AWS Open Source Blog

Category: Java

AWS Distro for OpenTelemetry adds StatsD and Java support

AWS Distro for OpenTelemetry (ADOT) 0.8.0 is now available with StatsD support in the Collector and stable Java 1.0 support with an auto-instrumentation agent for observing your Java applications. StatsD Receiver The StatsD receiver is part of the OpenTelemetry Collector and collects StatsD metrics for exporting to your choice of monitoring service. This StatsD receiver […]

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Testing AWS Lambda functions written in Java

Testing is an essential task when building software. Testing helps improve software quality by finding bugs before they reach production. The sooner we know there is a defect in code, the easier and cheaper it is to correct. Automated tests are a central piece in reducing this feedback loop. In association with a continuous integration […]

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Getting started with Spring Boot on AWS: Part 2

This is a guest post from Björn Wilmsmann, Philip Riecks, and Tom Hombergs, authors of the upcoming book Stratospheric: From Zero to Production with Spring Boot and AWS. In part 1 of this two-part Spring Boot tutorial, we provided a brief introduction to Spring Cloud for AWS and covered how to display content of an […]

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Getting started with Spring Boot on AWS: Part 1

This is a guest post from Björn Wilmsmann, Philip Riecks, and Tom Hombergs, authors of the upcoming book Stratospheric: From Zero to Production with Spring Boot and AWS. Spring Boot is the leading framework for building applications in the Java Virtual Machine (JVM) ecosystem. In a nutshell, open source Spring Boot adds auto-configuration on top […]

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Distributed tracing with OpenTelemetry

These days, more and more systems deploy as a set of services using containers. You may already be using services like Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS) for quickly getting started with container workloads. Separating out services enables separation of concerns that can enable teams to operate independently […]

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Diagram of the Lambda Powertools framework.

Simplifying serverless best practices with AWS Lambda Powertools Java

Modern applications are increasingly relying on compute platforms based on serverless technologies to provide scalability, cost efficiency, and agility. Distributed architectures have unlocked many benefits, and they have introduced new complexities in how the applications operate. With traditional architectures, debugging was as straightforward as logging into the server and inspecting the logs. Modern observability must […]

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How Amazon retail systems run machine learning predictions with Apache Spark using Deep Java Library

Today more and more companies are taking a personalized approach to content and marketing. For example, retailers are personalizing product recommendations and promotions for customers. An important step toward providing personalized recommendations is to identify a customer’s propensity to take action for a certain category. This propensity is based on a customer’s preferences and past […]

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Generate Python, Java, and .NET software libraries from a TypeScript source

As builders and developers, many of us are aware of the principle of Don’t Repeat Yourself (or DRY) and practice it every day. Entire runtimes and programming languages have been developed by taking that principle to an even higher level, with the core idea of writing software once and having it run on many different […]

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JVM pause times chart

Introducing Heapothesys, an open source Java GC latency benchmark with predictable allocation rates

The Amazon Corretto team introduces the open source Heapothesys benchmark, a synthetic workload that simulates fundamental application characteristics that affect garbage collector (GC) latency. The benchmark creates and tests GC load scenarios defined by object allocation rates, heap occupancy, and JVM flags, then reports the resulting JVM pauses. OpenJDK developers can thus produce reference points […]

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Adopting machine learning in your microservices with DJL (Deep Java Library) and Spring Boot

Many AWS customers—startups and large enterprises—are on a path to adopt machine learning and deep learning in their existing applications. The reasons for machine learning adoption are dictated by the pace of innovation in the industry, with business use cases ranging from customer service (including object detection from images and video streams, sentiment analysis) to […]

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