Migrating an on-prem database to a public cloud comes with a number of benefits, such as no longer needing to manage and maintain physical infrastructure, dynamic scaling, disaster recovery, and overall cost reduction. However, migrating to the cloud can often be a complex and daunting task. For instance, if an organization is a Microsoft shop with teams that rely on SQL Server databases, Azure is a natural fit for its needs.
TeamCity is a CI/CD server that provides out-of-the-box support for unit testing, code quality tracking, and build automation. Additionally, TeamCity integrates with your other tools—such as version control, issue tracking, package repositories, and more—to simplify and expedite your CI/CD workflows.
When containers and container orchestration were introduced, they opened the possibility of helping companies utilize physical resources like CPU and memory more efficiently. But as more companies and bigger enterprises have adopted Kubernetes, FinOps professionals may wonder why their cloud bills haven’t gone down—or worse, why they have increased.
In Part 2 of this series, we’ve seen how RabbitMQ ships with tools for monitoring different aspects of your application: how your queues handle message traffic, how your nodes consume memory, whether your consumers are operational, and so on. While RabbitMQ plugins and built-in tools give you a view of your messaging setup in isolation, RabbitMQ weaves through the very design of your applications.
While the output of certain RabbitMQ CLI commands uses the term “slave” to refer to mirrored queues, RabbitMQ has disavowed this term, as has Datadog. When collecting RabbitMQ metrics, you can take advantage of RabbitMQ’s built-in monitoring tools and ecosystem of plugins. In this post, we’ll introduce these RabbitMQ monitoring tools and show you how you can use them in your own messaging setup.
RabbitMQ is a message broker, a tool for implementing a messaging architecture. Some parts of your application publish messages, others consume them, and RabbitMQ routes them between producers and consumers. The broker is well suited for loosely coupled microservices. If no service or part of the application can handle a given message, RabbitMQ keeps the message in a queue until it can be delivered.
Modern applications running on distributed systems often complicate service ownership because of their ever-growing web of microservice dependencies. This complication challenges engineers’ ability to shepherd their software through every stage of the development life cycle, as well as teams’ ability to train new engineers on the application’s architecture. With increased complexity, clarity is key for quick, effective troubleshooting and delivering value to end users.
Modern, high-scale applications can generate hundreds of millions of logs per day. Each log provides point-in-time insights into the state of the services and systems that emitted it. But logs are not created in isolation. Each log event represents a small, sequential step in a larger story, such as a user request, database restart process, or CI/CD pipeline.
Many developers and product teams are iterating faster and deploying more frequently to meet user expectations for responsive and optimized apps. These constant deployments—which can number in the dozens or even hundreds per day for larger organizations—are essential for keeping your customer base engaged and delighted. However, they also make it harder to pinpoint the exact deployment that led to a rise in errors, a new error, or a performance regression in your app.
As the world’s leading local delivery platform, Delivery Hero brings groceries and household goods to customers in more than 70 countries. Their technology stack comprises over 200 services across 20 Kubernetes clusters running on Amazon EKS. This cloud-based, containerized infrastructure enabled them to scale their operation to support increasing demand as the volume of orders placed on their platform doubled during the pandemic.
