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AWS App Mesh is now generally available and supported for production use. App Mesh is a service mesh that provides application level networking to make it easy for your services to communicate with each other across multiple types of compute infrastructure. App Mesh standardizes how your services communicate, giving you end-to-end visibility and ensuring high-availability for your applications. Modern applications are typically composed of multiple services. Each service may be built using multiple types of compute infrastructure such as Amazon EC2 and AWS Fargate.

To tackle this monolith, we initially began exploring how the codebase was built. It had a high level of complexity with too many features baked into the all-in-one code, as well as thousands of unit tests. Without consistent APIs, many nonstandard integrations, or one-offs, had been deployed. Tight coupling of integrations existed at every level, including on modules and datastores, without boundaries. For functional test cases, quality took a big hit.

Prometheus 2.x has a very different ingestion system to 1.x, with many performance improvements. This time I’m also going to take into account the cost of cardinality in the head block. To start with I took a profile of a Prometheus 2.9.2 ingesting from a single target with 100k unique time series: This gives a good starting point to find the relevant bits of code, but as my Prometheus has just started doesn’t have quite everything.

Last week we made a fairly quiet (too quiet, in fact) announcement of our plan to slowly and carefully deprecate the path-based access model that is used to specify the address of an object in an S3 bucket. I spent some time talking to the S3 team in order to get a better understanding of the plan. We launched S3 in early 2006. Jeff Bezos’ original spec for S3 was very succinct – he wanted malloc (a key memory allocation function for C programs) for the Internet.

The Rook project, as well as its thriving community, has continued to grow and evolve since the initial public release in November 2016. As the code base has matured through a series of minor releases, starting with the humble beginnings of v0.1 and reaching v0.9 late last year, we are incredibly pleased to finally announce the first major release of Rook, version 1.0! We will dive into some of the exciting new features included in this release, but let’s start with a community stats update first.

The survey was announced on our Slack channel and on Twitter. Participation was anonymous and did not require to leave behind contact information. Most questions had a set of predefined answers plus a field to add additional answers. All questions were optional, some users did not answer all questions. Source: cilium.io

It’s now time to show case what it takes to run an existing OpenWhisk action on Knative. Matt Rutkowski and I are very excited to share our process of building and serving OpenWhisk actions on Knative here. We started prototyping OpenWhisk NodeJS Runtime with a hello world action. Later, extended the runtime to handle more complex use cases such as: It’s a six step process, first three steps are one time deployment per Knative installation.

Automation is becoming a powerful tool in every industry. With the pace of development at breakneck speed, test automation is a big asset in deploying applications quickly. The volume and complexity of testing environments mean that machines are well-suited for the job, and a modern QA strategy is all about leveraging that automation effectively. QASymphony recently surveyed testers and QA leaders at mid-size and large enterprises and found that a significant number of respondents expect to be making a big leap towards test automation in the next year: Almost half expect to be automating more than 50 percent in that time.

As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon.

We are excited to announce the Cilium 1.5 release. Cilium 1.5 is the first release where we primarily focused on scalability with respect to number of nodes, pods and services. Our goal was to scale to 5k nodes, 20k pods and 10k services. We went well past that goal with the 1.5 release and are now officially supporting 5k nodes, 100k pods and 20k services. Along the way, we learned a lot, some expected, some unexpected, this blog post will dive into what we learned and how we improved.