Over the past few years GraphQL has gained much traction, but what’s the state of GraphQL in Python. This talk describes what GraphQL is, why it has been created and the current state of GraphQL libraries in Python. There will be a short history of GraphQL, some examples of what makes it shine and why it can be preferred to REST. We will also analyse the current state of GraphQL in Python, having a brief overview of the current libraries and then having an in-depth look at Strawberry a new library based on dataclasses that makes it easy to create GraphQl APIs in Python. EVENT: EuroPython 2019 – Talk – 2019-07-11 SPEAKER: Patrick Arminio PUBLICATION PERMISSIONS: Original video was published with the Creative Commons Attribution license (reuse allowed). ATTRIBUTION CREDITS: Original video source: https://www.youtube.com/watch?v=38uCu6ZEvfo https://www.youtube.com/watch?v=EIGjT5NYjjM
Modern hardware is multi-core. It is crucial for Python to provide
high-performance parallelism. This talk will expose to both data-scientists and
library developers the current state of affairs and the recent advances for
parallel computing with Python. The goal is to help practitioners and
developers to make better decisions on this matter. I will first cover how Python can interface with parallelism, from leveraging
external parallelism of C-extensions –especially the BLAS family– to Python's
multiprocessing and multithreading API. I will touch upon use cases, e.g single
vs multi machine, as well as and pros and cons of the various solutions for
each use case. Most of these considerations will be backed by benchmarks from
the scikit-learn machine
learning library. From these low-level interfaces emerged higher-level parallel processing
libraries, such as concurrent.futures, joblib and loky (used by dask and
scikit-learn) These libraries make it easy for Python programmers to use safe
and reliable parallelism in their code. They can even work in more exotic
situations, such as interactive sessions, in which Python’s native
multiprocessing support tends to fail. I will describe their purpose as well as
the canonical use-cases they address. The last part of this talk will focus on the most recent advances in the Python
standard library, addressing one of the principal performance bottlenecks of
multi-core/multi-machine processing, which is data communication. We will
present a new API for shared-memory management between different Python
processes, and performance improvements for the serialization of large Python
objects ( PEP 574, pickle extensions). These performance improvements will be
leveraged by distributed data science frameworks such as dask, ray and pyspark. EVENT: EuroPython 2019 – Talk – 2019-07-12 SPEAKER: Pierre Glaser PUBLICATION PERMISSIONS: Original video was published with the Creative Commons Attribution license (reuse allowed). ATTRIBUTION CREDITS: Original video source: https://www.youtube.com/watch?v=UVL4LFy8ch0 https://www.youtube.com/watch?v=xYuQi7PTAcc
Watch this great talk by Ken Wheeler on performance. You will learn the following:
– How do you know that your app has a performance problem?
– What are the signs of the performance problem?
– How do you measure your app's performance?
– What are the performance problems specific to React apps?
– How do you solve your app's performance problems?
… and much more! EVENT: React Advanced 2019 PUBLICATION PERMISSIONS: The conference organizer provided Coding Tech with the permission to republish this video. Link to the conference: https://react.amsterdam/ ATTRIBUTION CREDITS: Original video source: https://www.youtube.com/watch?v=q6TD3PvmQts https://www.youtube.com/watch?v=6zpzo6y4PDo