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**The Python Podcast.__init__**

The podcast about Python and the people who make it great

Latest Episodes:

**Adding Observability To Your Python Applications With OpenTelemetry** Jun 23, 2020
Show notes

Summary

Once you release an application into production it can be difficult to understand all of the ways that it is interacting with the systems that it integrates with. The OpenTracing project and its accompanying ecosystem of technologies aims to make observability of your systems more accessible. In this episode Austin Parker and Alex Boten explain how the correlation of tracing and metrics collection improves visibility of how your software is behaving, how you can use the Python SDK to automatically instrument your applications, and their vision for the future of observability as the OpenTelemetry standard gains broader adoption.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Austin Parker and Alex Boten about the OpenTelemetry project and its efforts to standardize the collection and analysis of observability data for your applications

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what OpenTelemetry is and some of the story behind it?

How do you define observability and in what ways is it separate from the "traditional" approach to monitoring?

What are the goals of the OpenTelemetry project?

For someone who wants to begin using OpenTelemetry clients in their Python application, what is the process of integrating it into their application?

How does the definition and adoption of a cross-language standard for telemetry data benefit the broader software community?

How do you avoid the trap of limiting the whole ecosystem to the lowest common denominator?

What types of information are you focused on collecting and analyzing to gain insights into the behavior of applications and systems?

What are some of the challenges that are commonly faced in interpreting the collected data?

With so many implementations of the specification, how are you addressing issues of feature parity?

For the Python SDK, how is it implemented?

What are some of the initial designs or assumptions that have had to be revised or reconsidered as it gains adoption?

What is your approach to integration with the broader ecosystem of tools and frameworks in the Python community?

What are some of the interesting or unexpected challenges that you have faced or lessons that you have learned while working on instrumentation of Python projects?

Once an application is instrumented, what are the options for delivering and storing the collected data?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with the OpenTelemetry ecosystem?

What are some of the most interesting, innovative, or unexpected ways that you have seen components in the OpenTelemetry ecosystem used?

When is OpenTelemetry the wrong choice?

What is in store for the future of the OpenTelemetry project?

Keep In Touch

Austin

@austinlparker on Twitter

austinlparker on GitHub

Alex

LinkedIn

@codeboten on Twitter

codeboten on GitHub

Picks

Tobias

Pulumi

Podcast Episode

Austin

Helm 3

Alex

Algorithms To Live By: The Computer Science Of Everyday Decisions by Brian Christian and Tom Griffiths

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

OpenTelemetry

Lightstep

OpenTracing

OpenCensus

Distributed Tracing

Jaeger

Zipkin

Observability

Kubernetes

Spring

Flask

gRPC

Structlog

Filebeat

W3C Trace Context

OpenTelemetry Python SDK

OpenTelemetry Django

OpenTelemetry Flask

OpenTelemetry Collector

OTLP == Open Telemetry Protocol

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Build A Personal Knowledge Store With Topic Modeling In Contextualize** Jun 15, 2020
Show notes

Summary

Our thought patterns are rarely linear or hierarchical, instead following threads of related topics in unpredictable directions. Topic modeling is an approach to knowledge management which allows for forming a graph of associations to make capturing and organizing your thoughts more natural. In this episode Brett Kromkamp shares his work on the Contextualize project and how you can use it for building your own topic models. He explains why he wrote a new topic modeling engine, how it is architected, and how it compares to other systems for organizing information. Once you are done listening you can take Contextualize for a test run for free with his hosted instance.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Brett Kromkamp about Contextualise, a topic modeling application that helps you build a mind map for information-heavy projects

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Contextualize is and some of the types of projects that it can be used for?

What was your motivation for creating it?

How do you use topic maps in your own work and creative endeavors?

The space of personal note-taking and knowledge management is vast and varied. What does Contextualize do well that you have been unable to find or implement in other tools?

For someone using Contextualize, what does that workflow look like?

How are you approaching integration with different creative contexts (e.g. text editors, graphics editors, word processing, etc.)?

Can you describe how Contextualize is implemented?

How has the design evolved since you first began working on it?

