The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

December 10, 2018Gnocchi: A Scalable Time Series Database For Your Metrics with Julien Danjou
Summary
Do you know what your servers are doing? If you have a metrics system in place then the answer should be “yes”. One critical aspect of that platform is the timeseries database that allows you to store, aggregate, analyze, and query the various signals generated by your software and hardware. As the size and complexity of your systems scale, so does the volume of data that you need to manage which can put a strain on your metrics stack. Julien Danjou built Gnocchi during his time on the OpenStack project to provide a time oriented data store that would scale horizontally and still provide fast queries. In this episode he explains how the project got started, how it works, how it compares to the other options on the market, and how you can start using it today to get better visibility into your operations.
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 Julien Danjou about Gnocchi, an open source time series database built to handle large volumes of system metrics
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what Gnocchi is and how the project got started?
What was the motivation for moving Gnocchi out of the Openstack organization and into its own top level project?

The space of time series databases and metrics as a service platforms are both fairly crowded. What are the unique features of Gnocchi that would lead someone to deploy it in place of other options?
What are some of the tools and platforms that are popular today which hadn’t yet gained visibility when you first began working on Gnocchi?

How is Gnocchi architected?
How has the design changed since you first started working on it?
What was the motivation for implementing it in Python and would you make the same choice today?

One of the interesting features of Gnocchi is its support of resource history. Can you describe how that operates and the types of use cases that it enables?
Does that factor into the multi-tenant architecture?

What are some of the drawbacks of pre-aggregating metrics as they are being written into the storage layer (e.g. loss of fidelity)?
Is it possible to maintain the raw measures after they are processed into aggregates?

One of the challenging aspects of building a scalable metrics platform is support for high-cardinality data. What sort of labelling and tagging of metrics and measures is available in Gnocchi?

For someone who wants to implement Gnocchi for their system metrics, what is involved in deploying, maintaining, and upgrading it?
What are the available integration points for extending and customizing Gnocchi?

Once metrics have been stored, aggregated, and indexed, what are the options for querying and analyzing the collected data?

When is Gnocchi the wrong choice?

What do you have planned for the future of Gnocchi?

Keep In Touch
jd on GitHub
Website
@juldanjou on Twitter
Picks
Tobias
Marketplace Podcast

Julien
Mergify

Links
Gnocchi
RedHat
OpenStack
Object Oriented Programming
O’Reilly
Debian
Ceilometer
Prometheus
Time Series
MySQL
Gerrit
Zuul
Podcast Episode

GitHub

GitLab

Graphite
Podcast Episode

DataDog

RabbitMQ

InfluxDB

Ceph
Podcast Episode

S3

OpenStack Swift

Cassandra

Honeycomb Observability Service
Podcast Episode

AMQP

Redis

DSL (Domain Specific Language)

Golang

RBAC (Role-Based Access Control)

CollectD

StatsD

Gnocchi Client

Telegraf

Grafana

TimescaleDB
Podcast Episode

OpenStack Heat

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
40 min
December 03, 2018 Keeping Up With The Python Community For Fun And Profit with Dan Bader - Episode 188
Keeping up with the work being done in the Python community can be a full time job, which is why Dan Bader has made it his! In this episode he discusses how he went from working as a software engineer, to offering training, to now managing both the Real Python and PyCoders properties. He also explains his strategies for tracking and curating the content that he produces and discovers, how he thinks about building products, and what he has learned in the process of running his businesses.
58 min
December 03, 2018Keeping Up With The Python Community For Fun And Profit with Dan Bader
Summary
Keeping up with the work being done in the Python community can be a full time job, which is why Dan Bader has made it his! In this episode he discusses how he went from working as a software engineer, to offering training, to now managing both the Real Python and PyCoders properties. He also explains his strategies for tracking and curating the content that he produces and discovers, how he thinks about building products, and what he has learned in the process of running his businesses.
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 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 Dan Bader about finding, filtering, and creating resources for Python developers at Real Python, PyCoders, and his own trainings
Interview
Introductions
How did you get introduced to Python?
Let’s start by discussing your primary job these days and how you got to where you are.
In the past year you have also taken over management of the Real Python site. How did that come about and what are your responsibilities?
You just recently took over management of the PyCoders newsletter and website. Can you describe the events that led to that outcome and the responsibilities that came along with it?

What are the synergies that exist between your various roles and projects?
What are the areas of conflict? (e.g. time constraints, conflicts of interest, etc.)

Between PyCoders, Real Python, your training materials, your Python tips newsletter, and your coaching you have a lot of incentive to keep up to date with everything happening in the Python ecosystem. What are your strategies for content discovery?
With the diversity in use cases, geography, and contributors to the landscape of Python how do you work to counteract any bias or blindspots in your work?

There is a constant stream of information about any number of topics and subtopics that involve the Python language and community. What is your process for filtering and curating the resources that are ultimately included in the various media properties that you oversee?

In my experience with the podcast one of the most difficult aspects of maintaining relevance as a content creator is obtaining feedback from your audience. What do you do to foster engagement and facilitate conversations around the work that you do?

