RC4 of ArangoDB 3.5: Configurable Analyzers & other ArangoSearch Upgrades

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Step-by-step we are getting closer and closer to the official release of ArangoDB 3.5. First of all, we want to send a biiiiig “Thank You!” to all the testers so far and all your feedback! Super helpful for us!

This Release Candidate post is dedicated to the four new features of ArangoSearch which extend the capabilities and provide pretty huge performance improvements, especially for queries including search & sorting. Read more

RC2 ArangoDB 3.5: Data Masking & Time-to-Live Index

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Coding is finished. Tests are green. Today, we can share the second release candidate for ArangoDB 3.5. Get RC2 Community Edition or RC2 Enterprise Edition.

With this release candidate we want to highlight the next two features of the upcoming release. Data Masking and Time-To-Live indices (TTL) can come in very handy for developers when it comes to compliance with data privacy regulations like GDPR or the upcoming Consumer Privacy Act of California. Read more

ArangoDB 3.5 RC1: Graph Database Improvements with PRUNE & k Shortest Paths

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After a lot of planning, coding and testing we can finally share the first Release Candidate of ArangoDB 3.5 with you today. You can get it on our Technical Preview Download page: Tech Preview Community & Tech Preview Enterprise.

With this RC, we want to highlight two new features for all graph database enthusiasts: the new PRUNE Keyword & k Shortest Path Feature. As always, please note Release Candidates are for testing purposes only and should not be used in production. Please see limitations of RC1 at the bottom of the page! Read more

Static binaries for a C++ application

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TL;DR; This describes how to generate a completely static binary for a complex C++ application which runs on all variants of Linux without any library dependency.

ArangoDB is a multi-model database written in C++. It is a sizable application with an executable size of 38MB (stripped) and quite some library dependencies. We provide binary packages for Linux, Windows and MacOS, and for Linux we cover all major distributions and their different versions, which makes our build and delivery pipeline extremely cluttered and awkward. At the beginning of this story, we needed approximately 12 hours just to build and publish a release, if everything goes well. This is the beginning of a tale to attack this problem. Read more

Setting up Datacenter to Datacenter Replication in ArangoDB

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Please note that this tutorial is valid for the ArangoDB 3.3 milestone 1 version of DC to DC replication!

This milestone release contains data-center to data-center replication as an enterprise feature. This is a preview of the upcoming 3.3 release and is not considered production ready.

In order to prepare for a major disaster, you can setup a backup data center that will take over operations if the primary data center goes down. For a server failure, the resilience features of ArangoDB can be used. Data center to data center is used to handle the failure of a complete data center.

Data is transported between data-centers using a message queue. The current implementation uses Apache Kafka as message queue. Apache Kafka is a commonly used open source message queue which is capable of handling multiple data-centers. However, the ArangoDB replication is not tied to Apache Kafka. We plan to support different message queues systems in the future.

The following contains a high-level description how to setup data-center to data-center replication. Detailed instructions for specific operating systems will follow shortly. Read more

The new Satellite Collections Feature of ArangoDB

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With the new Version 3.2 we have introduced a new feature called Satellite Collections. This post explains what this is all about, how it can help you, and explains a concrete use case for which it is essential.

Background and Overview

Join operations are very useful but can be troublesome in a distributed database. This is because quite often, a join operation has to bring together different pieces of your data that reside on different machines. This leads to cluster internal communication and can easily ruin query performance. As in many contexts nowadays, data locality is very important to avoid such headaches. There is no silver bullet, because there will be many cases in which one cannot do much to improve data locality.

One particular case in which one can achieve something, is if you need a join operation between a very large collection (sharded across your cluster) and a small one, because then one can afford to replicate the small collection to every server, and all join operations can be executed without network communications.

Read more

ArangoDB 3.2 beta release:
Pluggable Storage Engine with RocksDB, Distributed Graph Processing and a ClusterFoxx

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We’re excited to release today the beta of ArangoDB 3.2. It’s feature rich, well tested and hopefully plenty of fun for all of you. Keen to take it for a spin? Get ArangoDB 3.2 beta here.

With ArangoDB 3.2, we’re introducing the long-awaited pluggable storage engine and its first new citizen, RocksDB from Facebook

  • RocksDB: You can now use as much data in ArangoDB as you can fit on your disk. Plus, you can enjoy performance boosts on writes by having only document-level locks (more info below).
  • Pregel: Furthermore, we implemented distributed graph processing with Pregel for discovering hidden patterns, identify communities and perform in-depth analytics of large graph data sets.
  • ClusterFoxx: Another important upgrade is what we internally and playfully call the ClusterFoxx. The Foxx management internals have been rewritten from the ground up to make sure multi-coordinator cluster setups always keep their services in sync and new coordinators are fully initialised even when all existing coordinators are unavailable.
  • Enterprise: Working with some of our largest customers, we’ve added further security and scalability features to ArangoDB Enterprise like LDAP integration, Encryption at Rest, and the brand new Satellite Collections.

The goal of the whole ArangoDB 3 release cycle has been to scale the multi-model idea to new heights. Getting ‘ready’ for large scale applications is not done overnight and it’s definitely not possible without the help of a strong community. We’d like to invite all of you to lend us a helping hand to make ArangoDB 3.2 the best release ever. Please push this beta to its limits: test it for your use cases and compare the performance of the new features like RocksDB. Let us know on Github any bug that you find. Don’t worry about hurting our feelings: we want to fix any problems.

Join the Beta Bug Hunt Challenge and win a $200 Amazon Gift Card as first prize. You can find more details about this reward program at the end of this post. Read more

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