Jan Steemann, Author at ArangoDB

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Alpha 1 of the upcoming ArangoDB 3.7

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Estimated reading time: 6 minutes

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. Our goal is get early user feedback during the development of ArangoDB, so we can validate our designs and implementations against the reality, and adjust them if it turns out to be necessary.

If you want to give some of the new features a test drive, you can download the 3.7 Alpha 1 from here – Community and Enterprise – for all supported platforms. Read more

Sort-Limit Optimization in AQL

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Sometimes we want sorted output from a query and, for whatever reason, cannot use an index to do the sorting. In ArangoDB, we already cover this critical case with finely tuned query execution code. Sometimes though, we do not need to return all output, and follow our SORT clause with a LIMIT. In ArangoDB 3.4 and earlier, we did not handle this case any differently from returning the full data, at least with respect to sorting – we would sort the full input, then apply the limit afterwards.

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Time traveling with graph databases

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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

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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

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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.
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An implementation of phase-fair reader/writer locks

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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

Configuring ArangoDB-PHP to use active failover

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This article is about setting up active failover for ArangoDB-PHP, the PHP client driver for ArangoDB. It requires ArangoDB-PHP 3.3.2 or higher, and an ArangoDB server version of 3.3.4 or higher.

Interested in trying out ArangoDB? Fire up your cluster in just a few clicks with ArangoDB Oasis: the Cloud Service for ArangoDB. Start your free 14-day trial here

Active failover: basic setup

Historically, ArangoDB-PHP has been able to connect to a single ArangoDB endpoint, i.e. one combination of IP address and port number.

To connect to an ArangoDB server that is running on localhost or on a remote server, simply set the OPTION_ENDPOINT item in the ConnectionOptions and connect: Read more