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ArangoDB 2013: Simplify Database Management with Auto-Increment
The most recent versions of ArangoDB offer more detailed control over what values will be auto-generated for the _key and _id attributes of documents. They also allow using predictable auto-increment sequences.
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German Video: NoSQL Meets Mobile Cologne | ArangoDB 2013
Last week Jan (core member of ArangoDB and the brain behind AQL and many other parts) was invited by “mobile.cologne” – a user group here in Cologne dealing with mobile development.
In this talk Jan gives a general overview on nosql databases and the different flavors. He explains how to query a nosql database and he evaluates how a nosql database can be used in a mobile app.
Warning: The talk is in German. If you want to hear it in English, let us know in the comments. 🙂
“NoSQL meets mobile.cologne” by Jan Steemann.
Getting Started with ArangoDB & Symfony: Part 1 | ArangoDB 2013
Benchmark Results – ArangoDB vs. Neo4j : ArangoDB up to 8x faster than Neo4j
Estimated reading time: 6 minutes
Introduction
This document presents the benchmark results comparing the ArangoDB’s Graph Analytics Engine (GAE) against Neo4j. The GAE is just one component of ArangoDB’s Data Science Suite.
This reproducible1 benchmark aims to provide a neutral and thorough comparison between the two databases, ensuring a fair and unbiased assessment.
We use the wiki-Talk dataset, a widely used, real-world graph dataset derived from the edit and discussion history of Wikipedia.
(more…)Vector Search in ArangoDB: Practical Insights and Hands-On Examples
Estimated reading time: 5 minutes
Vector search is gaining traction as a go-to tool for handling large, unstructured datasets like text, images, and audio. It works by comparing vector embeddings, numerical representations generated by machine learning models, to find items with similar properties. With the integration of Facebook’s FAISS library, ArangoDB brings scalable, high-performance vector search directly into its core, accessible via AQL (ArangoDB Query Language). Vector Search is now just another, fully-integrated data type/model in ArangoDB’s multi-model approach. The Vector Search capability is currently in Developer Preview and will be in production release in Q1, 2025.
This guide will walk you through setting up vector search, combining it with graph traversal for advanced use cases, and using tools like LangChain to power natural language queries that integrate Vector Search and GraphRAG.
(more…)Some Perspectives on HybridRAG in an ArangoDB World
Estimated reading time: 7 minutes
Introduction
Graph databases continue to gain momentum, thanks to their knack for handling intricate relationships and context. Developers and tech leaders are seeing the potential of pairing them with the creative strength of large language models (LLMs). This combination is opening the door to more precise, context-aware answers to natural language prompts. That’s where RAG comes in—it pulls in useful information, whether from raw text (VectorRAG) or a structured knowledge graph (GraphRAG), and feeds it into the LLM. The result? Smarter, more relevant responses that are grounded in actual data.
(more…)ArangoDB vs. Neo4J
Estimated reading time: 7 minutes
Advantages & Implications for Architects and Developers
ArangoDB and Neo4j are both powerful graph databases, but they have different strengths and features. Here’s a comparison showing how ArangoDB might be superior to Neo4j in certain scenarios, along with code snippets to illustrate:
(more…)ArangoDB 3.12 – Performance for all Your Data Models
Estimated reading time: 6 minutes
We are proud to announce the GA release of ArangoDB 3.12!
Congrats to the team and community for the latest ArangoDB release 3.12! ArangoDB 3.12 is focused on greatly improving performance and observability both for the core database and our search offering. In this blog post, we will go through some of the most important changes to ArangoDB and give you an idea of how this can be utilized in your products.
(more…)Advanced Fraud Detection in Financial Services with ArangoDB and AQL
Estimated reading time: 3 minutes
Advanced Fraud Detection: ArangoDB’s AQL vs. Traditional RDBMS
In the realm of financial services, where fraud detection is both critical and complex, the choice of database and query language can impact the efficiency and effectiveness of fraud detection systems. Let’s explore how ArangoDB – a multi-model graph database – is powered by AQL (ArangoDB Query Language) to handle multiple, real-world fraud detection scenarios in a much more seamless and powerful way compared to traditional Relational Database Management Systems (RDBMS).