You have also built a few different product offerings. Can you discuss the process involved in identifying the relevant opportunities and the creation and marketing of them?

Creating, collecting, and curating content takes a significant investment of time and energy. What are your avenues for ensuring the sustainability of your various projects?

What are your plans for the future growth and development of your media empire?

As someone who is so deeply involved in the conversations flowing through and around Python, what do you see as being the greatest threats and opportunities for the language and its community?

Keep In Touch
@dbader_org on Twitter
Website
dbader on GitHub
Picks
Tobias
Data Engineering Podcast

Dan
Black code formatter
Łukasz Langa

Links
Dan Bader
Nerd Lettering
Real Python
PyCoders
Computer Science
Vancouver, BC
Django
Raymond Hettinger
Data Science
Flask
Pythonista Cafe
Python Tricks
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
58 min
November 26, 2018 Using Calibre To Keep Your Digital Library In Order with Kovid Goyal - Episode 187
Digital books are convenient and useful ways to have easy access to large volumes of information. Unfortunately, keeping track of them all can be difficult as you gain more books from different sources. Keeping your reading device synchronized with the material that you want to read is also challenging. In this episode Kovid Goyal explains how he created the Calibre digital library manager to solve these problems for himself, how it grew to be the most popular application for organizing ebooks, and how it works under the covers. Calibre is an incredibly useful piece of software with a lot of hidden complexity and a great story behind it.
44 min
November 26, 2018Using Calibre To Keep Your Digital Library In Order with Kovid Goyal
Summary
Digital books are convenient and useful ways to have easy access to large volumes of information. Unfortunately, keeping track of them all can be difficult as you gain more books from different sources. Keeping your reading device synchronized with the material that you want to read is also challenging. In this episode Kovid Goyal explains how he created the Calibre digital library manager to solve these problems for himself, how it grew to be the most popular application for organizing ebooks, and how it works under the covers. Calibre is an incredibly useful piece of software with a lot of hidden complexity and a great story behind 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 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 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 Kovid Goyal about Calibre, the powerful and free ebook management tool
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what Calibre is and how the project got started?
How are you able to keep up to date with device support in Calibre, given the continual release of new devices and platforms that a user can read ebooks on?
What are the main features of Calibre?
What are some of the most interesting and most popular plugins that have been creatd for Calibre?

Can you describe the software architecture for the project and how it has evolved since you first started working on it?

You have been maintaining and improving Calibre for a long time now. What is your motivation to keep working on it?
How has the focus of the project and the primary use cases changed over the years that you have been working on it?

In addition to its longevity, Calibre has also become a de-facto standard for ebook management. What is your opinion as to why it has gained and kept its popularity?
What are some of the competing options and how does Calibre differentiate from them?

In addition to the myriad devices and platforms, there is a significant amount of complexity involved in supporting the different ebook formats. What have been the most challenging or complex aspects of managing and converting between the formats?

One of the challenges around maintaining a private library of electronic resources is the prevalence of DRM restricted content available through major publishers and retailers. What are your thoughts on the current state of digital book marketplaces?

What was your motivation for implementing Calibre in Python?
If you were to start the project over today would you make the same choice?
Are there any aspects of the project that you would implement differently if you were starting over?

What are your plans for the future of Calibre?

Keep In Touch
kovidgoyal on GitHub
Website
Patreon
Picks
Tobias
American Gods by Neil Gaiman

Kovid
Into Thin Air by John Krakauer
About how an expedition to climb Everest went wrong. Wonderful account of the difficulties of high altitude mountaineering and the determination it needs.
The Steerswoman’s Road by Rosemary Kirstein
About the spirit of scientific enquiry in a fallen civilization on an alien planet with partial terraforming that is slowly failing.

Links
Calibre
KDE
Caltech
Sony PRS500
Linux
Kindle
Kobo
ePUB
Calibre Recipes
Rapydscrypt NG
Goodreads
Qt
PyQt
build-calibre
Kitty
DRM (Digital Rights Management)
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
44 min
November 19, 2018 Entity Extraction, Document Processing, And Knowledge Graphs For Investigative Journalists with Friedrich Lindenberg - Episode 186
Investigative reporters have a challenging task of identifying complex networks of people, places, and events gleaned from a mixed collection of sources. Turning those various documents, electronic records, and research into a searchable and actionable collection of facts is an interesting and difficult technical challenge. Friedrich Lindenberg created the Aleph project to address this issue and in this episode he explains how it works, why he built it, and how it is being used. He also discusses his hopes for the future of the project and other ways that the system could be used.
40 min
November 19, 2018Entity Extraction, Document Processing, And Knowledge Graphs For Investigative Journalists with Friedrich Lindenberg
Summary
Investigative reporters have a challenging task of identifying complex networks of people, places, and events gleaned from a mixed collection of sources. Turning those various documents, electronic records, and research into a searchable and actionable collection of facts is an interesting and difficult technical challenge. Friedrich Lindenberg created the Aleph project to address this issue and in this episode he explains how it works, why he built it, and how it is being used. He also discusses his hopes for the future of the project and other ways that the system could be used.
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 podcastinit.com/linode today 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
Registration for PyCon US, the largest annual gathering across the community, is open now. Don’t forget to get your ticket and I’ll see you there!
Your host as usual is Tobias Macey and today I’m interviewing Friedrich Lindenberg about Aleph, a tool to perform entity extraction across documents and structured data
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what Aleph is and how the project got started?
What is investigative journalism?
How does Aleph fit into their workflow?
What are some other tools that would be used alongside Aleph?
What are some ways that Aleph could be useful outside of investigative journalism?

