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

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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