CodeIsGo.com

Go started in September 2007 when Robert Griesemer, Ken Thompson, and I began discussing a new language to address the engineering challenges we and our colleagues at Google were facing in our daily work. By Rob Pike.

When authors first released Go to the public in November 2009, tehy didn’t know if the language would be widely adopted or if it might influence future languages. Looking back from 2020, Go has succeeded in both ways: it is widely used both inside and outside Google, and its approaches to network concurrency and software engineering have had a noticeable effect on other languages and their tools.

Go has turned out to have a much broader reach than we had ever expected. Its growth in the industry has been phenomenal, and it has powered many projects at Google.

In the past year, there was sixteen case studies from end users around the world talking about how they use Go to build fast, reliable, and efficient software at scale. And another three new case studies from teams inside Google has been published recently. Great read!

Steven Staley published this guide on AWS tracing in AWS Lambda context. A serverless API on AWS in order to run in production needs a distributed tracing. Author wanted to see if he could use AWS X Ray to get everything he wanted out of a tracing solution.

The article then goes in great detail over:

• Requirements
• Setup
• X Ray
• Sampling
• Internal Tracing with X Ray
• Local Development
• Segments
• Express with Lambda & X Ray

… and much more. The X Ray SDK doesn’t really play nice with serverless offline. One option that at least prevents these errors from causing your lambda to fail is set up specific environment variable AWS_XRAY_CONTEXT_MISSING. This tells X Ray to log the error instead of throwing it. However, the log output is rather verbose and annoying.

This is really detailed and excellent guide for production ready monitoring and tracing fo your serverless. You will get all the code needed and resources to further reading. Well done!

A team of scientists from Arizona State University and Shanghai Jiao Tong University (SJTU) has announced the creation of a new type of meta-DNA structures that will open up the fields of optoelectronics, including information storage and encryption as well as synthetic biology. By Jenny Green.

It is common knowledge that the predictable nature of Watson-Crick base-pairing and the structural features of DNA have allowed DNA to be used as a versatile building block to engineer sophisticated nanoscale structures and devices.

“A milestone in DNA technology was certainly the invention of DNA origami, where a long single-stranded DNA is folded into designated shapes with the help of hundreds of short DNA staple strands,” professor Yan explained. “However it has been challenging to assemble larger (micron to millimeter) sized DNA architectures, which up until recently has limited the use of DNA origami.” The new micron-size structures are on the order of the width of a human hair, which is 1,000 times larger than the original DNA nanostructures.

The group demonstrated that a six-helix bundle DNA origami nanostructure in the submicrometer scale (meta-DNA) could be used as a magnified analog of single-stranded DNA, and that two meta-DNAs containing complementary “meta-base pairs” could form double helices with programmed handedness and helical pitches.

This research was published Sept. 7 in Nature Chemistry. Indeed, the meta-DNA self-assembly concept may totally transform the microscopic world of structural DNA nanotechnology. Super exciting!

In recent years, lots of new programming paradigms have emerged – going from monolithic architectures towards microservices and now serverless functions. As a result, less code needs to be deployed, and updating an application becomes easier and faster as only a part has to be built and deployed. By Andreas Muttscheller.

If you want to stay independent of cloud providers, an open-source solution that can be deployed on your own infrastructure can be beneficial. Cut to OpenFaaS, an open source serverless framework that runs on Kubernetes or Docker Swarm.

In this blog post, we will focus on setting up a Raspberry Pi cluster, using Ansible for reproducible provisioning, k3s as a lightweight Kubernetes distribution, and OpenFaaS to run serverless functions.

The article then describes:

• Building the Raspberry Pi cluster (4x Raspberry Pi 3)
• Provisioning the cluster with Ansible
• Master node and network setup
• Bootstrap
• LCD display monitoring

All the code is provided with some solid explanation. Loads of links to further reading, including to printable rack for cluster. Nice one!

Janakiram MSV put together this tutorial about TensorFlow models. We will explore the idea of running TensorFlow models as microservices at the edge. Jetson Nano, a powerful edge computing device will run the K3s distribution from Rancher Labs. It can be a single node K3s cluster or join an existing K3s cluster just as an agent.

The Jetson platform from NVIDIA runs a flavor of Debian called L4T (Linux for Tegra) which is based on Ubuntu 18.04. The OS along with the CUDA-X drivers and SDKs is packaged into JetPack, a comprehensive software stack for the Jetson family of products such as Jetson Nano and Jetson Xavier.

Accessing the GPU from a K3s cluster through custom Docker runtime is a powerful mechanism to run AI at the edge in a cloud native environment. With TensorFlow running at the edge within Kubernetes, you can deploy deep learning models as microservices.

The tutorial is split into:

• Configure Docker runtime
• Install K3s on Jetson Nano

You will also get code examples and screen shots to guide you through tutorial. Well done!