In the documentation for Contextualize it mentions that this is the latest in a string of topic mapping platforms that you have built. What are some of the lessons that you have learned from previous efforts that have influenced the design of this one?

One of the challenges with many knowledge management tools is that they are proscriptive in how to work with them. In what ways has your own preference for how to interact with information influenced the direction of Contextualize?

Being an open source application, how has its exposure to the public directed your software and user design?

How do you approach the challenge of reducing friction in adding content and relations while allowing for flexibility and context management?

What are some of the projects that you are using Contextualize for?

What are your thoughts on the utility of something like Contextualize for capturing and organizing the collective knowledge of a team of collaborators, whether in a work or casual context?

What have you found to be the most interesting, complex, or complicated aspects of building a topic mapping platform?

When is Contextualize the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

Website

@brettkromkamp on Twitter

brettkromkamp on GitHub

Picks

Tobias

Pydantic

Podcast Episode

MyPy

Podcast Episode

Brett

Black Lives Matter

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Contextualise

GitHub Repository

Norway

IBM Rexx

Java

Semantic Web

Topic Map

ISO standard for topic maps

RDF

Spain

Knowledge Management

Graph Database

Worldbuilding

Roam Research

TopicDB

Twitter Bootstrap

Hypergraph

Digital Gardening

Notion

TiddlyWiki

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Open Source Product Analytics With PostHog** Jun 08, 2020
Show notes

Summary

You spend a lot of time and energy on building a great application, but do you know how it’s actually being used? Using a product analytics tool lets you gain visibility into what your users find helpful so that you can prioritize feature development and optimize customer experience. In this episode PostHog CTO Tim Glaser shares his experience building an open source product analytics platform to make it easier and more accessible to understand your product. He shares the story of how and why PostHog was created, how to incorporate it into your projects, the benefits of providing it as open source, and how it is implemented. If you are tired of fighting with your user analytics tools, or unwilling to entrust your data to a third party, then have a listen and then test out PostHog for yourself.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

You listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code *AISPRINGBOARD* when you enroll.

Your host as usual is Tobias Macey and today I’m interviewing Tim Glaser about PostHog, an open source platform for product analytics

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what PostHog is and what motivated you to build it?

What are the goals of PostHog and who are the target audience?

In the description of PostHog it mentions being a product focused analytics platform, as opposed to session based. What are the meaningful differences between the two?

Customer analytics is a rather crowded market, with a large number of both commercial and open source offerings (e.g. Google Analytics, Heap, Matomo, Snowplow, etc.). How does PostHog fit in that landscape and what are the differentiating factors that would lead someone to select it over the alternativs?

For anyone interested in using PostHog, do you offer a migration path from other platforms?

necessary features for a customer analytics tool

privacy and security issues around analytics

How is PostHog implemented and how has its design evolved since you first began building it?

reason for choosing Python

benefits of Django

thoughts on introducing Channels

option to include it as a pluggable Django app

integration points

data lake integration

challenges of providing understandable statistics and exposing options for detailed analysis

Having data about how users are interacting with your site or application is interesting, but how does it help in determining the useful actions to drive success?

business model and project governance

What are the most complex, complicated, or misunderstood aspects of building a product analytics platform?

What have you found to be the most interesting, unexpected, or challenging lessons that you have learned in the process of building PostHog?

When is PostHog the wrong choice?

What do you have planned for the future of PostHog?

Keep In Touch

timgl on GitHub

LinkedIn

@timgl on Twitter

Picks

Tobias

Hitchhiker’s Guide To The Galaxy

Tim

Triumph Of The City by Edward Glaeser

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

PostHog

MixPanel

Amplitude

Heap

Data Engineering Podcast Episode

Snowplow

Data Engineering Podcast Episode

Looker

Data Engineering Podcast Episode

SnowflakeDB

Data Engineering Podcast Episode

Tableau

DOM == Document Object Model for web pages

Django

Django Rest Framework

React.js

Kea state management for React.js

Redux

TypeScript

Django Stubs

Django Channels

Sentry

Podcast Episode

Pluggable Django App

PostgreSQL

ELT

Data Lake

Optimizely

Feature Flags

Podcast Episode

PostHog Roadmap

PostHog Employee Handbook

Matomo (formerly Piwik)

