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ArangoDB Secures €2.2M Investment Led by Target Partners
Funding to accelerate the company’s product development and international expansion
Munich/Cologne (Germany), November 24th, 2016: – ArangoDB GmbH (www.arangodb.com), the company behind one of the fastest growing next generation databases, has landed a 2.2 million Euro investment led by Munich-based venture capital firm Target Partners (www.targetpartners.de). The company develops the open-source NoSQL database ArangoDB, which combines three major data models (graph, key/value, JSON-documents) in one database and one query language. ArangoDB’s product allows startups and enterprises alike to speed up their innovation cycles, simplify their technology stack and increase on-time and on-budget delivery of software projects.
Claudius Weinberger, Co-Founder and CEO, declares: “The previous funding round allowed us to build a rock-solid product and with this additional investment we can further accelerate our growth and expand internationally.” Read more
How to model customer surveys in a graph database
Use-Case
The graph database use-case we are stepping through in this post is the following: In our web application we have several places where a user is led through a survey, where she decides on details for one of our products. Some of the options within the survey depend on previous decisions and some are independent.
Examples:
- Configure a new car
- Configure a new laptop
- Book extras with your flight (meal, reserve seat etc.)
- Configure a new complete kitchen
- Collect customer feedback via logic-jump surveys
Learn ArangoDB while contributing
We are fortunate to live in an open-source world with a fairly large international community of users and contributors, which has been only growing more and more in the past year. (Big thanks for that, by the way 😉 ) Especially that we have recently received quite a few requests on how one can contribute to ArangoDB in an easy and quick way, we have decided that the time has come to get closer to our community and get even more involved.
A lot of great ideas came up during a chat with some of our long-term users on how we could improve on that front. Among all others, the most straightforward one is asking the community for some help and support on GitHub. So we took a bit of time to go through open issues and selected a few now tagged ‘Help Wanted’. Here is a selection of easy tasks that will get you started contributing to ArangoDB with code or ideas and concepts for features. It is only a few to start with, but if you want to get involved we would appreciate your helping hand! Read more
ArangoDB 3.1: Scaling Solutions, Part II
It’s not that long ago since we released ArangoDB 3.0 in which we introduced our binary storage format VelocyPack, the ArangoDB Agency for a self-managing cluster and the first persistent index by implementing Facebooks RocksDB. With all that we laid the foundation for a solid ground to scale with all three data-models.
With today’s ArangoDB 3.1 release we take things a few steps further and make cluster usage of ArangoDB more performant and convenient. Get ArangoDB 3.1.
General upgrades in 3.1
- Performance boost with our new boost-ASIO server infrastructure
- Performance boost by overhauling the ArangoDB query optimizer
- Improved internal abstraction for storage-engines as a preparation for MVCC and pluggable storage-engines
- VelocyPack over HTTP: Use our binary storage format VelocyPack over HTTP
- VelocyStream: for high performance needs you can now directly stream VelocyPack. This is already implemented in our Java driver (all other drivers maintained by ArangoDB will follow soon).
Cluster
- Parallel Intra-Cluster-Communication
- HLC: The Hybrid Logical Clock is used for timestamps in revision strings which is part of the preparation for cluster-wide transactions
- Auto-failover timeouts: you can now configure the timeouts for automatic failovers
- Progress Display when relocating shards
- Stand-Alone Agency: You can now use ArangoDB as a resilient, RAFT-based key/value store as an alternative to e.g. ZooKeeper or etcd. (You’ll surely ask yourself why we created it and we’ll answer this legitimate question in a blog post soon).
Graph Features
- Vertex-centric indices for graphs in AQL: You can now generate indices on edges which are a combination of the vertex and an attribute.
- SmartGraphs: This is a the big new feature of our Enterprise Edition and enables you to shard really huge graphs to a cluster and achieve close to the same performance as on a single instance. Read more about SmartGraphs and our Enterprise Edition.
