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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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, 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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Contributors for Python API wanted for nosql project

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Note: We changed the name of the database in May 2012. AvocadoDB is now called ArangoDB.

Are you a Python expert and want to contribute to an open source project? We need your help writing an API for Python for a new nosql database!

AvocadoDB is a rather new open source project – a fancy nosql database with a couple of interesting features:

  • Schema-free schemata
  • Usable as application server 
  • Consequent use of JavaScript
  • multi-threaded
  • Flexible data modeling (key value pairs, document store, graph database)
  • Free index choice
  • Configurable durability
  • Support for modern storage hardware like SSD and large caches

You’ll find more information on AvocadoDB here.

More info