Libraries

Building GraphQL APIs in Python Using Graphene with Syrus Akbary - Episode 192

Summary

The web has spawned numerous methods for communicating between applications, including protocols such as SOAP, XML-RPC, and REST. One of the newest entrants is GraphQL which promises a simplified approach to client development and reduced network requests. To make implementing these APIs in Python easier, Syrus Akbary created the Graphene project. In this episode he explains the origin story of Graphene, how GraphQL compares to REST, how you can start using it in your applications, and how he is working to make his efforts sustainable.

Preface

  • 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 check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Syrus Akbary about Graphene, a python library for building your APIs with GraphQL

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is GraphQL and what is the benefit vs a REST-based API?
    • How does it compare to specifications such as OpenAPI (formerly Swagger) or RAML?
  • Can you explain what Graphene is and your motivation for building it?
    • In addition to the Python implementation there is also a JavaScript library. Is that primarily for use as a client or can it also be used in Node for serving APIs?
  • What is involved in building a GraphQL API?
    • What does Graphene do to simplify this process?
  • How is Graphene implemented and how has that evolved since you first started working on it?
    • Is there a set of tests for verifying the compliance of Graphene or a specific API with the GraphQL specification?
  • What are some of the most complex or confusing aspects of building a GraphQL API?
  • What are some of the unique capabilities that are offered by building an application with GraphQL as the communication interface?
  • While reading through documentation in preparation for our conversation I noticed the Quiver project. Can you explain what that is and how it fits with the other Graphene projects?
    • What is it doing under the hood to optimize serving of the API?
  • For someone who is interested in adding a GraphQL interface to an existing application, what would be involved?
  • The documentation mentions creation of a schema, as well as defining queries. Is it possible for a client to craft queries that don’t match directly with those defined in the server layer?
  • What are some of the most interesting or surprising uses of Graphene and GraphQL that you have seeen?
  • What are some cases where it would be more practical to implement an API using REST instead of GraphQL?
  • What are some references that you would recommend for anyone who wants to learn more about GraphQL and its ecosystem?
  • What are your plans for the future of Graphene?

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

AIORTC: An Asynchronous WebRTC Framework with Jeremy Lainé - Episode 191

Summary

Real-time communication over the internet is an amazing feat of modern engineering. The protocol that powers a majority of video calling platforms is WebRTC. In this episode Jeremy Lainé explains why he wrote a Python implementation of this protocol in the form of AIORTC. He also discusses how it works, how you can use it in your own projects, and what he has planned for the future.

Preface

  • 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 check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Jeremy Lainé about AIORTC, an asynchronous implementation of the WebRTC and ObjectRTC protocols in Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what the WebRTC and ObjectRTC protocols are?
    • What are some of the main use cases for these protocols?
  • What is AIORTC and what was your motivation for creating it?
    • How does it compare to other implementations of the RTC protocols?
    • Why do you think there haven’t been any other Python implementations?
  • What are some of the benefits of having a Python implementation of the RTC protocol?
  • How is AIORTC implemented?
    • What have been some of the most difficult or challenging aspects of implementing a WebRTC compliant library?
    • What are some of the most interesting or useful lessons that you have learned in the process?
  • What is involved in building an application on top of AIORTC?
    • What would be required to integrate AIORTC into an existing application built with something such as Flask or Django?
  • What are some of the most interesting uses of AIORTC that you have seen?
  • What are some of the projects that you would like to build with AIORTC?
  • What are some cases where it would make more sense to use a different library or framework for your WebRTC projects?
  • What are your plans for the future of AIORTC?

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

Django, Channels, And The Asynchronous Web with Andrew Godwin - Episode 180

Summary

Once upon a time the web was a simple place with one main protocol and a predictable sequence of request/response interactions with backend applications. This is the era when Django began, but in the intervening years there has been an explosion of complexity with new asynchronous protocols and single page Javascript applications. To help bridge the gap and bring the most popular Python web framework into the modern age Andrew Godwin created Channels. In this episode he explains how the first version of the asynchronous layer for Django applications was created, how it has changed in the jump to version 2, and where it will go in the future. Along the way he also discusses the challenges of async development, his work on designing ASGI as the spiritual successor to WSGI, and how you can start using all of this in your own projects today.

Preface

  • 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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at podcastinit.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Andrew Godwin about Django Channels 2.x and the ASGI specification for modern, asynchronous web protocols

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start with an overview of the problem that Channels is aiming to solve?
  • Asynchronous frameworks have existed in Python for a long time. What are the tradeoffs in those frameworks that would lead someone to prefer the combination of Django and Channels?
  • For someone who is familiar with traditional Django or working on an existing application, what are the steps involved in integrating Channels?
  • Channels is a project that you have been working on for a significant amount of time and which you recently re-architected. What were the shortcomings in the 1.x release that necessitated such a major rewrite?
    • How is the current system architected?
  • What have you found to be the most challenging or confusing aspects of managing asynchronous web protocols both as an author of Channels/ASGI and someone building on top of them?
    • While reading through the documentation there were mentions of the synchronous nature of the Django ORM. What are your thoughts on asynchronous database access and how important that is for future versions of Django and Channels?
  • As part of your implementation of Channels 2.x you introduced a new protocol for asynchronous web applications in Python in the form of ASGI. How does this differ from the WSGI standard and what was your process for developing this specification?
    • What are your hopes for what the Python community will do with ASGI?
  • What are your plans for the future of Channels?
  • What are some of the most interesting or unexpected uses of Channels and/or ASGI?

