Tech IT Up - Growth and Learning for trivago Techies

What triggered you to choose the career you are in and still inspires you in your path today?

Role models are often sources of aspirations and inspirations in our careers. ​​Unfortunately, in this day and age, women are still underrepresented in Tech careers. This impacts the capability of future generations, as well as those women who may want or need to reconvert into Tech professions. ​​Many teams, — and companies as a whole — do not profit from the benefits of diversity and miss out on valuable talents. A white paper by eco-Association of the Internet highlights: “In order to get more women interested in IT and technology, the visibility of female role models plays a central role. This is underlined by both studies and empirical experiences of female IT professionals” (eco - Association of the Internet) [1].

As a part of the series of posts already mentioned on WARP - A Web Application Rewrite Project, we are disclosing our process of making technical decisions. We hope that you find this process helpful. Maybe you can even pull something out for your own projects.

Over the last few years, we completely refactored what was described in our previous article about how we use the ELK stack for an overview of our test automation results, but some core concepts remain valid and applicable.

Hello, I am a product manager here at trivago. I have worked on different parts of the product such as apps, alternative accommodations, landing pages, and search & flow. We work in cross-functional teams that solve problems within a defined scope. My key responsibilities as a product manager are developing a product vision & strategy, defining objectives & outcomes, driving product discovery efforts such as user research, solution ideation, solution testing, and supporting engineering in product delivery. Overall it is about helping the team to ship the right product.

You have always been an engineer, solving problems and writing code. Now, there is an opportunity to become an engineering manager. You are interested.

However, questions arise.

While working with data and analyzing the interactions of our users with the products we have today, it is essential to understand their behaviors by tracking their past actions, such as opening notifications, interacting with a blog, or creating a new login in the platform. In that context, the attribution study refers to the method of grouping together all of those actions in a specific pattern to generate one desired end result.

Imagine, out of thousands of accommodations that match a user search, you have to select the “best” 25 to show to the user. Which ones would you show- the ones you know perform well or ones that have never been shown before, so that you can discover new high-potential accommodations? In the Data Science world, this is known as exploitation (continue doing what works well) versus exploration (try something new to discover hidden potential) problem and is often explained using the well-known multi-armed bandit problem. The objective of the problem is to divide a fixed number of resources between competing choices to maximize their expected gains, given that the properties of each choice are not fully known at the time of allocation.

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!

In the last few years, organisations have been increasing their investments in building Machine Learning (ML) based systems. In practice, such systems often took longer than expected to be built or failed to deliver the promised outcome. Data availability and quality have been among the most significant reasons behind this phenomenon. Since each organisation had its custom problems, open datasets or even logged data were not always directly usable. As a result, data collection and annotation processes became more crucial, yet remained under-documented.

In 2020 we started to migrate one of our most significant workloads, our Node.js based GraphQL API and many of its microservices, from our datacenter to Google Kubernetes Engine. We deploy it in three GCP regions, each having its Kubernetes cluster. Since then, our monitoring infrastructure has changed due to various periods of instability and pandemic induced scaling challenges.