From Zero to Advanced Graph Query Knowledge with ArangoDB

00General, Graphs, Query LanguageTags: , ,

Thinking about your data as a highly connected set of information is a powerful way to gain insights, solve problems and bring products faster into the hands of your users.

Unlike other databases, relationships take the first priority in graph databases and with ArangoDBs multi-model approach for graphs, documents and key/value pairs you can even switch between models or combine them in a single query.

The graph concept is booming but still new to many. So we invested a few bazillion coffees and some night shifts to come up with a good plan for a Graph Course:

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Killing a long-running query

00General, Query LanguageTags: ,

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

AQL optimizer improvements for 2.8

00PerformanceTags: , ,

With the 2.8 beta phase coming to an end it’s time to shed some light on the improvements in the 2.8 AQL optimizer. This blog post summarizes a few of them, focusing on the query optimizer. There’ll be a follow-up post that will explain dedicated new AQL features soon. Read more

AQL Function Speedups in 2.8

00PerformanceTags: , ,

While working on the upcoming ArangoDB 2.8, we have reimplemented some AQL functions in C++ for improved performance. AQL queries using these functions may benefit from using the new implementation of the function.

The following list shows the AQL functions for which a C++ implementation has been added in 2.8. The other C++-based AQL function implementations added since ArangoDB 2.5 are also still available. Here’s the list of functions added in 2.8: Read more

Using Multiple Indexes Per Collection

03Documentation, PerformanceTags: ,

The query optimizer in ArangoDB 2.8 has been improved in terms of how it can make use of indexes. In previous versions of ArangoDB, the query optimizer could use only one index per collection used in an AQL query. When using a logical OR in a FILTER condition, the optimizer did not use any index for the collection in order to ensure the result is still correct.

This is much better in 2.8. Now the query optimizer can use multiple indexes on the same collection for FILTER conditions that are combined with a logical OR. Read more

Index Speedups in 2.8

01PerformanceTags: , ,

The upcoming 2.8 version of ArangoDB will provide several improvements in the area of index usage and query optimization.

First of all, hash and skiplist indexes can now index individual array values. A dedicated post on this will follow shortly. Second, the query optimizer can make use multiple indexes per collection for queries with OR-combined filter conditions. This again is a subject for another post. Third, there have been some speed improvements due to changes in the general index handling code. This is what this post is about. Read more

Modeling Data in MongoDB vs ArangoDB

05Architecture, Documentation, Graphs, Query LanguageTags: ,

MongoDB is a document DB whereas ArangoDB is a multi-model DB supporting documents, graphs and key/values within a single database. When it comes to data modeling and data querying, they pursue somewhat different approaches.


In a Nutshell: In MongoDB, data modeling is “aggregate-oriented”, avoiding relations and joins. On the other side, everybody has probably used relational databases which organize the data in tables with relations and try to avoid as much redundancy as possible. Both approaches have their pros and cons. ArangoDB is somewhat in-between: You can both model and query your data in a “relational way” but also in an “aggregate-oriented way”, depending on your use case. ArangoDB offers joins, nesting of sub-documents and multi-collection graphs. More info

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