Great news for driver maintainers that want access to the latest developments in ArangoDB. Many of you have asked us if we can provide a nightly build of our ArangoDB database to improve CI test automation using Travis-CI. The Travis builds for ArangoDB 2.6, 2.7 and devel will be generated and published shortly after midnight (GMT).

It’s time for another update of my NoSQL performance blog series. This hopefully concludes the first part of this series with the initial databases ArangoDB, MongoDB, Neo4J and OrientDB and I can now start to check out other databases. I’m getting a lot of requests to test others as well and I’ll try to add them as soon as possible. Pull requests to my repository are also more than welcome. Remember it is all open-source.

The first set of benchmarks was started as a proof that multi-model can..

For the ArangoDB 2.6 release from last week we’ve put some performance tests together. The tests will compare the AQL query execution times in 2.5 and 2.6.

The results look quite promising: 2.6 outperformed 2.5 for all tested queries, mostly by factors of 2 to 5. A few dedicated AQL features in the tests got boosted even more, resulting in query execution time reductions of 90 % and more. Finally, the tests also revealed a dedicated case for which 2.6 provides a several hundredfold speedup.

Also good news is that not a single of the test queries ran slower in 2.6 than in 2.5.

TL;DR: Our initial benchmark has raised a lot of interest. Initially we wanted to show that multi-model can compete with other solutions. Due to the open and competitive way we have conducted the benchmark, the discussions around it have lead to improvements in all products, better algorithms, faster drivers and better ways to use the databases.

General Setup

From the outset we published all code and data and asked the vendors of all tested products as well as the general public, not only to run..

Last week some further optimization slipped into 2.6. The optimization can provide significant speedups in AQL queries using huge array/object bind parameters and passing them into V8-based functions.

It started with an ArangoDB user reporting a specific query to run unexpectedly slow. The part of the query that caused the problem was simple and looked like this:

FOR doc IN collection
  FILTER doc.attribute == @value
  RETURN TRANSLATE(doc.from, translations, 0)

In the original query, translations was a big, constant object literal. Think of something like the following, but with a lot more..

My recent blog post “Native multi-model can compete” has sparked considerable interest on HN and other channels. As expected, the community has immediately suggested improvements to the published code base and I have already published updated results several times (special thanks go to Hans-Peter Grahsl, Aseem Kishore, Chris Vest and Michael Hunger).

Please note: An update is available (June ’15) and a new performance test with PostgreSQL added.

Here are the latest figures and diagrams:

Claudius Weinberger, CEO ArangoDB

TL;DR Native multi-model databases combine different data models like documents or graphs in one tool and even allow to mix them in a single query. How can this concept compete with a pure document store like MongoDB or a graph database like Neo4j? I myself and a lot of folks in the community asked that question.

So here are some benchmark results: 100k reads → competitive; 100k writes → competitive; friends-of-friends → superior; shortest-path → superior; aggregation → superior.

Feel free to comment, join the discussion on HN and contribute – it’s all on ..

ArangoDB can easily be configured to require HTTP authentication for access to the web admin frontend or the REST API. But while Basic Auth works fine for APIs, the user experience in the web admin frontend was decidedly sub-par: browsers would often persist the authentication credentials indefinitely, logging out was made difficult or impossible and switching users was hit-or-miss.

The upcoming ArangoDB 2.6 release introduces cookie-based authentication for the web admin frontend, allowing you to side-step the issue altogether by using the built-in session manager instead of the low-level..

Another performance improvement could be accomplished in the latest devel-branch: The handling of large IN-lists. Those become much faster than in the previous releases. Large IN-lists are normally used when comparing attribute or index values against some big array of lookup values or keys provided by the application.

This post is about improvements for the fulltext index in ArangoDB 2.6. The improvements address the problem that non-string attributes were ignored when fulltext-indexing.

Effectively this prevented string values inside arrays or objects from being indexed. Though this behavior was documented, it was limited the usefulness of the fulltext index much. Several users requested the fulltext index to be able to index arrays and object attributes, too.