Category: Document Stores

Feed: Blog – ArangoDB. Author: Simon Grätzer. With ArangoDB 3.4 we finally made the rocksdb storage engine the default. This decision was made after a year of constant improvements to the engine to make it suitable for all our customer’s use cases. Improved Read / Write-Performance We massively improved the binary on-disk storage format with the 3.4 release. This format allows inserting new documents in an order that RocksDB prefers. Using the new format will reduce the number of compactions that RocksDB needs to do for the ArangoDB documents stored, allowing for better long-term insertion performance. The sustained insertion performance ... Read More

Feed: Blog – ArangoDB. Author: Kaveh Vahedipour. “I must have a prodigious amount of mind;it takes me as much as a week, sometimes, to make it up!”― Mark Twain How many shards should one choose, when creating collections in ArangoDB clusters? TLDR: Don’t be too shy with sharding your data in many shards across your cluster. Be mindful however that AQL-heavy applications might not profit as much from heavy distribution. How a database works and performs in a single instance setup is becoming more and more irrelevant lately, if said database cannot scale. Distributed, clustered databases are, as far as ... Read More

Feed: Blog – ArangoDB. Author: Max Neunhöffer. This post uses the new .tar.gz binary distribution of ArangoDB to run multiple versions of ArangoDB alongside each other on the same machines. We will do a production-ready deployment on 3 cloud instances with authentication, TLS encryption, (self-signed) certificates and systemd service. In the end, we show how to perform a rolling upgrade for one of the clusters to a new version. The new .tar.gz binary archive Starting with Version 3.4.0 we have changed the way how we build our binary distribution. We are now using completely static binaries (see here for another ... Read More

Feed: Blog – ArangoDB. Author: Willi Goesgens. When using a database like ArangoDB it is also important to explore how it behaves once it reaches system bottlenecks, or which KPIs (Key Performance Indicators) it can achieve in your benchmarks under certain limitations. One can achieve this by torturing the system by effectively saturating the resources using random processes. This however will drown your system effectively – it may hinder you from capturing statistics, do debugging, and all other sorts of things you’re used to from a normally running system. The more clever way is to tell your system to limit ... Read More

Feed: Blog – ArangoDB. Author: Max Neunhöffer. It has been a few months since we first released the Kubernetes operator for ArangoDB and started to brag about it. Since then, quite a few things have happened. For example, we have done a lot of testing, fixed bugs, and by now the operator is declared to be production ready for three popular public Kubernetes offerings, namely Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS) and Pivotal Kubernetes Service (PKS) (see here for the current state of affairs). We have developed a semi-automatic “acceptance test” to validate production readiness for each ... Read More

Feed: Blog – ArangoDB. Author: Frank Celler. The ability to see your data from various perspectives is the idea of a multi-model database. Having the freedom to combine these perspectives into a single query is the idea behind native multi-model in ArangoDB. Extending this freedom is the main thought behind the release of ArangoDB 3.4. We’re always excited to put a new version of ArangoDB out there, but this time it’s something special. This new release includes two huge features: a C++ based full-text search and ranking engine called ArangoSearch; and largely extended capabilities for geospatial queries by integrating Google™ ... Read More

Feed: Blog – ArangoDB. Author: Luca Olivari. 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 hereWe are overwhelmed to be listed as one of six OPDBMS solutions in the Customers’ Choice Zone. We believe this is a remarkable achievement. As of April 30, 2018, ArangoDB has an Overall Rating of 4.7 out of ... Read More

Feed: Blog – ArangoDB. Author: Daniel Larkin-York. 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 ... Read More

Feed: Blog – ArangoDB. Author: Daniel Larkin-York. 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 ... Read More

Feed: Blog – ArangoDB. Author: Jan Steemann. 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.Previous versions of MMFiles fetched documents from these indexes one after the other, which had some considerable overhead compared to fetching many documents from inside the index code in a single batch. This ... Read More