(more…)Update: Evolving ArangoDB’s Licensing Model for a Sustainable Future
Estimated reading time: 7 minutes
Update: https://arangodb.com/2023/10/evolving-arangodbs-licensing-model-for-a-sustainable- future/
Last October the first iteration of this blog post explained an update to ArangoDB’s 10-year-old license model. Thank you for providing feedback and suggestions. As mentioned, we will always remain committed to our community and hence today, we are happy to announce yet another update that integrates your feedback.
(more…)The world is a graph: How Fix reimagines cloud security using a graph in ArangoDB
‘Guest Blog’
Estimated reading time: 5 minutes
In 2015, John Lambers, a Corporate Vice President and Security Fellow at Microsoft wrote “Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win.ˮ
(more…)Reintroducing the ArangoDB-RDF Adapter
Estimated reading time: 1 minute
ArangoRDF allows you to export Graphs from ArangoDB into RDFLib, the standard library for working with Resource Description Framework (RDF) in Python, and vice-versa.
(more…)Introducing ArangoDB’s Data Loader : Revolutionizing Your Data Migration Experience
Estimated reading time: 7 minutes
At ArangoDB, our commitment to empowering companies, developers, and data enthusiasts with cutting edge tools and resources remains unwavering. Today, we’re thrilled to unveil our latest innovation, the Data Loader, a game-changing feature designed to simplify and streamline the migration of relational databases to ArangoGraph. Let’s dive into what makes Data Loader a must-have tool for your data migration needs.
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ArangoDB & Symfony Integration: Part 2 | ArangoDB ’13
You can download the completed demo at Github.
The data structure
Our app deals with movies: each movie has a title, a year of release and it is assigned to a genre. Each movie deals with none, one or many topics.
In a relational database we would probably create a table movie and another table topic and join them with a shared movie id.
ArangoDB follows like other document stores the “aggregate model” approach: In short terms this means that you should put data that is often needed together into one document. Remember: documents are a bit like rows in the relation world; in nosql world they can have a nested structure.
ArangoDB & Symfony Integration: Part 3 Guide | ArangoDB ’13
You can download the completed demo at Github.
Working with forms
The next example shows the use of ArangoDB with Symfony’s form component.
This is how our form will look like. The topics are represented as a text field and the user is required to separate them by commas. This delivers questionable user experience, but leads to an interesting question: how can we transform our topics array from our movie document to a text field? And: we’ll need it at least two times, both for creating and editing movies.
ArangoDB & Symfony Integration: Part 4 Guide | ArangoDB ’13
You can download the completed demo at Github.
Extending Arangodb\Document
The next thing we want to improve are all those noisy set calls in the controller:
// src/Triagens/ArangodbBundle/Controller/DefaultController.php
if ($form->isValid()) {
$movie = new ArangoDocument();
$movie->set("title",$form->get("title")->getData());
$movie->set("released",$form->get("released")->getData());
$movie->set("genre",$form->get("genre")->getData());
// and so on ... my eyes hurt!
}
API Implementors Gathering: ArangoDB 2013
Dear API Implementors,
We want to make it simpler for you to keep up with the development of ArangoDB. We therefore created a dedicated place for all API implementors. If you are implementing a Driver, ODM or database access tool for ArangoDB, this is the right place for you. In addition to sharing information about the changing API, we want to discuss patterns and ideas with each other.
The team behind ArangoDB will announce API changes and enhancements in the issues of this project, so you have one place to get all the up to date information. Apart from that, the community is independent from the ArangoDB team.
We want to make this a central, open place for all API implementors. So if you want to be part of this group, just introduce yourself and we will add you to the list. Then just watch the Repository to get all information in your inbox.
We already have the people behind the PHP and Ruby drivers on board. We’re looking forward to discussing with you and build a multi-programming language community around ArangoDB. So join us.
