Rootconf Hyderabad edition

Absynthe came about in response to the need for test data for analysizing the performance and accuracy of log analysis algorithms. Even though plenty of real life logs are available, e.g. /var/log/ in unix-based laptops, they do not serve the purpose of test data. For that, we need to understand the core application logic that is generating these logs.

A more interesting situation arises while trying to test log analytic (and anomaly detection) solutions for distributed applications where multiple sources or modules emit their respective log messages in a single log queue or stream. This means that consecutive log lines could have originated from different, unrelated application components. Absynthe provides ground truth models to simulate such situations.

You need Absynthe if you wish test data to evaluate algorithms that model the behaviours of any well defined process -- whether it’s a computer application or a business process flow.

Outline

1. Motivation
2. Overview of absynthe package
3. Hands-on examples
4. What’s next

Requirements

1. Basic understanding of Python
2. Basic understanding of log analysis

Speaker bio

Namit Chaturvedi is a computer science researcher, currently working at LinkedIn. He obtained his PhD in logic and automata theory in 2015 and transitioned to the world of machine learning and AI. He has previously worked on diverse projects, from load balancing on distributed systems to applying automata theory for physical access control. He has 10 publications in peer-reviewed conferences and journals; and jointly holds 2 technology patents.

His interests include outdoor sports, history of science, effect of science and technology on societies, and beer.

Links

• https://github.com/chaturv3di/absynthe
• https://medium.com/@chaturv3di/absynthe-a-branching-behaviour-synthesiser-85fcfa7b466b

Slides

https://docs.google.com/presentation/d/1OYFpb4N1CZ_5RA-n8-KYRb8rRG5ZCZpdKIIMTdZWZJc/edit?usp=sharing