletter (AKA TWIN4j), a few queries to community graph surfaces each week’s content. Mark Needham Neo4j Community Graph Github This Week In Neo4j In this post I want to explain how we built out the trending activity feed feature on the Neo4j Community Forum using a GraphQL First development approach. Community Graph Data model for the Community Graph. Combining data sets and querying across them is a natural fit for a graph database like Neo4j. One of the benefits of a graph database like Neo4j is the ease with which it allows for combining datasets and querying across them. We’ve been using Neo4j to help keep track of what the Neo4j community has been working on. Neo4j Community Graph is powered by a set of serverless functions that run on AWS Lambda, periodically checking the APIs of GitHub, Twitter, StackOverflow, and Meetup for new Neo4j related updates. Any matches are imported into Neo4j. Mark has even created a so that anyone can spin up a community graph for their community! community graph CLI The Awesome Discourse API The first step was adding content from our new Discourse forum into community graph. Discourse exposes an API to enable developers to build tooling on top of Discourse. In this case Mark registered a webhook on the Discourse API so that events are sent to the serverless community graph ingest Lambda functions. So the new Discourse site becomes a new developer community data set in community graph. This allows us to capture new Discourse users and new posts, including likes and replies. We link Discourse users in the graph with their other accounts (Github, Twitter, Stackoverflow, etc) when a user fills in their Discourse profile with their user names across services. Community Trending Activity Feeds Trending activity feeds provide an overview of what is going on in the community at a glance and quickly show the posts, topics, and projects that are trending. Rather than just showing the most recent posts, we want to rank items to surface content of interest to viewers that show what is currently trending in the community. Exponential Time Decay Functions Discussion forums such as Hacker News and Reddit make use of to rank what posts float to the top and what are banished to the second page and beyond. The basis for these ranking algorithms are a function that calculates a score for each post which consists of a rating divided by the time since the post was created raised to some exponent. The rating can consist of things like upvotes, views, subjective scoring by a reviewer, or as in our case, all three. You can find , but for our purposes we’ll use a much simpler version. exponential time decay functions implementations of the functions used by Hacker News and Reddit A simple exponential time decay function for scoring posts in a discussion forum. Functions like this allow new content to appear high on the leaderboard, but quickly drop off if the community does not find it engaging. Here’s how we can query the community graph using Cypher to find our top community blog posts using an exponential decay function: const cypherQuery = ` (u: )-[: ]->(t: ) t.approved “Exclude” labels(t) *,1.0 * (duration.inSeconds(datetime(), t.createdAt)).seconds/10000 ago u, t, ( (10.0 * t.rating) + t.likeCount + t.replyCount)/(ago^2) score u, (t)[0] AS topic u, topic $first`; MATCH DiscourseUser POSTED_CONTENT DiscourseTopic WHERE AND NOT IN WITH AS WITH AS WITH COLLECT RETURN LIMIT An exponential time decay function with these parameters will surface almost every new post to the top, to give it a chance for the community to vote on it. Posts that the community votes up, or that involve active discussion stay at the top, while posts that are less captivating to the community drop off the list. GraphQL API We expose a GraphQL API on top of the Neo4j community graph instance hosted on that we can query from Discourse to populate the activity feed on the landing page. Neo4j Cloud The GraphQL Schema Defining the GraphQL schema was actually one of the first steps we took. Once we defined the schema we were able to just from the GraphQL API so and could work on the UI. Here’s the GraphQL schema for our top blog post query: return mocked data Jennifer David type {title: Stringurl: Stringauthor: DiscourseUser} CommunityBlog type {name: StringscreenName: Stringavatar: String} DiscourseUser type {topCommunityBlogsAndContent(first: Int = 10): [CommunityBlog]} Query Resolvers In a GraphQL server, resolvers are the functions that contain the logic for fetching data from our data layer. In our case each query entry point to the GraphQL API is a single Cypher query, so we can use the to execute our Cypher query and return the results, ordered by our time decay ranking score. Neo4j JavaScript driver export const resolvers = {Query: { (_, params, context) => { topCommunityBlogsAndContent: let session = context.driver.session();  
  const baseUrl = ‘[https://community.neo4j.com/’](https://community.neo4j.com/%27);

  // cypherQuery is defined above  
  return session.run(cypherQuery, params)  
  .then( result => {  
    const resData = result.records.map(record => {  
      const user = record.get(“u”).properties,  
           topic = record.get(“topic”).properties;

      return {  
        title: topic.title,  
        url: baseUrl + “t/” + topic.slug,  
        author: {  
          name: user.name,  
          screenName: user.screenName,  
          avatar: getAvatarUrl(user.avatarTemplate)  
        }  
      }  
    })  
    return resData;

  })  
  .catch(error => {  
    console.log(error);  
  })  
  .finally( ()=> {  
    session.close();  
  })  
} }}; Apollo Server We use in a Node.js app to allow us to build a GraphQL API from a GraphQL schema (type definitions) and resolvers. We inject a Neo4j driver instance into the context so that it is available in the resolver functions. Apollo Server import { typeDefs, resolvers } from “./graphql-schema”;import { ApolloServer, makeExecutableSchema } from “apollo-server”;import { v1 as neo4j } from “neo4j-driver”;import dotenv from “dotenv”; dotenv.config(); const schema = makeExecutableSchema({ , }); typeDefs resolvers const driver = neo4j.driver(process.env.NEO4J_URI || “bolt://localhost:7687”,neo4j.auth.basic(process.env.NEO4J_USER || “neo4j”,process.env.NEO4J_PASSWORD || “neo4j”)); const server = new ApolloServer({context: { driver },schema: schema}); server.listen(process.env.GRAPHQL_LISTEN_PORT, ‘0.0.0.0’).then(({ url }) => {console.log(`GraphQL API ready at ${url}`);}); One of the benefits of using Apollo Server is that we automatically get GraphQL Playground, a tool for exploring GraphQL APIs. GraphQL Playground allows us to explore the schema, including entry points and types, and to execute GraphQL requests against the API. makes it easy to explore GraphQL APIs. GraphQL Playground Try it here. The Neo4j Community Forum Landing Page The GraphQL first development approach was a fun and productive way for us to quickly build out a feature that allowed us to put it into production after a few hours of collaborative work. When it’s all said and done, here’s how the landing page looks: Not bad for a few hours hacking on a rainy day ☔ Check it out . here I skipped over how we populate the “This Week in Neo4j”, “Popular Community Projects”, and “New Certified Developers” sections, but it all comes from Neo4j Community Graph! You can find the GraphQL API code to see how those bits work. here neo4j-graphql-js and GRANDstack If you’re interested in exposing a GraphQL API on top of a graph database like Neo4j (we think GraphQL and graph databases are an obvious match for each other) be sure to check out the library and for building full stack applications. neo4j-graphql-js GRANDstack Be sure to join the Neo4j community forum on Discourse You can find the code for the GraphQL API . here Further Reading . A GraphQL API for the GraphQL Community Graph on top of Neo4j. GraphQL Community Graph And finally, here are a few more resources about Community Graph: — includes code for spinning up your own community graph Community Graph Github org GraphQL APIs for Developer Communities Five Common GraphQL Problems and How Neo4j-GraphQL Aims to Solve Them — GraphQL, React, Apollo, Neo4j Database full stack development GRANDstack.io

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