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Extending The Life Of Python 2 Projects With Tauthon** Jun 02, 2020
Show notes

Summary

The divide between Python 2 and 3 lasted a long time, and in recent years all of the new features were added to version 3. To help bridge the gap and extend the viability of version 2 Naftali Harris created Tauthon, a fork of Python 2 that backports features from Python 3. In this episode he explains his motivation for creating it, the process of maintaining it and backporting features, and the ways that it is being used by developers who are unable to make the leap. This was an interesting look at how things might have been if the elusive Python 2.8 had been created as a more gentle transition.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

You listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.

Your host as usual is Tobias Macey and today I’m interviewing Naftali Harris about his work on Tauthon, a fork of Python 2 that backports features from Python 3

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Tauthon is and your motivations for creating it?

What’s the story behind the name?

What types of applications and environments are you using Tauthon in?

How much adoption of Tauthon have you seen?

What are some of the different ways that your users are employing it?

Is this the missing "2.8" release? In other words, is this intended to be a bridge for simplifying the migration of existing Python 2 code to Python 3, or as an extended support window for Python 2?

What features have you backported from Python 3?

What is your process for identifying and prioritizing features to bring into Tauthon?

What is your workflow for implementing the backported functionality in Tauthon?

What are some of the cases where you have had to compromise on the functionality or syntax of a feature that you have backported in order to fit into Python 2?

What is your governing philosophy for how to manage syntax or behavior differences between Python 2 and 3?

What have been the most challenging features to backport and maintain?

What are some of the ways that Tauthon might break existing Python 2 code?

What is the story for compatibility with libraries that are Python 3 only?

What have you seen in terms of adoption of Tauthon?

Do you have any sense of the commonalities among those users?

What are some of the ecosystem challenges that faces users of Tauthon? (e.g. Pip support, package compatibility, etc.)

What are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of creating and maintaining Tauthon?

What are your long-term plans for Tauthon, and how have they changed since you first started working on it?

Keep In Touch

Website

@naftaliharris on Twitter

naftaliharris on GitHub

Picks

Tobias

Dagster

PyCon 2020 Online

Naftali

Sentilink

Timsort

Tim Peters

Links

Tauthon

Function Annotations

Tau

Nick Coghlan

MyPy

Podcast Episode

Matrix Multiplier Operator

Python 3.9 PEG Parser

lazysorted

nonlocal keyword

Valgrind

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Dependency Management Improvements In Pip's Resolver** May 25, 2020
Show notes

Summary

Dependency management in Python has taken a long and winding path, which has led to the current dominance of Pip. One of the remaining shortcomings is the lack of a robust mechanism for resolving the package and version constraints that are necessary to produce a working system. Thankfully, the Python Software Foundation has funded an effort to upgrade the dependency resolution algorithm and user experience of Pip. In this episode the engineers working on these improvements, Pradyun Gedam, Tzu-Ping Chung, and Paul Moore, discuss the history of Pip, the challenges of dependency management in Python, and the benefits that surrounding projects will gain from a more robust resolution algorithm. This is an exciting development for the Python ecosystem, so listen now and then provide feedback on how the new resolver is working for you.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.

Your host as usual is Tobias Macey and today I’m interviewing Tzu-ping Chung, Pradyun Gedam, and Paul Moore about their work to improve the dependency resolution capabilities of Pip and its user experience

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the focus of the work that you are doing?

What is the scope of the work, and what is the established criteria for when it is considered complete?

What is your history with working on the Pip source code and what interests you most about this project?

What are the main sources or manifestations of technical debt that exist in Pip as of today?

How does it currently handle dependency resolution?

What are some of the workarounds that developers have had to resort to in the absence of a robust dependency resolver in Pip?

How is the new dependency resolver implemented?

How has your initial design evolved or shifted as you have gotten further along in its implementation?

