RC1 ArangoDB 3.4 – What’s new?

00General, ReleasesTags: , , ,

For ArangoDB 3.4 we already added 100,000 lines of code, happily deleted 50,000 lines and changed over 13,000 files until today. We merged countless PRs, invested months of problem solving, hacking, testing, hacking and testing again and are super excited to share the feature complete RC1 of ArangoDB 3.4 with you today.

Download the technical preview package for your OS: RC1 3.4 Community and RC1 3.4 Enterprise.

The team implemented over 40 new features, improvements and optimizations. In this short post, we will highlight just a few of them. You can find a full list in the release notes and follow the changes in the changelog. Read more

ArangoDB Among Highest Rated Operational Databases Management Systems solutions in Gartner Report with 4.7/5 Rating

00General

Firstly, a huge thank you to all our customers that took the time to review ArangoDB for the Gartner Peer Insights “Voice of the Customer”: Operational Database Management Systems Market report. Without your help and assistance, the continued improvements and enhancements we make to our software wouldn’t be possible.

You can access the full report here Read more

Time traveling with graph databases

00GeneralTags:

Graph databases are often used to analyze relations within highly interconnected datasets. Social networks, recommendation engines, corporate hierarchies, fraud detection or querying a bill of materials are common use cases. But these datasets change over time and you as a developer or data scientist may want to time travel and analyze these changes.

While ArangoDB may not come with built-in support for managing the revision history of graph data, we’ll show in this article how to manage it in a performant manner for some general classes of graphs. Best of all, this won’t require any groundbreaking new ideas. We’ll simply borrow a few tools and tricks from the persistent data structure literature and adapt them for good performance within ArangoDB. We hope that this will help enable new ways to use everyone’s favorite avocado-fueled datastore, and power some useful applications. Read more

Speeding Up Dump & Restore

00GeneralTags: ,

Many ArangoDB users rely on our arangodump and arangorestore tools as an integral part of their backup and recovery procedures. As such, we want to make the use of these tools, especially arangodump, as fast as possible. We’ve been working hard toward this goal in preparation for the upcoming 3.4 release.

We’ve made a number of low-level server-side changes to significantly reduce overhead and improve throughput. Additionally, we’ve put some work into rewriting much of the code for the client tools to allow dumping and restoring collections in parallel, using a number of worker threads specified by --threads n. Read more

Data retrieval performance optimizations in ArangoDB 3.3.9

00General, ReleasesTags:

Our recent release 3.3.9 includes several performance optimizations for data retrieval cases. Benefits can be expected for both storage engines, MMFiles and RocksDB, AQL batch lookup queries, and cluster AQL queries.

MMFiles index batch lookups

For the MMFiles engine, an optimization has been made for retrieving multiple documents from an index (hash index, skiplist index or persistent index) in a batch.
Read more

An implementation of phase-fair reader/writer locks

00GeneralTags:

We were in search for some C++ reader/writer locks implementation that allows a thread to acquire a lock and then optionally pass it on to another thread. The C++11 and C++14 standard library lock implementations std::mutex and shared_mutex do not allow that (it would be undefined behaviour – by the way, it’s also undefined behaviour when doing this with the pthreads library).

Additionally, we were looking for locks that would neither prefer readers nor writers, so that there will be neither reader starvation nor writer starvation. And then, we wanted concurrently queued read and write requests that compete for the lock to be brought into some defined execution order. Ideally, queued operations that cannot instantly acquire the lock should be processed in approximately the same order in which they queued. Read more

ArangoSearch architecture overview

00GeneralTags: ,

In this article, we’re going to dive deeper into our recently released feature preview in Milestone ArangoDB 3.4 – ArangoSearch which provides a rich set of information retrieval capabilities. In particular, we’ll give you an idea of how our search engine works under the hood.

Essentially ArangoSearch consists of 2 components: A search engine and an integration layer. The former is responsible for managing the index, querying and scoring, whereas latter exposes search capabilities to the end user in a convenient way.

Read more

Foxx CLI – Managing Microservices

00GeneralTags: ,

Anyone who has ever worked with our JavaScript framework Foxx was faced at some point with the challenge to install its Foxx service in its current ArangoDB instance or to replace the installed service with local code changes. This is not a big deal and can easily be done through ArangoDB’s WebUI. However, we developers always want to become more productive and clicking through a graphical UI is not the best way. Furthermore, this procedure is almost impossible to use in an automated deployment process. That’s why we decided to develop a node-based CLI tool to manage Foxx services, called Foxx-CLI, which we already released in version 1.1.

Read more

Take Free ArangoDB Graph Course for Freshers

The course will take you all the way from zero knowledge about ArangoDB to advanced graph query techniques.
 
Get Started 
close-link