Author: ArangoDB Support

Feed: Blog – ArangoDB. Author: Joerg Schad. TL;DR ArangoDB has supported two storage engines for a while: RocksDB and MMFiles. While ArangoDB started out with just the MMFiles storage engine in its early days, RocksDB became the default storage engine in the 3.4 release. Due to its drawbacks ArangoDB 3.6 deprecated the old MMFiles storage engine and with the upcoming 3.7 release we plan to fully remove support. This blog post will provide the background of why storage engines matter, why we chose to deprecate the MMFiles storage engine, and what you should be aware of when migrating from MMFiles ... Read More

Feed: Blog – ArangoDB. Author: Kaveh Vahedipour. In each verse, a decision awaits us and we can’tchoose to close our eyes and let instinct work on its own.Octavio Paz tldr Please consider monitoring your productive ArangoDB installation as part of the best practices strategy. It is effortlessly done using established services Prometheus for data collection and Grafana for visualisation and alerting. Best practices Many of us have come to experience database infrastructure like a solid rock to rely upon when the going gets rough. It is the mission statement of any database, that comes to my mind right now: Trust ... Read More

Feed: Blog – ArangoDB. Author: Joerg Schad. Kube-ArangoDB, ArangoDB’s Kubernetes Operator first released two years ago and as of today is operating many ArangoDB production clusters (including ArangoDB’s Managed Service Oasis). With many exciting features we felt kube-arango really deserves to be released as 1.0. If you would like to see ArangoDB Kubernetes Operator in action, join our Head of Engineering and Machine Learning, Jörg Schad, on March 17, 2020 – 11am PDT/ 2pm EDT/ 7pm CET for a live webinar. ArangoDB in the Cloud Native Ecosystem You might wonder why is a Kubernetes operator such a big deal for ... Read More

Feed: Blog – ArangoDB. Author: Jan Stücke. Neo4j, Inc. is the well-known vendor of the Neo4j Graph Database, which solely supports the property graph model with graphs of previously limited size (single server, replicated). In early 2020, Neo4j finally released its 4.0 version which promises “unlimited scalability” by the new feature Neo4j Fabric. While the marketing claim of “scalability” is true seen from a very simplistic perspective, developers and their teams should keep a few things in mind – most importantly: True horizontal scalability with graph data is not achieved by just allowing distributing data to different machines. We refer ... Read More

Feed: Blog – ArangoDB. Author: Ewout Prangsma. Today we are excited to invite everybody to take the first public preview of Azure on ArangoDB Oasis for a test ride. In case you haven’t joined Oasis yet, please find more details about our offering and a 14-day free trial on cloud.arangodb.com. Just choose Microsoft Azure as your cloud provider and choose from the many regions we already support. You can share all feedback with us about regions you’d love to see added or other improvements on slack. Please use the #oasis channel on Community Slack or raise an issue via the ... Read More

Feed: Blog – ArangoDB. Author: Jan Steemann. We released ArangoDB version 3.6 in January this year, and now we are already 6 weeks into the development of its follow-up version, ArangoDB 3.7. We feel that this is a good point in time to share some of the new features of that upcoming release with you! We try not to develop new features in a vacuum, but want to solve real-world problems for our end users. To get an idea of how useful the new features are, we would like to make alpha releases available to everyone as soon as possible ... Read More

Feed: Blog – ArangoDB. Author: Joerg Schad. We all know how crucial training data for data scientists is to build quality machine learning models. But when productionizing Machine Learning, Metadata is equally important. Consider for example: Capture of Lineage Information (e.g., Which dataset influences which Model?) Capture of Audit Information (e.g, A given model was trained two months ago with the following training/validation performance) Reproducible Model Training Model Serving Policy (e.g., Which model should be deployed in production based on training statistics) If you would like to see a live demo of ArangoML Pipeline Cloud, join our Head of Engineering ... Read More

Feed: Blog – ArangoDB. Author: Bogdan Udubasa. Nothing performs faster than arangoimport and arangorestore for bulk loading or massive inserts into ArangoDB. However, if you need to do additional processing on each row inserted, this blog will help with that type of functionality. If the data source is a streaming solution (such as Kafka, Spark, Flink, etc), where there is a need to transform data before inserting into ArangoDB, this solution will provide insight into that scenario as well. Let’s delve into the specifics. ArangoDB is version v3.6, and we will assume that it has already been installed (I am ... Read More

Feed: Blog – ArangoDB. Author: Ingo Friepoertner. Welcome 2020! To kick off this new year, we are pleased to announce the next version of our native multi-model database. So here is ArangoDB 3.6, a release that focuses heavily on improving overall performance and adds a powerful new feature that combines the performance characteristics of a single server with the fault tolerance of clusters. If you would like to learn more about the released features in a live demo, join our Product Manager, Ingo Friepoertner, on January 22, 2020 – 10am PT/ 1pm ET/ 7pm CET for a webinar on “What’s ... Read More

Feed: Blog – ArangoDB. Author: Ingo Friepoertner. We are working on the release of ArangoDB 3.6 and today, just in time for the holiday season, we reached the milestone of RC2. You can download and take the RC2 for a spin: Community Edition and Enterprise Edition. The next version of the multi-model database will be primarily focused on major performance improvements. We have improved on many fronts of speeding up AQL and worked on things like: Subquery performance Parallel execution of AQL queries that allows to significantly reduce gathering time of data distributed over several nodes Late document materialization that ... Read More