For the Java World
We put a lot of effort into our new Java Driver which will only work with ArangoDB 3.1 onwards. Our Java team completely refactored the driver which is now up to 4x faster than the previous one. The new features include:
- multi document operations
- Uses VelocyPack
- VelocyStream ready
- asynchronous request handling
Read more about the features in the corresponding blog post. You can download the new Java drivers here: ArangoDB-Java-Driver 4.1.0 & ArangoDB-Java-Driver-Async 4.1.0. We also included a new detailed Java drivers documentation.
Web UI
- New Graph Viewer: The previous solution was not suitable for large graph visualizations. With an extended Canvas support the Graph Viewer is now feature-rich and can handle large graph visualizations. As a second engine we made a first implementation of WebGL. Feedback to our new GraphViewer is highly appreciated.
- AQL Editor: We invested a lot into usability and e.g. simplified the elaboration of performance issues of your queries. With the Query Performance Profiler you can now get info about the query performance so you can investigate which part of the execution took how long. You can also choose between JSON, tabular and graph output for your results.
We hope that we included many useful features for you into ArangoDB 3.1. We appreciate your feedback about the new release a lot. If you’re missing something, find a bug or want to talk about an idea with us, feel free to get in touch via our Slack Community channel or contact us form.
Have fun playing around with ArangoDB 3.1!
ArangoDB 3.1 Enterprise: Scaling Graphs
In addition to our community version of ArangoDB 3.1 we are excited to release our first Enterprise Edition today. The Enterprise Editions of ArangoDB focuses on enterprise-scale problems and provides useful features to meet the requirements of enterprise customers. You can download a free evaluation-only version here: Download Enterprise Edition. ArangoDB Enterprise Edition also comes with the Enterprise subscription, including comprehensive support SLA.
This first ArangoDB Enterprise Edition includes three major features:
- SmartGraphs: Scale with graphs to a cluster and stay performant. With SmartGraphs you can use the “smartness” of your application layer to shard your graph efficiently to your machines and let traversals run locally
- Encryption Control: Choose your level of SSL encryption
- Auditing: Keep a detailed log of all the important things that happened in ArangoDB
ArangoDB and DC/OS Webinar: Scaling Made Simple
Wednesday, October 26th
DC/OS provides ArangoDB with exactly the infrastructure it needs for implementing a modern distributed stateful service. Join this upcoming webinar to learn how DC/OS quickly and easily deploys ArangoDB to provide scaling and fault tolerance with automatic replacement of failed components. While DC/OS supplies the management of resources and hence allows multiple services to share a common infrastructure, ArangoDB provides a modern persistence layer with its multi-model, fault-tolerant datastore. Read more
DC/OS: Modernizing Distributed Database Management
The mission of ArangoDB is to simplify the complexity of data work. ArangoDB is a distributed native multi-model NoSQL database that supports JSON documents, graphs and key-value pairs in one database engine with one query language. The cluster management is based on Apache Mesos, a battle-hardened technology. With the launch of DC/OS by a community of more than 50 companies all ArangoDB users can easily scale. Read more
ArangoDB Spartan Mode: Optimize Performance and Resource Usage
Most of us saw the fantastic movie 300 (I did it last night…again) or at least read the comics. 300 spartans barely wearing anything but achieving a lot. This little how-to will show you how to put ArangoDB into Spartan-Mode and thereby reduce memory-footprint and CPU usage.
Big thanks to Conrad from L.A. for his time and for giving us the impulse for this little how-to!
Read more
Linenoise-ng: Command Line Editing Library
For projects that are BSD or Apache 2 licensed, Linenoise (by Salvatore Sanfilippo) is a pretty small, portable GNU readline (GPL) replacement. Based on the work of Salvatore and 10gen Inc. this Linenoise NG implementation adds UTF8 and Windows support, uses a BSD license and can be used in any kind of program.
Read more
Killing Long-Running Queries in ArangoDB
Suppose there is an AQL query that’s executing in the server for a long time already and you want to get rid of it. What can be done to abort that query?
If a connection to the server can still be established, the easiest is to use the ArangoShell to fetch the list of currently executing AQL queries and send a kill command to the server for the correct query. Read more
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