The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

June 30, 2020Build Your Own Domain Specific Language in Python With textX
Summary
Programming languages are a powerful tool and can be used to create all manner of applications, however sometimes their syntax is more cumbersome than necessary. For some industries or subject areas there is already an agreed upon set of concepts that can be used to express your logic. For those cases you can create a Domain Specific Language, or DSL to make it easier to write programs that can express the necessary logic with a custom syntax. In this episode Igor Dejanović shares his work on textX and how you can use it to build your own DSLs with Python. He explains his motivations for creating it, how it compares to other tools in the Python ecosystem for building parsers, and how you can use it to build your own custom languages.
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 to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
Your host as usual is Tobias Macey and today I’m interviewing Igor Dejanović about textX, a meta-language for building domain specific languges in Python
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what a domain specific language is and some examples of when you might need one?
What is textX and what was your motivation for creating it?
There are a number of other libraries in the Python ecosystem for building parsers, and for creating DSLs. What are the features of textX that might lead someone to choose it over the other options?
What are some of the challenges that face language designers when constructing the syntax of their DSL?
Beyond being able to parse and process an arbitrary syntax, there are other concerns for consumers of the definition in terms of tooling. How does textX provide support to those end users?
How is textX implemented?
How has the design or goals of textX changed since you first began working on it?
What is the workflow for someone using textX to build their own DSL?
Once they have defined the grammar, how do they distribute the generated interpreter for others to use?
What are some of the common challenges that users of textX face when trying to define their DSL?
What are some of the cases where a PEG parser is unable to unambiguously process a defined grammar?
What are some of the most interesting/innovative/unexpected ways that you have seen textX used?
What have you found to be the most interesting, unexpected, or challenging lessons that you have learned while building and maintaining textX and its associated projects?
While preparing for this interview I noticed that you have another parser library in the form of Parglare. How has your experience working with textX informed your designs of that project?
What lessons have you taken back from Parglare into textX?
When is textX the wrong choice, and someone might be better served by another DSL library, different style of parser, or just hand-crafting a simple parser with a regex?
What do you have planned for the future of textX?
Keep In Touch
Website
igordejanovic on GitHub
@dejanovicigor on Twitter
Picks
Tobias
wemake-python-styleguide
Igor
Interactive Fiction genre
Awesome Interactive Fiction
The Interactive Fiction Database
TADS
Inform 7
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 [email protected]) 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
textX
U of Novi Sad
Serbia
DSL course
Secondary Notation
Django
Xtext
Eclipse
PLY
SLY
PyParsing
Lark
PEG Grammar
Language Workbench
Language Server Protocol
Visual Studio Code
textX-LS
Arpeggio Parser
Context-Free Grammar
pyTabs
Guitar Tablatures
Parglare
GLR parsing
TEP 1
Evennia
Podcast Episode
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
55 min
June 23, 2020 Adding Observability To Your Python Applications With OpenTelemetry
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.
54 min
June 23, 2020Adding Observability To Your Python Applications With OpenTelemetry
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 [email protected]) 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
54 min
June 15, 2020 Build A Personal Knowledge Store With Topic Modeling In Contextualize
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.
59 min
June 15, 2020Build A Personal Knowledge Store With Topic Modeling In Contextualize
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 [email protected]) 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
59 min
June 08, 2020 Open Source Product Analytics With PostHog
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.
50 min
June 08, 2020Open Source Product Analytics With PostHog
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 [email protected]) 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
50 min
June 02, 2020 Extending The Life Of Python 2 Projects With Tauthon
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.
34 min
June 02, 2020Extending The Life Of Python 2 Projects With Tauthon
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
34 min
May 25, 2020 Dependency Management Improvements In Pip's Resolver
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.
1 hr 17 min