#174 Happy developers use Python 3
Published March 26, 2020
47 min
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    Topic #0: Quick chat about COVID 19.

    Brian #1: Documentation as a way to build Community

    • Melissa Mendonça
    • “… educational materials can have a huge impact and effectively bring people into the community.”
    • Quality documentation for OSS is often lacking due to:
      • decentralized development
      • documentation is not as glamorous or as praised as new features or major bug fixes
      • “Even when the community is welcoming, documentation is often seen as a "good first issue", meaning that the docs end up being written by the least experienced contributors in the community.”
    • Possible solution:
      • organize/re-organize docs into:
        • tutorials
        • how-tos
        • reference guide
        • explanations
      • consequences:
        • Improving on the quality and discoverability
        • Clear difference between docs aimed at different users
        • Give users more opportunities to contribute, generating content that can be shared directly on the official documentation
        • Building a documentation team as a first-class team in the project, which helps create an explicit role as documentation creator. This helps people better identify how they can contribute beyond code.
        • Diversifying our contributor base, allowing people from different levels of expertise and different life experiences to contribute. This is also extremely important so that we have a better understanding of our community and can be accessible, unbiased and welcoming to all people.
    • Referenced in article: "What nobody tells you about documentation"

    Michael #2: The Django Speed Handbook: making a Django app faster

    • By Shibel Mansour
    • Speed of your app is very important: 100ms is an eternity. SEO, user conversions, bounce rates, etc.
    • Use the tried-and-true django-debug-toolbar.
      • Analyze your request/response cycles and see where most of the time is spent.
      • Provides database query execution times and provides a nice SQL EXPLAIN in a separate pane that appears in the browser.
    • ORM/Database: Two ORM functionalities I want to mention first: these are select_related and prefetch_related. Nice 24x perf improvement example in the article. Basically, beware of the N+1 problem.
    • Indexes: Be sure to add them but they slow writes.
    • Pagination: Use it if you have lots of data
    • Async / background tasks.
    • Content size: Shrunk 9x by adding gzip middleware
    • Static files: minify and bundle as you can, cache, serve through nginx, etc.
      • At Python Bytes, Talk Python, etc, we use webassets, cssmin, and jsmin.
    • PageSpeed from Google, talk python’s ranking.
    • ImageOptim (for macOS, others)
    • Lazy-loading images: Lazily loading images means that we only request them when or a little before they enter the client’s (user’s) viewport. With excellent, dependency-free JavaScript libraries like LazyLoad, there really isn’t an excuse to not lazy-load images. Moreover, Google Chrome natively supports the lazy attribute.
    • Remember: Test and measure everything, before and after.

    Brian #3: dacite: simplifies creation of data classes from dictionaries

    • Konrad Hałas
    • dataclasses are awesome
      • quick and easy
      • fields can
        • have default values
        • be excluded from comparison and/or repr and more
    • data often gets to us in dictionaries
    • Converting from dict to dataclass is trivial for trivial cases: x = MyClass(**data_as_dict)
    • For more complicated conversions, you need dacite
    • dacite.from_dict supports:
      • nested structures
      • optional fields and unions
      • collections
      • type_hooks, which allow you to have custom converters for certain types
    • strict mode. Normally allows extra input data that is just ignored if it doesn’t match up with fields. But you can use strict to not allow that.
    • Raises exceptions when something weird happens, like the wrong type, missing values, etc.

    Michael #4: How we retired Python 2 and improved developer happiness

    • By Barry Warsaw
    • The Python Clock is at 0:00.
    • In 2018, LinkedIn embarked on a multi-quarter effort to fully transition to a Python 3 code base.
    • In total, the effort entailed the migration of about 550 code repositories.
    • They don't use Python in our product or as a monolithic web service, and instead have hundreds of independent microservices and tools, and dozens of supporting libraries, all owned by independent teams in separate repositories.
    • In the early days, most of internal libraries were ported to be “bilingual,” meaning they could be used in either Python 2 or 3.
    • Given that the migration affected all of LinkedIn engineering across so many disparate teams and thousands of engineers, the effort was overseen by our Horizontal Initiatives (HI) program.
    • Phase 1: In the first quarter of 2019, we performed detailed dependency graphing, identifying a number of repositories that were more foundational, and thus needed to be fully ported first because they blocked the ports of everything that depended on them.
    • Phase 2: In the second quarter of 2019, we identified the remainder of repositories that needed porting
    • Post-migration reflections: Our primary indicator for completing the migration of a multiproduct was that it built successfully and passed its unit and integration tests.
    • For other organizations planning or in the midst of their own migration paths, we offer the following guidelines:
      • Plan early, and engage your organization’s Python experts. Find and leverage champions in your affected teams, and promote the benefits of Python 3.
      • Adopt the bilingual approach to supporting libraries so that consumers of your libraries can port to Python 3 on their own schedules.
      • Invest in tests and code coverage—these will be your best success metrics.
      • Ensure that your data models are explicit and clear, especially in identifying which data are bytes and which are human-readable text.
    • Benefits:
      • No longer have to worry about supporting Python 2 and have seen our support loads decrease.
      • Can now depend on the latest open source libraries and tools, and free from the constrictions of having to write bilingual Python.
      • Opportunistically and enthusiastically adopting type hinting and the mypy type checker, improving the overall quality, craft, and readability of Python code bases.

    Brian #5: The Troublesome Active Record Pattern

    • Cal Paterson
    • "Object relational mappers" (ORMs) exist to bridge the gap between the programmers' friend (the object), and the database's primitive (the relation).
    • Examples include Django ORM and SQLAlchemy
    • The Active Record pattern of data access is marked by:
      1. A whole-object basis
      2. Access by key (mostly primary key)
    • Problem: Queries that don’t need all information for objects retrieve it all anyway, and it’s easy to code for loops to select or collect info that are wildly inefficient.
      • how many books are there
      • how many books about software testing written by Oregon authors
    • Problem: transactions. people can forget to use transactions, some ORMs don’t support them, they are not taught in beginner tutorials, etc.
      • SQLAlchemy has sessions
      • Django has atomic()
    • REST APIs can suffer the same problems.
    • Solutions:
      • just use SQL
      • first class queries
      • first class transactions
      • avoid Active Record style access patterns
      • Be careful with REST APIs
        • Alternatives:
          • GraphQL
          • RPC-style APIs

    Michael #6: Types at the edges in Python

    • By Steve Brazier
    • For a new web service in python there are 3 things to start with:
    • Why: Because what is this about? AttributeError: 'NoneType' object has no attribute 'strip' It should be: none is not an allowed value (type=type_error.none.not_allowed)
    • We then launch this code into production and our assumptions are tested against reality. If we’re lucky our assumptions turn out to be correct. If not we likely encounter some cryptic NoneType errors like the one at the start of this post.
    • Pydantic can help by formalizing our assumptions.
    • mypy carries on helping: Once you see the error at the start of this post (thanks error reporting) you know what is wrong about assumptions. Make the following change to your code: field: typing.Optional[str]
    • BTW: FastAPI integrates with Pydantic out of the box.
    • A mini-kata like exercise here that can be worked through: meadsteve/types-at-the-edges-minikata





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