CoffeeScript in ArangoDB: Feature Preview | ArangoDB 2013
In my blog post about npm packages, I tried to use underscore for ArangoDB. I found that the easiest way to archive this, is using the nodes package manager NPM. Node packages and modules follow the Common.JS specification, so they can be used by ArangoDB.
Why not try to use the package coffee-script as well? Install it using
npm install coffee-script
and that’s it. Unfortunately, CoffeeScript use a module “path”, which is not a CommonJS module. I assume that most of the functionality is part or will be part of the module “fs”. The “path.js” from node.js is simple JavaScript code with some references like
'path.exists is now called `fs.exists`'
Being brave, I simply copied the file into my module path and tried again. The next obstacle is node’s global variable “process”. Luckily, this is only used to check for Windows in the module “path”. Also ignoring the module “vm” used to execute JavaScript code, this allows one to actually load CoffeScript into ArangoDB.
arangosh> process = {};
{ }
arangosh> var cs = require("coffee-script");
arangosh> cs.compile("a = 1\nopposite = true\na = -a if opposite", {});
(function() {
var a, opposite;
a = 1;
opposite = true;
if (opposite) {
a = -a;
}
}).call(this);
So, I can now use CoffeeScript definition within ArangoDB. Some of the loaders must be adjusted to check for both “.js” and “.coffee” files. Afterwards it should be possible, to define an action in CoffeeScript as well as JavaScript.
ArangoDB 1.2.0 Released: What’s New? | ArangoDB 2013
Benchmark Results – ArangoDB vs. Neo4j : ArangoDB up to 8x faster than Neo4j
Estimated reading time: 6 minutes
Introduction
This document presents the benchmark results comparing the ArangoDB’s Graph Analytics Engine (GAE) against Neo4j. The GAE is just one component of ArangoDB’s Data Science Suite.
This reproducible1 benchmark aims to provide a neutral and thorough comparison between the two databases, ensuring a fair and unbiased assessment.
We use the wiki-Talk dataset, a widely used, real-world graph dataset derived from the edit and discussion history of Wikipedia.
(more…)Vector Search in ArangoDB: Practical Insights and Hands-On Examples
Estimated reading time: 5 minutes
Vector search is gaining traction as a go-to tool for handling large, unstructured datasets like text, images, and audio. It works by comparing vector embeddings, numerical representations generated by machine learning models, to find items with similar properties. With the integration of Facebook’s FAISS library, ArangoDB brings scalable, high-performance vector search directly into its core, accessible via AQL (ArangoDB Query Language). Vector Search is now just another, fully-integrated data type/model in ArangoDB’s multi-model approach. The Vector Search capability is currently in Developer Preview and will be in production release in Q1, 2025.
This guide will walk you through setting up vector search, combining it with graph traversal for advanced use cases, and using tools like LangChain to power natural language queries that integrate Vector Search and GraphRAG.
(more…)Some Perspectives on HybridRAG in an ArangoDB World
Estimated reading time: 7 minutes
Introduction
Graph databases continue to gain momentum, thanks to their knack for handling intricate relationships and context. Developers and tech leaders are seeing the potential of pairing them with the creative strength of large language models (LLMs). This combination is opening the door to more precise, context-aware answers to natural language prompts. That’s where RAG comes in—it pulls in useful information, whether from raw text (VectorRAG) or a structured knowledge graph (GraphRAG), and feeds it into the LLM. The result? Smarter, more relevant responses that are grounded in actual data.
(more…)ArangoDB vs. Neo4J
Estimated reading time: 7 minutes
Advantages & Implications for Architects and Developers
ArangoDB and Neo4j are both powerful graph databases, but they have different strengths and features. Here’s a comparison showing how ArangoDB might be superior to Neo4j in certain scenarios, along with code snippets to illustrate:
(more…)ArangoDB 3.12 – Performance for all Your Data Models
Estimated reading time: 6 minutes
We are proud to announce the GA release of ArangoDB 3.12!
