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Episode #94: Why don't you like notebooks?

Published Thurs, Sep 6, 2018, recorded Wed, Sep 5, 2018.

Sponsored by DigialOcean -- pythonbytes.fm/digitalocean

Brian #1: Python Patterns

Michael #2: Arctic: Millions of rows a sec (time data)

  • Arctic is a high-performance datastore for numeric data. It supports Pandas, numpy arrays and pickled objects out-of-the-box, with pluggable support for other data types and optional versioning.
  • Arctic can query millions of rows per second per client, achieves ~10x compression on network bandwidth, ~10x compression on disk, and scales to hundreds of millions of rows per second per MongoDB instance.
  • Arctic has been under active development at Man AHL since 2012.
  • Super fast, some latency numbers:
    • 1xDay Data 4ms for 10k rows, vs 2,210 ms from SQL Server)
    • Tick Data 1s for 3.5 MB (Python) or 15 MB (Java) vs 15-40sec from “other tick”
  • Versioned data
  • Built on MongoDB
  • Slides
  • Based on pandas
  • Tested with pytest

Brian #3: PyCon Australia videos

Michael #4: GAE: Introducing App Engine Second Generation runtimes and Python 3.7

  • Today, Google Cloud is announcing the availability of Second Generation App Engine standard runtimes, a significant upgrade to the platform that allows you to easily run web apps using up-to-date versions of popular languages, frameworks and libraries.
  • Python 3.7 is one of the new Second Generation runtimes that we announced at Cloud Next.
  • Based on technology from the gVisor container sandbox, these Second Generation runtimes eliminate many previous App Engine restrictions, giving you the ability to write portable web apps and microservices that take advantage of App Engine's unique auto-scaling, built-in security and pay-per-use billing model.
  • This new runtime allows you to take advantage of Python's vibrant ecosystem of open-source libraries and frameworks. While the Python 2 runtime only allowed the use of specific versions of whitelisted libraries, Python 3 supports arbitrary third-party libraries, including those that rely on C code and native extensions. Just add Django 2.0, NumPy, scikit-learn or your library of choice to a requirements.txt file. App Engine will install these libraries in the cloud when you deploy your app.

Brian #5: I don’t like notebooks

Michael #6: PEP 8000 -- Python Language Governance Proposal Overview

  • This PEP provides an overview of the selection process for a new model of Python language governance in the wake of Guido's retirement. Once the governance model is selected, it will be codified in PEP 13.
  • PEPs in the lower 8000s describe the general process for selecting a governance model.
    • PEP 8001 - Python Governance Voting Process
    • PEP 8002 - Open Source Governance Survey
  • PEPs in the 8010s describe the actual proposals for Python governance.
    • PEP 8010 - The BDFL Governance Model
    • PEP 8011 - The Council Governance Model
    • PEP 8012 - The Community Governance Model

Extras

  • Free Brian Granger ACM webcast on Jupyter Friday
  • TIOBE jump to #3: https://www.tiobe.com/tiobe-index/

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