Introduction to Fuerte – The ArangoDB C++ Driver

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In this post, we will introduce you to our new ArangoDB C++ diver fuerte. fuerte allows you to communicate via HTTP and VST with ArangoDB instances. You will learn how to create collections, insert documents, retrieve documents, write AQL Queries and how to use the asynchronous API of the driver.

Requirements (Running the sample)

Please download and inspect the sample described in this post. The sample consists of a C++ – Example Source Code – File and a CMakeLists.txt. You need to install the fuerte diver, which can be found on github, into your system before compiling the sample. Please follow the instructions provided in the drivers Read More

Performance analysis with pyArango: Part III Measuring possible capacity with usage Scenarios

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So you measured and tuned your system like described in the Part I and Part II of these blog post series. Now you want to get some figures how many end users your system will be able to serve. Therefore you define “scenarios” which will be typical for what your users do.
One such a user scenario could i.e. be:

  • log in
  • do something
  • log out

Since your users won’t nicely queue up and wait for other users to finish their business, the pace you need to test your defined system is “starting n scenarios every second”. Many scenarios simulating different users may be running in parallel. If your scenario would require 10 seconds to finish, and you’d start 1 per second, that means that your system needs to be capable to process 10 users in parallel. If it can’t handle that, you will see that more than 10 sessions are running in parallel, and the time required to handle such a scenario will lengthen. You will see the server resource usage go up and up, and finally have it burst in flames.
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Auto-Generate GraphQL for ArangoDB

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Currently, querying ArangoDB with GraphQL requires building a GraphQL.js schema. This is tedious and the resulting JavaScript schema file can be long and bulky. Here we will demonstrate a short proof of concept that reduces the user related part to only defining the GraphQL IDL file and simple AQL queries.

The Apollo GraphQL project built a library that takes a GraphQL IDL and resolver functions to build a GraphQL.js schema. Resolve functions are called by GraphQL to get the actual data from the database. I modified the library in the way that before the resolvers are added, I read the IDL AST and create resolver functions. Read more

Performance analysis with pyArango: Part II
Inspecting transactions

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Following the previous blog post on performance analysis with pyArango, where we had a look at graphing using statsd for simple queries, we will now dig deeper into inspecting transactions. At first, we split the initialization code and the test code.

Initialisation code

We load the collection with simple documents. We create an index on one of the two attributes: Read more

Performance analysis using pyArango Part I

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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, divided by the count.
  • minimum – fastest requests
  • maximum – slowest requests
  • the time “most” aka 95% of your users may expect an answer within – this is called 95% percentile

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Sorting number strings numerically


Recently I gave a talk about ArangoDB in front of a community of mathematicians. I advertised that nearly arbitrary data can “easily” be stored in a JSON based document store. The moment I had uttered the word “easily”, one of them asked about long integers. And if a mathematician says “long integer” they do not mean 64bit but “properly long”. He actually wanted to store orders of finite groups. I said one should use a JSON UTF-8 string for this but I should have seen the next question coming because he then wanted that a sorted index would actually sort the documents by the numerical value stored in the string. But most databases – and ArangoDB is no exception here – will compare UTF-8 strings lexicographically (dictionary order). Read more

Webinar: Use ArangoDB Agency as fault-tolerant persistent data store

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Join our Sr Distributed System Engineer, Kaveh Vahedipour, to learn more about ArangoDB Agency on September 19th, 2017 (6PM CEST/12PM ET/ 9AM PT)Join the webinar here.

Distributed systems have become the standard topology on which modern appliances live. While the advantages of distributing workload for both performance as well as fault-tolerance are obvious, the runtime flexible configuration of such deployment becomes non-trivial.

ArangoDB clusters are no different in that regard. A potentially large database cluster’s configuration is manipulated at runtime by addition, alteration and removal of collections, indexes, and even servers. All servers need to trust in a fault-tolerant centralized configuration tree, which we call “the agency” in arango-speak. Read more

Pronto Move Shard

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In July Adobe announced that they plan the End-of-Life for flash at around 2020.
As HTML5 progressed and due to a long history of critical security vulnerabilities this is – technologically speaking – certainly the right decision. However I tended to also become a bit sad.

Flash was the first technology that brought interactivity to the web. We tend to forget how static the web was in the early 2000s. Flash brought life to the web and there were plenty of stupid trash games and animations which I really enjoyed at the time. As a homage to the age of trashy flash games I created a game which resembles the games of this era: Read more

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