Category: Multi-Model Databases

Feed: Blog – ArangoDB. Author: Manuel Pöter. Over the past years, we have seen a shift in processors from the previously dominant x86 architecture to the more energy-efficient (and often cheaper) ARM architecture. This trend is true for both consumer hardware, e.g., Apple’s M1 SoC and also server hardware, e.g. AWS’s Gravitron processor. Given that the ARM architecture has a more relaxed memory model, this might have some subtle impact on C++ programs… With the introduction of multi-threaded execution in C++11, the standard also introduced a memory model. For many developers, this memory model is one of the least well-understood ... Read More

Feed: Blog – ArangoDB. Author: Christopher Woodward. Multi-Model Machine Learning This article looks at how a team collaborating on a real-world machine learning project benefits from using a multi-model database for capturing ML meta-data. The specific points discussed in this article are how: The graph data model is superior to relational for ML meta-data storage.Storing ML experiment objects is natural with multi-model.ArangoML promotes collaboration due to the flexibility of multi-model.ArangoML provides ops logging and performance analysis. Be sure to check out the other posts in this series:ArangoML Part 1: Where Graphs and Machine Learning MeetArangoML Part 2: Basic Arangopipe WorkflowArangoML ... Read More

Feed: Blog – ArangoDB. Author: Christopher Woodward. This post is the fifth in a series of posts introducing the ArangoML features and tools. This post introduces the NetworkX adapter, which makes it easy to analyze your graphs stored in ArangoDB with NetworkX. In this post we: Briefly introduce NetworkXExplore the IMDB user rating datasetShowcase the ArangoDB integration of NetworkXExplore the centrality measures of the data using NetworkXStore the experiment with arangopipe This notebook is just a slice of the full-sized notebook available in the ArangoDB NetworkX adapter repository. It is summarized here to better fit the blog post format and ... Read More

Feed: Blog – ArangoDB. Author: Andrei Lobov. Just in case you haven’t heard about ArangoSearch yet, it is a high-performance Full-Text Search engine integrated in ArangoDB (meaning connected with the other data-models and AQL). Feel free to check out ArangoSearch – Full-text search engine including similarity ranking capabilities for more details. In ArangoDB version 3.7 the ArangoSearch team added Fuzzy Search support (see the comprehensive article Fuzzy search by Andrey Abramov). With Fuzzy Search data preprocessing and filtering becomes even more important. In the upcoming ArangoDB 3.8 release, ArangoSearch efforts will be focused on improving this part. In this post ... Read More

Feed: Blog – ArangoDB. Author: Christopher Woodward. This post is the fifth in a series of posts introducing the ArangoML features and tools. This post introduces the ArangoDB NetworkX adapter, which makes it easy to analyze your graphs stored in ArangoDB with NetworkX. In this post we: Briefly introduce NetworkXExplore the IMDB user rating datasetShowcase the ArangoDB integration of NetworkXExplore the centrality measures of the data using NetworkXStore the experiment with arangopipe Going forward, the ArangoML Series will no longer be numbered. Parts 1-4 deal with the same housing dataset and focus on introducing some core machine learning concepts. We ... Read More

Feed: Blog – ArangoDB. Author: Christopher Woodward. This post is the third in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we: Introduce bootstrapping and bias-variance conceptsEstimate and analyze the variance of the model from part 2Capture the metadata for this activity with arangopipe These posts will hopefully appeal to two audiences: The first half of each post is for beginners in machine learningThe second half for those already using machine learningWe decided to do it this way to provide a jumping-off point for those interested in ... Read More

Feed: Blog – ArangoDB. Author: Christopher Woodward. This post is the second in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we: Introduce machine learning conceptsDemonstrate basic model buildingLog a model building activity with arangopipe This post is the second in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we: Introduce machine learning concepts Demonstrate basic model building Log a model building activity with arangopipe These posts will hopefully appeal to two audiences: The ... Read More

Feed: Blog – ArangoDB. Author: Christopher Woodward. This post is the first in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. In this first post, we look at what exactly ArangoML is, with later posts in this series showcasing the different tools and use cases. If you have a use case you would like to see highlighted as a part of this series, please let us know on the ArangoML Slack channel. Overview ArangoML is a set of tools and technologies that enable analytics and machine learning on graph data.  ArangoML Tools/Stack The ... Read More

Feed: Blog – ArangoDB. Author: Max Neunhöffer. Recently, we decided to investigate why our application ARANGOSYNC for synchronizing two ArangoDB clusters across data centers used up a lot of memory – around 2GB in certain cases. The environment contained ~1500 shards with 5000 GOroutines. Thanks to tools like pprof (to profile CPU and memory usage) it was very easy to identify the issue. The GO profiler showed us that memory was allocated in the function time.After() and it accumulated up to nearly 1GB. The memory was not released so it was clear that we had a memory leak. We will explain ... Read More

Feed: Blog – ArangoDB. Author: Jan Stücke. When our founders realized that data models can be features, we at ArangoDB set ourselves the big goal of developing the most flexible database. With today’s GA release of ArangoDB 3.7, the project reached an important milestone on this journey. Join our Head of Engineering & Machine Learning on September 10th for the ArangoDB 3.7 Release Webinar. Graph & Beyond – An Important Milestone for Multi-Model Being a suitable solution for graph, document and key/value needs was our first milestone, which we reached several years ago. Providing this flexibility also at scale has ... Read More