How is Aleph architected and how has it evolved since you first started working on it?

What are the major components of Aleph?
What are the types of documents and data formats that Aleph supports?

Can you describe the steps involved in entity extraction?
What are the most challenging aspects of identifying and resolving entities in the documents stored in Aleph?

Can you describe the flow of data through the system from a document being uploaded through to it being displayed as part of a search query?

What is involved in deploying and managing an installation of Aleph?

What have been some of the most interesting or unexpected aspects of building Aleph?

Are there any particularly noteworthy uses of Aleph that you are aware of?

What are your plans for the future of Aleph?

Keep In Touch
Website
@pudo on Twitter
pudo on GitHub
Picks
Tobias
Mechanical Soup

Friedrich
phonenumbers – because it’s useful
pyicu – super nerdy but amazing
sqlalchemy – my all-time favorite python package

Links
Aleph
Organized Crime and Corruption Reporting Project
OCR (Optical Character Recognition)
Jorge Luis Borges
Buenos Aires
Investigative Journalism
Azerbaijan
Signal
Open Corporates
Open Refine
Money Laundering
E-Discovery
CSV
SQL
Entity Extraction (Named Entity Recognition)
Apache Tika
Polyglot
SpaCy
Podcast.__init__ Episode

LibreOffice

Tesseract

followthemoney

Elasticsearch

Knowledge Graph

Neo4J

Gephi

Edward Snowden

Document Cloud

Overview Project

Veracrypt

Qubes OS

I2 Analyst Notebook

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
40 min
October 29, 2018 Bringing Python To The Spanish Language Community with Maricela Sanchez - Episode 185
The Python Community is large and growing, however a majority of articles, books, and presentations are still in English. To increase the accessibility for Spanish language speakers, Maricela Sanchez helped to create the Charlas track at PyCon US, and is an organizer for Python Day Mexico. In this episode she shares her motivations for getting involved in community building, her experiences working on Python Day Mexico and PyCon Charlas, and the lessons that she has learned in the process.
20 min
October 29, 2018Bringing Python To The Spanish Language Community with Maricela Sanchez
Summary
The Python Community is large and growing, however a majority of articles, books, and presentations are still in English. To increase the accessibility for Spanish language speakers, Maricela Sanchez helped to create the Charlas track at PyCon US, and is an organizer for Python Day Mexico. In this episode she shares her motivations for getting involved in community building, her experiences working on Python Day Mexico and PyCon Charlas, and the lessons that she has learned in the process.
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 Maricela Sanchez Miranda about her work in organizing PyCon Charlas, the spanish language track at PyCon US, as well as Python Day Mexico
Interview
Introductions
How did you get introduced to Python?
Can you briefly describe PyCon Charlas and Python Day Mexico?
What has been your motivation for getting involved with organizing these community events?

What do you find to be the unique characteristics of the Python community in Mexico?

What kind of feedback have you gotton from the Charlas track at PyCon?

What are your goals for fostering these Spanish language events?

What are some of the lessons that you have learned from PyCon Charlas that were useful in organizing Python Day Mexico?

What have been the most challenging or complicated aspects of organizing Python Day Mexico?
How many attendees do you anticipate? How has that affected your planning and preparation?

Are there any aspects of the geography, infrastructure, or culture of Mexico that you have found to be either beneficial or challenging for organizing a conference?

Do you anticipate PyCon Charlas and Python Day Mexico becoming annual events?

What is your advice for anyone who is interested in organizing a conference in their own region or language?

Keep In Touch
mayela on GitHub
@mayela0x14 on Twitter
Picks
Tobias
CardLine Dinosaurs

Maricela

Links
Python Day Mexico
PyCon Charlas
PyCon Hatchery
PyCon Latin America
Mexico City
Guadalajara
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
20 min
October 22, 2018 Of Checklists, Ethics, and Data with Emily Miller and Peter Bull - Episode 184
As data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps to reduce the overall effort of preventing negative outcomes from the use of the final product. Emily Miller and Peter Bull of Driven Data have created Deon to improve the communication and conversation around ethics among and between data teams. It is a Python project that generates a checklist of common concerns for data oriented projects at the various stages of the lifecycle where they should be considered. In this episode they discuss their motivation for creating the project, the challenges and benefits of maintaining such a checklist, and how you can start using it today.
46 min