At massive scale, indexing is done by distributing the data rather than relying on a single machine. The index is split into shards, each holding a subset of the data, commonly partitioned by hashing IDs or dividing term ranges. Every shard is replicated to multiple machines so reads can be load-balanced and failures do not take the system down.

Search queries are handled by a coordinator that sends the query to the relevant shards in parallel, collects their partial results, merges and ranks them, and returns the final result. Because all shards work at the same time, query latency depends on the slowest shard, not on total index size.

This setup is built on search engines based on inverted indexes, usually derived from Lucene, either via systems like Elasticsearch or via custom implementations. Metadata and related data are stored in distributed databases or key-value stores, while index updates are streamed asynchronously so writes do not block reads. Caching at multiple layers keeps frequently accessed data in memory, and the whole system runs on large clusters that automatically handle placement, scaling, and failures.

idk where you are, but where I live anybody can go to the university lectures for free, as long as they are not full. Or the library and browse the relevant section. Personally I learned everything IT related from uni courses and searching for my topics of interest in the uni lib. So thats my shitty recomendation, I'm sure there are online resources and courses on it though.