In-game information like: Live Scores, Live Updates or Commentary.

Interested in current news about the club:.

Interested in booking tickets, buy merchandise, register as an official fan (Official Website).

Researching about the club (in which case a Wikipedia snippet would be relevant).

If we try to interpret the intent from the query, we will figure out that the user may be: Now, this may seem a very generic query, but when issued, it touches several services within our system. Fully Automated and Near Real-time SearchР”В¬onsider a query like РІР‚Сљbayern munichРІР‚Сњ. In here, the depth of the search is unbounded but it is less demanding on latency (less than 1000 ms) as compared to the dropdown version. Search on a web page, the typical search engine results page everybody knows. Search in SERP Figure 2: Cliqz Search Engine Result Page This type of search requires fewer results (typically 3) but is extremely latency sensitive (less than 150 ms) otherwise the user experience suffers.

The search in the browser address bar, with results available on the dropdown. Search-as-you-type Figure 1: Cliqz Dropdown in the Browser The search engine at Cliqz has two consumers with different requirements. Local Development on Kubernetes - An end to end use case.Introduce our Kubernetes stack - How we deploy, run and manage Kubernetes and various add-ons and the problems they solve for us.Our need for using containers and a container orchestration system (Kubernetes).Microservices Architecture: Orchestrating services involved to deliver content for a search engine result page.How deployments were done in the past? The Pros and Cons of various approaches.Data Processing Platform: Facilitating near Real-time and Batch Indexing.Web Search Systems: A near real-time and truly automated search system.Cliqz search as a product and its system requirements.Given the nature of this post, we want to provide an ordered outline of all topics covered: But instead to add them one by one slowly and incrementally adding complexity one step at a time. As matter of fact, we really advise anyone, to never try to throw all the ingredients to the pot at once. РІР‚СљPremature optimization is the root of all evilРІР‚Сњ ~ Donald KnuthР”Р„nd we agree wholeheartedly. By no means, we would suggest that this is a recipe to build a search engine it is what is working today, as wiser people said: We had multiple architectural overhauls throughout the years, always considering constraints like costs, traffic and data size. Places where we havenРІР‚в„ўt found a solution in the open source world or commercial efforts, we have been prepared to dive deep and write some core systems from scratch, which has worked well for us at our scale.Р”Вisclaimer: We describe how our system is, as of today. We use a combination of prominent open source and cloud-native technologies wrapped with home grown tooling, which have been battle tested. The topic at hand is very broad and cannot be covered in a single sitting, but we hope to give you the gist of it. This article explores the technology stack we employ today and some of our choices and decisions, which have been taken and iterated upon over the years, to cater both external and internal users. On top of that it can easily become extremely costly to operate both in human resource and, of course, in money.

It is a distributed system with strong constraints on performance and latency. It is important to understand that a web scale search engine is highly complex. It is about time that we introduce the systems that bring everything together. In previous posts of this advent series, we have described some of the technologies that power our private search products.

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