Microsoft announced the first public release of Open Service Mesh (OSM) an open-source, lightweight, and extensible service mesh implementation, based on the Service Mesh Interface (SMI) specification. By Thorsten Hans.

A service mesh helps to manage service-to-service network communication in Kubernetes environments. Several features related to communication between individual application components are offloaded from the application code to the service mesh. Typically, service mesh implementations add those capabilities to an application by using Kubernetes’ sidecar-container pattern.

This article explains the core concepts of a service mesh, provides an Open Service Mesh introduction, and demonstrates some common service mesh use-cases.

It explains in great detail:

• What is a Service Mesh
• What is the Service Mesh Interface specification
• Should you consider using a Service Mesh
• Introducing Microsoft Open Service Mesh
• Traffic Access Control sor simple Applications
• Canary Deployments with Open Service Mesh
• The Open Service Mesh Dashboard

There is a lot to like about the simplicity and frictionless of Microsoft Open Service Mesh. Installing and onboarding applications is straight forward, and by relying on the SMI specification, fundamental tasks can be implemented quickly. You will also get example code in GutHub repository. Great article!

Changes in Spotlight Search on iOS and iPadOS 14 beta, a significant update to its Applebot support page, and an increase in crawling from AppleBot signify that Apple may be launching a search engine soon. By Jon Henshaw.

For several years, it’s been reported that Google pays billions of dollars to Apple to remain the default search engine on Safari for iOS, iPadOS, and macOS. The deal ensures that iPhone, iPad, and Mac users search with Google when they use Safari. That is unless they manually change the default search engine in Safari’s preferences.

The article then describes:

• Why Apple might be launching a search engine
• iOS and iPadOS 14 beta bypasses Google Search with Spotlight Search
• Apple recently updated its Applebot web crawler page
• Applebot has been busy crawling sites
• What Apple has to gain from launching a search engine
• What will this mean for SEO?

If Apple can capture enough market share, it will force SEOs to adapt and optimize for their search engine. In some ways, at least based on their Applebot documentation, tactics will remain the same. But SEOs can also expect to test and discover new opportunities that aren’t possible with Google or other modern search engines. Interesting!

Kubernetes is finding its way from the cloud to the edge via the data center. During the early days, Kubernetes was considered for hyperscale workloads running in the public cloud. It eventually became the consistent and unified infrastructure layer to run workloads in hybrid cloud and multicloud environments. By Janakiram MSV.

The rise of the Internet of Things and AI prompted the industry in moving the compute capabilities closer to the data which has become the edge computing layer.

The article is split into few sections:

• The building blocks of edge
• K3s – The Kubernetes distribution for the edge
• Portworx – The container-native storage layer
• Project Calico – Secure network for the edge

Project Calico brings fine-grained network policies to Kubernetes. While Kubernetes has extensive support for Role-Based Access Control (RBAC), the default networking stack available in the upstream Kubernetes distribution doesn’t support fine-grained network policies. Great read!

Graphtage is a command line utility and underlying library for semantically comparing and merging tree-like structures such as JSON, JSON5, XML, HTML, YAML, and TOML files. By Evan Sultanik.

Graphtage lets you see what’s different between two files quickly and easily, but it isn’t a standard line-oriented comparison tool like diff. Graphtage is semantically aware, which allows it to map differences across unordered structures like JSON dicts and XML element tags. You can even compare files that are in two different formats! And when paired with our PolyFile tool, you can semantically diff arbitrary file formats.

// original.json
{
    "foo": [1, 2, 3, 4],
    "bar": "testing"
}

// modified.json
{
    "foo": [2, 3, 4, 5],
    "zab": "testing",
    "woo": ["foobar"]
}

Source: https://blog.trailofbits.com/2020/08/28/graphtage/

Tree-like file formats are becoming increasingly common as a means for transmitting and storing data.Graphtage’s diffing algorithms operate on an intermediate representation rather than on the data structures of the original file format. This allows Graphtage to have generic comparison algorithms that can work on any input file type. Very interesting diff tool, please give it a go!

Scrum of Scrums is a technique used to scale Agile by dividing the groups into Agile teams of 5-10. Each daily scrum within a sub-team ends by designating one member as representative to participate in a daily meeting with ambassadors from other teams, called the Scrum of Scrums. This article provides some tips on how to succeed with Scrum of Scrums. By Sergio Fiorillo.

Everything is going swimmingly with Scrum and then one day, it is time to scale. Know here how to succeed when you start adding Scrum of Scrums meetings to the schedule. Understanding their purpose is the first step.

The article main focus is on:

• Make sure everyone agrees on the purpose of the Scrum of Scrums
• Avoid SoS mistakes by keeping an Agile mindset
• Make sure the right questions are being asked
• Choose your Scrum of Scrums representatives wisely

In an Agile environment, the Scrum of Scrums is one of the best scaling techniques that teams can adopt along with running scaled retrospectives. Good read!