AWS Compute Blog

Category: Best Practices

Spark Deployment on Kubernetes Cluster

Running cost optimized Spark workloads on Kubernetes using EC2 Spot Instances

This post is written by Kinnar Sen, Senior Solutions Architect, EC2 Spot  Apache Spark is an open-source, distributed processing system used for big data workloads. It provides API operations to perform multiple tasks such as streaming, extract transform load (ETL), query, machine learning (ML), and graph processing. Spark supports four different types of cluster managers (Spark standalone, Apache […]

Process to install and configure the CloudWatch agent

How to monitor Windows and Linux servers and get internal performance metrics

This post was written by Dean Suzuki, Solution Architect Manager. Customers who run Windows or Linux instances on AWS frequently ask, “How do I know if my disks are almost full?” or “How do I know if my application is using all the available memory and is paging to disk?” This blog helps answer these […]

Introducing Spot Blueprints, a template generator for frameworks like Kubernetes and Apache Spark

This post is authored by Deepthi Chelupati, Senior Product Manager for Amazon EC2 Spot Instances, and Chad Schmutzer, Principal Developer Advocate for Amazon EC2 Customers have been using EC2 Spot Instances to save money and scale workloads to new levels for over a decade. Launched in late 2009, Spot Instances are spare Amazon EC2 compute […]

Backend architecture

Application integration patterns for microservices: Running distributed RFQs

In this blog, I present the scatter-gather pattern, which is a composite pattern based on pub-sub and point-to-point messaging channels. It also employs correlation ID and return address. I show how this is implemented in the Wild Rydes example application. You can use this integration pattern for communication in your microservices.