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UID:pretalx-europython-2026-PPQ3KE@programme.europython.eu
DTSTART;TZID=CET:20260716T141500
DTEND;TZID=CET:20260716T144500
DESCRIPTION:We deployed Python in serverless environments and quickly saw t
 he performance limits. Serverless systems suffer from startup latency\, me
 mory overhead\, and repeated object creation as their executions start alm
 ost from scratch. Those extra seconds made our user experience painfully s
 low—and we couldn’t afford it.\n\nWe looked into the performance of ou
 r flight search engine\, ran profilers\, and applied optimizations. We fou
 nd real gains when **tuning Python’s GC**\, reducing **stop-the-world pa
 uses**\, and introducing an **asynchronous post-execution process** that r
 uns after the handler returns. We reduced execution time from **1.2 second
 s to 300 milliseconds—a 4× speedup** with just a few tweaks.\n\nUnderst
 anding **Python’s memory model** and its runtime behavior was essential\
 , and it’s something we’ll dive into during the talk. What we share is
  based on **AWS Lambda**\, but it can be applied to any short-lived Python
  system—whether serverless or containerized.\n\nAnother big gain came fr
 om replacing `Pydantic` with `TypedDict` for faster parsing\, using **Redi
 s** strategically to distribute operations\, and restructuring code to eli
 minate duplicated transformations. Our web API processes thousands of flig
 ht offers—deserialize\, enrich\, transform—and every millisecond count
 s.\n\nWhile covering optimization techniques\, when they matter\, and how 
 we measured their impact\, the main idea is that you will walk away with t
 actics that help your system stay fast while it grows.
DTSTAMP:20260524T130806Z
LOCATION:Conference Hall Complex (S4)
SUMMARY:Python on Serverless: Strategies for Peak Performance - Alejandro C
 abello Jiménez
URL:https://programme.europython.eu/europython-2026/talk/PPQ3KE/
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