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

Fast Stream Processing In Python Using Faust with Ask Solem - Episode 176

Summary

The need to process unbounded and continually streaming sources of data has become increasingly common. One of the popular platforms for implementing this is Kafka along with its streams API. Unfortunately, this requires all of your processing or microservice logic to be implemented in Java, so what’s a poor Python developer to do? If that developer is Ask Solem of Celery fame then the answer is, help to re-implement the streams API in Python. In this episode Ask describes how Faust got started, how it works under the covers, and how you can start using it today to process your fast moving data in easy to understand Python code. He also discusses ways in which Faust might be able to replace your Celery workers, and all of the pieces that you can replace with your own plugins.

Preface

  • 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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at podcastinit.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Ask Solem about Faust, a library for building high performance, high throughput streaming systems in Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Faust and what was your motivation for building it?
    • What were the initial project requirements that led you to use Kafka as the primary infrastructure component for Faust?
  • Can you describe the architecture for Faust and how it has changed from when you first started writing it?
    • What mechanism does Faust use for managing consensus and failover among instances that are working on the same stream partition?
  • What are some of the lessons that you learned while building Celery that were most useful to you when designing Faust?
  • What have you found to be the most common areas of confusion for people who are just starting to build an application on top of Faust?
  • What has been the most interesting/unexpected/difficult aspects of building and maintaining Faust?
  • What have you found to be the most challenging aspects of building streaming applications?
  • What was the reason for releasing Faust as an open source project rather than keeping it internal to Robinhood?
  • What would be involved in adding support for alternate queue or stream implementations?
  • What do you have planned for the future of Faust?

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

Michael Foord On Testing, Mock, TDD, And The Python Community - Episode 171

Summary

Michael Foord has been working on building and testing software in Python for over a decade. One of his most notable and widely used contributions to the community is the Mock library, which has been incorporated into the standard library. In this episode he explains how he got involved in the community, why testing has been such a strong focus throughout his career, the uses and hazards of mocked objects, and how he is transitioning to freelancing full time.

Preface

  • 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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at podcastinit.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Michael Foord mockingly, about his career in Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • One of the main threads in your career appears to be software testing. What aspects of testing do you find so interesting and how did you first get exposed to that aspect of building software?
    • How has the language and ecosystem support for testing evolved over the course of your career?
    • What are some of the areas that you find it to still be lacking?
  • Mock is one of your projects that has been widely adopted and ultimately incorporated into the standard library. What was your reason for starting it in the first place?
    • Mocking can be a controversial topic. What are your current thoughts on how and when to use mocks, stubs, and fixtures?
  • How do you view the state of the art for testing in Python as it compares to other languages that you have worked in?
  • You were fairly early in the move to supporting Python 2 and 3 in a single project with Mock. How has that overall experience changed in the intervening years since Python 2.4 and 3.2?
  • What are some of the notable evolutions in Python and the software industry that you have experienced over your career?
  • You recently transitioned to acting as a software trainer and consultant full time. Where are you focusing your energy currently and what are your grand plans for the future?

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

The Past, Present, and Future of Twisted with Moshe Zadka - Episode 170

Summary

Twisted is one of the earliest frameworks for developing asynchronous applications in Python and it has yet to fulfill its original purpose. It can be used to build network servers that integrate a multitude of protocols, increase the performance of your I/O bound applications, serve as the full web stack for your WSGI projects, and anything else that needs a battle tested and performant foundation. In this episode long time maintainer Moshe Zadka discusses the history of Twisted, how it has evolved over the years, the transition to Python 3, some of its myriad use cases, and where it is headed in the future. Try it out today and then send some thanks to all of the people who have dedicated their time to building it.

Preface

  • 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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at podcastinit.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Moshe Zadka about Twisted, the original multi-function tool for asynchronous operations and network protocols in Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • For anyone who isn’t familiar with Twisted can you share a brief overview of what it is?
    • What was the original motivation for creating it?
    • How did you get involved with the project and what is your current role in the team?
  • How can people learn to use Twisted?
    • What are some of the common difficulties that new users encounter?
  • What did you learn working on Twisted?
  • Who uses Twisted?
    • When is Twisted the wrong choice?
    • What are some examples of systems that aren’t using Twisted but should be?
  • What are some of the ways that Twisted has evolved and changed over the years?
  • What are some of the ways people can support Twisted?
  • What are some of the plans for the future of Twisted?

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

Pandas Extension Arrays with Tom Augspurger - Episode 164

Summary

Pandas is a swiss army knife for data processing in Python but it has long been difficult to customize. In the latest release there is now an extension interface for adding custom data types with namespaced APIs. This allows for building and combining domain specific use cases and alternative storage mechanisms. In this episode Tom Augspurger describes how the new ExtensionArray works, how it came to be, and how you can start building your own extensions today.

