Backend at trivago

Insights, experiences and learnings from trivago's tech teams.

Read How to substantially slow down your Node.js server

How to substantially slow down your Node.js server

Back in March 2022, after spending a considerable amount of effort migrating our monolithic Node.js GraphQL server from Express to Fastify, we noticed absolutely no performance improvements in production. That hit us like a bombshell, especially because Fastify performed exceptionally well in our k6 load tests in staging, where it responded to HTTP requests 107% (more than two times) faster on average than Express!

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Read How we got on top of our data

How we got on top of our data

Scalability and availability are key aspects of cloud native computing. If your microservice takes five minutes to start up, it becomes very difficult to meet the expectations because adjustments to traffic changes, regional failovers, hot-fixes and rollbacks are simply too slow. In this article, we show how we solved this and a few other problems by taking control of the process of updating our data and storing it in a highly available Redis setup.

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Read How we build the Image Gallery on trivago

How we build the Image Gallery on trivago

When was the last time you booked accommodation without checking its photos? Most probably never! Because having imagery information makes our decision-making process much easier and faster. However, picking up the best possible images of a hotel to show to the user is an interesting problem to solve, because it can be a naive random selection or a sophisticated machine learning model to know what the user truly wants at that moment.

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Read Proper (Java) application life cycle management in Kubernetes

Proper (Java) application life cycle management in Kubernetes

When operating applications in Kubernetes, proper lifecycle management is crucial to enable Kubernetes to manage applications correctly throughout their different phases: startup, runtime and shutdown. Improper or incomplete lifecycle management can lead to incidents with unforeseen and difficult to debug application behavior, such as random CrashLoopBackOffs, broken/zombie services not being restarted or even entire services not becoming healthy after a scheduled restart.

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Read Java Reactive Programming - Effective Usage in a Real World Application

Java Reactive Programming - Effective Usage in a Real World Application

This article presents how trivago's search backend team used reactive programming in Java effectively when designing and implementing one of our many Java backend services. Compared to traditional imperative and functional programming, reactive programming requires a mindset-shift in order to apply the concepts and techniques effectively. The benefits we gain support us in some key challenges that every engineer is facing with essentially every (micro-) service in today’s backend architectures: handling of blocking IO, backpressure, managing highly varying loads as well as message and error propagation.

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Read Reactive Programming - The Price You Have To Pay For A Responsive Backend

Reactive Programming - The Price You Have To Pay For A Responsive Backend

In the trivago backend, we use the reactive programming pattern for fetching prices from advertisers and updating our caches. This helps us to increase the responsiveness (i.e., scalability and resilience) of our backend. Thus, our backend system can alleviate high response times from internal components and our advertisers while staying responsive, even if downstream components fail entirely. Here is how we use the Java library Reactor Core to ensure those guarantees:

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