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The Spring Data ArangoDB integration is a library for accessing data stored in ArangoDB from Spring-based Java applicationSpring Data provides a consistent interface foraccessing various types of data sources. Spring Data ArangoDB implements thisfor ArangoDB and provides mapping of Java objects to ArangoDB documents (ODM).Repository Demo without Spring Boot Starter Demo with Spring Boot Starter Reference (version 4)Reference (version 3)JavaDoc Changelog MigrationSupported versions Spring Data ArangoDB is compatible with:all the still supported Spring Boot 3.x versions and related Spring Framework versionsall the still supported ArangoDB versions all the still supported Spring Boot 2.x versions and related Spring Framework versionsall the still supported ArangoDB versions Get started This tutorial is about how to configure Spring Data ArangoDB without using Spring Boot Starter ArangoDB.For a more extensive tutorial about the features of Spring Data ArangoDB andSpring Boot support, see the Spring Boot Starterdocumentation.Build a project with Maven Set up a project and add every needed dependency. This demo uses Maven andSpring Boot.Create a Maven pom.xml: xmlns=" xmlns:xsi=" xsi:schemaLocation=" 4.0.0 org.springframework.boot spring-boot-starter-parent 3.3.4 com.arangodb spring-data-arangodb-tutorial 1.0.0 spring-data-arangodb-tutorial ArangoDB Spring Data Tutorial 21 UTF-8 org.springframework.boot spring-boot-starter com.arangodb arangodb-spring-data 4.4.2 Substitute the versions with the latest available versions that are compatible.See the Supported versions for details.Entity classes For this tutorial we will model our entity with a Java record class:@Document("characters")public record Character( @Id String id, String name, String surname) {}Create a repository Now that we have our data model, we want to store data. For this, we create a repository interface whichextends ArangoRepository. This gives us access to CRUD operations, paging, and query by example mechanics.public interface CharacterRepository extends ArangoRepositoryCharacter, String> {}Create a Configuration class We need a configuration class to set up everything to connect to our ArangoDB instance and to declare that allneeded Spring Beans are processed by the Spring container.@EnableArangoRepositories: Defines where Spring can find your repositoriesarango(): Method to configure the connection to the ArangoDB instancedatabase(): Method to define the database namereturnOriginalEntities(): Method to configures the behavior of repository save methods to either return theoriginal entities (updated where possible) or new ones. Set to false to use java records.@Configuration@EnableArangoRepositories(basePackages = {"com.arangodb.spring.demo"})public class

ArangoDB vs. MongoDB MongoDB was the very first document-oriented database and it’s open source. ArangoDB adds many important features to the document model, like scalability, JOIN’s, complex transactions, lower operational costs and more. ArangoDB vs. Neo4j MongoDB was the very first document-oriented database and it’s open source. ArangoDB adds many important features to the document model, like scalability, JOIN’s, complex transactions, lower operational costs and more. ArangoDB vs. Cassandra MongoDB was the very first document-oriented database and it’s open source. ArangoDB adds many important features to the document model, like scalability, JOIN’s, complex transactions, lower operational costs and more. SQL vs AQL - A Declarative Query Language for Coders AQL provides a powerful way to handle all data models and even combine them in a single query. If you know SQL, you’ll feel at home in AQL’s clear & intuitive syntax. No more juggling various tech: one database and one query language supporting many needs. SQL vs AQL - A Declarative Query Language for Coders AQL provides a powerful way to handle all data models and even combine them in a single query. If you know SQL, you’ll feel at home in AQL’s clear & intuitive syntax. No more juggling various tech: one database and one query language supporting many needs.

“We use ArangoDB for storing and processing of geo locations (i.e., beacons, router, GPS information), storing and sending notifications, and processing campaigns. We even run the proximity based algorithm on ArangoDB to decide which notification or campaign will be shown to the user.” –Alex Pavlov, Lead Software Engineer Get started free today (no credit card required), and experience the shortest time to value for a hosted graph DB. Read the Case Studies Learn why companies across industries are switching to ArangoDB for Graph. Native Support For Spatial Data ArangoDB excels in geospatial applications with its native capability to efficiently store and process complex spatial data relationships. Integrating geospatial data with other data ypes on a single platform enables developers to build advanced analytics and location-based services across industries. Real-Time Fleet Tracking ArangoDB’s geospatial features simplify the integration of location data, which is essential for real-time tracking and logistics optimization. This eliminates the need for developers to merge location-based and database systems manually, greatly reducing errors and simplifying data management. Spatial Analytics for Retail Retailers face challenges in gaining insights into customer behavior, such as most visited sections, time spent in areas, and movement paths through the store. While technologies like Wi-Fi, Bluetooth, and heat maps can track this data, integrating it is complex. ArangoDB is the first viable solution to efficiently incorporate spatial data, offering a practical solution to leverage insights and optimize store layouts and product placement. Location-Based Recommendations Imagine a restaurant recommendation app powered by ArangoDB. It cross-references real-time location with user preferences and relationships to deliver personalized dining suggestions. This unique real-time analysis capability provides a richer, context-aware user experience without needing multiple databases or complex data transformations, profoundly streamlining and simplifying development. Geofencing for IoT For IoT applications needing to set up boundaries and take actions based on location, ArangoDB is an ideal choice. It can personalize services, send security alerts, and track assets. ArangoDB enables quick adaptation and response by seamlessly merging location and context, making it a practical and reliable choice for IoT solutions focused on real-time efficiency. ArangoDB vs. Commodity Graph DBs For Geospatial Explore Other Use Cases Get started free today (no credit card required), and experience the shortest time to value for a hosted graph DB. Read the Case Studies about how companies across industries are switching to Graph.