Preface

  • 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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Tom Augspurger about the extension interface for Pandas data frames and the use cases that it enables

Interview

  • Introductions
  • How did you get introduced to Python?
  • Most people are familiar with Pandas, but can you describe at a high level the new extension interface?
    • What is the story behind the implementation of this functionality?
    • Prior to this interface what was the option for anyone who wanted to extend Pandas?
  • What are some of the new data types that are available as external packages?
    • What are some of the unique use cases that they enable?
  • How is the new interface implemented within Pandas?
  • What were the most challenging or difficult aspects of building this new functionality?
  • What are some of the more interesting possibilities that you are aware of for new extension types?
  • What are the limitations of the interface for libraries that add new array functionality?
  • What is the next major change or improvement that you would like to add in Pandas?

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

Asking Questions From Data Using Active Learning with Tivadar Danka - Episode 162

Summary

One of the challenges of machine learning is obtaining large enough volumes of well labelled data. An approach to mitigate the effort required for labelling data sets is active learning, in which outliers are identified and labelled by domain experts. In this episode Tivadar Danka describes how he built modAL to bring active learning to bioinformatics. He is using it for doing human in the loop training of models to detect cell phenotypes with massive unlabelled datasets. He explains how the library works, how he designed it to be modular for a broad set of use cases, and how you can use it for training models of your own.

Preface

  • 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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Tivadar Danka about modAL, a modular active learning framework for Python3

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is active learning?
    • How does it differ from other approaches to machine learning?
  • What is modAL and what was your motivation for starting the project?
  • For someone who is using modAL, what does a typical workflow look like to train their models?
  • How do you avoid oversampling and causing the human in the loop to become overwhelmed with labeling requirements?
  • What are the most challenging aspects of building and using modAL?
  • What do you have planned for the future of modAL?

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

Synthetic Data Generation Using Mimesis with Nikita Sobolev - Episode 155

Summary

Most applications require data to operate on in order to function, but sometimes that data is hard to come by, so why not just make it up? Mimesis is a library for randomly generating data of different types, such as names, addresses, and credit card numbers, so that you can use it for testing, anonymizing real data, or for placeholders. This week Nikita Sobolev discusses how the project got started, the challenges that it has posed, and how you can use it in your applications.

Preface

  • 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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • Your host as usual is Tobias Macey and today I’m interviewing Nikita Sobolev about Mimesis, a library for quickly generating synthetic data

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is mimesis and how does it compare to other projects such as faker and factory_boy?
    • What was the motivation for creating it?
  • One of the features that is advertised is the speed of Mimesis. What techniques are used to ensure that the data is generated quickly?
  • What are the built in mechanisms for generating data?
    • What options do users have for customizing the types of data that can get generated?
  • What are some of the most complicated providers to write and maintain?
  • What are some of the use cases outside of unit or integration tests where Mimesis could be beneficial?
    • How would you use Mimesis to anonymize data from a production environment to be used for testing?
  • What are the most challenging aspects of maintaining the Mimesis project?
  • What are some of the plans that you have for the future of Mimesis?

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

Salabim: Logistics Simulation with Ruud van der Ham - Episode 151

Summary

Determining the best way to manage the capacity and flow of goods through a system is a complicated issue and can be exceedingly expensive to get wrong. Rather than experimenting with the physical objects to determine the optimal algorithm for managing the logistics of everything from global shipping lanes to your local bank, it is better to do that analysis in a simulation. Ruud van der Ham has been working in this area for the majority of his professional life at the Dutch port of Rotterdam. Using his acquired domain knowledge he wrote Salabim as a library to assist others in writing detailed simulations of their own and make logistical analysis of real world systems accessible to anyone with a Python interpreter.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Ruud van der Ham about Salabim, a Python library for conducting discrete event simulations

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what Discrete Event Simulation is and how Salabim helps with that?
    • Can you explain how you chose the name?
  • What was your motivation for creating Salabim and how does it compare to other tools for discrete event simulation?
  • How does discrete event simulation compare with state machines?
  • How is Salabim implemented and how has the design evolved over the time that you have been working on it?
  • I understand that you have done a majority of Salabim was written on an iPad. Can you speak about why you have chosen that as your development environment and your experience working in that manner?
  • What are some examples of the types of models that you can model with Salabim?
    • What would an implementation of one of these models look like for someone using Salabim?
  • What options does a user have to verify the accuracy of a simulation created with Salabim?
  • One of the nice aspects of Salabim is the fact that it provides a visual output as a simulation runs. Can you describe the workflow for someone who wants to use Salabim for modeling and visualizing a system?
  • At what point does a system become too complex to encapsulate in a simulation and what techniques can you use to modularize it to make a simulation useful?
  • When is Salabim not the right tool to use and what would you suggest for people who find themselves in that situation?
  • What have been some of the most complicated or difficult aspects of building and maintaining Salabim?
  • What are some of the new features or improvements that you have planned for the future of Salabim?

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