Hello community!
This month we post on the blog bearing gifts up upcoming events, content we think you might enjoy, as well as suggestions around how to get involved with the community. Read on!
We’d also love to hear from *you*! Drop us a line anytime at community [at] arangodb [dot] com.
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 parts of the C++ standard and a common source of confusion. While an in-depth explanation of the C++ memory model is beyond this blog post’s scope, we will cover some basics and take a brief look at two of the most common hardware memory models and how the C++ memory model maps to these hardware models.
But first, let’s take a step back and consider why we need a memory model in the first place.
More infoHappy New Year to our amazing community!
We hope your year is off to a good start. Even though things don’t seem too different (yet), we’re definitely feeling a fresh mindset and doing our best to stay hopeful and healthy.
Here’s what we’re excited about so far:
Read on for more!
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:
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 I’m going to uncover some of the new features we are proud to present.
More infoThis 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:
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 provide a quick introduction to using the NetworkX adapter.
This post is the fourth in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. Until now, we have focused on ArangoML’s ability to capture metadata for your machine learning projects, but it does much more.
In this post we:
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:
