Python Bytes
Python Bytes
Sep 5, 2020
#197 Structured concurrency in Python
36 min

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Michael #1: Structured concurrency in Python with AnyIO

  • AnyIO is a Python library providing structured concurrency primitives on top of asyncio.
  • Structured concurrency is a programming paradigm aimed at improving the clarity, quality, and development time of a computer program by using a structured approach to concurrent programming. The core concept is the encapsulation of concurrent threads of execution (here encompassing kernel and userland threads and processes) by way of control flow constructs that have clear entry and exit points and that ensure all spawned threads have completed before exit. — Wikipedia
  • The best overview is Notes on structured concurrency by Nathaniel Smith (or his video if you prefer).
  • Python has three well-known concurrency libraries built around the async/await syntax: asyncio, Curio, and Trio. (WHERE IS unsync?!?! 🙂 )
  • Since it's the default, the overwhelming majority of async applications and libraries are written with asyncio.
  • The second and third are attempts to improve on asyncio, by David Beazley and Nathaniel Smith respectively
  • The AnyIO library by Alex Grönholm describes itself as follows: > an asynchronous compatibility API that allows applications and libraries written against it to run unmodified on asyncio, curio and trio.

Example:

    import anyio

    async def task(n):
        await anyio.sleep(n)

    async def main():
        try:
            async with anyio.create_task_group() as tg:
                await tg.spawn(task, 1)
                await tg.spawn(task, 2)
        finally:
            # e.g. release locks
            print('cleanup')

    anyio.run(main)

Brian #2: The Consortium for Python Data API Standards

  • One unintended consequence of the advances in multiple frameworks for data science, machine learning, deep learning and numerical computing is fragmentation and differences in common function signatures.
  • The Consortium for Python Data API Standards aims to tackle this fragmentation by developing API standards for arrays (a.k.a. tensors) and dataframes.
  • They intend to work with library maintainers and the community and have a review process.
  • One example of the problem, “mean”. Five different interfaces over 8 frameworks:
    numpy:         mean(a, axis=None, dtype=None, out=None, keepdims=[HTML_REMOVED])
    dask.array:    mean(a, axis=None, dtype=None, out=None, keepdims=[HTML_REMOVED])
    cupy:          mean(a, axis=None, dtype=None, out=None, keepdims=False)
    jax.numpy:     mean(a, axis=None, dtype=None, out=None, keepdims=False)
    mxnet.np:      mean(a, axis=None, dtype=None, out=None, keepdims=False)
    sparse:        s.mean(axis=None, keepdims=False, dtype=None, out=None)
    torch:         mean(input, dim, keepdim=False, out=None)
    tensorflow:    reduce_mean(input_tensor, axis=None, keepdims=None, name=None,   
                               reduction_indices=None, keep_dims=None)
  • They are going to start with array API
  • Then dataframes
  • Also, it’s happening fast, hoping to make traction in next few months.

Michael #3: Ask for Forgiveness or Look Before You Leap?

  • via PyCoders
  • Think C++ style vs Python style of error handling
  • Or any exception-first/only language vs. some hybrid thing
  • If you “look before you leap”, you first check if everything is set correctly, then you perform an action.
  • Example:
    from pathlib import Path
    if Path("/path/to/file").exists():
        ...
  • With “ask for forgiveness,” you don’t check anything. You perform whatever action you want, but you wrap it in a try/catch block.
    try:
        with open("path/to/file.txt", "r") as input_file:
            return input_file.read()
    except IOError:
        # Handle the error or just ignore it
  • Their example, “Look before you leap” is around 30% slower (155/118≈1.314). Testing for subclass basically with no errors
  • But if there are errors: The tables have turned. “Ask for forgiveness” is now over four times as slow as “Look before you leap” (562/135≈4.163). That’s because this time, our code throws an exception. And handling exceptions is expensive.
  • If you expect your code to fail often, then “Look before you leap” might be much faster.
  • Michael’s counter example: gist.github.com/mikeckennedy/00828db1d49d2cd2dac8fa0295e54c23

Brian #4: myrepos

  • “You have a lot of version control repositories. Sometimes you want to update them all at once. Or push out all your local changes. You use special command lines in some repositories to implement specific workflows. Myrepos provides a mr command, which is a tool to manage all your version control repositories.”
  • Run mr register for all repos under a shared directory.
  • Then be able to do common operations on a subtree of repos, like mr status, mr update, mr diff, or really anything.
  • See also: Maintaining Multiple Python Projects With myrepos - Adam Johnson

Michael #5: A deep dive into the official Docker image for Python

  • by Itamar Turner-Trauring, via PyCoders
  • Wait, there’s an official Docker image for Python
  • The base image is Debian GNU/Linux 10, the current stable release of the Debian distribution, also known as Buster because Debian names all their releases after characters from Toy Story
  • Next, environment variables are added: ENV PATH /usr/local/bin:$PATH
  • Next, the locale is set: ENV LANG C.UTF-8
  • There’s also an environment variable that tells you the current Python version: ENV PYTHON_VERSION 3.8.5
  • In order to run, Python needs some additional packages (the dreaded certificates, etc)
  • Next, a compiler toolchain is installed, Python source code is downloaded, Python is compiled, and then the unneeded Debian packages are uninstalled. Interestingly, The packages—gcc and so on—needed to compile Python are removed once they are no longer needed.
  • Next, /usr/local/bin/python3 gets an alias /usr/local/bin/python, so you can call it either way
  • the Dockerfile makes sure to include that newer pip
  • Finally, the Dockerfile specifices the entrypoint: CMD ["python3"] Means docker run launches into the REPL:
    $ docker run -it python:3.8-slim-buster
    Python 3.8.5 (default, Aug  4 2020, 16:24:08)
    [GCC 8.3.0] on linux
    Type "help", "copyright", "credits" or "license" for more information.
    >>> 

Brian #6: “Only in a Pandemic” section nannernest: Optimal Peanut Butter and Banana Sandwiches

  • Ethan Rosenthal
  • Computer vision, deep learning, machine learning, and Python come together to make sandwiches.
  • Just a really fun read about problems called “nesting” or “packing” and how to apply it to banana slices and bread.

Extras:

Brian:

Michael:

Joke

via Eduardo Orochena

Kubernetes Podcast from Google
Kubernetes Podcast from Google
Adam Glick and Craig Box
KubeCon NA 2020, with Stephen Augustus
Join us for all the news from KubeCon NA 2020, and a conversation with conference co-chair Stephen Augustus. Stephen is a Senior Open Source Engineer on the VMware Tanzu team, a chair of Kubernetes’ SIG Release, and a leader in many other parts of the project, past and present. Do you have something cool to share? Some questions? Let us know: * web: kubernetespodcast.com * mail: kubernetespodcast@google.com * twitter: @kubernetespod Chatter of the week * The kākāpō wins Bird of the Year * We’re off for 2 weeks. See you on December 8! News of the week * Cisco acquires Banzai Cloud * CNCF announces Cloud Native Survey 2020 results * Red Hat: New edge features, industrial AI/ML blueprint and AWS launch * CNCF End User Tech Radar for storage * New End User benefits * Envoy Mobile joins the CNCF * New sandbox projects * cert-manager * cdk8s * Kyverno * OpenKruise * Pravega * SchemaHero * Tinkerbell * k8ssandra from Datastax * Episode 98 with Sam Ramji * k0s from Mirantis * Solo.io announces Gloo Mesh Enterprise and rebrands products * Episode 55, with Idit Levine * Pinniped * Shipa launches Ketch * Kinvolk launches Headlamp * The SPIFFE book “Solving The Bottom Turtle” * Episode 45, with Andrew Jessup * Anthos Developer Sandbox * GKE ingress features * Ambassador Labs takes in $18m and launches v1.9 * Tanzu SQL: Postgres on Kubernetes * Lightning round: * Accurics extends Terrascan * AWS adds containers to Lightsail * Arrikto takes $10m in funding * Brobridge releases Gravity * CircleCI runner is GA * Cloud66 for agencies and multiple database support * Cloudflare Origin CA cert-manager plugin * Cloudical Vanillastack * Cloudify version 5.1 * Codefresh launches GitOps 2.0 features * Commvault backup-as-a-service * Diamanti Spektra 3.1 and customer portal * Dynatrace PurePath 4 * Elastisys Compliant Kubernetes * The Fairwinds Kubernetes Maturity Model * Garden takes “seed” funding * Gremlin adds soundproofing * Humio Operator * Instana adds observability tools on Kubernetes * Intuit runs TurboTax on Kubernetes * Kioxia announces a new storage offering * Kubecost adds features for monitoring outside a cluster * KubeMQ adds automatic network creation * Kubermatic updates KubeOne to v1.1 * Kubernative SINA * Kublr 1.19 * Lablup announced Backend.ai 20.09 RC * Magalix launches KubeAdvisor 2.0 * Mayadata launches Kubera Propel and Kubera Chaos * Mirantis adds extensions to Lens * Puppet Labs adds Relay to Puppet Enterprise * Reblaze announces Curiefense to add WAF to Envoy * Replicates wants to help you Troubleshoot * Styra adds new editions to DAS * Sysdig introduces Kubernetes-native network security (ZTNSK) and partners with IBM Cloud * TrilioVault for Kubernetes v2.0 * Zerto for Kubernetes * Google Open Source Live Kubernetes Links from the interview * KubeCon NA 2020 * Episode 117, with Constance Caramanolis * CNCF Twitch * SIG Friday: ping Stephen for the current link * Slack * CNCF Slack * Kubernetes Slack * Hallway Track * Kubernetes Podcast chat * CoreOS * CoreOS Tectonic * CoreOS acquired by Red Hat * Tectonic on Azure * SIG Azure * SIG Release * SIG PM (retired) * Kubernetes Enhancement Process * Receipts process KEP * Sidecar containers - KEP closed! * Production readiness review * Episode 10, with Josh Berkus and Tim Pepper * Release managers * Black Lives Matter announcement banner * Better announcements * Kubernetes Naming working group * Inclusive Naming project * Dan Kohn memorial * Stephen Augustus on Twitter and on the web
53 min
Streaming Audio: A Confluent podcast about Apache Kafka
Streaming Audio: A Confluent podcast about Apache Kafka
Confluent, original creators of Apache Kafka®
Multi-Tenancy in Apache Kafka ft. Anna Pozvner
Multi-tenancy has been quite the topic within the Apache Kafka® community. Anna Povzner, an engineer on the Confluent team, spends most of her time working on multi-tenancy in Kafka in Confluent Cloud. Anna kicks off the conversation with Tim Berglund (Senior Director of Developer Experience, Confluent) by explaining what multi-tenancy is, why it is worthy to be desired, and advantages over single-tenant architecture. By putting more applications and use cases on the same Kafka cluster instead of having a separate Kafka cluster for each individual application and use case, multi-tenancy helps minimize the costs of physical machines and also maintenance. She then switches gears to discuss quotas in Kafka. Quotas are essentially limits—you must set quotas for every tenant (or set up defaults) in Kafka. Anna says it’s always best to start with bandwidth quotas because they’re better understood. Stick around until the end as Anna gives us a sneak peek on what’s ahead for multi-tenant Kafka, including KIP-612, the addition of the connection rate quota, which will help protect brokers. EPISODE LINKS * Sharing is Caring: Toward Creating Self-Tuning Multi-Tenant Kafka (Anna Povzner, Confluent) Join the Confluent Community Slack * Learn more with Kafka tutorials, resources, and guides at Confluent Developer * Live demo: Kafka streaming in 10 minutes on Confluent Cloud * Use *60PDCAST* to get an additional $60 of free Confluent Cloud usage (details)
44 min
Software Defined Talk
Software Defined Talk
Software Defined Talk LLC
Episode 272: This time we’re doing it in green
This week we discuss IBM buying Instana, highlights from Kubecon and the rise of Substack. Plus, Coté updates us on his quest to live the iPad lifestyle. 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1 hr 5 min
Azure DevOps Podcast
Azure DevOps Podcast
Jeffrey Palermo
Scott Nichols on the State of Azure - Part 2 - Episode 116
This episode is part 2 of the interview with Scott Nichols! Be sure to tune in to part 1 first before joining in on this episode’s conversation. Scott Nichols is a Sr. Cloud Solutions Architect, Scott works for the commercial enterprise division serving the west region. He is also the leader of the .NET and the Azure user groups in Boise, Idaho. He started his career in the IT profession in 1993 as a mainframe and web developer. Since then, he has served as a Lead Software Engineer/Solution Architect, a Software Development Engineering Manager, a Sr. Cloud Software Solution Architect, a Sr. Enterprise Solution Architect, and of course, most recently, a Sr. Cloud Solutions Architect at Microsoft since 2019. In this second part, the interview transitions from discussing the state of Azure and the Cloud industry into talking about how customers are modernizing their existing applications and infrastructure for Azure. Scott shares about infrastructure as code tools he sees as having the most traction right now, his recommendations for those looking to get their application into Azure, and what he sees as being the most successful pathways for his customers utilizing Azure infrastructure. Topics of Discussion: [:38] Be sure to visit AzureDevOps.Show for past episodes and show notes. [1:01] About The Azure DevOps Podcast and Jeffrey’s offer to speak at virtual user groups. [1:11] Clear Measure is hiring! Be sure to check out the link in the show notes. [1:33] Jeffrey welcomes Scott back to the podcast for part 2 and shares about this episode’s conversation. [2:06] The infrastructure as code tools Scott sees as having the most traction. [4:35] Does Scott have any customers that have used Azure Bicep yet? [4:56] How Scott thinks about Azure infrastructure and what he sees as being most successful with his customers. [7:42] For certain tweaks in the infrastructure, where does an ARM template work? [9:45] What is the mechanism in Azure that would know that the Powershell script has not been run yet or has already been run so that it doesn’t run it again? [11:07] A word from Azure DevOps Podcast’s sponsor: Clear Measure. [11:38] For people looking to get their application into Azure, what would Scott recommend their next steps be? [20:37] Jeffrey thanks Scott for joining the podcast and Scott offers some parting words of advice for developers. Mentioned in this Episode: Azure DevOps Clear Measure (Sponsor) .NET DevOps for Azure: A Developer's Guide to DevOps Architecture the Right Way, by Jeffrey Palermo — Available on Amazon! bit.ly/dotnetdevopsebook — Click here to download the .NET DevOps for Azure ebook! Jeffrey Palermo’s Youtube Jeffrey Palermo’s Twitter — Follow to stay informed about future events! The Azure DevOps Podcast’s Twitter: @AzureDevOpsShow Scott Nichols’ LinkedIn Scott Nichols’ Twitter @TheScottNichols Azure Architecture Center - Microsoft Amazon Web Services (AWS) Azure Architectures - Microsoft Docs Google Cloud (GCP) Microsoft Cloud Adoption Framework (CAF) Azure Bicep on GitHub .NET Conference 2020 Python Terraform Azure Resource Manager (ARM) CICD PowerShell HashiCorp Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.
23 min
The Cloudcast
The Cloudcast
Cloudcast Media
eBPF & Cilium Cloud-native Networking
Dan Wendlandt (@danwendlandt, CEO/Co-Founder @Isovalent talks about the evolution of cloud networking, eBPF and Cilium for programmable infrastructure, and blurring the lines between networking, security and service-mesh.  *SHOW: *476 *SHOW SPONSOR LINKS:* * CloudAcademy -Build hands-on technical skills. Get measurable results.  * Get 50% of the monthly price of CloudAcademy by using code CLOUDCAST * Datadog Security Monitoring Homepage - Modern Monitoring and Analytics * Try Datadog yourself by starting a free, 14-day trial today. Listeners of this podcast will also receive a free Datadog T-shirt. * Learn more about Fauna: https://www.fauna.com/serverless * Try FaunaDB for Free: https://dashboard.fauna.com/accounts/register *CLOUD NEWS OF THE WEEK *- http://bit.ly/cloudcast-cnotw *PodCTL Podcast is Back (Enterprise Kubernetes) *- http://podctl.com *SHOW NOTES:* * Isovalent (homepage) * Cilium (homepage) * What is eBPF?  *Topic 1 *- Welcome to the show. We’ve been following your work for a while (Nicira, OpenShift networking, etc), but tell our audience a little bit about your background. *Topic 2* - A few years ago I wrote an article that said, “if you’re in networking, the #1 skill you should learn is Linux”. Why has there been so much shift from “traditional networking” to so many new capabilities being implemented in software, and specifically Linux? *Topic 3 *- Help us understand these two new concepts - eBPF and Cilium. It’s new packet filtering, it’s container networking, it’s multi-cluster networking, it can help with observability - lots going on here.  *Topic 4 *- What are some of the gaps in today’s networking/filtering/observability stacks that can improve with eBPF/Cilium?*   * *Topic 5 *- We’ve seen quite a few companies evolve from expertise in an open-source project to commercial offerings. What lessons have you learned from other companies that shape how Isovalent will both go-to-market and also engage with ecosystem partners?* * *Topic 6 *- What are some of the common use-cases or applications you see that highlight the value of the Isovalent stack?  *FEEDBACK?* * Email: show at thecloudcast dot net * Twitter: @thecloudcastnet
36 min
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