Rootconf is HasGeek’s annual conference -- and now a growing community -- around DevOps, systems engineering, DevSecOps, security and cloud. The annual Rootconf conference takes place in May each year, with the exception of 2019 when the conference will be held in June.
Besides the annual conference, we also run meetups, one-off public lectures, debates and open houses on DevOps, systems engineering, distributed systems, legacy infrastructure, and topics related to Rootconf.
This is the place to submit proposals for your work, and get them peer reviewed by practitioners from the community.
##Topics for submission:
We seek proposals -- for short and long talks, as well as workshops and tutorials -- on the following topics:
- Case studies of shift from batch processing to stream processing
- Real-life examples of service discovery
- Case studies on move from monolith to service-oriented architecture
- Network security
- Monitoring, logging and alerting -- running small-scale and large-scale systems
- Cloud architecture -- implementations and lessons learned
- Optimizing infrastructure
- Immutable infrastructure
- Aligning people and teams with infrastructure at scale
- Security for infrastructure
If you have questions/queries, write to us on firstname.lastname@example.org
The curious case of slow/fast grequests code
If you have made a single HTTP request in Python, you have probably used the requests module.
If you have made concurrent HTTP requests in Python, you have probably used the grequests module.
I ran into an issue in production which made a grequests based code execute HTTPS GET requests serially without utilising the concurrency goodness promised by grequests. What was more interesting is that depending on the Python modules installed on my system, the same codebase ran fast or slow.
Tumbling down that rabbit hole made me understand Python profiling, function tracing, understanding how gevent works and opening a pull request to fix the issue with a gevent based Python module.
In this talk, I want to share my learnings with the audience through a working dockerized demo which showcases the following stages for the same codebase while explaining why each stage behaves the way it does:
- Stage-0 - Trigger which led to the exploration - May run slow/fast for Python 2.7 and slow/fast for Python 3.7 depending on the modules installed on your system.
- Stage-1 - Baseline with dockerized environment - Runs fast both for Python 2.7 and Python 3.7
- Stage-2 - Detection of slowness and verification - Runs slow both for Python 2.7 and Python 3.7
- Stage-3 - Python 2.7 fix - Runs fast for Python 2.7 but slow for Python 3.7
- Stage-4 - Python 3.7 fix - Patching a gevent based Python module which fixes the issue and makes Python 3.7 fast again.
Please check out the preview video to get a sneak peek into the issue and watch the same codebase run slow/fast depending on the Python version chosen.
Curiosity + programming basics.
- Saurabh Hirani normally doesn’t talk about himself in third person, but when he does, he is writing a speaker bio.
- He enjoys programming and infrastructure automation.
- He is currently working as a Site Reliability Engineer at Autodesk and has spoken at Python meetups, Rootconf conferences and firmly believes in sharing hard earned knowledge that everyone can benefit from.