Design YouTube Search.

This question revolves around system design principles applied to designing the search functionality for a massive content platform like YouTube. The underlining factors to consider would include designing an efficient search algorithm, factoring in large scale data handling, ranking algorithms, and user experience. Key points to highlight would be scalability, reliability, accuracy, real-time responsiveness, and personalization.

A scalable way to design YouTube search would begin with an Inverted Index. By defining an index of keywords leading to videos containing those words in titles, descriptions, or tags, it enables the search feature to find records in a fast and efficient way. This process is similar to how search engines index webpages for quick retrieval.

Next, we must consider Scaling the Inverted Index. Considering YouTube's size, a single machine handling the Inverted Index wouldn't be ideal. It would require a distributed system where the index divides across multiple servers, and the search queries should be serviced in parallel.

Lastly, Ranking of Search Results plays an important role. Once we have a subset of videos that match the search query, ranking them by relevance ensures that the most suitable videos are displayed first. Factors considered for ranking include the matching score from the inverted index, the number of views, likes, dislikes, and time since publishing among others. Machine Learning algorithms like Logistic Regression, XGBoost, etc., can be used here to rank the videos based on features extracted from users' past interactions.

The search system design for YouTube should adhere to few essential components:

1. Preprocessing - Textual data from each video’s title, description and tags should be cleaned and tokenized.

2. Inverted Indexing - Mapping from keywords to corresponding videos that contain those words would form the backbone of the search functionality. This would require efficient data structures like Tries and Hash Tables.

3. Distributed Systems - A single machine will not be feasible to store the Inverted Index due to the enormous volume of YouTube's content. So, it would require creating clusters of servers, maybe with the help of technologies like Elasticsearch.

4. Query Processing and Ranking - After retrieving relevant videos against a search query, the system needs to rank these videos effectively based on various signals like matching score from the inverted index, user preferences, historical data like number of views, likes, dislikes, etc.

5. Caching - Frequently searched and viewed videos should be stored in a cache system to improve response times. LRU (Least Recently Used) or LFU (Least Frequently Used) cache eviction policies can be considered.

Overall, the design must be optimized for efficiency, scalability and the ability to deliver the most relevant search results to the user.