How do you build Python applications that can handling literally billions of requests. I has certainly been done to great success with places like YouTube (handling 1M requests / sec) and Instagram as well as internal pricing APIs at places like PayPal and other banks.
While Python can be fast at some operations and slow at others, it's generally not so much about language raw performance as it is about building an architecture for this scale. That's why it's great to have Julian Danjou on the show today. We'll dive into his book "The Hacker's Guide to Scaling Python" as well as some of his performance work he's doing over at Datadog.
Links from the showJulian on Twitter: @juldanjou
Scaling Python Book: scaling-python.com
DD Trace production profiling code: github.com
Futurist package: pypi.org
Tenacity package: tenacity.readthedocs.io
Cotyledon package: cotyledon.readthedocs.io
Locust.io Load Testing: locust.io
Datadog: datadoghq.com
daiquiri package: daiquiri.readthedocs.io
YouTube Live Stream Video: youtube.com
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