This is Part I of Performance analysis using pyArango blog series. Please refer here for: Part II (cluster) and Part III (measuring system capacity).

Usually, your application will persist of a set of queries on ArangoDB for one scenario (i.e. displaying your user’s account information etc.) When you want to make your application scale, you’d fire requests on it, and see how it behaves. Depending on internal processes execution times of these scenarios vary a bit.

We will take intervals of 10 seconds, and graph the values we will get there:

• average – all times measured during the interval,..

– law of the polish-lithuanian common-wealth, 1505

A warning aforehand: this is a rather longish post, but hang in there it might be saving you a lot of time one day.

The upcoming 3.1 release comes with a binary protocol – VelocyStream – to transport VelocyPack (internal storage format of ArangoDB introduced with the 3.0 release) data between ArangoDB and client applications. VelocyPack stores a superset of JSON, is more compact and has a fast attribute lookup. On the other hand, VelocyStream allows to send VelocyPack in an optimized form over the network. We think it would be the right time to update our official Java Driver to modernize it and to let it be the first to fully support VelocyStream.

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.

Array indexes

2.8 allows creating hash and skiplist indexes on attributes which are arrays. Creating such index works similar to creating a non-array index, with the exception that the name of the array attribute needs to be followed by a [*] in the index fields definition:

db._create("posts"); db.posts.ensureIndex({ type:..

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:

• document-related functions: DOCUMENT, EDGES, PARSE_IDENTIFIER
• numerical functions: ABS, FLOOR, RAND, ROUND, SQRT
• statistical functions:..

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.

For all following queries, I have set up a collection named test, which has..

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.

In order to assess the speedups in 2.8, I have run some..

The upcoming ArangoDB version 2.8 (currently in devel) will provide a much better deadlock detection mechanism than its predecessors.

The new deadlock detection mechanism will kick in automatically when it detects operations that are mutually waiting for each other. In case it finds such deadlock, it will abort one of the operations so that the others can continue and overall progress can be made.

In previous versions of ArangoDB, deadlocks could make operations wait forever, requiring the server to be stopped and restarted.

How deadlocks can occur

Here’s a simple example for getting into a..

In this blog post – which is a roundup of the performance blog series – I want to complete the picture of our NoSQL performance test and include some of the supportive feedback from the community. First of all, thanks for all your comments, contributions and suggestions to improve this open source NoSQL performance test (Github). This blog post describes a complete overhaul of the test with no need to read all the previous articles to get the picture – have a look at the appendix below to get all the details on..

We’ve recently been working on improving ArangoDB’s throughput, especially when using the ArangoDB’s interface.

In this post, I will show some of the improvements already achieved, though the work is not yet finished. Therefore, the results shown here are still somewhat preliminary.

We wanted to measure improvements for ArangoDB’s HTTP interface, and so we used wrk as an external HTTP load generator.

During the tests, wrk called some specific URLs on a local ArangoDB instance on an otherwise idle machine. The test was run with ArangoDB 2.6 and devel. The ArangoDB instances were started with..