What are the pieces of information that the resolver will rely on for determining which packages and versions to install? (e.g. will it install setuptools > 45.x in a Python 2 virtualenv?)

What are the new capabilities in Pip that will be enabled by this upgrade to the dependency resolver?

What projects or features in the encompassing ecosystem will be unblocked with the introduction of this upgrade?

What are some of the changes that users will need to make to adopt the updated Pip?

How do you anticipate the changes in Pip impacting the viability or adoption of Python and its ecosystem within different communities or industries?

What are some of the additional changes or improvements that you would like to see in Pip or other core elements of the Python landscape?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on these updates to Pip?

Keep In Touch

Pradyun

Website

pradyunsg on GitHub

@pradyunsg on Twitter

Paul

pfmoore on GitHub

Tzu-Ping

uranusjr on GitHub

Website

@uranusjr on Twitter

Picks

Tzu-ping

Python Launcher

Joe Abercrombie author

The Shattered Sea Trilogy

Anime

PipX Standalone

Paul

pipx

Black

nox

tox

scoop

Neil Gaiman

Good Omens

Book

TV Series

Pradyun

because my picks can be anything — things that have kept me sane in this lockdown world

Music: Chris Daughtry

Video Game: Parkitect

Tobias

Language Server Protocol

Emacs lsp-mode

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Pip

Podcast interview with Donald Stufft

Macdown

Taiwan

Pipenv

PyPI

Podcast Episode

TOML

Python Package Metadata Standards

iBook G4

Acorn Computer

distutils

easy_install

Python Eggs

setuptools

Python Wheels

CPAN

Conda

Inside The Cheeseshop

Google Summer of Code

Zazo

PEP517

pip-tools

Poetry

resolvelib

SAT Solver

Trove Classifiers

PyPA

pyproject.toml

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Easy Data Validation For Your Python Projects With Pydantic** May 18, 2020
Show notes

Summary

One of the most common causes of bugs is incorrect data being passed throughout your program. Pydantic is a library that provides runtime checking and validation of the information that you rely on in your code. In this episode Samuel Colvin explains why he created it, the interesting and useful ways that it can be used, and how to integrate it into your own projects. If you are tired of unhelpful errors due to bad data then listen now and try it out today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show because you love Python and want to keep your skills up to date. Machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.

Your host as usual is Tobias Macey and today I’m interviewing Samuel Colvin about Pydantic, a library for enforcing type hints at runtime

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Pydantic is and what motivated you to create it?

What are the main use cases that benefit from Pydantic?

There are a number of libraries in the Python ecosystem to handle various conventions or "best practices" for settings management. How does pydantic fit in that category and why might someone choose to use it over the other options?

There are also a number of libraries for defining data schemas or validation such as Marshmallow and Cerberus. How does Pydantic compare to the available options for those cases?

What are some of the challenges, whether technical or conceptual, that you face in building a library to address both of these areas?

The 3.7 release of Python added built in support for dataclasses as a means of building containers for data with type validation. What are the tradeoffs of pydantic vs the built in dataclass functionality?

How much overhead does pydantic add for doing runtime validation of the modelled data?

In the documentation there is a nuanced point that you make about parsing vs validation and your choices as to what to support in pydantic. Why is that a necessary distinction to make?

What are the limitations in terms of usage that you are accepting by choosing to allow for implicit conversion or potentially silent loss of precision in the parsed data?

What are the benefits of punting on the strict validation of data out of the box?

What has been your design philosophy for constructing the user facing API?

How is Pydantic implemented and how has the overall architecture evolved since you first began working on it?

What have you found to be the most challenging aspects of building a library for managing the consistency of data structures in a dynamic language?

What are some of the strengths and weaknesses of Python’s type system?

What is the workflow for a developer who is using Pydantic in their code?

What are some of the pitfalls or edge cases that they might run into?

What is involved in integrating with other libraries/frameworks such as Django for web development or Dagster for building data pipelines?

What are some of the more advanced capabilities or use cases of Pydantic that are less obvious?

What are some of the features or capabilities of Pydantic that are often overlooked which you think should be used more frequently?

