Design an auto-complete feature.

An auto-complete feature requires efficient handling of partial string matching across a large dataset, with low latency being paramount. Key functional requirements include supporting searches for users, products, and keywords, with options for additional filtering and the exclusion of blacklisted terms. Non-functional requirements emphasize high throughput for read operations, the ability to scale to billions of records, and caching of recent searches to reduce latency.

A robust design would leverage a combination of data structures and caching strategies. A Trie (prefix tree) is ideal for prefix-based auto-completion, allowing fast retrieval of matching terms. For broader search capabilities and filtering, an inverted index can be employed.

To meet the performance demands, a multi-layered caching approach is essential. This includes:

1. In-memory cache: For frequently accessed terms and recent search history (e.g., using LRU or LFU eviction policies).
2. Distributed cache: A cluster of caching servers (like Redis or Memcached) to handle a larger cache footprint and distribute load.

Data can be pre-processed and indexed into the Trie and inverted index, potentially in batches. Read operations would first check the in-memory cache, then the distributed cache, and finally query the primary data store or search index if a cache miss occurs. Cache invalidation and periodic refreshes are crucial to maintain data freshness. For extremely large datasets, consider sharding the data and indices across multiple servers.