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ArangoDB Logstash Output: Efficient Data Integration
Inspired by a question on StackOverflow, I did some investigation about how to make Logstash send log events to ArangoDB.
There is no dedicated Logstash output plugin for ArangoDB on the Logstash plugins page, so I had already accepted to write one on my own.
Browsing the plugins page for inspiration, I found an HTTP output plugin for Logstash. It seems to be general enough that it can send the log event in JSON format to any HTTP-speaking backend.
ArangoDB’s API is JSON over HTTP, so it sounded like a perfect match. I briefly tried it out and it seemed to work fine.
Explaining AQL Queries in a Fancier Way: ArangoDB Tutorial
I have been looking at many AQL queries during the last few weeks…
Looking back, I can say that the JSON query execution plans provided by the explain() method have provided me with a lot of useful information about how the AQL optimizer had transformed a given query. This has helped testing and improving the query optimizer a great deal.
However, the JSON output produced by explain() is so detailed that even for the simplest cases queries it will span multiple screens. This is far too much for quickly assessing what a query will be doing and how it will be executed.
I therefore quickly put together a function that provides a much more compact explain output. Its input parameter is a query string, which it will send to the ArangoDB server to have it explained.
But it doesn’t print a voluminous JSON object. This one is for developers with a full schedule.
ArangoDB AQL Optimization: Returning & Modifying in v2.4
ArangoDB provides many options for finding and modifying data. Though there are several more specialized operation, data-modification AQL queries are the most general solution in ArangoDB. They allow to find documents using arbitrary filter criteria, and to modify or remove the documents once found.
Read in Jan’s blog how INSERT, UPDATE, REMOVE and REPLACE operations can now return modified documents and allow to find, modify and return documents from the same AQL query. Read on
Bulk Inserts: MongoDB vs CouchDB vs ArangoDB (Dec 2014)
More than two years ago, we compared the bulk insert performance of ArangoDB, CouchDB and MongoDB in a blog post.
The original blog post dates back to the times of ArangoDB 1.1-alpha. We have been asked several times to re-run the tests with the current versions of the databases. So here we go.
Enhanced AQL in ArangoDB 2.4: Boost Query Performance
While on a retreat in Belgium, we found some spare time to work on improvements for AQL. These will be shipped with ArangoDB version 2.4, and are already available in the devel version for testing from now on.
Here’s a short overview of the improvements:
Collect with count
A common use case in query languages is to count the number of documents returned by a query. The AQL solution for this has been to use the LENGTH function and a subquery:
RETURN LENGTH((
FOR doc IN collection
FILTER doc.someAttribute == someValue
RETURN doc
))
This is quite long and probably unintuitive for people which have used SQL for years.
We therefore now allow using the following alternative version:
Tour Around the New AQL Query Optimizer | ArangoDB Blog
The major new feature in ArangoDB 2.3 is the shiny new AQL query optimizer and executor. These parts of ArangoDB have been rewritten in 2.3 to make AQL much better for our end users.
Since one of the initial releases, ArangoDB has been shipped with AQL, the ArangoDB Query Language. AQL has since then been ArangoDB’s most versatile way of executing simple and also the not-so-simple queries.
I’ll start with an overview of query execution in previous versions of ArangoDB, and then explain the new engine and explain the differences.
Improved Non-Unique Hash Indexes in ArangoDB 2.3
With ArangoDB 2.3 now getting into the beta stage, it’s time to spread the word about new features and improvements.
Today’s post will be about the changes made to non-unique hash indexes.
Setting Up Test Data: ArangoDB Tutorial & Best Practices
Today I was asked to look at code that was supposed to read data from a MySQL data source, process it and then import it into ArangoDB.
To run and debug the code I had to have some MySQL data source. So I thought I’d quickly set up a simple example table with a few rows. It turned out that this took more time than what I had expected.
Maybe I’m spoilt by JavaScript-enabled, schema-free databases where creating such test setups is so much easier.
Set Up Bash Completion for ArangoDB: Step-by-Step Guide
I was interested in how bash auto-completion works and how to write a custom completer. After about an hour of work, I came up with a solution that at least seems to work on Ubuntu. I now have auto-completion for ArangoDB and all its client tools!
The problem
I use the command-line for almost everything, including starting and stopping ArangoDB and its client tools. They provide lots of options which I cannot completely memorize.
The bash solution for “I don’t know what I am looking for” is to press the TAB key. This will bring up a list of suggestions for how to complete the currently entered word. I thought using the same thing for ArangoDB’s command-line options would be nice, too.
Compile ArangoDB Source: Step-by-Step Guide | ArangoDB Blog
Though we provide a lot of pre-built packages for the stable versions of ArangoDB here, it is often more interesting to play with the bleeding edge development version. New ArangoDB features are normally added to the devel branch, where they can be tested, documented and improved. When a feature matures, it is either backported to a stable branch or will eventually be released when the next stable branch is forked from devel.
Contributing to the core of ArangoDB is also much easier with a ready-to-go devel version. This post explains how to set one up from scratch.