News

Loading Android data with coroutines

Many moons ago, I was working at the New York Times and created a library called Store, which was “a Java library for effortless, reactive data loading.” We built Store using RxJava and patterns adopted from Guava’s Cache implementation. Today’s app users expect data updates to flow in and out of the UI without having to do things like pulling to refresh or navigating back and forth between screens.

Personalizing Spotify Home with Machine Learning

Machine learning is at the heart of everything we do at Spotify. Especially on Spotify Home, where it enables us to personalize the user experience and provide billions of fans the opportunity to enjoy and be inspired by the artists on our platform. This is what makes Spotify unique.

Istio as an Example of When Not to Do Microservices

Database Migration To Amazon Aurora

In this blog post we’ll show you how we migrated a critical Postgres database with 18Tb of data from Amazon RDS (Relational Database Service) to Amazon Aurora, with minimal downtime. To do so, we’ll discuss our experience at Codacy.

How does a Prometheus Histogram work?

How does a Prometheus Histogram work? We looked previously at thecounter, gauge, and summary, how does the Prometheus histogram work? The histogram has several similarities to the summary.

Building a Service Mesh with Envoy

Service Mesh is the communication layer in a microservice setup. All requests, to and from each of the services go through the mesh. Also known as an infrastructure layer in a microservices setup, the service mesh makes communication between services reliable and secure.

Monitoring blocked and passthrough external service traffic

What are BlackHole and Passthrough clusters? Understanding, controlling and securing your external service access is one of the key benefits that you get from a service mesh like Istio. From a security and operations point of view, it is critical to monitor what external service traffic is getting blocked as they might surface possible misconfigurations or a security vulnerability if an application is attempting to communicate with a service that it should not be allowed to.

Scaling a Mature Data Pipeline—Managing Overhead

Before delving into our specifics, I want to take a moment to discuss the technical stack backing our pipeline. Our platform uses a mixture of Spark and Hive jobs. Our core pipeline is primarily implemented in Scala.

Peloton – Uber’s Webscale Unified Scheduler on Mesos & Kubernetes

Cloud-Powered, Next-Generation Banking

Traditional banks make extensive use of labor-intensive, human-centric control structures such as Production Support groups, Security Response teams, and Contingency Planning organizations. These control structures were deemed necessary in order to segment responsibilities and to maintain a security posture that is risk averse. Unfortunately, this traditional model tends to keep the subject matter experts in these organizations at a distance from the development teams, reducing efficiency and getting in the way of innovation.