What are some of the most interesting, innovative, or unexpected ways that you have seen Pydantic used?

What are some of the most interesting, challenging, or unexpected lessons that you have learned through your work on or with Pydantic?

When is Pydantic the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

samuelcolvin on GitHub

Website

LinkedIn

@samuel_colvin on Twitter

Picks

Tobias

Devil Sticks

Samuel

Flash Boys by Michael Lewis

Algorithms To Live By by Brian Christian and Tom Griffiths

NGrok.com

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Pydantic

Matlab

C#

FastAPI

Podcast Episode

Marshmallow

Podcast Episode

Cerberus

12 Factor App

Django

Python Type Hints

Cython

Podcast Episode

MyPy

Podcast Episode

Duck Typing

Haskell

Higher Order Types

PyCharm Pydantic Plugin

Django Rest Framework

Avro

Parquet

Dagster

Data Engineering Podcast Episode

Starlette

Flask

Ludwig

Deep Pavlov

Fast MRI

Reagent

Pynt

Open Source Has Failed article

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Managing Distributed Teams In The Age Of Remote Work** May 11, 2020
Show notes

Summary

More of us are working remotely than ever before, many with no prior experience with a remote work environment. In this episode Quinn Slack discusses his thoughts and experience of running Sourcegraph as a fully distributed company. He covers the lessons that he has learned in moving from partially to fully remote, the practices that have worked well in managing a distributed workforce, and the challenges that he has faced in the process. If you are struggling with your remote work situation then this conversation has some useful tips and references for further reading to help you be successful in the current environment.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to pythonpodcast.com/tidydata today and get started for free with no credit card required.

Your host as usual is Tobias Macey and today I’m interviewing Quinn Slack about his experience managing a fully remote company and useful tips for remote work

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of the team structure at Sourcegraph?

You recently moved to being fully remote. What was the motivating factor and how has it changed your personal workflow?

What is your prior history with working remote?

team practices for visibility of progress

impact of remote teams on how code is written and organized

reducing review burden by writing clearer code

structuring meetings when remote

points of friction for remote developer teams

benefits of being fully remote

incentivizing documentation

compensation structure

Keep In Touch

LinkedIn

@sqs on Twitter

sqs on GitHub

Picks

Tobias

Joplin App

Quinn

Skunkworks by Ben Rich

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Sourcegraph

Quinn’s Python Search Engine

Sourcegraph Employee Handbook

Gitlab

Gitlab Handbook

Zapier

Zapier Guide To Remote Work

Automattic

Automattic Blog On Distributed Work

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Maintainable Infrastructure As Code In Pure Python With Pulumi** May 04, 2020
Show notes

Summary

After you write your application, you need a way to make it available to your users. These days, that usually means deploying it to a cloud provider, whether that’s a virtual server, a serverless platform, or a Kubernetes cluster. To manage the increasingly dynamic and flexible options for running software in production, we have turned to building infrastructure as code. Pulumi is an open source framework that lets you use your favorite language to build scalable and maintainable systems out of cloud infrastructure. In this episode Luke Hoban, CTO of Pulumi, explains how it differs from other frameworks for interacting with infrastructure platforms, the benefits of using a full programming language for treating infrastructure as code, and how you can get started with it today. If you are getting frustrated with switching contexts when working between the application you are building and the systems that it runs on, then listen now and then give Pulumi a try.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to pythonpodcast.com/tidydata today and get started for free with no credit card required.

Your host as usual is Tobias Macey and today I’m interviewing Luke Hoban about building and maintaining infrastructure as code with Pulumi

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the concept of "infrastructure as code"?

What is Pulumi and what is the story behind it?

Where does the name come from?

How does Pulumi compare to other infrastructure as code frameworks, such as Terraform?

What are some of the common challenges in managing infrastructure as code?

How does use of a full programming language help in addressing those challenges?

What are some of the dangers of using a full language to manage infrastructure?

How does Pulumi work to avoid those dangers?

Why is maintaining a record of the provisioned state of your infrastructure necessary, as opposed to relying on the state contained by the infrastructure provider?

