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Has anyone ever come across any authoritative statement from Google on their official position of whether RSS feeds should be noindexed or not?
Indexes are there to simplify our work when searching for data: they speed up SELECT queries at the expense of slowing down other kinds of queries like DELETEs, UPDATEs, and INSERT s instead. However, as awesome as indexes might be, they also need a lot of work to get right — in this blog, we will tell you how you should go about deriving ideal indexes for your database. The majority of the examples in this article will focus on MySQL: however, the concept is the same for all major database management systems available on the market today.
If you are familiar with database structures, great — because that's essentially what indexes are! Indexes are database structures that can be used to quickly find rows having specific column values. At the expense of taking up disk space and time if your tables are big and you find yourself adding indexes on top of them, indexes allow databases to skip reading through entire tables and instead, only scan relevant rows which means that databases have less data to scan through.
One of the greatest questions to consistently badger a developer is "what technology should I use?". Days of thought and analysis determines which option(s) (from an increasingly growing number) best suits the need, manages volume and demand, plans for long-term strategy, simplifies/reduces support, and gets approved by colleagues and management.
Those steps may even seem easy compared to real life. The decision's complexity can get compounded by how much buy-in is needed, and the current constraints of existing technology plus developer knowledge. For instance, investing in an unknown or newer solution means allocation for learning costs.