Cloud Native Computing Foundation Announces CoreDNS Graduation

SAN FRANCISCO, Calif., January 24, 2018 – The Cloud Native Computing Foundation® (CNCF®), which sustains open source technologies like Kubernetes® and Prometheus™, today announced that CoreDNS is the first project of 2019 to graduate, following last year’s graduations of Kubernetes, Prometheus, and Envoy. To move from the maturity level of incubation to graduation, projects must demonstrate thriving adoption, diversity, a formal governance process, and a strong commitment to community sustainability and inclusivity.
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More-Efficient “Kernel Methods” Dramatically Reduce Training Time for Natural-Language-Understanding Systems

Machine learning systems often act on “features” extracted from input data. In a natural-language-understanding system, for instance, the features might include words’ parts of speech, as assessed by an automatic syntactic parser, or whether a sentence is in the active or passive voice. Some machine learning systems could be improved if, rather than learning from extracted features, they could learn directly from the structure of the data they’re processing.
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Server Name Indication (SNI) Support Now in Ambassador

We’ve discussed many interesting use cases for SNI support within the edge proxy/gateway with both open source and commercially supported users of Ambassador. In a nutshell (and with thanks to Wikipedia), SNI is an extension to the TLS protocol which allows a client to indicate which hostname it is attempting to connect to at the start of the TCP handshaking process. This allows the server to present multiple certificates on the same IP address and TCP port number, which in turn enables the serving of multiple secure websites or API services without requiring all those sites to use the same certificate.
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AI Blueprints: Implementing content-based recommendations using Python

In this article, we’ll have a look at how you can implement a content-based recommendation system using Python and the scikit-learn library. But before diving straight into this, it’s important to have some prerequisite knowledge of the different ways by which recommendation systems can recommend an item to users. Content-based: A content-based recommendation finds similar items to a given item by examining the item’s properties, such as its title or description, category, or dependencies on other items (for example, electronic toys require batteries).
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Dockter: A Docker image builder for researchers

Dependency hell is ubiquitous in the world of software for research, and this affects research transparency and reproducibility. Containerization is one solution to this problem, but it creates new challenges for researchers. Docker is gaining popularity in the research community—but using it efficiently requires solid Dockerfile writing skills. As a part of the Stencila project, which is a platform for creating, collaborating on, and sharing data-driven content, we are developing Dockter, an open source tool that makes it easier for researchers to create Docker images for their projects.
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Jaeger integration in Kiali

Kiali has the ability to show traces obtained from Istio. Jaeger collects traces for monitoring and troubleshooting microservices-based distributed systems, and both Istio and Kiali use the data that Jaeger provides. Originally this was done via a separate tab in the UI. But that turned out to be impractical. So the Kiali team has been working with the Jaeger team on the Jaeger integration. By improving the Jaeger UI components to make them embeddable, there is better integration between a Kiali selection and a Jaeger selection, making it easier to enrich Jaeger with Kiali information.
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Lyft’s Envoy dashboards

I’ve given quite a few talks about observability in the age of the service mesh (most recent slides, unfortunately this talk series has not been recorded yet). Visibility into the inherently unstable network is one of the most important thing that Envoy provides and I’m asked repeatedly for the source of the dashboards that we use at Lyft. In the interest of “shipping” and getting something out there that can help folks, we are releasing a snapshot of our internal Envoy dashboards.
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Optimising Prometheus 2.6.0 Memory Usage with pprof

There have been some reportsthat compaction was causing larger memory spikes than was desirable. I dug into this and improved it for Prometheus 2.6.0, so let’s see how. Firstly I wrote a test setup that created some samples for 100k time series, in a way that would require compaction. It would be nice to get the active heap usage at its peak, however we don’t know where in the code this peak is to take a profile then.
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Help! Something is wrong with my Jaeger installation!

Jaeger is composed of different components, each potentially running in its own host. It might be the case that one of these moving parts isn’t working properly, causing spans to not be processed and stored. When something goes wrong, make sure to check the items listed here. Before everything else, make sure to confirm what is the sampling strategy. By default, Jaeger uses a probabilistic sampling strategy, with a 1/1000th chance that the span will be reported.
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Automating Datacenter Operations at Dropbox

Switch provisioning at Dropbox is handled by a Pirlo component called the TOR Starter. The TOR Starter is responsible for validating and configuring switches in our datacenter server racks, PoP server racks, and at the different layers of our datacenter fabric that connect racks in the same facility together. Writing the TOR Starter on top of the ClusterOps queue provides us with a basic manager-worker queuing service. We also have the ability to customize the queue to fit our needs in switch provisioning.
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