What are some of the design principles and constraints that developers should be considering as they architect their infrastructure with Pulumi?

Can you describe how Pulumi is implemented?

How does Pulumi manage support for multiple languages while maintaining feature parity across them?

How do you manage testing and validation of the different providers?

The strength of any tool is largely measured in the ecosystem that exists around it, which is one of the reasons that Terraform has been so successful. How are you approaching the problem of bootstrapping the community and prioritizing platform support?

Can you talk through the workflow of working with Pulumi to build and maintain a proper infrastructure?

What are some of the ways to approach testing of infrastructure code?

What does the CI/CD lifecycle for infrastructure look like?

What are the limitations of infrastructure as code?

How do configuration management tools fit with frameworks such as Pulumi?

The core framework of Pulumi is open source, and your business model is focused around a managed platform for tracking state. How are you approaching governance of the project to ensure its continued viability and growth?

What are some of the most interesting, innovative, or unexpected design patterns that you have seen your users include in their infrastructure projects?

When is Pulumi the wrong choice?

What do you have planned for the future of Pulumi?

Keep In Touch

LinkedIn

lukehoban on GitHub

@lukehoban on Twitter

Picks

Tobias

Bookshelf App

Luke

GoBinaries.com

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Pulumi

Terraform

IronPython

HCL == Hashicorp Config Language

Kubernetes

TypeScript

DevOps

CloudFormation

ARM == Azure Resource Manager

AWSx

GCP == Google Cloud Platform

Pulumi SaaS

SaltStack

Podcast Episode

Ansible

Elastic Beanstalk

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Build The Next Generation Of Python Web Applications With FastAPI Apr 20, 2020

Show notes

Summary

Python has an embarrasment of riches when it comes to web frameworks, each with their own particular strengths. FastAPI is a new entrant that has been quickly gaining popularity as a performant and easy to use toolchain for building RESTful web services. In this episode Sebastián Ramirez shares the story of the frustrations that led him to create a new framework, how he put in the extra effort to make the developer experience as smooth and painless as possible, and how he embraces extensability with lightweight dependency injection and a straightforward plugin interface. If you are starting a new web application today then FastAPI should be at the top of your list.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
Your host as usual is Tobias Macey and today I’m interviewing Sebastián Ramirez about FastAPI, a framework for building production ready APIs in Python 3

Interview

Introductions
How did you get introduced to Python?
Can you start by describing what FastAPI is?
- What are the main frustrations that you ran into with other frameworks that motivated you to create an entirely new one?
What are some of the main use cases that FastAPI is designed for?
Many web frameworks focus on managing the end-to-end functionality of a website, including the UI. Why did you focus on just API capabilities?
- What are the benefits of building an API only framework?
- If you wanted to integrate a presentation layer, what would be involved in that effort?
What API formats does FastAPI support?
- What would be involved in adding support for additional specifications such as GraphQL or JSON-LD?
There are a huge number of web frameworks available just in the Python ecosystem. How does FastAPI fit into that landscape and why might someone choose it over the other options?
Can you share your design philosophy for the project?
- What are your main sources of inspiration for the framework?
- You have also built the Typer CLI library which you refer to as the little sibling of FastAPI. How have your experiences building these two projects influenced their counterpart’s evolution?
What are the benefits of incorporating type annotations into a web framework and in what ways do they manifest in its functionality?
What is the workflow for a developer building a complex application in FastAPI?
Can you describe how FastAPI itself is architected and how its design has evolved since you first began working on it?
- What are the extension points that are available for someone to build plugins for FastAPI?
What are some of the challenges that you have faced in building an async framework that is leveraging the new ASGI specification?
What are some sharp edges that users should keep an eye out for?
What are some unique or underutilized features of FastAPI that users might not be aware of?
What are some of the most interesting, unexpected, or innovative ways that you have seen FastAPI used?
When is FastAPI the wrong choice?
What are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of building and maintaining FastAPI?
What do you have planned for the future of the project?

Keep In Touch

@tiangolo on Twitter. @tiangolo on GitHub.

Picks

Tobias
- Once Upon A Time TV Show
Sebastián

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

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