InfoQ Homepage Scaling Content on InfoQ
-
How Pinterest Scaled up Its Ad-Serving Architecture
On the Pinterest Engineering Blog, Nishant Roy wrote about their strategy to overcome a scaling problem with their ad corpus. Their existing solution had hit its scaling limit, but further growth was necessary. Its redesign offloaded the ad index to a key-value store and optimised garbage collection in their Go applications to increase their ad corpus size by a factor of 60.
-
Google Introduces Minimum Instances to Reduce Cold Starts
Google’s Function as a Service (FaaS) offering Cloud Functions now supports minimum (“min”) instances. With this new feature, Google aims to take away a well-known friction point of FaaS called "cold-starts".
-
Paving the Road to Production at Coinbase: QCon Plus Q&A
As Coinbase scaled both their number of engineers and customers, they needed more projects, faster iteration, and more control over their growing infrastructure. In developing their in-house deployment tool by looking at what developers were doing and trying to help them, they created a culture of self-service.
-
Safe and Fast Deploys at Planet Scale: QCon Plus Q&A
Uber has automated the deployment of services using a hybrid cloud model. All services are deployed using the same rollout techniques and workflows, ensuring safe deployment and mitigation of any issues. Abstracting away the differences between clouds supports engineers in building services that run on any platform.
-
Q&A on Container Scaling with Fargate
Vlad Ionescu, an AWS Container Hero, in early April reported on his experiments with scaling Amazon Fargate for batch processing or background jobs.
-
How N26 Scales Technology through Hypergrowth
As N26 grew fast, they had to scale their technology to keep up. This meant scaling not only their infrastructure, but also their teams; for instance, they had to decide how to distribute work over teams and what technology to use or not use. Folger Fonseca, software engineer and Tech Lead at N26, shared his experience from scaling technology at N26 at QCon London 2020.
-
Scaling Tech to Keep Building the Right Product During Hyper-Growth
When your organization is growing fast and steadily, change has to be part of your culture. People are recruited, people leave, and people change teams; you have to learn to adapt fast and keep tech and business synchronized. At FlowCon France 2019 Nicholas Suter and Nicolas Nallet spoke about scaling tech at Younited.
-
Microsoft Announces 1.0 Release of Kubernetes-Based Event-Driven Autoscaling (KEDA)
Microsoft has announced the 1.0 version of the Kubernetes-based event-driven autoscaling (KEDA) component, an open-source project that can run in a Kubernetes cluster to provide "fine grained autoscaling (including to/from zero)" for every container. KEDA also serves as a Kubernetes Metrics Server and allows users to define autoscaling rules using a dedicated Kubernetes custom resource.
-
Google Software Engineering Culture
Several Google engineering practices have been largely adopted across the company until today and still contribute to the company's success. In 2017, a staff software engineer published some of these practices, not limited to software development. Today, Google fosters a team culture of creativity, autonomy, and innovation.
-
Scaling, Incident Management and Collaboration at New York Times Engineering
The New York Times Engineering Team wrote about their approach to scaling and incident management against the backdrop of increased traffic during the November 2018 US midterm elections.
-
Inner-Sourcing as Catalyst for DevOps Transformation at Verizon
Verizon successful scaled their DevOps execution at enterprise level by focusing on three key areas: migrating to the cloud, modernizing their technologies and transforming their culture. Verizon transformed into an inner-sourcing culture based on participation, empowerment, rapid prototyping and meritocracy.
-
Sony Trains ResNet-50 on ImageNet in 224 Seconds
Researchers from Sony announced that they trained a ResNet 50 architecture on ImageNet in only 224 seconds. The resulting network has a top-1 accuracy of 75% on the validation set of ImageNet. They achieved this record by using 2.100 Tesla V100 Tensor Core GPUs from NVIDIA. Besides this record, they also got a 90% GPU scaling efficiency using 1.088 Tesla V100 Tensor Core GPUs.
-
Building Production-Ready Applications: Michael Kehoe Shares Lessons Learned from LinkedIn
At QCon San Francisco, Michael Kehoe presented “Building Production-Ready Applications”. Drawing on his experience with site reliability engineering (SRE), he introduced the tenets of “production-readiness” that all engineers across the organisation should focus on as: stability and reliability; scalability and performance; fault tolerance and disaster recovery; monitoring; and documentation.
-
Atlassian Releases Escalator, an Autoscaling Tool for Kubernetes Nodes
Atlassian released their in-house tool Escalator as an open source project. It provides configuration-driven preemptive scale-up and faster scale-down for Kubernetes nodes.
-
Netflix Open Sources Its Container Management Platform "Titus"
Netflix announced the open sourcing of their container management platform called Titus. Titus is built on top of Apache Mesos and runs on AWS EC2.