ArangoDB Community Edition Unlock the Power of Multi-Model Databases with ArangoDB Community Edition: A Free, Open-Source Solution for Agile Teams Seeking Scalability, Flexibility, and High Performance. Native Graph Store both data and relationships, for faster queries even with multiple levels of joins and deeper insights that simply aren’t possible with traditional relational and document databases. Document Store Every node in your graph is a JSON document: flexible, extensible, and easily imported from your existing document database. ArangoSearch Natively integrated cross-platform indexing, text-search and ranking engine for information retrieval, optimized for speed and memory. Get started with Graph today with no credit card required. Read the Graph Done Right White Paper to understand everything about Graph Databases and their use cases Read the Case Studies about how companies across industries are switching to Graph. ArangoDB Query Language AQL provides a powerful way to access and combine all data access strategies in ArangoDB. Foxx Microservices Unify your data storage logic, reduce network overhead and secure sensitive data with Foxx. Full GeoJSONSupport Enrich your graph, document or search queries with geo-locational aspects. MongoDB ArangoDB vs. Neo4j ArangoDB vs. Cassandra Get started with Graph today with no credit card required. Read the Graph Done Right White Paper to understand everything about Graph Databases and their use cases Read the Case Studies about how companies across industries are switching to Graph.

Pregel enables you to do online analytical processing directly on graphs stored in ArangoDBDistributed graph processing enables you to do online analytical processingdirectly on graphs stored in ArangoDB. This is intended to help you gainanalytical insights on your data, without having to use external processingsystems. Examples of algorithms to execute are PageRank, Vertex Centrality,Vertex Closeness, Connected Components, Community Detection.For more details, see all available algorithmsin ArangoDB.Check out the hands-onArangoDB Pregel Tutorial to learn more.The processing system inside ArangoDB is based on:Pregel: A System for Large-Scale Graph Processing – Malewicz et al. (Google), 2010.This concept enables us to perform distributed graph processing, without theneed for distributed global locking.This system is not useful for typical online queries, where you just work ona small set of vertices. These kind of tasks are better suited forAQL traversals.Prerequisites If you run a single ArangoDB instance in single-server mode, there are norequirements regarding the modeling of your data. All you need is at least onevertex collection and one edge collection.In cluster deployments, the collections need to be sharded in a specific way toensure correct results: The outgoing edges of a vertex need to be on the sameDB-Server as the vertex. This is guaranteed by SmartGraphs.Thus, Pregel in cluster deployments is not usable in the Community Edition.Note that the performance may be better, if the number of your shards /collections matches the number of CPU cores.JavaScript API Starting an Algorithm Execution The Pregel API is accessible through the @arangodb/pregel package.To start an execution, you need to specify the algorithm name and anamed graph (SmartGraph in cluster). Alternatively, you can specify the vertexand edge collections. Additionally, you can specify custom parameters which varyfor each algorithm. The start() method always returns a unique ID(a numeric string) which you can use to interact with the algorithm later on.The following example shows the start() method variant for using a named graph:var pregel = require("@arangodb/pregel");var params = {};var execution = pregel.start("", "", params);You can also specify the vertex and edge collections directly. In this case,the second argument must be an object with the keys vertexCollectionsand edgeCollections:var execution = pregel.start("", { vertexCollections: ["vertices"], edgeCollections: ["edges"] }, params);The params argument needs to be an object with the algorithm settings asdescribed in Pregel Algorithms.Status of an Algorithm Execution You can call pregel.status() and use the ID returned by the pregel.start(...)method to track the status of your algorithm:var execution = pregel.start("sssp", "demograph", { source: "vertices/V" });var status = pregel.status(execution);It tells you the current state of the execution, the currentglobal superstep, the runtime, the global aggregator values as well as thenumber of send and received messages.The state field has one of the following values:StateDescription"none"The Pregel run has not started yet."loading"The graph data is being loaded from the database into memory before executing the algorithm."running"The algorithm is executing normally."storing"The algorithm finished, but the results are still being written back into the collections. Only occurs if the store parameter is set to true."done"The execution is done. This means that storing is also done. This event is announced in the