Congrats to the team and community for the latest ArangoDB release 3.12! ArangoDB 3.12 is focused on greatly improving performance and observability both for the core database and our search offering. In this blog post, we will go through some of the most important changes to ArangoDB and give you an idea of how this can be utilized in your products.
(more…)Advanced Fraud Detection in Financial Services with ArangoDB and AQL
Estimated reading time: 3 minutes
Advanced Fraud Detection: ArangoDB’s AQL vs. Traditional RDBMS
In the realm of financial services, where fraud detection is both critical and complex, the choice of database and query language can impact the efficiency and effectiveness of fraud detection systems. Let’s explore how ArangoDB – a multi-model graph database – is powered by AQL (ArangoDB Query Language) to handle multiple, real-world fraud detection scenarios in a much more seamless and powerful way compared to traditional Relational Database Management Systems (RDBMS).
(more…)Update: Evolving ArangoDB’s Licensing Model for a Sustainable Future
Estimated reading time: 7 minutes
Update: https://arangodb.com/2023/10/evolving-arangodbs-licensing-model-for-a-sustainable- future/
Last October the first iteration of this blog post explained an update to ArangoDB’s 10-year-old license model. Thank you for providing feedback and suggestions. As mentioned, we will always remain committed to our community and hence today, we are happy to announce yet another update that integrates your feedback.
(more…)The world is a graph: How Fix reimagines cloud security using a graph in ArangoDB
‘Guest Blog’
Estimated reading time: 5 minutes
In 2015, John Lambers, a Corporate Vice President and Security Fellow at Microsoft wrote “Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win.ˮ
(more…)Reintroducing the ArangoDB-RDF Adapter
Estimated reading time: 1 minute
ArangoRDF allows you to export Graphs from ArangoDB into RDFLib, the standard library for working with Resource Description Framework (RDF) in Python, and vice-versa.
(more…)Introducing ArangoDB’s Data Loader : Revolutionizing Your Data Migration Experience
Estimated reading time: 7 minutes
At ArangoDB, our commitment to empowering companies, developers, and data enthusiasts with cutting edge tools and resources remains unwavering. Today, we’re thrilled to unveil our latest innovation, the Data Loader, a game-changing feature designed to simplify and streamline the migration of relational databases to ArangoGraph. Let’s dive into what makes Data Loader a must-have tool for your data migration needs.
(more…)Get the latest tutorials, blog posts and news:
Thanks for subscribing! Please check your email for further instructions.
Using NPM Packages for ArangoDB: Enhance Functionality | 2013
ArangoDB follows the Common.JS specification for modules. However, it would be very convenient, if there was an easy way to install a package like “underscore.js”. These package are, for instance, available using NPM. There is a draft for packages on Common.JS which seems to be compatible with NPM.
NPM has a neat way of dealing with version conflicts. Basically, it allows multiple versions to exists simultaneously. For example, assume you have 4 packages A, B, C, D. A requires B and C and D, B requires C. Then directory layout might be as follows.
node_modules
|
+- A
| |
| +- node_modules
| |
| +- B
| | |
| | +- node_modules
| | |
| | +- C (1.0.0)
| |
| +- C (2.0.0)
|
+- D
Package B will see package C in version 1.0.0, while package A sees package C in version 2.0.0.
<!--more-->
This behaviour is easy to implement in ArangoDB. In addition to “Module” there is now a “Package”. Each package has it own module cache. When a package requires a module, the package hierarchy is traversed from the current package to the root (or global) package, until the module is found.
In order to use underscore, switch into the package directory and use NPM to install it
unix> cd /tmp/packages
unix> npm install underscore
npm http GET https://registry.npmjs.org/underscore
npm http 304 https://registry.npmjs.org/underscore
underscore@1.4.4 node_modules/underscore
Now start arangosh with the new “–javascript.package-path” option and enjoy underscore.
unix> arangosh --javascript.package-path /tmp/packages
arangosh> var _ = require("underscore");
arangosh> _.max([1,2,3])
3
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